technical cooperation with the philippines department...

National REDD+ System Philippines Project

Deutsche Forstservice GmbH

Component 4 implemented

on behalf of Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Technical Cooperation with the Philippines

Department of Environment and Natural Resources (DENR)

National REDD+ System Philippines

Component 4:

Forest Land Use Planning and REDD+ Implementation in Selected Areas

PN 12.9022.0-001.00 / VN 81162755

Methodology and Results of the 2014 - 2015 Forest Resources Assessment

in the selected Project sites in Eastern Samar

February 2016

Ralph LENNERTZ


Deutsche Forstservice GmbH

Component 4 implemented

on behalf of Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Eastern Samar FRA Results i


TABLE OF CONTENT

TABLE OF CONTENT ............................................................................................................. i

ANNEXES ............................................................................................................................. iii

TABLES ................................................................................................................................ iv

FIGURES ............................................................................................................................... v

ACRONYMS ......................................................................................................................... vi

EXECUTIVE SUMMARY ........................................................................................................ 1

1. INTRODUCTION AND BACKGROUND .......................................................................... 2

1.1 National REDD+ System Philippines Project .......................................................... 2

1.2 Methodological Framework ..................................................................................... 3

1.3 Forest Resources Assessment Objectives, Scale and Scope ................................. 3

2. SOURCES OF INFORMATION ....................................................................................... 6

2.1 Forest Definition ..................................................................................................... 6

2.2 Forest Areas / Stratification .................................................................................... 6

2.3 Carbon Stocks in Mangroves .................................................................................. 8

2.4 Wood Specific Gravity ............................................................................................ 8

2.5 Soil Classes............................................................................................................ 8

3. INVENTORY DESIGN ................................................................................................... 10

3.1 Inventory Method .................................................................................................. 10

3.2 Areal Sampling Frame .......................................................................................... 10

3.3 Elements Sampled ............................................................................................... 10

3.4 Number of Sample Points ..................................................................................... 11

3.5 Distribution of Sample Points ................................................................................ 11

3.6 Configuration of Sampling Units ........................................................................... 11

3.6.1 Observations and measurements at and around the Sample Points ......... 14

3.6.2 Observations and measurements at and around the Satellite Centers ...... 14

3.7 Estimation Design ................................................................................................ 16

3.7.1 Tree volume equations and calculation of merchantable volume............... 16

3.7.2 Allometric equations and calculation of biomass ....................................... 16

3.7.3 Carbon fraction of dry matter ..................................................................... 19

3.7.4 Statistical parameters ................................................................................ 20

4. FIELD IMPLEMENTATION ............................................................................................ 21

4.1 Retrieval and Permanent Marking of Sampling Units ............................................ 21

4.1.1 Approach of Sample Points using GPS receivers ...................................... 21

4.1.2 Location of Sample Points and Satellite Centers using compass and distance tape or laser rangefinder ............................................................. 22

Eastern Samar FRA Results ii


4.1.3 Permanent marking of Sample Points and Satellite Centers ..................... 22

4.1.4 Inaccessible Sample Points and Satellite Centers ..................................... 22

4.2 Variables of Interest Assessed / Measured ........................................................... 24

4.2.1 Administrative location .............................................................................. 24

4.2.2 Actual coordinates..................................................................................... 24

4.2.3 Elevation ................................................................................................... 24

4.2.4 Slope......................................................................................................... 24

4.2.5 Slope orientation ....................................................................................... 24

4.2.6 Terrain ...................................................................................................... 24

4.2.7 Land classification ..................................................................................... 25

4.2.8 Land cover ................................................................................................ 25

4.2.9 Forest type ................................................................................................ 26

4.2.10 Tree crown cover ...................................................................................... 26

4.2.11 Plant diversity ............................................................................................ 26

4.2.12 Ground coverage classes by vegetation layers ......................................... 27

4.2.13 Ground coverage and average depth of litter ............................................ 27

4.2.14 Mid-diameter and length of lying dead wood sections ............................... 27

4.2.15 Observations / measurements on live trees and standing dead wood ....... 28

5. ORGANIZATIONAL ASPECTS ..................................................................................... 32

5.1 Inventory Instructions and Field Data Forms......................................................... 32

5.2 Inventory Teams ................................................................................................... 32

5.3 Inventory Equipment ............................................................................................. 33

5.4 Training ................................................................................................................ 33

5.5 Inventory Camps .................................................................................................. 34

5.6 Time and Costs of the Field Work ......................................................................... 34

5.7 Data Processing and Analysis .............................................................................. 36

6. QUALITY ASSURANCE / QUALITY CONTROL ............................................................ 37

6.1 Quality Assurance ................................................................................................ 37

6.2 Quality Control ...................................................................................................... 37

7. DETAILED RESULTS OF THE FOREST RESOURCES ASSESSMENT ...................... 39

7.1 Species Diversity .................................................................................................. 39

7.1.1 Species diversity of Closed Forests .......................................................... 39

7.1.2 Species diversity of Open Forests ............................................................. 41

7.2 Stand Composition ............................................................................................... 44

7.2.1 Stand composition of Closed Forests ........................................................ 44

7.2.2 Stand composition of Open Forests .......................................................... 46

7.3 Stand Structure .................................................................................................... 48

7.3.1 Stand structure of Closed Forests ............................................................. 48

7.3.2 Stand structure of Open Forests ............................................................... 54

7.4 Timber Stocks ...................................................................................................... 60

Eastern Samar FRA Results iii


7.4.1 Timber stocks of Closed Forests ............................................................... 60

7.4.2 Timber stocks of Open Forests ................................................................. 62

7.5 Carbon Stocks ...................................................................................................... 64

7.5.1 Carbon stocks of Closed Forests .............................................................. 64

7.5.2 Carbon stocks of Open Forests ................................................................. 65

7.5.3 Carbon stocks of Mangroves ..................................................................... 66

8. UNCERTAINTY OF THE ESTIMATES .......................................................................... 67

8.1 Statistical Sampling Error ..................................................................................... 67

8.2 Poor Representativeness of the Sampling Network .............................................. 68

8.3 Measurement Errors ............................................................................................. 68

8.4 Data Encoding Errors ........................................................................................... 68

8.5 Estimation Design Uncertainties ........................................................................... 68

8.6 Overall Error Budget ............................................................................................. 69

9. REFERENCES .............................................................................................................. 70

ANNEXES

Appendix 1: List of Recorded Species

Appendix 2: List of Inventoried Sampling Units in Eastern Samar

Appendix 3: Field Data Forms

Appendix 4: Detailed Results - Closed Forests

Appendix 5: Detailed Results - Open Forests

Appendix 6: Statistical Parameters - Closed Forests

Appendix 7: Statistical Parameters - Open Forests

Eastern Samar FRA Results iv


TABLES

Table 1: IPCC Tier 1 Soil Organic Matter stocks of the Eastern Samar soil classes ........................................................................................................ 9

Table 2: 2010 forest strata areas inventoried .......................................................... 10

Table 3: Overview of plot sizes and observations / measurements made on live trees1 and dead wood ......................................................................... 15

Table 4: Time and costs the FRA field work in Eastern Samar ................................ 35

Table 5: Deviation of initial from control measurements .......................................... 38

Table 6: Deviation of encoded from field data ......................................................... 38

Table 7: Relative frequency, density and dominance, importance and rank of the 20 most "important" species in Closed Forests .................................... 40

Table 8: Threatened species in Closed Forests ...................................................... 41

Table 9: Relative frequency, density and dominance, importance and rank of the 20 most "important" species in Open Forests ...................................... 42

Table 10: Threatened species in Open Forests ........................................................ 43

Table 11: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Closed Forests ...... 44

Table 12: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Open Forests ........ 46

Table 13: Stand structure in terms of N/ha of Closed Forests ................................... 48

Table 14: Stand structure in terms of G/ha of Closed Forests ................................... 50

Table 15: Stand structure in terms of AGB/ha of Closed Forests .............................. 52

Table 16: Stand structure in terms of N/ha of Open Forests ..................................... 54

Table 17: Stand structure in terms of G/ha of Open Forests ..................................... 56

Table 18: Stand structure in terms of AGB/ha of Open Forests ................................. 58

Table 19: Merchantable volume in Closed Forests ................................................... 60

Table 20: Merchantable volume in Open Forests ...................................................... 62

Table 21: Carbon stocks of Closed Forests .............................................................. 64

Table 22: Carbon stocks of Open Forests ................................................................. 65

Table 23: Carbon stocks of Mangroves ..................................................................... 66

Table 24: Statistical sampling errors of the main variables of interest in Closed and Open Forests ..................................................................................... 67

Table 25: Overall error budget for V/ha ..................................................................... 69

Table 26: Overall error budget for AGB/ha ................................................................ 69

Eastern Samar FRA Results v


FIGURES

Figure 1: 2010 NAMRIA land cover of Borongan City and Maydolong ....................... 7

Figure 2: 2013 BSWM FAO soil classes of Eastern Samar ........................................ 9

Figure 3: Distribution of the Sampling Units effectively (re-)measured in Borongan City and Maydolong .................................................................. 12

Figure 4: Configuration of the sampling unit (cluster) ............................................... 13

Figure 5: Apple Map ................................................................................................. 21

Figure 6: Open Cycle Map with "Outdoors" base layer ............................................. 22

Figure 7: Re-location of inaccessible "satellites" ...................................................... 23

Figure 8: Measurements on lying dead wood sections ............................................. 28

Figure 9: DBH / DAB measurements ........................................................................ 30

Figure 10: Diameter estimates for inaccessible measurement points ......................... 31

Figure 11: N/ha, G/ha, V/ha and AGB/ha by number of species in Closed Forests ...................................................................................................... 41

Figure 12: N/ha, G/ha, V/ha and AGB/ha by number of species in Open Forests....... 43

Figure 13: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Closed Forests ...... 45

Figure 14: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Open Forests ........ 47

Figure 15: Stand structure in terms of N/ha of Closed Forests ................................... 49

Figure 16: Stand structure in terms of G/ha of Closed Forests ................................... 51

Figure 17: AGB/ha of Closed Forests by DBH / DAB threshold .................................. 52

Figure 18: Stand structure in terms of AGB/ha of Closed Forests .............................. 53

Figure 19: Stand structure in terms of N/ha of Open Forests ..................................... 55

Figure 20: Stand structure in terms of G/ha of Open Forests ..................................... 57

Figure 21: AGB/ha of Open Forests by DBH / DAB threshold .................................... 58

Figure 22: Stand structure in terms of AGB/ha of Open Forests ................................. 59

Figure 23: Merchantable volume in Closed Forests ................................................... 61

Figure 24: Merchantable volume in Open Forests ...................................................... 63

Figure 25: Carbon stocks of Closed Forests .............................................................. 64

Figure 26: Carbon stocks of Open Forests ................................................................. 65

Figure 27: Carbon stocks of Mangroves ..................................................................... 66

Eastern Samar FRA Results vi


ACRONYMS

AD Ancestral Domain

AFOLU Agriculture, Forest and Other Land Use

AFP Armed Forces of the Philippines

AGB Above-Ground Biomass

ALOS Advanced Land Observing Satellite

a.s.l. above sea level

AVNIR Advanced Visible and Near Infrared Radiometer

BCEF Biomass Conversion and Expansion Factor

BGB Below-Ground Biomass

BMUB Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit (Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety)

BSWM Bureau of Soils and Water Management

C Carbon

CADT Certificate of Ancestral Domain Title

CBFM Community-Based Forest Management

CBFMA Community-Based Forest Management Agreement

CCC Climate Change Commission

CENRO Community Environment and Natural Resources Office(r)

CLUP Comprehensive Land Use Plan

CMA Co-Management Agreement

DAB Diameter Above Buttress

DBH Diameter at Breast Height

DEM Digital Elevation Model

DENR Department of Environment and Natural Resources

DFS Deutsche Forstservice GmbH

DOM Dead Organic Matter

Dref Reference Diameter

FLUP Forest Land Use Planning

FMB Forest Management Bureau

FRA Forest Resources Assessment

FREL Forest Reference Emissions Level

FRL Forest Reference Level

GADM Global Administrative Areas

GHG Greenhouse Gas

GIS Geographic Information System

GIZ Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH

GPG Good Practice Guidance

GPS Global Positioning System

HWSD Harmonized World Soil Database

ICC Indigenous Cultural Communities

IP Indigenous People

IPCC Intergovernmental Panel on Climate Change

IUCN International Union for Conservation of Nature and Natural Resources

Eastern Samar FRA Results vii


JDK Java Development Kit

JRE Java Runtime Environment

LB Living Biomass

LDW Lying Dead Wood

LGU Local Government Unit

LI Litter

LULUCF Land Use and Land-Use Change and Forestry

MAD Mean Absolute Deviation

MRV Measurement, Reporting and Verification

NAMRIA National Mapping and Resource Information Authority

NCIP National Commission on Indigenous People

NFRI National Forest Resources Inventory

NGP National Greening Program

NSCB National Statistical Coordination Board

NTFP Non-Timber Forest Product

ODBC Open Database Connectivity

OGC Open GeoSpatial Consortium

ORDBMS Object Relational Database Management System

PENRO Provincial Environment and Natural Resources Office(r)

PNRPS The Philippine National REDD-Plus Strategy

POI Point Of Interest

PSC Project Steering Committee

PSGC Philippine Standard Geographic Code

QA Quality Assurance

QC Quality Control

REDD+ Reducing Emissions from Deforestation and forest Degradation, and conservation, sustainable management of forests and enhancement of carbon stocks

RMSD Root Mean Square Deviation

SDW Standing Dead Wood

SINP Samar Island Natural Park

SLC Scan Line Corrector

SOM Soil Organic Matter

SOP Standard Operating Procedure

TLA Timber License Agreement

UNFCCC United Nations Framework Convention on Climate Change

UTM Universal Transverse Mercator

VSU Visayas State University

WGS World Geodetic System

WRB World Reference Base for soil resources

Eastern Samar FRA Results 1


EXECUTIVE SUMMARY

The present report describes the methodology and the results of the Forest Resources Assessment (FRA) conducted from 10 December 2014 until 24 July 2015 in the sites of the National REDD+ System Philippines Project in Eastern Samar selected for Forest Land Use Planning (FLUP) and the implementation of REDD+ eligible activities (Borongan City and Maydolong). The methodology used is a refinement of the forest carbon baseline study carried out from 2011 to 2012 in Leyte in the framework of the Climate relevant Modernization of Forest Policy and Piloting of Reducing Emissions from Deforestation and Forest Degradation (REDD) Project. It takes into account evolving international standards and good practices with regards to forest carbon stock assessment for the estimation of Greenhouse Gas (GHG) emissions and removals in compliance with the latest (2006) Intergovernmental Panel on Climate Change (IPCC) guidelines for national GHG inventories. The FRA pursued the objectives of providing for the forests of the selected Project sites (i) stand and stock data estimates reflecting the forest resources conditions as well as (ii) carbon stock estimates for the key carbon pools:

Above-Ground Biomass (AGB) at Tier 3 level;

Below-Ground tree Biomass (BGB) at Tier 1 level;

Dead Organic Matter (DOM) at Tier 3 level;

Soil Organic Matter (SOM) at Tier 1 level;

of key forest strata according to the 2010 forest cover map prepared by the National Mapping and Resource Information Authority (NAMRIA). The report successively provides details about:

the background and purpose, including (i) a brief introduction to the Project, (ii) the methodological framework for which the FRA is to provide biomass / carbon stock estimates, and (iii) the FRA objectives, scale and scope (Chapter 1).

the sources of information used, notably regarding (i) the forest definition, (ii) the forest areas and their stratification, (iii) the carbon stocks in mangroves, (iv) the wood specific gravity, and (v) the soil classes (Chapter 2);

the inventory design, providing details about (i) the inventory method, (ii) the areal sampling frame, (iii) the elements sampled, (iv) the number of Sampling Units (SUs), their (v) distribution and (vi) configuration, and (vii) the estimation design (Chapter 3);

the field implementation, describing (i) the retrieval and permanent marking of the SUs and (ii) the variables of interest assessed / measured (Chapter 4);

the organizational aspects, covering (i) the inventory instructions and field data forms, (ii) the inventory team composition, (iii) the equipment used, (iv) the training of the inventory teams, (v) the use of inventory camps, (vi) the time and costs of the field work, and (vii) data processing and analysis (Chapter 5);

the quality assurance and quality control measures (Chapter 6);

the detailed results of the FRA (Chapter 7);

the uncertainties of the estimates (Chapter 8).

In the open and closed forests of Borongan City and Maydolong, 102 plus 18 Sampling Units (SUs) have been retrieved in the field, permanently marked and measured. The inventory has sampled and identified 236 different species with a Diameter at Breast Height (DBH) / Diameter Above Buttress (DAB) ≥ 5.0 cm. The total forest carbon stock is estimated to amount to 1.97 million t C (on average 338 t C/ha) in closed forests, to 10.16 million t C/ha (280 t C/ha) in open forests, and to 265 800 t C/ha (526 t C/ha) in mangroves.



1. INTRODUCTION AND BACKGROUND

1.1 National REDD+ System Philippines Project

The German Federal Ministry for the Environment, Nature Conservation, Housing and Nuclear Safety (BMUB) funded National REDD+ System Philippines Project contributes to the overall goal that Department of Environment and Natural Resources (DENR), relevant government agencies, local government units and local communities / indigenous people in the Philippines use a national framework, based on internationally recognized ecological and social safeguards, to reduce Greenhouse Gas (GHG) emissions from deforestation and forest degradation and to achieve co-benefits (biodiversity conservation and livelihoods improvement). The Project supports the implementation of the Philippine National REDD-Plus Strategy (PNRPS) by assisting the process towards REDD+ Readiness. Considering the variability of the prevailing natural, cultural and institutional conditions throughout the Philippines, 3 replications (Project field sites) were deemed necessary to validate lessons learned in the field from activities contributing to the up-scaling of the implementation of the PNRPS. Following the geographical division into major island groups, one replication each is implemented in Luzon, the Visayas and Mindanao, respectively. Cities / Municipalities as territorial / jurisdictional units for FLUPs and Comprehensive Land Use Plans (CLUPs) were selected applying criteria regarding (i) forests and threats, (ii) priority watersheds, (iii) biodiversity conservation, (iv) poverty, (v) logistics, (vi) replicability, (vii) preparedness and commitment of the LGUs, and (viii) for one site at least priority areas of the National Commission on Indigenous People (NCIP). The following sites were retained:

in Albay (Luzon) the Municipalities of Ligao City and Oas;

in Eastern Samar (Visayas) the Municipalities of Borongan City and Maydolong; and

in Davao Oriental (Mindanao) the Municipalities of Caraga, Manay and Tarragona.

Project Component 4 shall achieve the following indicators:

Forest Land Use Plans (FLUPs) and co-management agreements with clear land tenure arrangements for local communities and Indigenous People (IP) groups and biodiversity conservation agreements with local actors are in place for at least three pilot areas covering a total forest area of at least 150,000 ha.

REDD+ eligible activities (avoided deforestation and degradation, reforestation, assisted natural regeneration, sustainable forest management) in at least three pilot areas for emissions reduction and CO2 removals have been implemented.

Moreover, Component 4 shall furnish substantial contributions to:

Forest Reference (Emissions) Levels (FR[E]Ls) for the three sites;

Concept for a REDD+ Measurement, Reporting and Verification (MRV) system;

Forest policy and regulatory frameworks related to Community-Based Forest Management (CBFM), tenure arrangements, Co-Management Agreements (CMAs), IP/ICC concerns, and biodiversity conservation;

Knowledge management and Project monitoring and reporting.

It is in support of the elaboration and pilot testing of FR(E)Ls and the MRV system that FRAs were carried out in Eastern Samar and Davao Oriental.



1.2 Methodological Framework

The 2006 Intergovernmental Panel on Climate Change (IPCC) guidelines for national GHG inventories propose two methods of calculating carbon stock changes in a given carbon pool for a given land-use category in the Agriculture, Forestry and Other Land Use (AFOLU) sector:

the "Gain - Loss Method", estimating the difference between increases (transfer from another carbon pool or increase of biomass [removal]) and decreases (transfer to another carbon pool or emissions) of the amount of carbon;

the "Stock Difference Method", estimating the change of carbon stocks through measurements at two (or more) points in time (which reflects the emissions and removals).

The "Stock Difference Method" is robust and transparent, particularly to monitor carbon stock changes from forest degradation, which, in the Philippines, is a GHG emission source key category (category "3 B 1 a Forest Land Remaining Forest Land") with a presumably higher emission level than deforestation (sub-category "3 B 2 b i Forest Land Converted to Cropland"). The "Stock Difference Method" requires two estimations: (i) forest area (preferably by strata that are correlated to carbon stocks) and (ii) carbon stock per unit area of forest. The forest area by strata has been mapped nationwide by the National Mapping and Resource Information Authority (NAMRIA), through visual classification of medium- to high- resolution multi spectral satellite data (116 ALOS AVNIR-2, 40 SPOT 5 and 29 LANDSAT 7 gap-filled Scan Line Corrector [SLC] off scenes covering the national territory) acquired mainly 2010. A new wall-to-wall mapping assessing the 2015 land cover is under way. The results, however, won't be available before 2017. Carbon stock per unit area of forest for the different strata must be determined using appropriate probabilistic (statistical) field sampling inventory methods. The adopted inventory methodology is a refinement of the forest carbon baseline study carried out from mid-2011 until end 2012 in Leyte in the framework of the BMUB funded, GIZ-assisted Climate relevant Modernization of Forest Policy and Piloting of Reducing Emissions from Deforestation and Forest Degradation (REDD) Project (SCHADE J. and R. LUDWIG, 2013), building on the experience gained during this inventory, and taking into account evolving international standards and good practices (see Chapter 9).

1.3 Forest Resources Assessment Objectives, Scale and Scope

The FRA conducted in Eastern Samar is expected to provide for the forests of the selected Project sites (Municipalities of Borongan City and Maydolong) stand and stock data estimates reflecting the forest resources conditions such as

tree species variety,

stand density (N/ha),

basal area (G/ha), and

merchantable volume (V/ha).

stand composition (proportions of species / species groups in terms of N/ha, G/ha and V/ha), and

stand structure (distribution of N/ha, G/ha and V/ha by diameter classes).



In addition, the FRA shall also furnish forest carbon stock estimates for

key carbon pools (with definitions according to the 2006 IPCC guidelines for national GHG inventories)

o Living Biomass (LB), composed of

- Above-Ground Biomass (AGB), defined as follows: "All biomass of living vegetation, both woody and herbaceous, above the soil including stems, stumps, branches, bark, seeds, and foliage. In cases where forest understory is a relatively small component of the above-ground biomass carbon pool, it is acceptable for the methodologies and associated data used in some tiers to exclude it, provided the tiers are used in a consistent manner throughout the inventory time series."

- Below-Ground tree Biomass (BGB), defined as follows: "All biomass of live roots. Fine roots of less than (suggested) 2 mm diameter are often excluded because these often cannot be distinguished empirically from soil organic matter or litter."; and

o Dead Organic Matter (DOM), composed of

- Dead Wood (DW), defined as follows: "Includes all non-living woody biomass not contained in the litter, either standing, lying on the ground, or in the soil. Dead wood includes wood lying on the surface, dead roots, and stumps, larger than or equal to 10 cm in diameter (or the diameter specified by the country)." (for the forest resources assessments in the "National REDD+ System Philippines" Project sites, the inventory threshold / minimum diameter for dead wood is set to 5.0 cm), and

- Litter (LI), defined as follows: "Includes all non-living biomass with a size greater than the limit for soil organic matter (suggested 2 mm) and less than the minimum diameter chosen for dead wood (e.g. 10 cm), lying dead, in various states of decomposition above or within the mineral or organic soil. This includes the litter layer as usually defined in soil typologies. Live fine roots above the mineral or organic soil (of less than the minimum diameter limit chosen for below-ground biomass) are included in litter where they cannot be distinguished from it empirically." (for the forest resources assessments in the "National REDD+ System Philippines" Project sites, the inventory threshold / minimum diameter for dead wood is set to 5.0 cm);

if applicable disaggregated by species and diameter classes;

of key forest strata according to the 2010 forest cover map prepared by the National Mapping and Resource Information Authority (NAMRIA), distinguishing:

o Closed Forests (forests with a tree crown cover of more than 40%) and

o Open Forests (forests with a tree crown cover of more than 10% up to 40%);

at T0 (prior to the implementation of REDD+ eligible activities);

using permanent Sampling Units in view of the implementation of the "Stock Difference Method" for determining GHG emissions and removals.

Considering that more than 96% of the AGB biomass of tropical forest is found in trees with a Diameter at Breast Height (DBH) / Diameter Above Buttress (DAB) ≥ 10.0 cm (GILLESPIE A. et al., 1992; in the Philippines, LASCO et al., 2006, report from Surigao del Sur 98% of the AGB in trees with DBH / DAB ≥ 19.5 cm), the biomass of trees with a DBH / DAB < 5.0 cm and the non-tree biomass (except for bamboos and palms, which are also included in the Philippine forest definition, cf. Chapter 2.1) is not key and have not be included in the FRA / forest carbon stock estimates. BGB is not estimated directly, but calculated using the IPCC Tier 1 BGB to AGB ratio (R).



The carbon stocks of mangroves (a non-key forest stratum, since of the total 2010 forest area, mangroves represent only 1.20% in the selected Project sites [Municipalities of Borongan City and Maydolong], 4.07% in Eastern Samar) are estimated using IPCC Tier 1 data (IPCC, 2013: Supplement to the 2006 IPCC guidelines for national GHG - Coastal wetlands: Tables 4.3, 4.5, 4.7 and 4.11). IPCC Tier 1 data are also be used to account for Soil Organic Matter (SOM) (IPCC, 2006: IPCC guidelines for national GHG inventories - AFOLU: Table 2.3), defined as follows: "Includes organic carbon in mineral soils to a specified depth chosen by the country and applied consistently through the time series. Live and dead fine roots and DOM within the soil, that are less than the minimum diameter limit (suggested 2 mm) for roots and DOM, are included with soil organic matter where they cannot be distinguished from it empirically. The default for soil depth is 30 cm.".



2. SOURCES OF INFORMATION

2.1 Forest Definition

DENR Memorandum Circular 2005-005 of 26 May 2005 defines forests as "Land with an area of more than 0.5 hectare and tree crown (or equivalent stocking level) of more than 10 percent. The trees should be able to reach a minimum height of 5 meters at maturity in situ. It consists either of closed forest formations where trees of various storeys and undergrowth cover a high portion of the ground or open forest formations with a continuous vegetation cover in which tree crown cover exceeds 10 percent. Young natural stands and all plantations established for forestry purposes, which have yet to reach a crown density of more than 10 percent or tree height of 5 meters are included under forest. These are normally forming part of the forest area which are temporarily unstocked as a result of human intervention or natural causes but which are expected to revert to forest. It includes forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, cleared tracts, firebreaks and other small open areas; forest within protected areas; windbreaks and shelter belts of trees with an area of more than 0.5 hectare and width of more than 20 meters; plantation primarily used for forestry purposes, including rubber wood plantations. It also includes bamboo, palm and fern formations (except coconut and oil palm)."

2.2 Forest Areas / Stratification

The forest areas and their stratification are taken from the 2010 NAMRIA national forest cover map, released in 2013, where forests (and other land cover) have been classified through visual interpretation of medium- to high- resolution multi spectral satellite data (116 ALOS AVNIR-2, 40 SPOT 5 and 29 LANDSAT 7 gap-filled SLC off scenes covering the national territory, acquired mainly 2010), adopting a minimum mapping area of 0.5 ha in accordance with the 2005 DENR forest definition (see Chapter 2.1 above), distinguishing the following 3 forest strata:

Closed Forests: tree crown cover > 40%;

Open Forests: 10% < tree crown cover ≤ 40%;

Mangroves.

Tree plantations have not been mapped as a separate class, since the satellite data did not warrant their comprehensive and systematic identification. The documentation of the classification and its accuracy (confusion matrix) has not been published yet. Figure 1 shows an excerpt from this map, clipped to the Project sites (Borongan City and Maydolong) in Eastern Samar.



Figure 1: 2010 NAMRIA land cover of Borongan City and Maydolong



2.3 Carbon Stocks in Mangroves

Based on the 2010 NAMRIA land cover map, mangroves represent only 1.20% of the total forest area in the selected Project sites (Municipalities of Borongan City and Maydolong), and only 4.07% of the total forest area in Eastern Samar. As a non-key forest stratum, they have not been included in the FRA. The estimate of the forest carbon stocks in mangroves can be made using the IPCC Tier 1 data for tropical wet climate (IPCC, 2013: Supplement to the 2006 IPCC guidelines for national GHG - Coastal wetlands: Tables 4.3, 4.5, 4.7 and 4.11):

Above-Ground Biomass (AGB): 192 t d.m./ha;

Below-Ground Biomass (BGB) to Above-Ground Biomass (AGB) ratio (R): 0.49;

Dead Wood (DW): 10.7 t C/ha;

Litter (LI): 0.7 t C/ha;

Soil Organic Matter (SOM): 386 t C/ha.

2.4 Wood Specific Gravity

Wood Specific Gravity (p, expressed in g / cm³ or t / m³), as required by the estimation of AGB using the equation developed by J. CHAVE et al. (CHAVE J. et al., 2014: Improved allometric models to estimate the aboveground biomass of tropical trees; see equation {5} in Chapter 3.7.2) has been looked up (and averaged whenever several gravities are available) by species / species group growing in South-East Asia from the following sources:

preferably in A. ZANNE et al. (ZANNE A. et al., 2009: Global wood density database);

else in G. REYES et al. (REYES G. et al., 1992: Wood densities of tropical tree species).

For species not found in any of the above cited sources, the average Wood Specific Gravity for tropical tree species in Asia of 0.57 g/cm³ published by S. BROWN (FAO, 1997: Estimating biomass and biomass change of tropical forests - A primer: Chapter 3.1.1) has been used. Appendix 1 provides a list of species recorded by the inventory with the corresponding values of p.

2.5 Soil Classes

The World Reference Base (WRB) soil classes have been looked up from the 2013 FAO soil map of the Philippines prepared by BSWM, see Figure 2 (BSWM, 2013: Updating the Harmonized World Soil Database [HWSD]: Correlation of Philippine Soils into FAO's WRB for Soil Resources). Under the tropical wet climate prevailing in Eastern Samar, these classes yield according to the 2006 IPCC Tier 1 data the Soil Organic Matter (SOM) C-stocks (IPCC, 2006: IPCC guidelines for national GHG inventories - AFOLU: Table 2.3) summarized in Table 1.



Figure 2: 2013 BSWM FAO soil classes of Eastern Samar

Table 1: IPCC Tier 1 Soil Organic Matter stocks of the Eastern Samar soil classes

Climate region FAO soil class Soil SOM

Tropical, wet Humic Acrisols Low Activity Clay 60 t C/ha

Tropical, wet Orthic Acrisols Low Activity Clay 60 t C/ha

Tropical, wet Eutric Cambisols High Activity Clay 44 t C/ha

Tropical, wet Orthic Luvisols High Activity Clay 44 t C/ha

Tropical, wet Haplic Phaeozems High Activity Clay 44 t C/ha



3. INVENTORY DESIGN

3.1 Inventory Method

The inventory adopted a stratified probabilistic (statistical) sampling. Conceptionally, the population from which the sample is drawn is not the biological population of trees1. Sampling is rather considered to be based on the selection of Sample Points, each with observations and measurements of single tree1-, stand- and site- variables of interest derived from associated Sampling Units (SUs, see Chapter 3.6). Since Sample Points are dimensionless, the population is infinite even in a limited area of interest ("infinite population approach"). Hence, the sampling frame required by the statistical theory cannot be defined through a list of all elements that can be drawn during sampling, but rather through the area (areal sampling frame) to be covered.

3.2 Areal Sampling Frame

The areal sampling frame of the FRA in Eastern Samar consists of the key forest strata (Closed Forests and Open Forests) of the selected Project sites (Borongan City and Maydolong) (i) according to the 2010 NAMRIA national forest cover map (see Figure 3 and Table 2), (ii) referring in the absence of official / authoritative administrative boundaries to the municipal boundaries downloadable from the GADM database of Global Administrative Areas (see http://www.gadm.org/). In total, the areal sample frame measures 42,079 ha.

Table 2: 2010 forest strata areas inventoried

LGU Closed Forests Open Forests Mangroves Others Total Land Area

[ha] [ha] [ha] [ha] [ha]

Borongan City 4,675 27,851 476 67,896 100,898

Maydolong 1,140 8,413 29 21,097 30,679

Total 5,815 36,264 505 88,993 131,577

3.3 Elements Sampled

The elements sampled to estimate the forest biomass and carbon stock consists of the following:

live trees1 with a DBH / DAB ≥ 5.0 cm;

dead wood, both standing and lying, down to a small end diameter of 5.0 cm (the smaller fractions are part of the litter);

litter.

For live trees1 and dead wood, the inventory threshold consistently amounts to 5.0 cm (in diameter).

1 including bamboos, palms, rattan and tree ferns.



3.4 Number of Sample Points

It was initially expected that 200 Sampling Units could be measured with the available budget, 150 thereof in the Municipalities with Project field activities (Borongan City and Maydolong), and the remaining 50 in the other Municipalities of Eastern Samar (to serve as "zero-"plots [without Project-supported field activities]). Since the inventory could be implemented before the start of the REDD+ eligible field activities, the 50 Sampling Units outside the Municipalities with Project field activities were no longer needed. This was fortunate, since the available budget and time was barely sufficient to effectively measure 120 Sampling Units in the Municipalities with Project field activities.

3.5 Distribution of Sample Points

The 150 Sample Points were drawn at random without replacement from the 415 nodes of a quadratic grid with a side length of 1 km located within the areal sampling frame (Closed Forests and Open Forests according to the 2010 NAMRIA national forest cover map in the Municipalities of Borongan City and Maydolong). The Sample Points were numbered consecutively in the sequence they were drawn, from "EASM0001" to "EASM0150" ("EAS" identifying the Province, "M" signifying "measurement", "C" signifying "control", i.e. the mandatory independent re-measurement of 10% of the Sampling Units for Quality Control). Figure 3 shows the grid and the distribution of the 120 effectively measured SUs in Borongan City and Maydolong. Appendix 2 provides the list of these SUs with their Universal Transverse Mercator (UTM) and World Geodetic System (WGS) 84 geographic coordinates.

3.6 Configuration of Sampling Units

Each Sampling Unit consists of a cluster (offering the advantage of lowering the coefficient of variation between SUs) centered on the Sample Point, composed of the following elements (see Figure 4):

1 circular plot with 25 m radius centered on the Sample Point for the ocular assessment of the land cover, to serve as Remote Sensing (RS) training and validation data;

4 "satellites" with their centers at 40 m horizontal distance from the Sample Point in the 4 cardinal directions (North, East, South and West), each consisting of 2 concentric circular plots (featuring the best circumference : surface ratio, hence limiting the number of "boundary" trees, and moreover easy to establish even in steep terrain):

o 5 m radius plot (corresponding to an area of 0.0079 ha) for:

- the sampling of "small-sized" live trees1 (all species) with 5 cm ≤ DBH / DAB < 20 cm for the estimation of their contribution to the AGB and BGB (an average of 7.7 trees1 were actually sampled in these plots);

- the sampling of standing dead wood with DBH / DAB ≥ 5.0 cm for the estimation of their contribution to the DOM (an average of 0.4 standing dead wood were actually sampled in these plots);

- the sampling of lying dead wood down to a diameter of 5.0 cm for the estimation of their contribution to the DOM (an average of 0.9 pieces of lying dead wood were actually sampled in these plots;

- the sampling of litter for the estimation of its contribution to the DOM.

o 10 m radius plot (corresponding to an area of 0.0314 ha) for:

- the sampling of "big-sized" live trees1 (all species) with DBH / DAB ≥ 20 cm for the estimation of their contribution to the AGB and BGB (an average of 7.3 trees1 were actually sampled in these plots);



Figure 3: Distribution of the Sampling Units effectively (re-)measured in Borongan City and Maydolong



Figure 4: Configuration of the sampling unit (cluster)

On average, 15.0 trees1 were sampled in each satellite, which is well within the commonly recommended range of 12 to 20 trees in uneven-aged forests reputed to offer the best compromise in terms of sampling efficiency, considering the ratio between the "unproductive" time invested in retrieving SUs and the "productive" time measuring them. The entire cluster is inscribed in an area of 100 m x 100 m (1 ha). Statistically, one cluster constitutes one Sampling Unit. For the computation of the results per ha, the following blow-up factors are applicable:

parameters measured in the 10 m radius plots: 10 000 / (4 × 𝜋 × 102) = 7.9577;

parameters measured in the 5 m radius plots: 10 000 / (4 × 𝜋 × 52) = 31.8310.

The Sampling Units were marked permanently to be prepared for their periodic re-measurement.



3.6.1 Observations and measurements at and around the Sample Points

The following variables of interest were observed / measured at the Sample Points:

Administrative location: Province, City / Municipality and Barangay.

Actual coordinates.

Elevation.

Slope.

Slope orientation.

Terrain: 11 classes (plateau; summit / crest; upper slope; middle slope; lower slope; bench / terrace; valley; plain; narrow depression; water course; dunes).

Land classification: Legal status (forest land or alienable and disposable).

The following variables of interest were assessed within a radius of 25 m horizontal distance around the Sample Points:

Land cover: 12 classes (forest; marshland / swamp; fallow; shrubs; wooded grassland; grassland; annual crop; perennial crop; open / barren land; built-up area; fishpond; inland water).

Forest type: 10 types (dipterocarp old growth forest; dipterocarp residual forest; mossy forest; submarginal forest; closed pine forest; open pine forest; mangrove of growth forest; mangrove reproduction forest; native tree plantation forest; other plantation forest).

Tree crown cover: 3 classes (tree crown cover ≤ 10%; 10% < tree crown cover ≤ 40%); tree crown cover > 40%).

3.6.2 Observations and measurements at and around the Satellite Centers

The following variables of interest are observed / measured at the Satellite Centers (similar to the observations / measurements at the Sample Points):

Administrative location: Province, City / Municipality and Barangay.

Actual coordinates.

Elevation.

Slope.

Slope orientation.

Terrain: 11 classes (plateau; summit / crest; upper slope; middle slope; lower slope; bench / terrace; valley; plain; narrow depression; water course; dunes).

Land classification: Legal status (forest land or alienable and disposable).

The following variables of interest are observed / measured within a radius of 5 m horizontal distance around the Satellite Centers:

Plant diversity.

Ground coverage classes for six vegetation layers according to height (< 50 cm; 50 cm ≤ height < 130 cm; 130 cm ≤ height < 200 cm; 2.0 m ≤ height < 4.0 m; 4.0 m ≤ height < 10.0 m; height > 10.0 m): 4 classes (none; coverage ≤ 10%; 10% < coverage ≤ 50%; coverage > 50%).

For each of the sampled "small-sized" live trees1 with 5 cm ≤ DBH / DAB < 20 cm: species, azimuth and horizontal distance (from the Satellite Center), and DBH / DAB.

For each of the sampled standing dead wood (including stumps) with DBH / DAB ≥ 5.0 cm: species, azimuth and horizontal distance (from the Satellite Center), DBH / DAB and merchantable height.



For each of the sampled lying dead wood sections (those portions that are within the 5 m horizontal distance radius plot) down to a diameter of 5.0 cm: mid-diameter and length.

Litter: ground coverage percentage plus average depth.

The following variables of interest are observed / measured within a radius of 10 m horizontal distance around the Satellite Centers:

Land cover: 12 classes (forest; marshland / swamp; fallow; shrubs; wooded grassland; grassland; annual crop; perennial crop; open / barren land; built-up area; fishpond; inland water).

Forest type: 10 types (dipterocarp old growth forest; dipterocarp residual forest; mossy forest; submarginal forest; closed pine forest; open pine forest; mangrove of growth forest; mangrove reproduction forest; native tree plantation forest; other plantation forest).

Tree crown cover: 3 classes (tree crown cover ≤ 10%; 10% < tree crown cover ≤ 40%); tree crown cover > 40%).

For each of the sampled "big-sized" live trees1 with DBH / DAB ≥ 20.0 cm: species, azimuth and horizontal distance (from the Satellite Center), DBH / DAB and merchantable height.

Table 3 summarizes the circular plot sizes and the observations / measurements made on live trees1 and dead wood.

Table 3: Overview of plot sizes and observations / measurements made on live trees1 and dead wood

Live Trees1 Dead Wood

"Small-Sized" "Big-Sized" Standing Lying

5 cm ≤ Dref* < 20 cm Dref* ≥ 20 cm Dref* ≥ 5 cm Dref* ≥ 5 cm

Plot radius 5.0 m 10.0 m 5.0 m 5.0 m

Species Species Species Species -

Azimuth Azimuth Azimuth Azimuth -

Hor. Distance Hor. Distance Hor. Distance Hor. Distance -

Diameter DBH / DAB DBH / DAB DBH / DAB Mid-Diameter

Height / Length - Merch. Height Merch. Height Section Length

* Dref of live trees1 and standing dead wood refers to DBH / DAB, Dref for lying dead wood refers to the small end diameter



3.7 Estimation Design

3.7.1 Tree volume equations and calculation of merchantable volume

The merchantable volume (V, expressed in cubic meter [m³] inside bark) of live trees and standing dead wood is calculated based on the diameter at breast height or above buttress (Dref) and the merchantable height (H) of sampled live trees and standing dead wood with a Dref ≥ 5.0 cm using the Philippine regional volume equations for dipterocarps and non-dipterocarps (DENR, 2012: FMB Technical Bulletin No. 3 - Measurement standards in the conduct of timber inventory):

𝑉 = 0.00005231 × 𝐷𝑟𝑒𝑓2 × 𝐻 dipterocarps, Region 8 & Bohol

Equation {1}

𝑉 = 0.00005109 × 𝐷𝑟𝑒𝑓2 × 𝐻

non-dipterocarps, Region 8 & Bohol

Equation {2}

with

V merchantable volume inside bark of Standing Dead Wood, in m³

Dref diameter at breast height (1.30 m) or above buttress (30 cm) of Standing Dead Wood, in cm

H merchantable height of Standing Dead Wood, in m

Eventually missing merchantable heights have been estimated using height curves fitted separately for dipterocarps and non-dipterocarps through simple linear regression using the least squares approach based on the (Dref, H) data pairs measured during the inventory:

𝐻 =𝐷𝑟𝑒𝑓2

(2.190660809 + 0.239530478 × 𝐷𝑟𝑒𝑓)2+ 1.3

dipterocarps

Borongan City & Maydolong Equation {3}

𝐻 =𝐷𝑟𝑒𝑓2

(2.325400551 + 0.299572389 × 𝐷𝑟𝑒𝑓)2+ 1.3

non-dipterocarps

Borongan City & Maydolong Equation {4}

3.7.2 Allometric equations and calculation of biomass

In the absence of allometric equations specifically developed for the trees, bamboos, palms, rattan and tree ferns found in the tropical rainforests of the Philippines, the biomass (expressed in kg or t of dry matter [d.m.]) is calculated using the following available equations found in the literature and databases:

Above-Ground Biomass (AGB) of live trees:

Calculated based on the diameter at breast height or above buttress (Dref) of sampled live trees with a Dref ≥ 5.0 cm, using according to the preference of the user one of the two following equations:

o equation developed by J. CHAVE et al. (CHAVE J. et al., 2014: Improved allometric models to estimate the aboveground biomass of tropical trees), based on the destructive measurement of 4,004 trees, with 5.0 cm ≤ Dref ≤ 180.0 cm:

𝐴𝐺𝐵 = exp (−1.803 − 0.976 × 𝐸 + 0.976 × ln(𝑝) +2.673 × ln(𝐷𝑟𝑒𝑓) − 0.0299 × (ln(𝐷𝑟𝑒𝑓))2)

Equation {5}

with

- AGB oven-dry Above-Ground Biomass of live trees, in kg d.m.

- p wood specific gravity, in g / cm³; p by species or species groups (see Chapter 2.4)

- Dref diameter at breast height (1.30 m) or above buttress (30 cm), in cm



- E environmental variable measuring stress, defined as:

𝐸 = (0.178 × 𝑇𝑆 − 0.938 × 𝐶𝑊𝐷 − 6.61 × 𝑃𝑆) × 10−3 Equation {6}

with

- TS temperature seasonality, the standard deviation of the monthly mean temperature over a year, expressed in degrees Celsius multiplied by 100

- CWD climatological water deficit in mm per year, computed by summing the difference between monthly rainfall and monthly evapotranspiration, only when this difference is negative

- PS precipitation seasonality, the coefficient of variation in monthly rainfall values, expressed in percent of the mean value

A global gridded layer of E at 2.5 arc-minute resolution is available at http://chave.ups-tlse.fr/pantropical_allometry.htm#E and has been integrated into the FRA Database System Application used store, manage and analyze the inventory data (see Chapter 5.7); the values of E are extrapolated from the gridded layer based on the geographic coordinates of the Sampling Units (more precisely of the satellite center, cf. Chapter 3.6).

o equation developed by S. BROWN (FAO, 1997: Estimating biomass and biomass change of tropical forests - A primer: Chapter 3.2.1) for moist climatic zones, based on the destructive measurement of 170 trees, with 5.0 cm ≤ Dref ≤ 148.0 cm:

𝐴𝐺𝐵 = exp (−2.134 + 2.530 × ln(𝐷𝑟𝑒𝑓)) (R² = 0.97) Equation {7}

with

- AGB oven-dry Above-Ground Biomass of live trees, in kg d.m.

- Dref diameter at breast height (1.30 m) or above buttress (30 cm), in cm

AGB of live bamboos:

Calculated based on the diameter at breast height (Dref) of sampled live bamboos with a Dref ≥ 5.0 cm, using the following equation, developed by R. PRIYADARSINI (1998, notably cited in ZEMEK O., 2009: Biomass and carbon stocks inventory of perennial vegetation in the Chieng Khoi watershed, NW Viet Nam), based on the destructive measurement of Dendrocalamus asper in Indonesia, with 3.0 cm ≤ Dref ≤ 7.0 cm:

𝐴𝐺𝐵 = 0.1312 × 𝐷𝑟𝑒𝑓2.2784 (R² = 0.95) Equation {8}

with

o AGB oven-dry Above-Ground Biomass of bamboos, in kg d.m.

o Dref diameter at breast height (1.30 m), in cm

AGB of live arborescent palms:

Calculated based on the diameter at breast height (Dref) of sampled live arborescent palms with a Dref ≥ 5.0 cm, using the following equations, developed by R. GOODMAN et al. (GOODMAN R. et al., 2013: Amazon palm biomass and allometry), based on the destructive measurement of 97 palms in Western Amazonia, with 6.0 cm ≤ Dref < 40.0 cm:

𝐴𝐺𝐵 = exp(−3.3488 + 2.7483 × ln(𝐷𝑟𝑒𝑓)) (R² = 0.80) Equation {9}

with

o AGB oven-dry Above-Ground Biomass of arborescent palms, in kg d.m.

o Dref diameter at breast height (1.30 m), in cm

Below-Ground Biomass (BGB) of live trees, bamboos and arborescent palms:

Calculated based on the AGB of sampled live trees (cf. Equations {5} or {7}), bamboos (cf. Equation {8}) and arborescent palms (cf. Equation {9}), using the BGB to AGB ratio



(R) of the 2006 IPCC guidelines (IPCC, 2006: IPCC guidelines for national GHG inventories - AFOLU: Table 4.4):

𝐵𝐺𝐵 = 𝑅 × 𝐴𝐺𝐵 Equation {10}

with

o BGB oven-dry Below-Ground Biomass of live trees, bamboos and arborescent palms, in kg d.m.

o R BGB to AGB ratio: 0.37

o AGB oven-dry Above-Ground Biomass, in kg d.m.

Biomass of Standing Dead Wood (SDW):

Calculated in two steps:

a) Calculation of the merchantable volume (V) of sampled Standing Dead Wood based on the diameter at breast height or above buttress (Dref) and the merchantable height (H) of sampled dead wood with a Dref ≥ 5.0 cm using the Philippine regional volume equations for dipterocarps and non-dipterocarps, see Chapter 3.7.1.

b) Conversion of V into biomass (SDW) using the Biomass Conversion and Expansion Factor (BCEFs) of merchantable growing stock volume to AGB of the 2006 IPCC guidelines (IPCC, 2006: IPCC guidelines for national GHG inventories - AFOLU: Table 4.5), divided by 2 (THIELE T. et al., 2010: Monitoring, assessment and reporting for sustainable forest management in Pacific Island Countries: Chapter 4.3.2.2) to account for decay:

𝑆𝐷𝑊 = 𝑉 × 𝐵𝐶𝐸𝐹𝑠 / 2 Equation {11}

with

o SDW biomass of Standing Dead Wood, in t d.m.

o V merchantable volume inside bark of Standing Dead Wood, in m³

o BCEFs Biomass Conversion and Expansion Factor of merchantable growing stock volume to AGB for humid tropical natural forests, in t / m³, depending on the growing stock level:

9.0 t d.m. / m³ for V < 10 m³ / ha

4.0 t d.m. / m³ for 10 m³ / ha < V ≤ 20 m³ / ha

2.8 t d.m. / m³ for 20 m³ / ha < V ≤ 40 m³ / ha

2.05 t d.m. / m³ for 40 m³ / ha < V ≤ 60 m³ / ha

1.7 t d.m. / m³ for 60 m³ / ha < V ≤ 80 m³ / ha

1.5 t d.m. / m³ for 80 m³ / ha < V ≤ 120 m³ / ha

1.3 t d.m. / m³ for 120 m³ / ha < V ≤ 200 m³ / ha

0.95 t d.m. / m³ for V > 200 m³ / ha

Biomass of Lying (downed) Dead Wood (LDW):


a) Calculation of the volume (V) of sampled Lying Dead Wood sections up to a minimum diameter of 5.0 cm based on the mid-diameter (Dref) and the length (L) using the cylinder formula:

𝑉 = π × 𝐷𝑟𝑒𝑓² / 40,000 × 𝐿 Equation {12}

with

o V volume of Lying Dead Wood section, in m³

o Dref mid-diameter of Lying Dead Wood section, in cm

o L length of Lying Dead Wood section within the sample plot, in m

b) Conversion of V into biomass (LDW) using the average wood density for Asia (FAO, 1997: Estimating biomass and biomass change of tropical forests - A



primer: Chapter 3.1.2) divided by 2 (THIELE T. et al., 2010: Monitoring, assessment and reporting for sustainable forest management in Pacific Island Countries: Chapter 4.3.2.2) to account for decay:

𝐿𝐷𝑊 = 𝑉 × 𝐷 / 2 Equation {13}

with

o SDW biomass of Lying Dead Wood, in t d.m.

o D average wood density for Asia: 0.57 t d.m. / m³

Biomass of Litter (LI):


a) Calculation of the volume (V) of sampled litter based on the ground coverage percentage (C) and the average depth (DPT) of the litter:

𝑉 = C × 𝐷𝑃𝑇 × 10,000 Equation {14}

with

o V volume of litter, in m³ / ha

o C ground coverage percentage of litter, in%

o DPT average depth of litter, in m

b) Conversion of V into biomass (LI) using the average density of litter (CHOJNACKY D. et al., 2009: Separating duff and litter for improved mass and carbon estimates: Table 2):

𝐿𝐼 = 𝑉 × 𝐷 Equation {15}

with

o LI biomass of litter, in kg d.m. / ha

o V volume of litter, in m³ / ha

o D average density of litter: 40 kg d.m. / m³

3.7.3 Carbon fraction of dry matter

The following Carbon Fractions (CF) of the 2006 IPCC guidelines are used for the calculation of the C equivalent of the various carbon pools calculated in terms of Dry Matter:

Carbon Fraction of Dry Matter for Living Biomass:

0.47 t C / t d.m. Equation {16}

according to the IPCC guidelines (IPCC, 2006: IPCC guidelines for national greenhouse gas inventories - Agriculture, forestry and other land uses: Table 4.3).

Carbon Fraction of Dry Matter for Dead Organic Matter:

0.37 t C / t d.m. Equation {17}

according to the IPCC guidelines (IPCC, 2006: IPCC guidelines for national greenhouse gas inventories - Agriculture, forestry and other land uses: Equation 2.19).



3.7.4 Statistical parameters

Assuming for simplicity's sake a random distribution of the Sampling Units, the estimated stratum and total means, variances, standard errors and margins of error are computed using the following formulas (cf. ZÖHRER F., 1980: Forstinventur: Ein Leitfaden für Studium und Praxis):

Stratum means

�̅�𝑗 =∑ 𝑦𝑖𝑗

𝑛𝑗𝑖=1

𝑛𝑗 Equation {18}

Stratum variances

𝑠𝑗2 =

∑ 𝑦𝑖𝑗2 − (∑ 𝑦𝑖𝑗

𝑛𝑗𝑖=1

)2

/ 𝑛𝑗

𝑛𝑗𝑖=1

𝑛𝑗−1 Equation {19}

Stratum standard errors

𝑆𝑗 =𝑠𝑗

√𝑛𝑗 Equation {20}

Stratum margins of error

𝐸𝑗 =𝑠𝑗

√𝑛𝑗× 𝑡𝑗 Equation {21}

Total mean

�̅� = ∑𝑛𝑗

𝑛× �̅�𝑗

𝑀𝑗=1 Equation {22}

Total variance

𝑠2 = ∑𝑛𝑗

𝑛× 𝑠𝑗

2𝑀𝑗=1 Equation {23}

Total standard error

𝑆 = √1

𝑛× (∑ 𝑃𝑗

𝑀𝑗=1 × 𝑠𝑗)

2 Equation {24}

Total margin of error

𝐸 = √1

𝑛× (∑ 𝑃𝑗

𝑀𝑗=1 × 𝐸𝑗)

2 Equation {25}

with

o 𝑦𝑖𝑗 variable (such as number of trees per ha, basal area per ha, volume per ha,

biomass per ha, etc.) of sampling unit i in stratum j;

o �̅�𝑗 arithmetic mean of variable 𝑦 in stratum j;

o �̅� total arithmetic mean of variable 𝑦;

o 𝑠𝑗2 variance of variable 𝑦 in stratum j;

o 𝑠2 total variance of variable 𝑦;

o 𝑆𝑗 standard error of the mean of variable 𝑦 in stratum j;

o 𝑆 total standard error of the mean of variable 𝑦;

o 𝐸𝑗 margin of error of the mean of variable 𝑦 in stratum j;

o 𝐸 total margin of error of variable 𝑦;

o 𝑀 number of strata;

o 𝑛𝑗 number of sampling units in stratum j;

o 𝑡𝑗 two-tailed Student t-value with 𝑛𝑗 degrees of freedom in stratum j;

o 𝑛 total number of sampling units;

o 𝑃𝑗 weight of stratum j.



4. FIELD IMPLEMENTATION

4.1 Retrieval and Permanent Marking of Sampling Units

4.1.1 Approach of Sample Points using GPS receivers

The Sample Points were accessed / retrieved on the basis of their geographic coordinates using handheld GPS stand-alone receivers. The Sample Points were uploaded from a computer as "Points of Interest (POIs)" rather than as "waypoints", using the "GARMIN POI loader" software (freeware downloadable from http://www8.garmin.com/support/ mappingsw.jsp). "POIs" offer the advantage that unlike "waypoints", they cannot be edited nor erased from the GPS receivers (unless connected to a computer and with the use of the aforementioned software). Good sources of information to study the approach of Sample Points are the following:

Satellite images in Google Maps (http://www.google.com/maps), Bing Maps (http://www.bing.com/maps) and Apple Maps (only available on Apple Mac OS and iPhone / iPad iOS operating systems), particularly where high resolution satellite data are available (see Figure 5), which was not the case for most of the upland areas in Eastern Samar; however, the images are regularly updated, and it is worthwhile to compare the different sources for best results;

Topographic maps in Open Cycle Map (http://www.opencyclemap.org) showing the "Outdoors" base layer, which is particularly useful for the appreciation of the relief (see Figure 6).

As much as possible, the approach of a targeted Sample Point was studied together with local helpers / guides, who are well versed with the terrain, existing trails, unsurmountable barriers and/or obstacles such as steep hills or waterlogged areas to be avoided.

Barangays Pinanag-an (Borongan City) and Patag (Maydolong) on the Suribao River

Figure 5: Apple Map



Mouth of the Suribao River

Figure 6: Open Cycle Map with "Outdoors" base layer

4.1.2 Location of Sample Points and Satellite Centers using compass and distance tape or laser rangefinder

Considering the limited positional precision of stand-alone GPS measurements / navigation (in practice ± 10 m, as evidenced by the virtual movement of an immobilized GPS receiver, which is a remarkable precision to come close to any point on the globe from whatever origin over considerable distances, but insufficient to measure distances of less than 100 m to 200 m, since the relative precision deteriorates to 10% - 5%), the location of Sample Points was determined covering the last 10 m to 15 m by compass and horizontal distance measurement (referring to the azimuth / bearing and distance to the Sample Point displayed by the GPS receiver once the distance to the destination was less than 15 m) using a distance tape or a ranging laser, in order to prevent bias (preference for easily accessible areas) when closing in on the Sample Point. The same applied to the location of the four (4) Satellite Centers of each Sampling Unit, situated at 40 m in the four (4) cardinal directions (North = 0°; East = 90°, South = 180°; West = 270°) from the Sample Point. The azimuth / bearing was measured with the help of a handheld precision compass.

4.1.3 Permanent marking of Sample Points and Satellite Centers

The Sample Points and the 4 Satellite Centers of each SU were permanently marked with an iron rod (of at least 1 cm diameter and 50 cm length), forced at least 4/5 of its length into the ground, topped with a 50 cm bright-colored 1/2 " PVC pipe to facilitate the retrieval for Quality Control (QC) purposes (cf. Chapter 6.2).

4.1.4 Inaccessible Sample Points and Satellite Centers

In the rare event that one of the Satellite Centers turned out to be inaccessible, it was re-located at 80 m horizontal distance from the Sample Point in the next cardinal direction, turning clockwise (see Figure 7: if the Western Satellite Center is inaccessible, its center may be re-located at 80 m horizontal distance to the West + 90° = North from the Sample Point).



Figure 7: Re-location of inaccessible "satellites"

In the equally rare event that a Sample Point turned out to be inaccessible, the SU was abandoned. A replacement Sample Point was drawn at random from those nodes of the quadratic grid with a side length of 1 km (see Figure 3) located (i) in the same forest stratum and (ii) at a similar elevation as the inaccessible Sample Point.. If one of the accessible Satellite Centers fell on an area whose "land cover" assessed in the field was other than "forest", it was not re-located, but observed / measured as is.



4.2 Variables of Interest Assessed / Measured

4.2.1 Administrative location

The administrative location, comprising at least the Region, Province and Municipality, and as much as possible the Barangay, was observed at and recorded for the Sample Points and all Satellite Centers. Hence, five such observations were recorded per SU (in some cases, a Sampling Unit may be crossed by an administrative boundary).

4.2.2 Actual coordinates

The actual UTM coordinates, comprising the Zone (in the Philippines 50 in Palawan, 52 in the Eastern-most portions of Mindanao, 51 elsewhere), the Northing in m and the Easting in m, were measured at and recorded for the Sample Points and all Satellite Centers. Hence, five coordinate measurements were performed per SU. The coordinates were read from the GPS stand-alone receiver, immobilized at the Sample Point or Satellite Center, using "averaging".

4.2.3 Elevation

The elevation in m above sea level was measured at and recorded for the Sample Points and all Satellite Centers. Hence, five elevation measurements were performed per SU. The elevation was read from the GPS stand-alone receiver.

4.2.4 Slope

The slope was measured at and recorded for the Sample Points and all Satellite Centers. Hence, five slope measurements were performed per SU. The slope corresponds to the average inclination in % measured with a handheld precision clinometer in two opposite directions along 10 m segments (oblique distance) of an imaginary straight line passing through the Sample Point / Satellite Center and following the steepest slope gradient (where water would run off).

4.2.5 Slope orientation

The slope orientation was measured at and recorded for the Sample Points and all Satellite Centers. Hence, five slope orientation measurements were performed per SU. The slope orientation corresponds to the azimuth / bearing in ° of the downhill direction of the imaginary straight line used for the measurement of the slope gradient, read from a handheld precision compass.

4.2.6 Terrain

The terrain / topography class was observed at and recorded for the Sample Points and all Satellite Centers. Hence, five terrain / topography classes assessments were performed per SU. The assessment through ocular inspection distinguished the 11 classes defined by FAO (FAO, 2012: National Forest Monitoring and Assessment - Manual for integrated field data collection. Version 3.0):

Plateau: Relatively flat (slope ≤ 5%); terrain of great extent and high elevation, above adjacent lowlands limited by an abrupt descent scarp on at least one side; may be dissected by deep valleys and deeply incised rivers.

Summit / crest: Crest of any kind or hilltop; can be sharp or rounded.



Upper slope: Upper slope of hillside (located on the upper 1/3 of the slope) (shoulder).

Middle slope: Middle slope of hillside (slope > 5%) (back slope).

Lower slope: Lower slope of hillside (foot slope).

Bench / terrace: Horizontal zone of average width over 30 m interposed in the valley side (slope < 15%) or a terrace over 6 m width.

Valley: Very wide, gently sloping depression with predominant extent in one direction commonly situated between two mountains or ranges of hills; the profile may be U- or V-shaped; includes river valley (formed by flowing water) or glacier valleys.

Plain: A large flat to very gently undulating area at a low elevation with reference to surroundings

Narrow depression: Enclosed depression or small, narrow valley or distinct crater (including ravine, gorges, gullies, canyons, etc.).

Water course: Permanent or temporary water course (river, etc.).

Dunes: Sandy hills developed through sand deposits from wind erosion / storms, often unstable and moving.

4.2.7 Land classification

The land classification (legal status) was observed at and recorded for the Sample Points and all Satellite Centers. Hence, five land classification assessments were performed per SU. The assessment through consultation of the latest available land classification map from DENR distinguished 2 classes:

Forest land.

Alienable and disposable.

4.2.8 Land cover

The land cover was observed at and recorded for the Sample Points within a radius of 25 m horizontal distance and all Satellites within a radius of 10 m horizontal distance from the centers. Hence, five land cover assessments were performed per SU. The assessment through ocular inspection distinguished forests (further classified according to their type, see Chapter 4.2.9) and the 11 non-forest land cover classes used in the 2010 NAMRIA national forest cover map:

Forest: Land with an area of more than 0.5 ha and trees able to reach a minimum height of 5 m in situ with a crown cover of more than 10% (see Chapter 2.1 for the detailed definition).

Marshland / swamp.

Fallow.

Shrubs.

Wooded grassland.

Grassland.

Annual crop.

Perennial crop.

Open / barren land.

Built-up area.

Fishpond.

Inland water.



4.2.9 Forest type

The forest type was observed at and recorded for the Sample Points within a radius of 25 m horizontal distance and all Satellites within a radius of 10 m horizontal distance from the centers. Hence, five forest type assessments were performed per SU. The assessment through ocular inspection distinguished the 8 natural forest types used in the conduct of the second National Forest Resources Inventory (1979 - 1988), plus 2 additional types for planted (man-made) forests:

Dipterocarp old growth forest: Tropical rain forest dominated by Dipterocarpaceae with traces of commercial logging.

Dipterocarp residual forest: Tropical rainforest dominated by Dipterocarpaceae after commercial logging.

Mossy forest: Tropical rainforests of the high elevations dominated by Podocarpaceae, Myrtaceae and Fagaceae with trees of medium height and short boled, covered with epiphytes.

Submarginal forest: Tropical rainforest dominated by Leguminosae and lesser utilized species, mainly restricted to shallow and excessively drained lime stone soils.

Closed Pine forest: Pure stands of Benguet or Minodoro Pine with crown cover > 30%.

Open Pine forest: Pure stands of Benguet or Minodoro Pine with 10% < crown cover ≤ 30%.

Mangrove old growth forest: Tidal forests dominated by Rhizophoraceae located on mud flats at the mouths of streams along the shore of protective bays, without traces of exploitation.

Mangrove reproduction forest: Tidal forests dominated by Rhizophoraceae and Verbenaceae dominated by Api-api (Avicennia officinalis) located on mud flats at the mouths of streams along the shore of protective bays, where utilization had been intensive and big trees had been removed.

Native tree plantation forest: Planted forest dominated by native rainforest species.

Other plantation forest: Planted forest dominated by non-native, often fast growing tree species.

4.2.10 Tree crown cover

The tree crown cover was observed at and recorded for the Sample Points within a radius of 25 m horizontal distance and all Satellites within a radius of 10 m horizontal distance from the centers. Hence, five tree crown cover assessments were performed per SU. The assessment through ocular inspection distinguished the 3 classes currently used by NAMRIA for forest cover mapping:

Non-forest: tree crown cover ≤ 10%).

Open forest: 10% < tree crown cover ≤ 40%.

Closed forest: Tree crown cover > 40%.

4.2.11 Plant diversity

The plant diversity was counted at and recorded for all Satellites within a radius of 5 m horizontal distance from the centers. Hence, four plant diversity counts were performed per SU. The inventory consisted of the counting of distinct higher plant species observed, even if not known by their local, official common or scientific names. To avoid repeated counting of the same species, the count was done by only one person, systematically collecting specimen of leaves from plants that can be reached from the ground.



4.2.12 Ground coverage classes by vegetation layers

Ground coverage classes for six vegetation layers were observed and recorded for all Satellites within a radius of 5 m horizontal distance from the centers. Hence, four times six ground coverage classes assessments were performed per SU. For each of the following 6 vegetation layers:

Grass, herbs and mosses.

Tree regeneration, shrubs and plants with 50 cm ≤ height < 130 cm.

Tree regeneration, bushes and plants with 130 cm ≤ height < 200 cm.

Undergrowth of any kind with 2.0 m ≤ height < 4.0 m.

Lower trees and other plants with 4.0 m ≤ height < 10.0 m.

High trees with height > 10.0 m.

the following 4 ground coverage classes were assessed through ocular inspection:

None.

Coverage ≤ 10%.

10% < coverage ≤ 50%.

Coverage > 50%.

4.2.13 Ground coverage and average depth of litter

Litter, defined as all non-living biomass with a size > 2 mm and < 5.0 cm (i.e. the minimum diameter / inventory threshold for dead wood), lying dead, in various states of decomposition above or within the mineral or organic soil, was inventoried and recorded for all Satellites within a radius of 5 m horizontal distance from the centers through ocular estimates of

the ground coverage in%, and

the average depth in cm.

4.2.14 Mid-diameter and length of lying dead wood sections

Lying dead wood, defined as all non-living woody biomass lying on the ground with a diameter ≥ 5.0 cm (i.e. the inventory threshold for dead wood and live trees) not contained in the litter, was inventoried and recorded for all Satellites within a radius of 5 m horizontal distance from the centers. For each lying dead wood section within the 5 m radius plot (without considering those portions extending beyond the plot, see Figure 8, the following measurements were performed:

Mid-diameter: Mid-diameter outside bark in cm, rounded to 0.1 cm, of the dead wood section within the 5 m radius plot, without considering those portions (i) extending beyond the plot, or (ii) with a diameter < 5 cm. The mid-diameter was measured using a caliper or a diameter tape.

Length: Length in m, rounded to 0.1 m, of the dead wood section within the 5 m radius plot, without considering those portions (i) extending beyond the plot, or (ii) with a diameter < 5 cm. The length was measured using a distance tape.

If a lying dead wood section featured branches, these were measured separately. In total, 414 pieces of lying dead wood have been sampled.



Figure 8: Measurements on lying dead wood sections

4.2.15 Observations / measurements on live trees and standing dead wood

Live trees1 and standing dead wood with DBH / DAB ≥ 5.0 cm were inventoried and recorded for all Satellites within a radius of

5 m horizontal distance from the Satellite Centers for

o "small-sized" live trees1 (all species) with 5.0 cm ≤ DBH / DAB < 20.0 cm;

o standing dead wood with DBH / DAB ≥ 5.0 cm;

10 m horizontal distance from the Satellite Centers for "big-sized" live trees1 (all species) with DBH / DAB ≥ 20.0 cm.

For each of the sampled live trees1 and standing dead wood, (i) the species, (ii) azimuth and (iii) horizontal distance from the Satellite Center, (iv) DBH / DAB and for standing dead wood with DBH / DAB ≥ 5.0 cm as well live trees with DBH / DAB ≥ 20.0 cm (v) the merchantable height were observed / measured and recorded as described hereafter. In total, 8,031 live trees1 and 207 standing dead wood have been sampled.



4.2.15.1 Species

The species of each sampled live tree1 and, as much as possible, of each standing dead wood was recorded as identified by the team mates or the local guides / helpers, referring to the official common name or the scientific name. Local names are not suited to unequivocally identify a species, because they vary from dialect to dialect, and even from place to place. In cases where a tree1 could only be identified through its local name, the latter was recorded, as much as possible together with other information (such as digital pictures) that could facilitate the later identification of the species by its scientific name with the help of the taxonomy / dendrology expert, Dr. Dennis PEQUE of the Visayas State University (VSU). Appendix 1 provides the list of species recorded by the inventory, including the official common names, the scientific family, genus and species names and the wood specific gravity.

4.2.15.2 Azimuth

The azimuth / bearing in ° of the center of each sampled live tree1 and standing dead wood at its basis / ground level was recorded as measured from the Satellite Center using a handheld precision compass.

4.2.15.3 Horizontal distance

The horizontal distance in m, rounded to 0.1 m, of the center of each sampled live tree1 and standing dead wood at its basis / ground level was recorded as measured from the Satellite Center using a distance tape or a laser rangefinder.

4.2.15.4 Diameter at breast height / above buttress

The diameter at breast height / above buttress outside bark in cm, rounded to 0.1 cm, of each sampled live tree1 and standing dead wood was recorded as measured using a diameter tape at the following measurement points (see also Figure 9):

in general at "breast height", i.e. 1.3 m above ground ("Diameter at Breast Height [DBH]") as measured from the uphill side of the stem;

for trees with prominent buttresses / basal flanges at breast height, the diameter is measured 30 cm above the end of the buttresses / flanges ("Diameter Above Buttress [DAB]");

for trees with bulges, swellings, depressions, branches or other abnormalities at breast height, the diameter is measured just below and above the abnormality at a point where it ceases to affect normal stem form, and computed as the average of the two measurements;

for stumps with a total height < 1.3 m at the section.

If a live tree / standing dead wood forks immediately above breast height, the diameter was measured below the swell resulting from the fork. If a live tree / standing dead wood forks below breast height, the stems were considered as separate trees / standing dead woods. On leaning live trees / standing dead woods, the "breast height" was determined along the axis of the stem.



Source: ZÖHRER F., 1980: Forstinventur: Ein Leitfaden für Studium und Praxis

Figure 9: DBH / DAB measurements

Whenever it proved impossible to measure the DBH / DAB with a diameter tape as described above (e.g. when the measurement point is inaccessible), it was approximated by comparison with a metric tape held horizontally at the base of the tree (see Figure 10).



Figure 10: Diameter estimates for inaccessible measurement points

4.2.15.5 Merchantable height

The merchantable height in m, rounded to 0.1 m, of each sampled live tree with DBH / DAB ≥ 20.0 cm and of each sampled standing dead wood with DBH / DAB ≥ 5.0 cm including stumps was recorded as measured using either a laser hypsometer or a handheld precision clinometer. Merchantable height of trees with DBH / DAB ≥ 35.0 cm is defined as the linear distance along the axis of the stem from the stump height to the top merchantability limit which is restricted by forks, large limbs, sweep, crook or decay, which make segments of the stem un-merchantable for saw logs. For trees with 15.0 cm ≤ DBH / DAB < 35.0 cm, the volume section is limited by a minimum top diameter inside bark which is fixed at 60% of DBH / DAB. By this definition, the measurement to the base of the tree has to be a measurement to the place where the felling cut would be applied, usually about 50 cm above ground, or above the buttresses. Limits for merchantability are the following:

Size of limbs and knots: The sum of diameters in any ¼ m segment ½ the diameter of the log at that point. Where limb and knot diameters exceed this limit, the merchantable height cannot extend through that point, unless there is a merchantable section of 3 m or more in length above that point.

Sweep: Sweep is a curvature in a tree section. Sweep is measured in centimeters of departure of the center line of the section from a straight line joining the centers of each end of the section. The departure is measured at the midpoint of the section containing the sweep. A simple rule for maximum sweep is that departure minus allowance for long taper cannot exceed ½ the small end diameter of the section. Merchantable length is terminated below a section with excessive sweep unless there is a merchantable section of 3 m or more in length above that section.

Crook: Crook is a more or less abrupt bending or angle in a tree section. Crook is measured in cm of maximum departure of the section center line from an extension of the center line of the straight portion of the log. The maximum departure cannot exceed ½ the small end diameter of the log. Excessive crook should terminate the merchantable length unless there is a merchantable section of 3 m or more in length above that section.



5. ORGANIZATIONAL ASPECTS

5.1 Inventory Instructions and Field Data Forms

A specific and detailed FRA field manual (LENNERTZ R., FIEL R. and MEGRASO C.P., 2014: Field Manual for the Forest Resources Assessments in Eastern Samar and Davao Oriental) explaining the use and care of the equipment, the configuration of the SUs as well as the orderly sequencing of the field operations to retrieve, establish, permanently mark and assess / measure the SUs was prepared to ensure that the field work follows Standard Operating Procedures (SOPs), minimizing operating errors and maximizing the homogeneity of the data acquisition. The data were recorded with pencils on sets of purposely designed paper field data forms (see Appendix 3). The latter were regularly collected by the Junior Advisor, Mr. Cyrus Peter MEGRASO, coordinating and supervising the field works, and taken to the office for electronic data encoding and processing.

5.2 Inventory Teams

Two Teams carried out the FRA field work between 01 December 2014 and 24 July 2015 (with a short break for Christmas and New Year) during a net assignment (including training) of 6.5 months (a limit imposed by the available budget). Each team was composed of the following:

Team Leader:

o Mr. Jose PALAÑA, B.Sc. Forestry (Central Mindanao University);

o Mr. Yolises POLEA, B.Sc. Forestry (Visayas State University);

Assistant:

o Mr. Christian Roquelo GONZALES, B.Sc. Forestry (Visayas State University);

o Mr. Val Jeason SOLANO, B.Sc. Forestry (Visayas State University);

Two to four Helpers (according to the accessibility of the SUs being measured), recruited locally, familiar with the area and preferably knowledgeable about tree species / forest products.

The Team Leaders were responsible for the security of the team, for the equipment entrusted to them, and for the work of their members. They directed the members, validated the data observed or measured by their Assistants, and completed the field data forms. The Assistants manipulated the equipment and carried out the observations and measurements. The Helpers advised on the retrieval of the sample points, carried the equipment, opened / brushed trails, access and sighting lines, marked the sample points and centers of the "satellites", helped the Assistants in carrying out the measurements, and marked the trees. The teams were occasionally accompanied by staff from DENR CENRO Borongan City. One Control Team, composed of:

Senior Advisor Mr. Ransom FIEL, B.Sc. Forestry (Visayas State University), and/or

Junior Advisor Mr. Cyrus Peter MEGRASO, M.Sc. Forestry (Visayas State University),

Two to four Helpers,

often accompanied by staff notably from FMB, DENR VIII, CENRO Borongan City and CLGU Borongan City, re-measured for Quality Control purposes (see Chapter 6.2) 12 SUs (10% of the measured SUs) between 24 April 2015 and 10 February 2016.



5.3 Inventory Equipment

To carry out the field work, each team was equipped with the following:

One handheld IPX7 waterproof GPS receiver (GARMIN GPSMap 78 series) with proven sensitivity / ability to operate under difficult signal reception conditions (under tree cover), to retrieve the Sample Points and measure coordinates.

One handheld IPX7 waterproof precision compass (SUUNTO KB-14/360) graduated in degrees for the measurement of bearings / azimuth.

One handheld IP54 laser hypsometer (LASER TECHNOLOGY Inc. [LTI] TruPulse Laser 200 rangefinder) for the measurement of tree heights using the trigonometric principle, hence capable of measuring distances and inclination angles. Regrettably, the LTI TruPulse Laser 200 hypsometer is not waterproof. Once to be replaced, it is recommended to consider the IP55 waterproof LTI TruPulse Laser 200X instead.

One handheld IPX7 waterproof precision clinometer (SUUNTO PM-5/360) as alternative to and backup for the laser hypsometer (a strategy that paid off when the laser hypsometers failed to work during and after heavy rain).

One fiberglass distance tape, 30 m, to measure distances.

One steel diameter tape, 5 m, to measure tree diameters. Upon request of the teams, the steel tapes were rapidly replaced with fiberglass tapes to lessen the risk of injuries from the sharp cutting edges of the steel tapes.

Per SU five iron rods (of at least 1 cm diameter and 50 cm length) to permanently mark the Sample Points and the Satellite Centers, forced at least 4/5 of its length into the ground, topped each with a 50 cm bright-colored 1/2 " PVC pipe to facilitate the retrieval for Quality Control (QC) purposes (cf. Chapter 6.2).

One hatchet to force the iron rods used to permanently mark the Sample Points and the Satellite Centers into the ground.

One first aid kit.

One backpack to carry the equipment.

Personal field work gear for the Team Leaders and Assistants (boots, rain coats, head lamps, sleeping bags, etc.)

Camping equipment (tents, mobile stoves, etc.).

5.4 Training

The teams were to undergo a 6-day training, scheduled from 01 - 06 December 2014, with the following program:

Day 1: Taxonomy and dendrology, common tree / bamboo / palm / rattan species of Eastern Samar and their identification, inventory methodology, data processing and analysis, quality assurance and quality control, work plan for the implementation of the FRA, presentation of instruments and measurements.

Day 2: Common tree / bamboo / palm / rattan species of Eastern Samar and their identification, presentation of instruments and measurements, use (hands-on) / configuration / care of the instruments (GPS receiver, compass, hypsometer, clinometer).

Day 3: Retrieval, establishment and measurement of a training SU.

Day 4: Measurement of the training SU (continuation).

Day 5: Retrieval, establishment and measurement of an actual SU.

Day 6: Tree / bamboo / palm / rattan species identification (on field).

The taxonomy / dendrology subjects were administered by Dr. Dennis PEQUE from the Visayas State University (VSU), who also continued during the implementation of the FRA



assisting in the determination of species not known to the Team Mates, based on local names or digital pictures of samples forwarded to him. The inventory-related subjects were administered by Chief Advisor Mr. Ralph LENNERTZ, Senior Advisor Mr. Ransom FIEL and Junior Advisor Mr. Cyrus Peter MEGRASO. Unfortunately, the training had to be aborted after the third day (03 December 2014) due to the threatening typhoon Hagupit (Ruby), which effectively made landfall over Dolores, Eastern Samar, at 21:15 of 06 December 2015. Since all four FRA team mates had previous experience in forest inventory, they were deemed to be properly trained to proceed with the field work beginning of January 2015.

5.5 Inventory Camps

Considering the location of the SUs to be inventoried and their accessibility, studied on the basis of all available information (see Chapter 4.1.1), the SUs were grouped into batches assigned to inventory camps strategically located, preferably in Barangays or Sitios. A reasonable compromise had to be found between (i) the number of SUs assigned to a specific inventory camp (ideally not less than the number of SUs that can be observed / measured in one field mission, and (ii) the distance from the inventory camp to the furthest SU. The output could have been higher if the teams had agreed to operate separately, since this would have reduced the average distance between the inventory camp and its assigned SUs. However, they insisted to camp together for safety reasons.

5.6 Time and Costs of the Field Work

Based on the experience gained in the implementation of the forest carbon baseline study from mid-2011 until end 2012 in Leyte in the framework of the BMUB funded, GIZ-assisted Climate relevant Modernization of Forest Policy and Piloting of Reducing Emissions from Deforestation and Forest Degradation (REDD) Project, it was initially expected that one Inventory Team working 2 x 12 days without break per month (to reduce the proportion of time spend in mobilization / de-mobilization) could establish and measure an average of 16 SUs per month. As a matter of fact, the average output turned out to be much lower: 9 to 10 SUs per month and per team, in total 120 SUs over the 6.5 months available. The factors that have contributed to the lower than expected output are the following:

remoteness and very limited accessibility of the area to be inventoried (the furthest Sample Points are located 28 km [straight distance] from the sea shore / the only paved road):

o the Sample Points in the North-Western portion are located up to 18 km (straight distance) from Barangay San Gabriel, Borongan City, accessible from Borongan City by a graveled, partly paved road;

o the Sample Points in the Center are located up to 10 km (straight distance) from Barangay Cabalagnan, Borongan City, accessible from Barangay Camada, Borongan City, by a graveled road;

o the Sample Points in the South-Western portion are located up to 13 km (straight distance) from Barangay Patag, Maydolong, accessible from Barangay Camada, Borongan City, along the coastal road by a 2h30' boat ride on the Suribao river;



landslides and fallen trees caused by typhoon Ruby, which struck the area in December 2014, right before the start of the field work;

information of and coordination with local officials (Barangay Captains) prior to the hiring of local helpers / guides, preventing the teams to swiftly proceed to the Sample Points or inventory camps;

a considerable number of short field missions of 5 working days only, increasing the proportion of the "unproductive" time, since SUs could generally not be established / measured during the first and last day of a mission essentially dedicated to the traveling.

The costs of the field work are summarized in Table 4.

Table 4: Time and costs the FRA field work in Eastern Samar

Item Unit Quantity Cost / Total

Unit Cost

[PHP/Unit] [PHP]

Personnel Costs

Team Leaders (2 #) person-month 13 32,000 416,000

Assistants (2 #) person-month 13 28,000 364,000

Helpers (4 - 8 #) person-day 620 250 155,000

Operational Costs

Consumables (stationaries, batteries, paint, steel rods, etc.)

68,000

Transportation 16,000

Total 1,019,000

Future inventories should consider higher personnel costs, and foresee a food allowance for the helpers.



5.7 Data Processing and Analysis

To reduce licensing fees and to promote open standards, the FRA data were encoded and processed using the leading open source cross platform Object Relational Database Management System (ORDBMS) database engine MySQL (cf. http://www.mysql.com/) using the popular Structured Query Language (SQL). The database architecture is described in detail in a separate document (BARROIS V., 2015: Forest Resources Assessment Database Architecture). A user-friendly FRA Database System Application has been developed using the equally cross platform Java Development Kit (JDK) (cf. http://www.oracle.com/technetwork/java/ javase/overview/index.html). Its installation, including the installation of the required free software (MySQL Community Server 5.6.27 and Java Runtime Environment [JRE] 8 Update 66) under MICROSOFT Windows operating system environments, is described in a separate installation guide (BARROIS V., 2015: Forest Resources Assessment Database System Application installation guide). Version 3.1 of the FRA Database System Application features:

all essential data management operations: add, delete, edit, print to PDF, backup, restore data;

a series of data integrity checks, attracting the data encoder's attention with the help of "traffic lights" (green = integrity check passed; orange = warning; red = integrity check failed) to missing, out-of-range, incompatible and unusual values;

populated reference tables, notably:

o all Philippine LGUs (currently 18 Regions, 81 Provinces, 144 Cities, 1,504 Municipalities and 42,036 Barangays), following the Philippine Standard Geographic Code (PSGC), published by the National Statistical Coordination Board (NSCB, cf. http://www. nscb.gov.ph/activestats/psgc/), in its version of 31 December 2015;

o a growing number of Philippine tree / bamboo / palm / rattan / tree fern species (currently 518 species), whose scientific names have been validated / updated consulting The Plant List Version 1.1 (cf. http://www.theplantlist.org/), a joint effort of Kew and Missouri Botanical Gardens, completed with the wood specific gravity (see Chapter 2.4);

o a global gridded layer of E at 2.5 arc-minute resolution for the use of the latest allometric equations developed 2014 by CHAVE J. et al. (see Chapter 3.7.2, equation {6});

a comprehensive and versatile data analysis framework:

o computing relative frequency, relative density, relative dominance, importance, N/ha, G/ha, V/ha, AGB/ha, BGB/ha, LB/ha by tree / bamboo / palm / rattan / tree fern species and diameter class,

o computing SDW/ha, LDW/ha, LI/ha, DOM/ha, SOM/ha and Total C/ha,

o providing tabular results including statistical precision estimates that can be exported to MS Excel, printed to PDF or depicted as pie or bar charts, and

o displaying species occurrence by geographic coordinates and/or in Google Earth).



6. QUALITY ASSURANCE / QUALITY CONTROL

6.1 Quality Assurance

Apart from the selection of experienced inventory team mates (see Chapter 5.2) and their training (see Chapter 5.4), the key elements of the Quality Assurance (QA) were the following:

A specific and detailed FRA field manual (LENNERTZ R., FIEL R. and MEGRASO C.P., 2014: Field Manual for the Forest Resources Assessments in Eastern Samar and Davao Oriental), with instructions to be complied with to ensure that the field work followed SOPs, minimizing operating errors and maximizing the homogeneity of the data acquisition. The manual was designed to be incremental, i.e. to be enhanced and/or amended based on the feedback from the teams for situations initially not covered or procedures not clearly described. Actually, no technical concerns have been raised, apart from the (granted) request to replace the steel diameter tapes with fiberglass tapes. The team's grievances essentially concerned (i) organizational matters, notably the need for sufficient prior coordination with the local officials and the Armed Forces of the Philippines (AFP), (ii) security issues considering the presence of insurgents, and (iii) long hiking distances to the Sample Points.

Regular accompaniment of the inventory teams by the Senior and/or Junior Advisor assigned in Borongan City, more frequently in the beginning, to observe whether the inventory procedures, assessments and measurements are carried out correctly. Actually, the accompaniment was limited, due to the busy schedule of these Advisors.

Data encoding by a person with technical background (as a matter of fact by the Junior Advisor) closely following the data acquisition in the field, so that eventual gaps and errors observed could be ironed out with minimal effort, and the inventory teams be cautioned on typical and critical issues.

Thorough verification of the encoded inventory data by the Chief Advisor, paying attention to unusual values and the coherence of the data.

6.2 Quality Control

Ten percent (10%) of the SUs chosen at random and without prior knowledge of the Inventory Teams were subject to an independent re-measurement, conducted under the lead of the Senior or Junior Advisor, in cooperation with representatives from DENR and the partner-LGUs. The re-measurement concerned SUs No. EASM0009, EASM0013, EASM0036, EASM0048, EASM0054, EASM0059, EASM0072, EASM0084, EASM0091, EASM0118, EASM0120 and EASM0126. Table 5 summarizes the differences between the initial measurements and the (presumably correct) re-measurements (serving as reference) can be assessed through the mean absolute deviation (MAD) and the root mean square deviation (RMSD). Such deviations must be interpreted cautiously as long as the number of re-measured SUs remains low (say less than about 16 SUs). The following differences between the initial and re-measurements have been observed:

very frequently diverging merchantable height measurements, because of the reduced visibility in the stands; under such conditions, height measurements tend to be made from positions too close to the trees1, leading to steep sighting angles, resulting in inaccurate estimates; this source of error was anticipated, hence the preference for allometric equations relying on DBH / DAB measurements only;

diverging DBH / DAB measurements, at times observed during the re-measurements to be due to the non-removal of vines during the initial measurements; other sources of these differences are non-standard measurement points above ground, and diameter



tapes either not tightened or not held horizontally;

diverging assessments of "borderline" trees1 (at the fringe of the 5 m and 10 m radii plots), falsely considered either to be part or not to be part of the sample; hence the importance of a through checking of such trees1;

trees1 qualified by the initial measurements as dead though found alive during the re-measurements; this could be due to the defoliating effect of typhoon Hagupit (Ruby), which affected the area shortly before the beginning of the field work;

diverging species identifications.

Table 5: Deviation of initial from control measurements

Variable of Interest MAD RMSD

[%]* [%]*

Density (N/ha) 2.8 5.6

Basal Area (G/ha) 3.6 5.5

Merchantable Volume (V/ha) 14.7 27.4

Above-Ground Biomass (AGB/ha) 5.2 10.2

Standing Dead Wood (SDW/ha) 7.8 15.9

Lying Dead Wood (LDW) 19.2 49.0

Litter (LI/ha) 5.0 14.5

Number of plant species 5.3 9.6

* with reference to the control measurement

Overall, the mean deviations are reasonable, as expected higher when height measurements are involved (i.e. for the estimation of V/ha, and of SDW/ha). Another ten percent (10% of the encoded SUs chosen at random were printed and subject to an independent comparison with the original field data forms. The comparison was done for SUs No. EASM0006, EASM0015, EASM0027, EASM0035, EASM0046, EASM0066, EASM 0089, EASM0108, EASM0109, EASM0126, EASM0141 and EASM0143. For 58% of the SUs, no discrepancies were found between the original field data and the encoded data. For the remaining half of the SUs, the following differences between the original field data and the encoded data have been observed:

typing errors with minimal impact on the variables of interest;

one tree1 encoded twice.

Table 6: Deviation of encoded from field data

Variable of Interest MAD RMSD

[%]* [%]*

Density (N/ha) 0.2 0.6

Basal Area (G/ha) 0.1 0.1

Merchantable Volume (V/ha) 0.0 0.1

Above-Ground Biomass (AGB/ha) 0.0 0.1

Standing Dead Wood (SDW/ha) 0.0 0.0

Lying Dead Wood (LDW) 0.4 1.3

Litter (LI/ha) 0.0 0.0

Number of plant species 0.0 0.0

* with reference to the field data forms



7. DETAILED RESULTS OF THE FOREST RESOURCES ASSESSMENT

The detailed results of the FRA are provided in Appendix 4 (Closed Forests, based on 18 SUs) and Appendix 5 (Open Forests, based on 102 SUs), as computed and printed to PDF by the FRA Database System Application (see Chapter 5.7). A summary analysis is presented hereafter, focusing successively on the following:

species diversity, see Chapter 7.1;

stand composition, see Chapter 7.2;

stand structure, see Chapter 7.3;

timber stocks, see Chapter 7.4;

carbon stocks (including a Tier 1 carbon stocks estimate for Mangroves), see Chapter 7.5.

The results pertain to trees1 with DBH / DAB ≥ 5.0 cm. The merchantable volume in cubic meter (m³) inside bark has been estimated using the Philippine regional volume equations for dipterocarps and non-dipterocarps, see Chapter 3.7.1. The AGB of live trees has been estimated using the allometric equation developed by CHAVE J. et al., 2014, see Chapter 3.7.2, equation {5} (the FRA Database System Application offers the option to alternatively estimate the AGB of live trees using the allometric equation developed by S. BROWN [FAO, 1997], see Chapter 3.7.2, equation {7}).

7.1 Species Diversity

In ecological studies, the terms "relative frequency", "relative density", "relative dominance" and "importance", used to analyze and particularly to compare species diversity are defined as follows:

The relative frequency of a particular species is defined as the proportion in percent (%) of the SUs where that species has been sampled.

The relative density of a particular species is defined as its proportion in percent (%) of the total stand density (N/ha), all species combined.

The relative dominance of a particular species is defined as its proportion in percent (%) of the total basal area (G/ha), all species combined.

The importance of a particular species, typically used to determine the rank of species, is defined as the sum of its relative frequency, density and dominance.

The following brief analysis of the species diversity refers to these definitions.

7.1.1 Species diversity of Closed Forests

A total of 121 different species have been found and identified in the 18 SUs in the Closed Forests, including three species whose local names could not be translated into common or scientific names. This is relatively few, compared to the 236 different species found and identified in the 120 SUs across Closed and Open Forests. With more / additional SUs in Closed Forests, the number of species would certainly still increase. From 22 to 56, on average 33 different higher plant species have been observed per SU. Table 7 lists the 20 most "important" species (in the sense of the definition given in Chapter 7.1), led by Yakal, closely followed by Ulayan. As expected, 7 Dipterocarps (Yakal, Mayapis, Guijo, Almon, Yakal-kaliot, Red Lauan and Tangile) are among the most



"important" species, but also Ulayan, Bitanghol, two Sapotaceae (Malak-malak and Nato) and a palm (Sagisi).

Table 7: Relative frequency, density and dominance, importance and rank of the 20 most "important" species in Closed Forests

Species Relative Frequency

Relative Density

Relative Dominance

Importance

[%] Rank [%] Rank [%] Rank [-] Rank

Yakal 72,22 2 11,02 1 9,79 2 93,03 1

Ulayan 77,78 1 6,31 3 4,05 7 88,13 2

Bitanghol 66,67 3 8,58 2 3,21 10 78,46 3

Mayapis 61,11 4 2,82 6 12,35 1 76,28 4

Guijo 66,67 3 1,65 13 5,01 6 73,33 5

Malak-malak 61,11 4 5,22 4 5,14 5 71,48 6

Kalipapa 61,11 4 2,44 8 2,28 12 65,82 7

Ebony 55,56 5 1,60 14 1,07 20 58,22 8

Nato 55,56 5 0,87 24 1,48 14 57,91 9

Sagisi 50,00 6 2,27 9 1,12 17 53,39 10

Almon 44,44 7 0,81 25 7,36 4 52,62 11

Badling 50,00 6 1,35 15 0,86 21 52,22 12

Tiga 44,44 7 2,95 5 3,49 8 50,88 13

Yakal-Kaliot 44,44 7 2,95 5 2,39 11 49,79 14

Yabnob 44,44 7 1,33 16 0,68 24 46,45 15

Red Lauan 33,33 9 1,25 18 8,92 3 43,50 16

Tangile 38,89 8 0,41 37 1,43 15 40,72 17

Lanete 38,89 8 0,60 32 0,27 51 39,75 18

Duguan 33,33 9 1,92 11 1,96 13 37,21 19

Mankono 33,33 9 2,76 7 1,10 18 37,20 20

Figure 11 shows that relatively few species, ranked in decreasing order of their contribution to N/ha, G/ha, V/ha and AGB/ha, constitute 50% of the totals:

four species, namely Mayapis, Red Lauan, Almon and Yakal together represent nearly 56% of the merchantable volume;

six species, namely Red Lauan, Yakal, Mayapis, Guijo, Almon and Tiga represent together almost 55% of the AGB;

seven species, namely Mayapis, Yakal, Red Lauan, Almon, Malak-malak, Guijo and Ulayan represent together more than 52% of the basal area;

twelve species represent together some 51% of the density.

However, it takes 79, 66, 32 and 53 species to "explain" 95% of the total N/ha, G/ha, V/ha and AGB/ha, respectively.



Figure 11: N/ha, G/ha, V/ha and AGB/ha by number of species in Closed Forests

Table 8 lists the "threatened" species (according to the International Union for Conservation of Nature and Natural Resources [IUCN] red list of threatened species, see http://www.iucnredlist.org/) sampled in the Closed Forests. Practically all Dipterocarps (14 species) are considered "critically endangered" by IUCN.

Table 8: Threatened species in Closed Forests

Vulnerable (VU) Endangered (EN) Critically Endangered (CR)

Antipolo, Balobo, Dalingdingan, Kalingag, Katmon, Laneteng gubat, Lanutan, Malak-malak, Malasantol, Nato, Piling-liitan, Takip-asin

Mankono, Narig Almon, Apitong, Bagtikan, Gisok-gisok, Guijo, Hagakhak, Manggachapui, Mayapis, Panau, Red Lauan, Tangile, White Lauan, Yakal, Yakal-Kaliot

7.1.2 Species diversity of Open Forests

A total of 246 different species have been found and identified in the 102 SUs in the Open Forests, including fourteen species whose local names could not be translated into common or scientific names. From 14 to 54, on average 30 different plant species have been observed per SU. Table 9 lists the 20 most "important" species (in the sense of the definition given in Chapter 7.1), led by Ulayan, closely followed by Yakal. 7 Dipterocarps (Yakal, Mayapis, Guijo, Red Lauan, Almon, Yakal-kaliot and Palosapis) are among the most "important" species, but also Bitanghol, two Sapotaceae (Malak-malak and Nato) and a palm (Sagisi).



Table 9: Relative frequency, density and dominance, importance and rank of the 20 most "important" species in Open Forests

Species Relative Frequency

Relative Density

Relative Dominance

Importance

[%] Rank [%] Rank [%] Rank [-] Rank

Ulayan (Oak) 71,57 1 4,64 2 3,98 5 80,19 1

Yakal 63,73 2 5,39 1 8,69 3 77,80 2

Mayapis 56,86 4 3,10 6 11,69 1 71,65 3

Guijo 61,76 3 2,87 7 4,81 4 69,44 4

Red Lauan 55,88 5 2,81 8 10,28 2 68,97 5

Bitanghol 56,86 4 4,59 3 2,51 8 63,97 6

Sagisi 50,98 6 3,56 5 1,19 17 55,72 7

Malak-malak 44,12 8 2,08 10 2,89 6 49,09 8

Duguan 45,10 7 1,76 11 1,67 13 48,53 9

Kalipapa 42,16 9 1,65 14 1,05 19 44,86 10

Apanang 38,24 10 4,10 4 1,78 11 44,11 11

Almon 36,27 11 0,70 34 2,49 9 39,47 12

Mankono 34,31 12 1,24 17 1,92 10 37,47 13

Dalunot 30,39 13 1,70 13 0,43 45 32,53 14

Nato 30,39 13 0,73 32 1,19 16 32,31 15

Yakal-Kaliot 28,43 15 1,74 12 1,03 20 31,20 16

Malatambis 29,41 14 0,93 22 0,51 35 30,86 17

Badling 27,45 16 1,15 20 0,66 27 29,26 18

Piling-liitan 25,49 17 0,78 29 0,45 40 26,72 19

Palosapis 24,51 18 1,00 21 1,22 15 26,72 20

Figure 12 shows that relatively few species, ranked in decreasing order of their contribution to N/ha, G/ha, V/ha and AGB/ha, constitute 50% of the totals:

four species, namely Mayapis, Red Lauan, Yakal and Guijo together represent some 53% of the merchantable volume;

seven species, namely Red Lauan, Yakal, Mayapis, Guijo, Bansalangin, Ulayan and Malak-malak represent together almost 52% of the AGB;

ten species, namely Mayapis, Red Lauan, Yakal, Guijo, Ulayan, Malak-malak, Bansalangin, Bitanghol, Almon and Mankono represent together more almost 52% of the basal area;

twenty one species represent together some 51% of the density.

However, it takes 138, 115, 62 and 97 species to "explain" 95% of the total N/ha, G/ha, V/ha and AGB/ha, respectively.



Figure 12: N/ha, G/ha, V/ha and AGB/ha by number of species in Open Forests

Table 10 lists the "threatened" species (according to the IUCN red list of threatened species) sampled in the Open Forests. Practically all Dipterocarps (16 species) sampled in the Open Forests are considered "critically endangered" by IUCN.

Table 10: Threatened species in Open Forests

Vulnerable (VU) Endangered (EN) Critically Endangered (CR)

Antipolo, Balobo, Dalingdingan, Dalinsi, Hamindang, Ipil, Is-is, Kalingag, Katmon, Laneteng gubat, Lanutan, Malakape, Malak-malak, Malasantol, Molave, Nato, Pahutan, Piling-liitan, Pili, Puso-puso, Takip-asin, Tanglin, Tindalo

Mahogani, Mankono, Narig, Yakal-Mabolo

Almon, Apitong, Bagtikan, Gisok-gisok, Guijo, Hagakhak, Highland Panau, Malapanau, Manggachapui, Mayapis, Panau, Red Lauan, Tangile, White Lauan, Yakal, Yakal-Kaliot

The relatively limited number of SUs in the Closed Forests (18) precludes a thorough comparison of the species diversity with the Open Forests. Apanang and Malatambis seem to occupy significantly higher "importance" ranks in Open than in Closed Forests.



7.2 Stand Composition

7.2.1 Stand composition of Closed Forests

Table 11 summarizes and Figure 13 illustrates the stand composition of the Closed Forests in terms of N/ha, G/ha, V/ha and AGB/ha. Except in terms of density, the stands are clearly dominated by Dipterocarps, whose share exceeds 50% in terms of basal area, and even more (71.1%) in terms of merchantable volume. This stems from the fact that the average size of Dipterocarps, as shown through the quadratic mean diameter (Dg), is considerably larger (25.7 cm) than the Dg of Non-Dipterocarps (15.0 cm).

Table 11: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Closed Forests

Species / Dg N/ha G/ha V/ha AGB/ha

Species Group [cm] [/ha] [%] [m²/ha] [%] [m³/ha] [%] [t. d.m./ha] [%]

Dipterocarps

Mayapis 38.3 46.0 2.8 5.30 12.3 45.56 19.8 47.34 10.8

Yakal 17.2 179.9 11.0 4.20 9.8 20.83 9.1 48.96 11.2

Red Lauan 49.0 20.3 1.2 3.83 8.9 35.15 15.3 49.16 11.3

Almon 55.0 13.3 0.8 3.16 7.4 27.67 12.0 33.39 7.7

Guijo 31.8 27.0 1.7 2.15 5.0 18.28 7.9 35.84 8.2

Other Dipterocarps 17.4 134.8 8.3 3.22 7.5 15.96 6.9 37.23 8.5

Sub-Total Dipterocarps 25.7 421.3 25.8 21.86 50.9 163.44 71.1 251.91 57.7

Non-Dipterocarps

Malak-malak 18.1 85.8 5.3 2.21 5.1 8.88 3.9 15.45 3.5

Ulayan 14.7 103.0 6.3 1.74 4.1 3.76 1.6 13.37 3.1

Tiga 19.9 48.2 3.0 1.50 3.5 5.62 2.4 23.96 5.5

Bansalangin 23.8 31.8 1.9 1.42 3.3 8.47 3.7 22.05 5.1

Bitanghol 11.2 140.1 8.6 1.38 3.2 2.24 1.0 7.33 1.7

Kalipapa 17.7 39.8 2.4 0.98 2.3 3.82 1.7 9.44 2.2

Duguan 18.5 31.4 1.9 0.84 2.0 3.20 1.4 4.42 1.0

Nato 24.0 14.1 0.9 0.64 1.5 3.98 1.7 6.05 1.4

Sudiang 28.7 9.3 0.6 0.60 1.4 2.82 1.2 9.26 2.1

Mankono 11.5 45.1 2.8 0.47 1.1 0.79 0.3 3.19 0.7

Other Non-Dipterocarps 13.4 608.8 37.3 8.62 20.1 22.95 10.0 67.94 15.6

Sub-Total Non-Dipterocarps 15.0 1 157.0 70.8 20.39 47.5 66.52 28.9 182.46 41.8

Palms 54.8 3.4 0.68 1.6 2.09 0.5

Total 1 633.1 100.0 42.92 100.0 229.97 100.0 436.46 100.0

The five most dominant Dipterocarps in terms of basal area are Mayapis, Yakal, Red Lauan, Almon and Guijo. Together, they represent 90% of the total Dipterocarp merchantable volume, and around 64% of the total merchantable volume, all species combined.



N/ha [/ha]

G/ha [m²/ha]

V/ha [m³/ha]

AGB/ha [t d.m./ha]

Dipterocarps Non-Dipterocarps

Palms Mayapis Yakal Red Lauan Almon Guijo Other Dipterocarps

Figure 13: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Closed Forests



The ten most dominant Non-Dipterocarps in terms of basal area, led by Malak-malak closely followed by Ulayan, Tiga, Bansalangin and Bitanghol, represent together the lion share of G/ha (57.8%), V/ha (65.5%) and AGB/ha (62.8%) of their group. The palms encountered are essentially Sagisi, and to a lesser extent Anibong, Anahaw and Ulango. In the Closed Forests, not a single Coconut has been sampled!

7.2.2 Stand composition of Open Forests

Table 12 summarizes and Figure 14 illustrates the stand composition of the Open Forests in terms of N/ha, G/ha, V/ha and AGB/ha. Like the Closed Forests, the Open Forests are dominated by Dipterocarps, however, only in terms of merchantable volume (67.1%) and AGB (52.8%). This is thanks to their average size, in terms of Dg, which is again considerably larger (28.3 cm) than the Dg of Non-Dipterocarps (16.1 cm).

Table 12: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Open Forests

Species / Dg N/ha G/ha V/ha AGB/ha

Species Group [cm] [/ha] [%] [m²/ha] [%] [m³/ha] [%] [t. d.m./ha] [%]

Dipterocarps

Mayapis 37.3 37.8 3.1 4.12 11.7 39.57 19.0 36.63 10.7

Red Lauan 36.7 34.3 2.8 3.62 10.3 36.67 17.6 44.64 13.0

Yakal 24.3 65.8 5.4 3.06 8.7 21.32 10.2 39.87 11.6

Guijo 24.8 35.0 2.9 1.69 4.8 13.33 6.4 21.93 6.4

Almon 36.3 8.5 0.7 0.88 2.5 8.48 4.1 7.33 2.1

Other Dipterocarps 21.6 72.9 6.0 2.67 7.6 20.46 9.8 30.52 8.9

Sub-Total Dipterocarps 28.3 254.4 20.8 16.05 45.6 139.82 67.1 180.93 52.8

Non-Dipterocarps

Ulayan 17.7 56.7 4.6 1.40 4.0 5.09 2.4 12.27 3.6

Malak-malak 22.6 25.4 2.1 1.02 2.9 5.31 2.6 8.10 2.4

Bansalangin 32.5 11.0 0.9 0.91 2.6 6.18 3.0 14.62 4.3

Bitanghol 14.2 56.1 4.6 0.89 2.5 2.63 1.3 5.43 1.6

Mankono 23.9 15.1 1.2 0.68 1.9 4.03 1.9 7.31 2.1

Apanang 12.7 50.1 4.1 0.63 1.8 1.03 0.5 4.15 1.2

Duguan 18.7 21.5 1.8 0.59 1.7 2.87 1.4 3.27 1.0

Nato 24.5 8.9 0.7 0.42 1.2 3.22 1.5 4.15 1.2

Tiga 25.5 8.0 0.7 0.41 1.2 2.04 1.0 6.85 2.0

Kalipapa 15.3 20.1 1.6 0.37 1.1 1.17 0.6 3.32 1.0

Other Non-Dipterocarps 15.0 585.9 48.0 10.29 29.2 34.84 16.7 88.56 25.9

Sub-Total Non-Dipterocarps 16.1 858.9 70.3 17.59 49.9 68.41 32.9 158.03 46.2

Palms 106.3 8.7 1.57 4.5 0.0 3.42 1.0

Rattan 0.9 0.1 0.0 0.0 0.0

Tree ferns 1.2 0.1 0.01 0.0 0.0 0.0

Total 1 221.7 100.0 35.22 100.0 208.23 100.0 342.37 100.0



N/ha [/ha]

G/ha [m²/ha]

V/ha [m³/ha]

AGB/ha [t d.m./ha]

Dipterocarps Non-Dipterocarps

Palms Mayapis Yakal Red Lauan Almon Guijo Other Dipterocarps

Figure 14: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Open Forests



The five most dominant Dipterocarps in terms of basal area are Mayapis, Red Lauan, Yakal, Guijo and Almon (the same species as in the Closed Forests, however in a slightly different order). Together, they represent 85% of the total Dipterocarp merchantable volume, and around 57% of the total merchantable volume, all species combined. The ten most dominant Non-Dipterocarps in terms of basal area, led by Ulayan followed by Malak-malak, Bansalangin and Bitanghol, together represent only 41.6% of G/ha, 49.1% of V/ha and 44.0% of AGB/ha of their group. The palms encountered are essentially Sagisi and Anibong, and to a lesser extent Coconut, Anahaw and Ulango.

7.3 Stand Structure

7.3.1 Stand structure of Closed Forests

The stand structure of the Closed Forests is summarized hereafter in terms of the following:

density (N/ha) by diameter class, summarized in Table 13 and illustrated in Figure 15;

basal area (G/ha) by diameter class, summarized in Table 14 and illustrated in Figure 16; and

above-ground biomass (AGB/ha) by diameter class, summarized in Table 15 and illustrated in Figure 18.

Table 13: Stand structure in terms of N/ha of Closed Forests

Species / Density by Diameter Class

Species Group [5 cm - 20 cm[ [20 cm - 40 cm[ [40 cm - 60 cm[ [60 cm - 80 cm[ [80 cm - Total

[/ha] [/ha] [/ha] [/ha] [/ha] [/ha]

Dipterocarps

Almon 3.6 4.9 1.7 1.7 1.3 13.3

Guijo 17.7 6.2 1.7 0.8 0.4 27.0

Mayapis 14.1 15.9 11.9 3.1 0.9 46.0

Red Lauan 8.8 4.9 3.1 0.4 3.1 20.3

Yakal 153.9 21.6 2.2 2.2 - 179.9

Other Dipterocarps 118.4 11.5 3.3 1.5 0.4 134.8

Total Dipterocarps 316.5 65.0 23.9 9.7 6.1 421.3

Non-Dipterocarps

Bitanghol 134.4 5.7 - - - 140.1

Malak-malak 65.5 18.6 1.3 - - 85.3

Malatambis 14.1 1.3 0.4 - - 15.9

Mankono 42.4 2.6 - - - 45.1

Nato 8.9 4.0 1.3 - - 14.1

Ulayan 95.5 6.2 1.3 - - 103.0

Other Non-Dipterocarps 675.5 66.4 9.0 1.8 0.8 753.5

Total Non-Dipterocarps 1,036.3 104.8 13.3 1.8 0.8 1,157.0

Palms 54.8 - - - - 54.8

Total 1,407.6 169.7 37.1 11.5 7.1 1,633.1

Standing Dead Wood 44.2 21.2 3.5 1.8 1.8 72.5



Figure 15: Stand structure in terms of N/ha of Closed Forests

On average, the Closed Forests count per hectare 421 Dipterocarp trees, 1,157 Non-Dipterocarp trees, 55 Palms and 73 Standing Dead Wood.



As expected, N/ha by diameter class shows a typical inverse "J"-shaped distribution. The rise of N/ha for trees with DBH / DAB ≥ 100 cm is due to a few very large "overstaying" Dipterocarps (essentially Red Lauan, Almon and Mayapis), which had not been harvested by the former Timber License Agreement (TLA) holder, probably because of their sheer size, or of defects. The relative density of Dipterocarps by diameter class reveals that Yakal has a very big share in the lower diameter classes (hinting that it is regeneration well), quite to the opposite of Mayapis, Red Lauan and Almon. Among the Non-Dipterocarps, a similar, though less pronounced trend can be observed for Bitanghol as opposed to Nato.

Table 14: Stand structure in terms of G/ha of Closed Forests

Species / Basal Area by Diameter Class


[m²/ha] [m²/ha] [m²/ha] [m²/ha] [m²/ha] [m²/ha]

Dipterocarps

Almon 0.03 0.37 0.34 0.68 1.76 3.16

Guijo 0.18 0.52 0.32 0.37 0.78 2.15

Mayapis 0.18 0.97 2.26 1.14 0.74 5.30

Red Lauan 0.16 0.39 0.63 0.14 2.52 3.83

Yakal 1.64 1.42 0.36 0.79 - 4.20



Non-Dipterocarps

Bitanghol 1.06 0.32 - - - 1.38

Malak-malak 0.81 1.16 0.24 - - 2.21

Malatambis 0.14 0.05 0.08 - - 0.26

Mankono 0.34 0.14 - - - 0.47

Nato 0.11 0.30 0.23 - - 0.64

Ulayan 1.10 0.43 0.21 - - 1.74


Total Non-Dipterocarps 10.36 6.52 2.18 0.68 0.67 20.39

Palms 0.68 - - - - 0.68

Total 14.46 10.79 6.67 4.21 6.80 42.92


On average, the basal area of the Closed Forests amounts to 42.9 m²/ha, a respectable level for lowland Dipterocarp Forests (most of which have disappeared or been utterly degraded), and actually not much less than the 36.3 m²/ha (for trees with DBH / DAB ≥ 15 cm) observed 1986 to 1987 by the second National Forest Resources Inventory (NFRI) in 82 SUs in "Old Growth Forests" of Region VIII. The distribution of G/ha by diameter class does not reveal any particularity, for DBH / DAB < 80 cm, it likely reflects the biology of the composing species (many Non-Dipterocarps do not grow to bigger sizes, hence their relative weight in the lower diameter classes.



Figure 16: Stand structure in terms of G/ha of Closed Forests



Table 15: Stand structure in terms of AGB/ha of Closed Forests

Species / Above-Ground Biomass by Diameter Class


[t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha]

Dipterocarps

Almon 0,08 2,44 2,74 6,34 21,79 33,39

Guijo 1,34 6,18 4,43 6,14 17,76 35,84

Mayapis 0,91 6,57 19,71 11,39 8,75 47,34

Red Lauan 0,98 3,40 6,75 1,66 36,36 49,16

Yakal 13,08 16,94 5,22 13,71 - 48,96

Other Dipterocarps 9,28 6,41 8,22 5,29 8,06 37,22

Total Dipterocarps 25,67 41,94 47,07 44,53 92,72 251,91

Non-Dipterocarps

Bitanghol 4,99 2,33 - - - 7,33

Malak-malak 4,38 8,87 2,21 - - 15,45

Malatambis 1,06 0,49 1,16 - - 2,71

Mankono 1,93 1,27 - - - 3,19

Nato 0,68 2,80 2,57 - - 6,05

Ulayan 6,99 4,00 2,38 - - 13,37

Other Non-Dipterocarps 45,46 41,71 20,78 10,85 15,55 134,36

Total Non-Dipterocarps 65,49 61,47 29,10 10,85 15,55 182,46

Palms 2,09 - - - - 2,09

Total 93,25 103,41 76,17 55,38 108,26 436,46

On average, the above-ground biomass of the Closed Forests amounts to 436 t d.m./ha, which is above the median of the range from 280 t d.m./ha to 520 t d.m./ha referred to by IPCC as Tier 1 estimate for tropical rainforest of insular Asia. The distribution of AGB/ha by diameter class does not reveal any particularity. For trees1 with DBH / DAB < 80 cm, it likely reflects the biology of the composing species (many Non-Dipterocarps do not grow to bigger sizes, hence their relative weight in the lower diameter classes. Figure 17 shows that 96% of AGB/ha is composed of trees1 with DBH / DAB ≥ 10 cm.

Figure 17: AGB/ha of Closed Forests by DBH / DAB threshold



Figure 18: Stand structure in terms of AGB/ha of Closed Forests



7.3.2 Stand structure of Open Forests

The stand structure of the Open Forests is summarized hereafter in terms of the following:

density (N/ha) by diameter class, summarized in Table 16 and illustrated in Figure 15;

basal area (G/ha) by diameter class, summarized in Table 17 and illustrated in Figure 16; and

above-ground biomass (AGB/ha) by diameter class, summarized in Table 18 and illustrated in Figure 18.

Table 16: Stand structure in terms of N/ha of Open Forests

Species / Density by Diameter Class


[/ha] [/ha] [/ha] [/ha] [/ha] [/ha]

Dipterocarps

Almon 3.4 2.8 1.4 0.6 0.3 8.5

Guijo 23.1 8.7 2.4 0.7 0.2 35.0

Mayapis 16.9 9.2 7.8 3.2 0.9 37.8

Red Lauan 21.9 5.4 3.2 2.1 1.8 34.3

Yakal 44.0 15.2 5.7 0.6 0.3 65.8



Non-Dipterocarps

Bitanghol 50.8 4.7 0.5 - - 56.1

Malak-malak 17.8 5.4 2.1 0.1 - 25.4

Malatambis 10.0 1.2 0.2 - - 11.4

Mankono 11.2 2.5 1.1 0.2 0.2 15.1

Nato 5.6 2.4 0.9 - 0.1 8.9

Ulayan 46.8 7.9 2.0 0.1 - 56.7



Palms 97.7 8.4 0.2 - - 106.3

Rattan 0.9 - - - - 0.9

Tree ferns 1.2 - - - - 1.2

Total 1,018.0 146.5 42.8 10.0 4.5 1,221.7


On average, the Open Forests count per hectare 254 Dipterocarp trees, 859 Non-Dipterocarp trees, 106 Palms and 48 Standing Dead Wood. A t-test confirms that at a confidence level of 95%, the density of live trees1 (1,222 /ha) is significantly lower than in the Closed Forests (1,633 /ha). The distribution of N/ha by diameter class follows a similar pattern as in the Closed Forests, though at a lower level.



Figure 19: Stand structure in terms of N/ha of Open Forests

The rise of N/ha for trees with DBH / DAB ≥ 100 cm is observed again, due to a few very large Dipterocarps (essentially Red Lauan).



The relative density of Dipterocarps by diameter class reveals that Almon has a reduced share in the lower diameter classes (hinting that it is not regeneration well). Among the Non-Dipterocarps, a similar, though less pronounced trend can be observed for Malak-malak.

Table 17: Stand structure in terms of G/ha of Open Forests

Species / Basal Area by Diameter Class


[m²/ha] [m²/ha] [m²/ha] [m²/ha] [m²/ha] [m²/ha]

Dipterocarps

Almon 0.04 0.20 0.27 0.19 0.17 0.88

Guijo 0.29 0.63 0.46 0.22 0.10 1.69

Mayapis 0.24 0.70 1.49 1.10 0.57 4.12

Red Lauan 0.27 0.37 0.61 0.77 1.59 3.62

Yakal 0.57 1.07 1.04 0.20 0.19 3.06



Non-Dipterocarps

Bitanghol 0.53 0.30 0.07 - - 0.89

Malak-malak 0.22 0.39 0.37 0.03 - 1.02

Malatambis 0.09 0.07 0.03 - - 0.18

Mankono 0.14 0.16 0.19 0.05 0.14 0.68

Nato 0.06 0.16 0.14 - 0.05 0.42

Ulayan 0.55 0.50 0.32 0.03 - 1.40



Palms 0.96 0.58 0.03 - - 1.57

Tree ferns 0.01 - - - - 0.01

Total 10.71 9.60 7.72 3.53 3.66 35.22


On average, the basal area of the Open Forests amounts to 35.2 m²/ha. This is significantly less than G/ha of the Closed Forests (42.9 m²/ha), as confirmed by a t-test at a confidence level of 95%. The order of magnitude is reasonable, and actually higher than the 25.4 m²/ha (for trees with DBH / DAB ≥ 15 cm) observed 1986 to 1987 by the second NFRI in 88 SUs in "Residual Forests" of Region VIII. The distribution of G/ha by diameter class does not reveal any particularity, but confirms the observation derived from the distribution of N/ha that Almon is underrepresented in the lower diameter classes.



Figure 20: Stand structure in terms of G/ha of Open Forests



On average, the above-ground biomass of the Open Forests amounts to 342 t d.m./ha, which is significantly less than AGB/ha of the Closed Forests (436 t d.m./ha), as confirmed by a t-test at a confidence level of 95%.

Table 18: Stand structure in terms of AGB/ha of Open Forests

Species / Above-Ground Biomass by Diameter Class


[t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha]

Dipterocarps

Almon 0.18 1.27 2.17 1.85 1.85 7.33

Guijo 2.32 7.43 6.67 3.62 1.90 21.93

Mayapis 1.19 5.00 13.00 10.93 6.51 36.63

Red Lauan 1.56 3.23 6.48 9.29 24.07 44.64

Yakal 4.74 12.81 15.46 3.37 3.49 39.87



Non-Dipterocarps

Bitanghol 2.64 2.20 0.59 - - 5.43

Malak-malak 1.17 3.09 3.46 0.37 - 8.10

Malatambis 0.65 0.76 0.40 - - 1.82

Mankono 0.86 1.56 2.19 0.70 1.99 7.31

Nato 0.39 1.53 1.54 - 0.69 4.15

Ulayan 3.54 4.63 3.72 0.38 - 12.27



Palms 2.84 0.57 - - - 3.42

Total 63.30 86.66 90.81 44.90 56.70 342.37

The distribution of AGB/ha by diameter class does not reveal any particularity. Figure 21 shows that 97% of AGB/ha is composed of trees1 with DBH / DAB ≥ 10 cm.

Figure 21: AGB/ha of Open Forests by DBH / DAB threshold



Figure 22: Stand structure in terms of AGB/ha of Open Forests



7.4 Timber Stocks

7.4.1 Timber stocks of Closed Forests

Table 19 summarizes and Figure 23 illustrates the distribution of the merchantable volume in the Closed Forests by diameter class and main species.

Table 19: Merchantable volume in Closed Forests

Species / Merchantable Volume by Diameter Class

Species Group [5 cm - 40 cm[ [40 cm - 60 cm[ [60 cm - 80 cm[ [80 cm - Total

[m³/ha] [m³/ha] [m³/ha] [m³/ha] [m³/ha]

Dipterocarps

Almon 3.03 3.04 6.98 14.63 27.67

Guijo 3.92 2.83 3.56 7.97 18.28

Mayapis 7.59 20.79 9.80 7.37 45.56

Red Lauan 3.38 7.33 1.15 23.29 35.15

Yakal 10.44 2.88 7.51 - 20.83

Other Dipterocarps 4.20 4.60 3.81 3.35 15.95

Total Dipterocarps 32.56 41.47 32.81 56.61 163.44

Non-Dipterocarps

Bitanghol 2.24 - - - 2.24

Malak-malak 7.29 1.60 - - 8.88

Malatambis 0.23 0.62 - - 0.85

Mankono 0.79 - - - 0.79

Nato 2.22 1.76 - - 3.98

Ulayan 2.65 1.10 - - 3.76

Other Non-Dipterocarps 25.84 9.14 5.07 5.97 46.02

Total Non-Dipterocarps 41.26 14.22 5.07 5.97 66.52

Total 73.83 55.69 37.88 62.58 229.97

On average, the merchantable volume in the Closed Forests amounts to 230 m³/ha, dominated by Dipterocarps (163 m³/ha corresponding to a share of 71.1%). This is actually in the same order of magnitude as the 227 m³/ha (for trees with DBH / DAB ≥ 15 cm) observed 1986 to 1987 by the second National Forest Resources Inventory (NFRI) in 82 SUs in "Old Growth Forests" of Region VIII. However, while by that time, 25% of V/ha was concentrated on trees with DBH / DAB ≥ 75 cm, it is now almost 30%, due to the considerable amount of merchantable volume of trees with DBH / DAB ≥ 100 cm. As already noted in Chapter 7.3.1, these trees correspond to a few very large Dipterocarps (essentially Red Lauan, Almon and Mayapis, with an average merchantable volume per tree of 10.9 m³), which had not been harvested by the former Timber License Agreement (TLA) holder, probably because of their size, or of defects. The actively growing trees are presumably those with DBH / DAB < 80 cm. In this range of DBH / DAB, the increasing proportion of Yakal (not only in terms of V/ha) towards the smaller diameter classes is remarkable.



Figure 23: Merchantable volume in Closed Forests



7.4.2 Timber stocks of Open Forests

Table 20 summarizes and Figure 24 illustrates the distribution of the merchantable volume in the Open Forests by diameter class and main species.

Table 20: Merchantable volume in Open Forests

Species / Merchantable Volume by Diameter Class

Species Group [5 cm - 40 cm[ [40 cm - 60 cm[ [60 cm - 80 cm[ [80 cm - Total

[m³/ha] [m³/ha] [m³/ha] [m³/ha] [m³/ha]

Dipterocarps

Almon 1.70 2.80 2.30 1.65 8.48

Guijo 5.81 4.46 2.27 0.80 13.33

Mayapis 5.93 15.86 11.38 6.40 39.57

Red Lauan 3.52 6.74 8.28 18.14 36.67

Yakal 7.97 9.19 2.01 2.14 21.32

Other Dipterocarps 6.46 7.32 1.93 4.77 20.45

Total Dipterocarps 31.39 46.37 28.17 33.90 139.82

Non-Dipterocarps

Bitanghol 2.08 0.55 - - 2.63

Malak-malak 2.70 2.38 0.23 - 5.31

Malatambis 0.38 0.18 - - 0.56

Mankono 1.20 1.46 0.49 0.88 4.03

Nato 1.35 1.36 - 0.50 3.22

Ulayan 2.91 1.95 0.23 - 5.09

Other Non-Dipterocarps 23.36 14.39 5.27 4.56 47.57

Total Non-Dipterocarps 33.98 22.27 6.22 5.94 68.41

Total 65.36 68.64 34.39 39.85 208.23

On average, the merchantable volume in the Open Forests amounts to 208 m³/ha, dominated by Dipterocarps (140 m³/ha corresponding to a share of 67.1%). Although slightly less than the V/ha in Closed Forests (230 m³/ha), t-tests show that the difference is not significant, neither at 95%, nor at 90% confidence level. The merchantable volume in the Open Forests is actually considerably higher than the 149 m³/ha (for trees with DBH / DAB ≥ 15 cm) observed 1986 to 1987 by the second National Forest Resources Inventory (NFRI) in 88 SUs in "Residual Forests" of Region VIII. At that time, trees with DBH / DAB ≥ 75 cm represented only about 10% of V/ha, compared to currently 21%. As already pointed out in Chapter 7.3.1, these trees correspond to a few very large "overstaying" Dipterocarps (essentially Red Lauan, with an average merchantable volume per tree of 16.0 m³).



Figure 24: Merchantable volume in Open Forests



7.5 Carbon Stocks

7.5.1 Carbon stocks of Closed Forests

Table 21 summarizes, and Figure 25 illustrates the carbon stocks of the Closed Forests.

Table 21: Carbon stocks of Closed Forests

Carbon Pool Biomass/ha by Diameter Class Carbon/ha

[5 cm - 40 cm[ [40 cm - Total Total

[t d.m./ha] [t d.m./ha] [t d.m./ha] [t C/ha] [%]

Living Biomass (LB)

Above-Ground Biomass (AGB) 196.66 239.80 436.46 205.14 60.7

Below-Ground Biomass (BGB) 72.76 88.73 161.49 75.90 22.4

Total Living Biomass 269.42 328.53 597,95 281.04 83.1

Dead Organic Matter (DOM)

Standing Dead Wood (SDW) 19.40 7.18 2.1

Lying Dead Wood (LDW) 9.22 3.41 1.0

Litter (LI) 6.57 2.43 0.7

Total Dead Organic Matter (DOM) 35.20 13.02 3.9

Soil Organic Matter (SOM) 44.00 13.0

Total 338.06 100.0

C/ha [t C/ha] C/ha [t C/ha]

Figure 25: Carbon stocks of Closed Forests

On average, the Closed Forests feature a Living Biomass of 598 t d.m./ha, Dead Organic Matter of 13.02 t C/ha, composed of (i) 10.59 t C/ha of Dead Wood and (ii) 2.43 t C/ha of Litter, plus 44.00 t C/ha of Soil Organic Matter. The bulk of the carbon stock is in the Above-Ground Biomass (60.7%), and thereof in Dipterocarps (57.5%). Extrapolated to the 5.815 ha of Closed Forests in Borongan City and Maydolong, the forest carbon stock amounts to 1.97 million t C.



7.5.2 Carbon stocks of Open Forests

Table 22 summarizes, and Figure 26 illustrates the carbon stocks of the Open Forests.

Table 22: Carbon stocks of Open Forests

Carbon Pool Biomass/ha by Diameter Class Carbon/ha

[5 cm - 40 cm[ [40 cm - Total Total

[t d.m./ha] [t d.m./ha] [t d.m./ha] [t C/ha] [%]

Living Biomass (LB)

Above-Ground Biomass (AGB) 149.96 192.41 342.37 160.91 57.4

Below-Ground Biomass (BGB) 55.48 71.20 126.68 59.54 21.3

Total Living Biomass 205.44 263.61 469.05 220.45 78.7


Standing Dead Wood (SDW) 20.63 7.63 2.7

Lying Dead Wood (LDW) 10.19 3.77 1.3

Litter (LI) 6.46 2.39 0.9

Total Dead Organic Matter (DOM) 37.28 13.79 4.9


Total 280.13 100.0

C/ha [t C/ha] C/ha [t C/ha]

Figure 26: Carbon stocks of Open Forests

On average, the Open Forests feature a Living Biomass of 469 t d.m./ha, Dead Organic Matter of 13.79 t C/ha, composed of (i) 11.40 t C/ha of Dead Wood and (ii) 2.39 t C/ha of Litter, plus 45.88 t C/ha of Soil Organic Matter. The bulk of the carbon stock is in the Above-Ground Biomass (57.4%), and thereof in Dipterocarps (52.9%), though comparatively less than in Closed Forests. Extrapolated to the 36,264 ha of Open Forests in Borongan City and Maydolong, the forest carbon stock amounts to 10.16 million t C.



7.5.3 Carbon stocks of Mangroves

Table 22 provides an estimate, illustrated in Figure 26, of the carbon stocks of the Mangroves using IPCC Tier 1 data.

Table 23: Carbon stocks of Mangroves

Carbon Pool Biomass/ha Carbon/ha

Total Total

[t d.m./ha] [t C/ha] [%]

Living Biomass (LB)

Above-Ground Biomass (AGB) 192.00 86.59 16.5

Below-Ground Biomass (BGB) 94.08 42.43 8.0

Total Living Biomass 286.08 129.02 24.5


Dead Wood (DW) 10.70 2.0

Litter (LI) 0.70 0.1

Total Dead Organic Matter (DOM) 11.40 2.2


Total 526.42 100.0

C/ha [t C/ha]

Figure 27: Carbon stocks of Mangroves

Accordingly, the Mangroves feature a Living Biomass of 286.08 t d.m./ha, Dead Organic Matter of 11.40 t C/ha, composed of (i) 10.70 t C/ha of Dead Wood and (ii) 0.70 t C/ha of Litter, plus considerable 386 t C of Soil Organic Matter. The bulk of the carbon stock is in the Soil Organic Matter (73.3%). Extrapolated to the 505 ha of Mangroves in Borongan City and Maydolong, the forest carbon stock amounts to 265,800 t C.



8. UNCERTAINTY OF THE ESTIMATES

The estimates of all variables of interest, such as N/ha, G/ha, V/ha, AGB/ha, DOM/ha, SOM/ha and C/ha, to cite the most important ones that are summarily presented in Chapter 7 are affected with uncertainties. An evaluation of these uncertainties is presented hereafter, analyzing successively the following five main sources:

statistical sampling error (see Chapter 8.1);

poor representativeness of the sampling network (see Chapter 8.2);

measurements errors (see Chapter 8.3);

data encoding errors (see Chapter 8.4);

estimation design uncertainties (see Chapter 8.5).

Chapter 8.6 combines the different sources of uncertainty for the estimates of V/ha and AGB/ha to summarize the overall error budget.

8.1 Statistical Sampling Error

The detailed statistical parameters of the FRA estimates (in terms of number of SUs, arithmetic mean, variance, standard deviation, coefficient of variation, standard error of the mean and margin of error at confidence levels of 90%, 95% and 99%, respectively) are provided in Appendix 6 (Closed Forests) and Appendix 7 (Open Forests), as computed and printed to PDF by the FRA Database System Application (see Chapter 5.7). Table 24 summarizes the statistical sampling error in terms of the margin of error (E%) at a confidence level of 95% for the main variables of interest.

Table 24: Statistical sampling errors of the main variables of interest in Closed and Open Forests

Variable Closed Forests Open Forests

Based on 18 Sampling Units Based on 102 Sampling Units

Mean Margin of Error* Mean Margin of Error*

N/ha [/ha] 1,633.1 ± 23.00% 1,221.8 ± 6.86%

G/ha [m²/ha] 42.92 ± 16.77% 35.22 ± 8.57%

V/ha [m³/ha] 229.97 ± 23.51% 208.23 ± 14.72%

AGB/ha [t. d.m./ha] 436.46 ± 19.78% 342.37 ± 12.06%

BGB/ha [t. d.m./ha] 161.49 ± 19.78% 126.68 ± 12.06%

LB/ha [t C/ha] 281.04 ± 19.78% 220.45 ± 12.06%

SDW/ha [t C/ha] 7.18 ± 65.35% 7.63 ± 50.10%

LDW/ha [t C/ha] 3.41 ± 91.90% 3.77 ± 50.93%

LI/ha [t C/ha] 2.43 ± 26.96% 2.39 ± 9.48%

DOM/ha [t C/ha] 13.02 ± 43.63% 13.79 ± 31.07%

SOM/ha [t C/ha] 44.00 ± 0.00% 45.88 ± 2.22%

Total C/ha [t C/ha] 338.06 ± 16.53% 280.13 ± 9.62%

* 95% confidence level

The margin of error is inversely proportional to the square root of the number of SUs. This explains that the achieved E% in Open Forests with 112 SUs is lower than in Closed Forests with 18 SUs, although the coefficients of variation (s%) are generally higher in Open Forests than in Closed Forests (for AGB/ha for instance 61.4% compared to 39.8%).



The margin of error of the SOM/ha estimates appears to be very low. This is due to the fact that following the Tier 1 estimate, only two (2) soil types were found in the Closed and Open Forests of Borongan City and Maydolong: High Activity Clays (HAC) and Low Activity Clays (LAC), see Chapter 2.5, with two (2) corresponding SOM/ha stocks: 44 t C/ha and 60 t C/ha, respectively. Hence, there is little variation.

8.2 Poor Representativeness of the Sampling Network

The design of the sampling network (see Chapter 3.5) has been made in accordance with the statistical theory to prevent poor representativeness of the SUs. However, not all the 150 SUs initially distributed in the Closed and Open Forests of Borongan City and Maydolong have ultimately been measured, partly for security reasons, but mainly because of the limited available resources and time, combined with a lower than expected output (see Chapter 5.6). It cannot be excluded that the failure to measure all allocated SUs slightly affects the representativeness of the sampling network. An uncertainty of an order of magnitude of 2.5% may conservatively be assumed.

8.3 Measurement Errors

The impact of the measurement errors has been evaluated through the re-measurement of 10% of the SUs (see Chapter 6.2). While the estimates of N/ha, G/ha and AGB/ha are affected by relatively limited uncertainties related to measurement errors (approximately 2%, 1% and 4%, respectively), those of V/ha are affected by a hefty 15%, i.e. about as much as the statistical sampling error.

8.4 Data Encoding Errors

The effect of the data encoding errors has been evaluated through the comparison of the original field data and the encoded data of 10% of the SUs (see Chapter 6.2). The estimates of the variables of interest are affected by minimal uncertainties not exceeding 0.5% related to data encoding errors.

8.5 Estimation Design Uncertainties

Except for N/ha and G/ha, where no allometric models, volume equations, wood specific gravities nor conversion and/or extrapolation factors are used, the estimates of all other variables of interest are affected by uncertainties due to the lack of fit of the estimation design (see Chapter 3.7) used. The uncertainty arising from the use of the regional volume equations for dipterocarps and non-dipterocarps (see Chapter 3.7.1) for the estimation of V/ha is not documented. It may conservatively be estimated to be of an order of magnitude of 15%. According to the authors, the uncertainty arising from the use of the allometric equation developed by CHAVE J. et al., 2014 (see Chapter 3.7.2, equation {5}) for the estimation of AGB/ha is of the order of magnitude of 10%. The uncertainties of the other metrics used to estimate BGB/ha (the root to shoot ratio), SDW/ha (the Biomass Conversion and Expansion Factor [BCEFs]), LDW/ha, LI/ha, and to convert the biomass to carbon equivalent (Carbon Fraction [CF] of dry matter) are difficult to evaluate.



8.6 Overall Error Budget

Table 25 and Table 26 show the overall error budget of the estimates of V/ha and AGB/ha, respectively.

Table 25: Overall error budget for V/ha

Source of Uncertainty Stratum Uncertainty

Statistical sampling error Closed Forests ± 23.51%

Open Forests ± 14.72%

Representativeness of the sampling network Closed & Open Forests ± 2.50%

Measurement errors Closed & Open Forests ± 14.70%

Data encoding errors Closed & Open Forests ± 0.50%

Estimation design uncertainties Closed & Open Forests ± 15.00%

Table 26: Overall error budget for AGB/ha

Source of Uncertainty Stratum Uncertainty

Statistical sampling error Closed Forests ± 19.78%

Open Forests ± 12.06%

Representativeness of the sampling network Closed & Open Forests ± 2.50%

Measurement errors Closed & Open Forests ± 4.00%

Data encoding errors Closed & Open Forests ± 0.50%

Estimation design uncertainties Closed & Open Forests ± 10.00%

The largest uncertainties pertain to the statistical sampling error, followed by measurement errors when height measurements are involved (for the estimation of V/ha) and estimation design uncertainties. The statistical sampling error can be reduced by augmenting the number of SUs. However, one has to keep in mind that to halve the statistical sampling error, four times more SUs must be measured, since the sampling error is inversely proportional to the square root of the number of SUs.



9. REFERENCES

ABED T. et al., 2003: Tree measurement manual for farm foresters

ANSAB et al., 2010: Forest carbon stock measurement - Guidelines for measuring carbon stocks in community-managed forests

AUSTRALIAN GREENHOUSE OFFICE, 2002: Field measurement procedures for carbon accounting - Field measurement procedures

AUSTRALIAN GREENHOUSE OFFICE, 2002: Field measurement procedures for carbon accounting - Field sheets and appendices

AUSTRALIAN GREENHOUSE OFFICE, 2002: Field measurement procedures for carbon accounting - Reference document

BARROIS V., 2015: Forest Resources Assessment Database Architecture. National REDD+ System Philippines

BARROIS V., 2015: Forest Resources Assessment Database System Application installation guide. National REDD+ System Philippines

BARROIS V. and R. LENNERTZ, 2015: Forest Resources Assessment Database System Application user guide. National REDD+ System Philippines

BFD, 1963: Regional volume equations and tables for Philippine timber species

BSWM, 2013: Updating the Harmonized World Soil Database (HWSD): Correlation of Philippine Soils into FAO’s World Reference Base for Soil Resources (WRB)

CARBONFIX E.V., 2011: Forest inventory guideline

CHAVE J. et al., 2004: Error propagation and scaling for tropical forest biomass estimates

CHAVE J. et al., 2014: Improved allometric models to estimate the aboveground biomass of tropical trees

CHOJNACKY D. et al., 2009: Separating duff and litter for improved mass and carbon estimates

DENR, 1987: Forest resources of Region 8. Philippine - German Forest Resources Inventory Project

DENR, 1988: Natural forest resources of the Philippines. Philippine - German Forest Resources Inventory Project

DENR, 2012: FMB Technical Bulletin No. 3 - Measurement standards in the conduct of timber inventory

DHARWAMAN I. et al., 2010: Standard operating procedures for field measurement

FAO, 1997: Estimating biomass and biomass change of tropical forests

FAO - IUFRO, 2004: Knowledge reference for national forest assessments - Sample designs.

FAO, 2008: Technical review of FAO's approach and methods for national forest monitoring and assessment

FAO, 2012: National Forest Monitoring and Assessment - Manual for integrated field data collection. Version 3.0.

FERNANDO E., 2012: Forest stratification on ecological terms and forest categories in the Philippines

FORESTRY AND FOREST PRODUCTS RESEARCH INSTITUTE, 2012: REDD-plus cookbook

FRANGI J. and A. LUGO, 1985: Ecosystem dynamics of a subtropical floodplain forest

GARMIN, 2013: GPSMAP 78 series owner's manual

GILLESPIE, A. et al. 1992: Tropical forest biomass estimation from truncated stand tables

GOFC-GOLD, 2015: A sourcebook of methods and procedures for monitoring and reporting anthropogenic greenhouse gas emissions and removals associated with deforestation, gains and losses of carbon stocks in forests remaining forests, and forestation - COP 21 Version 1



GREGOIRE T., 1998: Design-based and model-based inference in survey sampling: Appreciating the difference

HAIRIAH K. et al., 2001: Methods for sampling carbon stocks above and below ground.

HAIRIAH K. et al., 2011: Measuring carbon stocks across land use systems - A manual

HEWSON J. et al., 2013: REDD+ Measurement, Reporting and Verification (MRV) manual

IPCC, 1996: Revised guidelines for national greenhouse gas inventories - Reference manual

IPCC, 2003: Good practice guidance for land use, land-use change and forestry

IPCC, 2006: IPCC guidelines for national greenhouse gas inventories, Volume 4 - Agriculture, forestry and other land use

IPCC, 2013: 2013 revised supplementary methods and good practice guidance arising from the Kyoto Protocol

IPCC, 2013: Supplement to the 2006 IPCC guidelines for national greenhouse gas inventories - Coastal wetlands

JOHNSON E., 2000: Forest sampling desk reference

KAUFFMAN J. et al., 2012: Protocols for the measurement, monitoring and reporting of structure, biomass and carbon stocks in mangrove forests

LASCO R. et al., 2006: Carbon stocks assessment of a selectively logged Dipterocarp forest and wood processing mill in the Philippines

LENNERTZ R. and J. SCHADE, 2014: Methodology of the Forest Resources Assessments in the selected sites. National REDD+ System Philippines

LENNERTZ R., FIEL R. and C.P. MEGRASO, 2014: Field Manual for the Forest Resources Assessments in Eastern Samar and Davao Oriental. National REDD+ System Philippines

LENNERTZ R., 2015: Forest inventory techniques training manual

MACDICKEN K., 1997: A guide to monitoring carbon storage in forestry and agroforestry projects

MANDALLAZ D., 2008: Sampling techniques for forest inventories

MANIATIS D., 2010: Methodologies to measure aboveground biomass in the Congo Basin forest in a UNFCCC REDD+ context

PEARSON T. et al., 2005: Sourcebook for land use, land-use change and forestry projects

PHUONG V. et al., 2012: Tree allometric equation development for estimation of forest above-ground biomass in Viet Nam - Evergreen broadleaf, deciduous, and bamboo forests in the Central Highland region

POLANSKY C., 2003: Guide to low-cost practical forest resources inventory in the context of participatory management

SCHADE J. and R. LUDWIG, 2013: Forest carbon baseline study in Leyte

SEIFERT-GRANZIN J., 2014: Design of REDD+ interventions in Project sites and further development of baseline and MRV system for REDD+ in the Philippines

SCHREUDER H. et al., 2004: Statistical techniques for sampling and monitoring natural resources

SKOLE D. et al., 2012: Field data collection for landscape carbon inventories

SKOLE D. et al., 2012: Guidelines for measuring carbon in biomass of agro-forestry systems

SKOLE D. et al., 2012: Guidelines for measuring carbon in forest change

TCG, 2009; Measuring and monitoring terrestrial carbon

THIELE T. et al., 2010: Monitoring, assessment and reporting for sustainable forest management in Pacific Island Countries - Manual

TOMPPO E. et al, 2008: Technical review of FAO's approach and methods to National Forest Monitoring and Assessment

VCS, 2010: REDD methodological module - Estimation of carbon stocks in the above- and belowground biomass in live tree and non-tree pools



VCS, 2010: REDD methodological module - Estimation of carbon stocks in the dead wood pool

VCS, 2010: REDD methodological module - Estimation of carbon stocks in the litter pool

WALKER S. et al., 2012: Standard operating procedures for terrestrial carbon measurement

WALKER W. et al., 2011, Field guide for forest biomass and carbon estimation V. 1.0

WONG J., 2000: The biometrics of NTFP resource assessment

ZEMEK O., 2009: Biomass and carbon stocks inventory of perennial vegetation in the Chieng Khoi watershed, NW Vietnam

ZÖHRER F., 1980: Forstinventur: Ein Leitfaden für Studium und Praxis

Eastern Samar FRA Results Appendix 1

List of Recorded Species Page 1


Appendix 1:

List of Recorded Species

Common name Scientific name Family Gravity* [gr /cm³]

Adina Pertusadina multifolia (Havil.) Ridsdale Rubiaceae

Agoho Casuarina equisetifolia L. Casuarinaceae 0.80

Agoho del Monte Gymnostoma rumphianum (Miq.) L.A.S. Johnson Casuarinaceae 0.86

Alagasi Leucosyke capitellata Wedd. Urticaceae

Alagau Premna odorata Blanco Lamiaceae

Alas-as Pandanus luzonensis Merr. Pandanaceae

Alim Melanolepis multiglandulosa (Reinw. ex Blume) Rchb. & Zoll. Euphorbiaceae 0.34

Almon Shorea almon Foxw. Dipterocarpaceae 0.39

Alupag Dimocarpus longan subsp. malesianus Leenh. Sapindaceae 0.70

Alupag-amo Litchi chinensis Sonn. Sapindaceae 0.80

Amamali Leea aculeata Blume ex Spreng Vitaceae

Amugis Koordersiodendron pinnatum Merr. Anacardiaceae 0.61

Anabiong Trema orientalis (L.) Blume Cannabaceae 0.33

Anahaw Saribus rotundifolius (Lam.) Blume Arecaceae

Anang Diospyros pyrrhocarpa Miq. Ebenaceae 0.64

Anibong Oncosperma tigillarium (Jack) Ridl. Arecaceae

Anii Erythrina fusca Lour Leguminosae 0.25

Anilao Colona serratifolia Cav. Malvaceae 0.38

Anislag Flueggea flexuosa Muell. Arg. Euphorbiaceae 0.69

Antipolo Artocarpus blancoi (Elmer) Merr. Moraceae 0.43

Anubing Artocarpus ovatus Blanco Moraceae 0.61

Apanang Mallotus cumingii Muell. Arg. Euphorbiaceae 0.49

Apitong Dipterocarpus grandiflorus (Blanco) Blanco Dipterocarpaceae 0.67

Aplas Ficus ampelas Burm.f. Moraceae 0.38

Aunasin Ardisia paniculata Roxb. Primulaceae

Auri Acacia auriculiformis Benth. Leguminosae

Badling Astronia cumingiana S. Vidal Melastomataceae

Bagalunga Melia azedarach L. Meliaceae 0.46

Bago Gnetum gnemon L. Gnetaceae 0.61

Bagtikan Shorea malaanonan Blume Dipterocarpaceae 0.51

Bahai Ormosia calavensis Blanco Leguminosae 0.43

Bakan Litsea philippinensis Merr. Lauraceae

Bakauan-gubat Carallia brachiata (Lour.) Merr. Rizophoraceae 0.66

Balanti Homalanthus populneus (Geiseler) Pax Euphorbiaceae 0.29

Balat-buaya Fagraea racemosa Jack Gentianaceae 0.64

Balete Ficus balete Merr. Moraceae 0.65

Balitbitan Cynometra ramiflora L. Leguminosae 0.79

Balobo Diplodiscus paniculatus Turcz. Malvaceae 0.63

Balukanag Chisocheton cumingianus (C.DC.) Harms Meliaceae 0.55

Banaba Lagerstroemia speciosa (L.) Pers. Lythraceae 0.55

Banai-banai Radermachera pinnata (Blanco) Seem. Bignoniaceae 0.46

Bangkal Nauclea orientalis (L.) L. Rubiaceae 0.47

Bangkal, Kaatoan Breonia chinensis (Lam.) Capuron Rubiaceae 0.34

Bangkal, Southern / Hambabalud

Neonauclea formicaria (Elmer) Merr. Rubiaceae

Bansalangin Mimusops elengi L. Sapotaceae 0.82

Banuyo Wallaceodendron celebicum Koord. Leguminosae 0.56

Batete Kingiodendron alternifolium (Elmer) Merr. & Rolfe Leguminosae 0.49

Batino Alstonia macrophylla Wall. ex G.Don Apocynaceae 0.64

Bayag-usa Voacanga globosa (Blanco) Merr. Apocynaceae





Bayanti Aglaia rimosa (Blanco) Merr. Meliaceae 0.69

Bayok Pterospermum diversifolium Blume Sterculiaceae 0.57

Bignai Antidesma bunius (L.) Spreng. Phyllanthaceae 0.51

Binggas Terminalia citrina Roxb. ex Fleming Combretaceae 0.71

Bingliu Polyscias cenabrei (Merr.) Lowry & G.M. Plunkett Araliaceae

Binuang Octomeles sumatrana Miq. Datiscaceae 0.30

Binucao Garcinia binucao (Blanco) Choisy Clusiaceae 0.75

Binunga Macaranga tanarius (L.) Muell. Arg. Euphorbiaceae 0.43

Bitanghol Calophyllum blancoi Planch. & Triana Clusiaceae 0.46

Bitaog-Palomaria Calophyllum inophyllum L. Clusiaceae 0.60

Bolong-eta Diospyros pilosanthera Blanco Ebenaceae 0.65

Botinag Homalanthus fastuosus (Linden) Fern.-Vill. Euphorbiaceae 0.57

Bugawak Melicope confusa (Merr.) P.S. Liu Rutaceae 0.38

Bulala (Wild Rambutan)

Dimocarpus fumatus (Blume) Leenh. Sapindaceae

Bunga Areca catechu L. Arecaceae

Bunguas Homalium gitingense Elmer Salicaceae 0.76

Buntan Engelhardtia rigida Blume Juglandaceae 0.42

Buri Corypha utan Lam. Arecaceae

Caimito Chrysophyllum cainito L. Sapotaceae

Coconut Cocos nucifera L. Arecaceae

Dacrydium beccarii Dacrydium beccarii Parl. Podocarpaceae 0.61

Dalingdingan Hopea foxworthyi Elmer Dipterocarpaceae 0.51

Dalinsi Terminalia pellucida C. Presl Combretaceae

Dalunot Pipturus arborescens (Link) C.B. Rob. Urticaceae

Dalutan Lithocarpus coopertus (Blanco) Rehd. Fagaceae 0.70

Dao Dracontomelon dao (Blanco) Merr. & Rolfe Anacardiaceae 0.40

Dita Alstonia scholaris (L.).R. Br. var. scholaris Apocynaceae 0.39

Duguan Myristica philippinensis Gand. Myristicaceae 0.36

Duklitan Planchonella duclitan (Blanco) Bakh.f. Sapotaceae 0.51

Dungon-late Heritiera littoralis Aiton Malvaceae 0.87

Ebony Diospyros vera (Lour.) A.Chev. Ebenaceae 0.85

Gisok-Gisok Hopea philippinensis Dyer Dipterocarpaceae 0.67

Gubas Endospermum peltatum Merr. Euphorbiaceae 0.30

Guijo Shorea guiso Blume Dipterocarpaceae 0.71

Hagakhak Dipterocarpus validus Blume Dipterocarpaceae 0.54

Hagimit Ficus minahassae (Teijsm. & Vriese) Miq. Moraceae 0.32

Hamindang Macaranga bicolor Muell. Arg. Euphorbiaceae 0.30

Haras / Ituman Garcinia ituman Merr. Clusiaceae

Hawili Ficus septica Burm.f. Moraceae 0.42

Highland Panau Dipterocarpus hasseltii Blume Dipterocarpaceae 0.56

Himbabao Broussonetia luzonica (Blanco) Bureau Moraceae 0.50

Hindang Myrica javanica Blume Myricaceae

Igang Syzygium garciae (Merr.) Merr. Myrtaceae 0.73

Igyo Dysoxylum gaudichaudianum (A. Juss.) Miq. Meliaceae 0.45

Ilang-ilang Cananga odorata (Lam.) Hook.f. & Thomson Annonaceae 0.29

Ipil Intsia bijuga (Colebr.) Kuntze Leguminosae 0.72

Is-is Ficus ulmifolia Lam. Moraceae 0.38

Kahoi dalaga Mussaenda philippica A. Rich. Rubiaceae

Kakaag Commersonia bartramia (L.) Merr. Malvaceae 0.34

Kalantas Toona calantas Merr. & Rolfe Meliaceae 0.29

Kalingag / Cinamomon

Cinnamomum mercadoi S. Vidal Lauraceae 0.43

Kalipapa Vitex quinata (Lour.) F.N.Williams Lamiaceae 0.65

Kalokoi Ficus callosa Willd. Moraceae 0.29





Kalubkub Syzygium calubcob (C.B.Rob.) Merr. Myrtaceae 0.73

Kalumpang Sterculia foetida L. Sterculiaceae 0.45

Kalumpit Terminalia microcarpa Decne. Combretaceae 0.53

Kamagong Diospyros discolor Willd. Ebenaceae 0.88

Kamandiis Garcinia rubra Merr. Clusiaceae

Kanapai Ficus magnoliifolia Blume Moraceae 0.28

Katmon Dillenia philippinensis Rolfe Dilleniaceae 0.63

Kubi Artocarpus nitidus Trécul Moraceae 0.48

Kulatingan Pterospermum obliquum Blanco Sterculiaceae

Kupang Parkia timoriana (DC.) Merr. Leguminosae 0.34

Laloi Turpinia sphaerocarpa Hassk. Staphyleaceae

Lamio Dracontomelon dao (Blanco) Merr. & Rolfe Anacardiaceae 0.40

Lamog Planchonia spectabilis Merr. Lecythidaceae 0.58

Lanete Wrightia pubescens subsp. laniti (Blanco) Ngan Apocynaceae

Laneteng gubat Kibatalia gitingensis (Elmer) Woodson Apocynaceae

Langil Albizia lebbeck (L.) Benth. Leguminosae 0.59

Langosig Trichospermum involucratum Elmer Malvaceae

Lanipau Terminalia copelandi Elmer Combretaceae 0.46

Lanipga Toona philippinensis Elmer Meliaceae

Lanutan Mitrephora lanotan (Blanco) Merr. Annonaceae

Lapnisan Polyalthia oblongifolia Burck Annonaceae

Lapo-lapo Gyrocarpus americanus Jacq. Hernandiaceae

Ligas Semecarpus cuneiformis Blanco Anacardiaceae

Lingaton Dendrocnide stimulans (L.f.) Chew Urticaceae

Lingo-lingo Vitex turczaninowii Merr. Lamiaceae 0.49

Lipang-kalabaw Dendrocnide meyeniana (Walp.) Chew Urticaceae

Lisak Neonauclea bartlingii (DC.) Merr. Rubiaceae

Loktob Duabanga moluccana Blume Lythraceae 0.34

Mabunot Gomphandra luzoniensis (Merr.) Merr. Stemonuraceae

Maguilik Premna cumingiana Schauer Lamiaceae

Mahogany Swietenia mahagoni (L.) Jacq. Meliaceae 0.51

Malaanonan Shorea polita S. Vidal Dipterocarpaceae 0.51

Malabagang Phyllanthus albus (Blanco) Muell. Arg. Phyllanthaceae

Malabatino Alyxia concatenata (Blanco) Merr. Apocynaceae

Malabayabas Tristaniopsis decorticata (Merr.) Peter G. Wilson & J.T. Waterh. Myrtaceae 0.91

Malabunga Alseodaphne malabonga (Blanco) Kosterm. Lauraceae

Malaikmo Celtis philippensis Blanco Cannabaceae 0.69

Malak-malak Palaquium philippense (Perr.) C.B. Rob. Sapotaceae 0.46

Malakalumpit Terminalia calamansanay Rolfe Combretaceae 0.50

Malakape Psydrax dicoccos Gaertn. Rubiaceae

Malakauayan Podocarpus rumphii Blume Podocarpaceae 0.46

Malanangka Parartocarpus venenosa Becc. Moraceae 0.35

Malapanau Dipterocarpus kerrii King Dipterocarpaceae 0.61

Malapapaya Polyscias nodosa (Blume) Seem. Araliaceae 0.32

Malaputat Terminalia darlingii Merr. Combretaceae

Malaruhat / Panglomboyen

Syzygium claviflorum (Roxb.) Wall. ex A.M. Cowan & Cowan Myrtaceae 0.64

Malasantol Sandoricum vidalii Merr. Meliaceae 0.45

Malasapsap Ailanthus integrifolia Lam. Simaroubaceae 0.31

Malatambis Syzygium hutchinsonii (C.B. Robinson) Merr. Myrtaceae 0.73

Malatapai Alangium longiflorum Merr. Cornaceae 0.68

Malatibig Ficus congesta Roxb. Moraceae

Malubago Hibiscus tilliaceus L. Malvaceae 0.45

Malugai Allophylus cobbe (L.) Raeusch. Sapindaceae 0.58

Manggachapui Hopea acuminata Merr. Dipterocarpaceae 0.54





Manggasinoro Shorea assamica var. philippinensis (Brandis ex Koord.) Y.K. Yang & J.K. Wu

Dipterocarpaceae 0.46

Mankono Xanthostemon verdugonianus Náves ex Fern. - Vill. Myrtaceae

Marang Litsea perrottetii (Blume) Fern.-Vill. Lauraceae 0.45

Matang-araw Melicope triphylla (Lam.) Merr. Rutaceae 0.39

Matang-hipon Breynia vitis-idaea (Burm.f.) C.E.C. Fisch. Euphorbiaceae

Mayapis Shorea palosapis Merr. Dipterocarpaceae 0.42

Milipili Canarium hirsutum Willd. Burseraceae 0.49

Moluccan sau Falcataria moluccana (Miq.) Barneby & J.W.Grimes Leguminosae 0.37

Nangka Artocarpus heterophyllus Lam. Moraceae 0.49

Narig Vatica mangachapoi Blanco Dipterocarpaceae 0.75

Nato Palaquium luzoniense (Fern.-Vill.) Vidal Sapotaceae 0.55

Pagpago Platea excelsa var. borneensis (Heine) Sleumer Icacinaceae 0.36

Pagsahingin-bulog Canarium asperum Benth. Burseraceae 0.47

Paguringon Cratoxylum sumatranum (Jack) Blume Hypericaceae 0.59

Pahutan Mangifera altissima Blanco Anacardiaceae 0.59

Pakiling Ficus odorata (Blanco) Merr. Moraceae 0.32

Pakong buwaya Cyathea contaminans (Wall. ex Hook.) Copel. Cyatheaceae

Palosapis Anisoptera thurifera (Blanco) Blume Dipterocarpaceae 0.59

Pamintaogon Calophyllum soulattri Burm. f. Clusiaceae 0.43

Pamitaogen Calophyllum whitfordii Merr. Clusiaceae

Panau Dipterocarpus gracilis Blume Dipterocarpaceae 0.60

Pandakaking-gubat Tabernaemontana pandacaqui Lam. Apocynaceae

Pangi Pangium edule Reinw. Achariaceae 0.50

Panglongboien Syzygium simile (Merr.) Merr. Myrtaceae 0.56

Pangnan Lithocarpus sulitii Soepadmo Fagaceae 0.86

Patsaragon Syzygium crassibracteatum (Merr.) Merr. Myrtaceae 0.73

Pili Canarium ovatum Engl. Burseraceae

Piling-liitan Canarium luzonicum (Blume) A. Gray Burseraceae 0.31

Pugahan Caryota cumingii Lodd. ex Mart. Arecaceae

Pulahan Lansium parasiticum (Osbeck) K.C. Sahni & Bennet Meliaceae 0.71

Puso-puso Neolitsea vidalii Merr. Lauraceae

Putat Barringtonia racemosa (L.) Spreng. Lecythidaceae 0.36

Putian Alangium javanicum (Blume) Wang. var. jaheri Bloem. Cornaceae 0.73

Red Lauan Shorea negrosensis Foxw. Dipterocarpaceae 0.51

Sagisi Heterospathe elata Scheff. Arecaceae

Saguimsim Syzygium brevistylum (C.B. Rob.) Merr Myrtaceae

Salaguisog Angiopteris palmiformis (Cav.) C. Chr. Marattiaceae

Salinggogon Cratoxylum formosum (Jacq.) Benth. & Hook.f. ex Dyer Hypericaceae 0.72

Salingkugi Albizia saponaria (Lour.) Miq. Leguminosae 0.57

Sarawag Pinanga insignis Becc. Arecaceae

Spike pepper Piper aduncum L. Piperaceae

Subiang Bridelia insulana Hance. Phyllanthaceae

Sudiang Ctenolophon parvifolius Oliv. Ctenolophonaceae

0.74

Taba Tristaniopsis littoralis (Merr.) Peter G. Wilson & J.T. Waterh. Myrtaceae

Tabau Lumnitzera littorea (Jack) Voigt Combretaceae 0.69

Tabian Elaeocarpus monocera Cav. Elaeocarpaceae

Tagatoi Palaquium foxworthyi Merr. Sapotaceae

Taipo Polyosma apoensis Elmer Escalloniaceae

Takip-asin Macaranga grandifolia (Blanco) Merr. Euphorbiaceae

Talisay Terminalia catappa L. Combretaceae 0.46

Talisay-gubat Terminalia foetidissima Griff. Combretaceae 0.60

Taluto Pterocymbium tinctorium Merr. Sterculiaceae 0.25

Tamayuan Strombosia philippinensis S. Vidal Olacaceae 0.70





Tambalau Myristica glomerata (Blanco) Kudô & Masam. Myristicaceae 0.52

Tambis Syzygium aqueum (Burm. f.) Alston Myrtaceae

Tangile Shorea polysperma Merr. Dipterocarpaceae 0.51

Tangisang-bayawak Ficus variegata Blume Moraceae 0.31

Tangisang-layugan Ficus aurita Blume Moraceae 0.31

Tanglin Adenanthera intermedia Merr. Leguminosae 0.78

Tara-tara Dysoxylum cumingianum C.DC. Meliaceae 0.72

Tibig Ficus nota (Blanco) Merr. Moraceae

Tiga Tristaniopsis micrantha (Merr.) Peter G. Wilson & J.T. Waterh. Myrtaceae 0.89

Tikas-pula Canna indica L. Cannaceae

Tindalo Afzelia rhomboidea (Blanco) S.Vidal Leguminosae 0.59

Toog Petersianthus quadrialatus (Merr.) Merr. Lecythidaceae 0.54

Tuai Bischofia javanica Blume Euphorbiaceae 0.61

Tumalim Calamus mindorensis Becc. Arecaceae

Ulango Pandanus acladus Merr Pandanaceae

Ulayan (Oak) Lithocarpus caudatifolius (Merr.) Rehder Fagaceae

Upling buntotan Ficus heteropleura Blume Moraceae 0.32

Upling gubat Ficus ampelas Burm.f. Moraceae 0.38

White Lauan Shorea contorta S. Vidal Dipterocarpaceae 0.43

White Nato Pouteria macrantha (Merr.) Baehni Sapotaceae 0.52

Yabnob Horsfieldia costulata Warb. Myristicaceae

Yakal Shorea astylosa Foxw. Dipterocarpaceae 0.73

Yakal-Gisok Shorea gisok Foxw. Dipterocarpaceae 0.76

Yakal-Kaliot Hopea malibato Foxw. Dipterocarpaceae 0.89

Yakal-Mabolo Shorea ciliata King Dipterocarpaceae 0.89

Yemane Gmelina arborea Roxb. Lamiaceae 0.43

* for tree species without specific wood gravity, the average wood specific gravity for tropical tree species in Asia of 0.57 g/cm³ published by Brown (1997) has been used


List of Inventoried Sampling Units in Eastern Samar Page 1


Appendix 2:

List of Inventoried Sampling Units in Eastern Samar

Sampling UTM Coordinates WGS 84 Geographic Coordinates Unit No. Zone East North Longitude Latitude

[m] [m] [°] [°]

0002 51N 747000 1269000 125.2641198 11.4707301

0003 51N 755000 1292000 125.3391366 11.6779824

0004 51N 748000 1286000 125.2745195 11.6242796

0005 51N 753000 1290000 125.3206506 11.6600589

0006 51N 744000 1288000 125.2379979 11.6426399

0007 51N 755000 1276000 125.3379318 11.5334054

0008 51N 765000 1275000 125.4294813 11.5236179

0009 51N 747000 1281000 125.2649885 11.5791691

0010 51N 763000 1273000 125.4110029 11.5056992

0012 51N 765000 1273000 125.4293261 11.5055469

0013 51N 755000 1285000 125.3386075 11.6147302

0014 51N 750000 1289000 125.2930744 11.6512433

0015 51N 753000 1284000 125.3202013 11.6058420

0016 51N 760000 1265000 125.3829112 11.4336388

0017 51N 747000 1289000 125.2655726 11.6514613

0018 51N 751000 1282000 125.3017220 11.5879161

0020 51N 755000 1279000 125.3381564 11.5605137

0021 51N 749000 1286000 125.2836859 11.6242071

0022 51N 755000 1286000 125.3386829 11.6237662

0024 51N 752000 1279000 125.3106644 11.5607343

0026 51N 747000 1279000 125.2648431 11.5610960

0027 51N 754000 1289000 125.3297425 11.6509487

0028 51N 744000 1280000 125.2374214 11.5703463

0029 51N 758000 1273000 125.3651939 11.5060750

0030 51N 749000 1267000 125.2822975 11.4525144

0031 51N 753000 1276000 125.3196056 11.5335524

0032 51N 763000 1282000 125.4116980 11.5870198

0033 51N 767000 1272000 125.4475709 11.4963580

0034 51N 750000 1291000 125.2932228 11.6693160

0035 51N 758000 1264000 125.3645170 11.4247513

0036 51N 764000 1274000 125.4202418 11.5146588

0037 51N 749000 1283000 125.2834652 11.5970979

0038 51N 761000 1272000 125.3926032 11.4968146

0039 51N 757000 1269000 125.3557316 11.4700053

0040 51N 754000 1281000 125.3291420 11.5786598

0041 51N 751000 1273000 125.3010585 11.5065891

0042 51N 755000 1262000 125.3368907 11.4068993

0044 51N 751000 1292000 125.3024651 11.6782789

0045 51N 751000 1272000 125.3009851 11.4975528

0046 51N 765000 1276000 125.4295590 11.5326534

0048 51N 745000 1280000 125.2465863 11.5702753

0050 51N 746000 1284000 125.2560412 11.6063505

0053 51N 759000 1275000 125.3745075 11.5240722

0054 51N 758000 1262000 125.3643673 11.4066793

0055 51N 754000 1291000 125.3298933 11.6690209

0057 51N 748000 1267000 125.2731367 11.4525858

0058 51N 756000 1263000 125.3461239 11.4158624

0059 51N 764000 1281000 125.4207845 11.5779076

0060 51N 752000 1280000 125.3107386 11.5697705





[m] [m] [°] [°]

0061 51N 755000 1287000 125.3387583 11.6328022

0062 51N 760000 1273000 125.3835177 11.5059256

0065 51N 754000 1283000 125.3292918 11.5967321

0066 51N 763000 1271000 125.4108492 11.4876279

0068 51N 762000 1274000 125.4019179 11.5148106

0070 51N 762000 1290000 125.4031536 11.6593810

0071 51N 743000 1281000 125.2283281 11.5794537

0072 51N 756000 1270000 125.3466454 11.4791151

0074 51N 753000 1286000 125.3203508 11.6239143

0075 51N 749000 1268000 125.2823700 11.4615509

0076 51N 754000 1288000 125.3296673 11.6419126

0077 51N 745000 1288000 125.2471651 11.6425685

0080 51N 765000 1272000 125.4292486 11.4965113

0081 51N 747000 1283000 125.2651342 11.5972422

0082 51N 744000 1283000 125.2376372 11.5974564

0083 51N 742000 1282000 125.2192341 11.5885610

0084 51N 748000 1290000 125.2748135 11.6604253

0085 51N 744000 1282000 125.2375652 11.5884197

0086 51N 750000 1285000 125.2927783 11.6150980

0088 51N 766000 1271000 125.4383321 11.4873993

0089 51N 757000 1270000 125.3558066 11.4790413

0091 51N 746000 1288000 125.2563323 11.6424968

0092 51N 753000 1289000 125.3205756 11.6510228

0093 51N 762000 1275000 125.4019947 11.5238463

0094 51N 763000 1291000 125.4123984 11.6683398

0095 51N 760000 1278000 125.3838988 11.5511046

0097 51N 751000 1268000 125.3006921 11.4614072

0098 51N 751000 1291000 125.3023905 11.6692426

0099 51N 746000 1286000 125.2561866 11.6244236

0100 51N 745000 1289000 125.2472378 11.6516051

0101 51N 759000 1266000 125.3738269 11.4427491

0102 51N 756000 1280000 125.3473956 11.5694757

0103 51N 745000 1286000 125.2470201 11.6244952

0107 51N 758000 1265000 125.3645920 11.4337873

0108 51N 745000 1284000 125.2468752 11.6064220

0109 51N 760000 1272000 125.3834416 11.4968897

0111 51N 752000 1285000 125.3111102 11.6149517

0112 51N 747000 1285000 125.2652801 11.6153152

0113 51N 750000 1269000 125.2916040 11.4705157

0114 51N 741000 1283000 125.2101396 11.5976681

0115 51N 754000 1276000 125.3287687 11.5334791

0116 51N 744000 1287000 125.2379256 11.6336032

0117 51N 767000 1274000 125.4477271 11.5144288

0118 51N 765000 1271000 125.4291712 11.4874758

0119 51N 758000 1270000 125.3649677 11.4789672

0120 51N 757000 1274000 125.3561071 11.5151853

0121 51N 751000 1290000 125.3023160 11.6602064

0122 51N 754000 1282000 125.3292169 11.5876959

0123 51N 750000 1273000 125.2918961 11.5066614

0124 51N 756000 1292000 125.3483043 11.6779075

0125 51N 751000 1286000 125.3020185 11.6240613

0126 51N 753000 1283000 125.3201266 11.5968058

0128 51N 759000 1267000 125.3739023 11.4517850

0129 51N 761000 1275000 125.3928323 11.5239219





[m] [m] [°] [°]

0130 51N 757000 1275000 125.3561824 11.5242213

0131 51N 763000 1283000 125.4117756 11.5960553

0132 51N 766000 1277000 125.4387994 11.5416120

0133 51N 749000 1281000 125.2833184 11.5790250

0134 51N 743000 1286000 125.2286868 11.6246375

0135 51N 765000 1279000 125.4297924 11.5597599

0138 51N 743000 1287000 125.2287587 11.6336743

0140 51N 766000 1279000 125.4389557 11.5596829

0141 51N 759000 1276000 125.3745834 11.5331081

0142 51N 756000 1262000 125.3460497 11.4068263

0143 51N 763000 1289000 125.4122423 11.6502687

0144 51N 744000 1284000 125.2377092 11.6064931

0145 51N 754000 1275000 125.3286942 11.5244429

0147 51N 754000 1292000 125.3299688 11.6780569

0148 51N 754000 1286000 125.3295169 11.6238404

0149 51N 763000 1292000 125.4124765 11.6773753

0150 51N 751000 1287000 125.3020928 11.6330976


Field Data Forms Page 1


Appendix 3:

Field Data Forms


Detailed Results - Closed Forests


Appendix 4:





Appendix 5:

Detailed Results - Open Forests


Statistical Parameters - Closed Forests


Appendix 6:

Statistical Parameters - Closed Forests


Statistical Parameters - Open Forests


Appendix 7:

Statistical Parameters - Open Forests

technical cooperation with the philippines department...

Documents