USAID LEAF Regional Climate Change Curriculum DevelopmentModule: Carbon Measurement and Monitoring (CMM)

Section 4. Carbon Stock Measurement Methods4.2. Design of field sampling framework for carbon stock inventory

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mName Affiliation Name Affiliation

Deborah Lawrence, Co-lead University of Virginia Megan McGroddy, Co-lead University of Virginia

Bui The Doi, Co-lead Vietnam Forestry University Ahmad Ainuddin Nuruddin

Universiti Putra Malaysia

Prasit Wang, Co-lead Chiang Mai University, Thailand

Mohd Nizam Said Universiti Kebangsaan Malaysia

Sapit Diloksumpun Kasetsart University, Thailand Pimonrat Tiansawat Chiang Mai University, Thailand

Pasuta Sunthornhao Kasetsart University, Thailand Panitnard Tunjai Chiang Mai University, Thailand

Wathinee Suanpaga Kasetsart University, Thailand Lawong Balun University of Papua New Guinea

Jessada Phattralerphong Kasetsart University, Thailand Mex Memisang Peki PNG University of Technology

Pham Minh Toai Vietnam Forestry University Kim Soben Royal University of Agriculture, Cambodia

Nguyen The Dzung Vietnam Forestry University Pheng Sokline Royal University of Phnom Penh, Cambodia

Nguyen Hai Hoa Vietnam Forestry University Seak Sophat Royal University of Phnom Penh, Cambodia

Le Xuan Truong Vietnam Forestry University Choeun Kimseng Royal University of Phnom Penh, Cambodia

Phan Thi Quynh Nga Vinh University, Vietnam Rajendra Shrestha Asian Institute of Technology, Thailand

Erin Swails Winrock International Ismail Parlan FRIM Malaysia

Sarah Walker Winrock International Nur Hajar Zamah Shari FRIM Malaysia

Sandra Brown Winrock International Samsudin Musa FRIM Malaysia

Karen Vandecar US Forest Service Ly Thi Minh Hai USAID LEAF Vietnam

Geoffrey Blate US Forest Service David Ganz USAID LEAF Bangkok

Chi Pham USAID LEAF Bangkok

Acknowledgements

I OVERVIEW: CLIMATE CHANGE AND FOREST CARBON

1.1 Overview: Tropical Forests and Climate Change

1.2 Tropical forests, the global carbon cycle and climate change

1.3 Role of forest carbon and forests in global climate negotiations

1.4 Theoretical and practical challenges for forest-based climate mitigation

II FOREST CARBON STOCKS AND CHANGE

2.1 Overview of forest carbon pools (stocks)

2.2 Land use, land use change, and forestry (LULUCF) and CO2 emissions and sequestration

2.3 Overview of Forest Carbon Measurement and Monitoring

2.4 IPCC approach for carbon measurement and monitoring

2.5 Reference levels – Monitoring against a baseline (forest area, forest emissions)

2.6 Establishing Lam Dong’s Reference Level for Provincial REDD+ Action Plan : A Case Study

III CARBON MEASUREMENT AND MONITORING DESIGN

3.1 Considerations in developing a monitoring system

IV CARBON STOCK MEASUREMENT METHODS

4.1 Forest Carbon Measurement and Monitoring

4.2 Design of field sampling framework for carbon stock inventory

4.3 Plot Design for Carbon Stock Inventory

4.4 Forest Carbon Field Measurement Methods

4.5 Carbon Stock Calculations and Available Tools

4.6 Creating Activity Data and Emission Factors

4.7 Carbon Emission from Selective Logging

4.8 Monitoring non-CO2 GHGs

V NATIONAL SCALE MONITORING SYSTEMS

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Session Outline

Lecture (50 minutes) Why sampling is important Major sampling approach Stratification Examples of stratification approaches used in forests Class activity (15 minutes) Homework

At the end of this session, learners will be able to: Explain why sampling is necessary Distinguish among random, stratified, and systematic

sampling, and know where each is appropriate Determine the advantages and drawbacks of different

sampling schemes:

Learning Objectives

Class Exercise

Class Exercise

What is sampling?

Often it is impractical to examine an entire population Instead, we select a sample from our population of

interest and, on the basis of this sample, information about the entire population will be inferred

Reasons for sampling

It is extremely unlikely that we would have the time and resources needed to measure the entire carbon stock in a forest or landscape

Value of sampling

Instead we select a sample from an area of interest, on the basis of this sample, we can infer information about the entire area

Conclusions about an entire population will be drawn based on the sample information through statistical inference

Carbon sampling example

1. Measure carbon stocks in sampled areas

2. Assume sampled carbon stocks represent a reasonable estimate of population carbon stocks,

3. Multiply measured carbon per unit area by entire area of interest to calculate the carbon stocks

4. Use the variation among your plot values to estimate uncertainty

Sampling theory

The sample must provide an accurate picture of the population from which it is drawn

The sample should be random; each individual in the population should have an equal chance of being selected

Sampling theory

Different sampling schemes can be used:

i. Simple random sampling

ii. Systematic sampling

iii. Stratified sampling

iv. Cluster sampling

i

Sampling theory

Different sampling schemes can be used:

i. Simple random sampling

ii. Systematic sampling

iii. Stratified sampling

iv. Cluster sampling

i

ii

Sampling theory

Different sampling schemes can be used:

i. Simple random sampling

ii. Systematic sampling

iii. Stratified sampling

iv. Cluster sampling

i

ii

iii

Sampling theory

Different sampling schemes can be used:

i. Simple random sampling

ii. Systematic sampling

iii. Stratified sampling

iv. Cluster sampling

i

ii

iii

iv

Simple random sampling

Sampling units are independently selected one at a time until the desired sample size is achieved

Each study unit in the finite population has an equal chance of being included in sample without any bias

http://www.youtube.com/watch?v=yx5KZi5QArQ

Simple random sampling

A random sample

Advantages: Representativeness and

freedom from bias Ease of sampling and analysis

Disadvantages: Errors in sampling Time and labor requirements

Systematic sampling

Distributes the sample evenly over the entire population

Bias may arise if there is some type of periodic variation in carbon stocks, but such patterns are rare

http://www.youtube.com/watch?v=QFoisfSZs8I

Advantages: Spatially well distributed Small standard errors Long history of use

Disadvantages: Bias in overestimating the

actual standard error Less flexible to increase or

decrease the sampling size Not applicable for fragmented

strata

Systematic sampling

Stratified sampling

Involves grouping the population of interest into strata to estimate characteristics of each stratum and to improve the precision of an estimate for entire population

http://www.youtube.com/watch?v=sYRUYJYOpG0

Stratified sampling

Advantages: Allows specifying the sample

size within each stratum Allows for different sampling

design for each stratum

Disadvantages: Yields large standard error if

the sample size selected is not appropriate

Not effective if all variables are equally important

Cluster sampling

Involves a grouping of the spatial units or objects sampled

All observations in the selected clusters are included in the sample

http://www.youtube.com/watch?v=QOxXy-I6ogs

Cluster sampling

Primary Sampling Unit (PSU)

Secondary Sampling Unit (SSU) - cluster

Advantages Can reduce the time and

expense of sampling by reducing travel distance

Disadvantages Can yield higher sampling error Can be difficult to select

representative clusters

Class Homework

i. Divide class in 4 groups (pick students randomly or systematically)

ii. Randomly assign each group one of the sampling techniques and a map of land cover either national or regional

iii. Each group should meet outside of class and decide on how to locate sampling plots to estimate per cent of each major land cover class based on the technique they were assigned. Next class they should be prepared to present their maps with sampling plots marked on them

Forest Carbon Stratification Techniques

Why stratify for carbon inventory?

Allows for measuring and monitoring areas where changes are likely to occur

Reduces sampling effort while maintaining accuracy and precision in carbon stocks estimates

Allows for wise spending of the resources

Types of stratification

By threat of deforestation Use historical evidence to identify critical factors of deforestation

Create potential for deforestation map

Identify areas with high probability of deforestation

By forest type Use existing maps of vegetation types

Use existing forest inventory

By accessibility Define accessibility criteria (e.g. 5 km accessibility to main roads)

Use spatial analysis to model accessibility

Stratification by carbon stocks

Stratifying by carbon stock reduces the sampling effort required to achieve targeted precision level

Stratification by carbon stocks & forest type

Develop initial stratification plan Land use Vegetation Slope Drainage Proximity to settlement

Collect preliminary data (~10 plots per stratum)

Not all forests are equally threatened

Stratification by threat

1. Use spatially explicit land use change model

2. Identify key factors impacting historical deforestation patterns

3. Identify areas with high suitability for deforestation

4. Create deforestation threat map

Sampling design

TAKE HOME MESSAGE

Sampling is very important in forest inventory in order to estimate information about an entire population

There are a number of sampling techniques but stratified sampling is most commonly used in forest carbon inventory

Forest types (or Carbon stocks) and threat of deforestation/ degradation are two main factors that are used to stratify the study area.

References

Asner, G.P. 2009. Tropical forest carbon assessment:integrating satellite and airborne mappingApproaches. Environ. Res. Lett. 4 034009

Czaplewski, R., R. McRoberts and E. Tomppo. 2004. Sample designs. FAO-IUFRO National Forest Assessments Knowledge reference http://www.fao.org/forestry/7367/en/

Maniatis, D. and D. Mollicone. 2010. Options for sampling and stratification for national forest inventories to implement REDD+ under the UNFCCC Carbon Balance and Management, 5:9 doi:10.1186/1750-0680-5-9