Forest Dragon 3 Project Id. 10666

Principle Investigator: Prof. Li, Academy of Forest Sciences Prof. Schmullius, University of Jena Co-Investigator: Prof. Pang, Dr. Feilong, Dr. Santoro Young Scientists: Wang Zhichao, Johannes Balling, Patrick Schratz, John Truckenbrodt

The seven objectives of the “FOREST DRAGON 3“ project are

1) the investigation of scaling effects in forest ecosystem mapping,

2) the long-term analysis of GSV and forest structure over NE China,

3) linking forest DRAGON products with existing land use, land cover and/or fire products and

4) the synergy of optical and radar data for mapping forest ecosystems,

5) adapt current forest mapping algorithms to Eastern Russia,

6) adapt current and develop new forest mapping algorithms in Continental Southeast Asia, and

7) use the Sentinels-1/-2 data for forest map updating.

First project year, KO / KO+12 I-i) Preparation of forest GSV maps and forest structure maps with different spatial resolutions. I-ii) Investigation of the scaling effects in mapping forest GSV and forest structure. I-iii) Further validation of the forest GSV- and forest structure 2005- and 2010- maps created within the Forest DRAGON 2 Project for Northeast China. I-iv) Preparation ERS and Envisat SAR data for Eastern Russia and Continental Southeast Asia.

BIOMASAR-II pan-boreal data products

BIOMASAR-II Final Presentation

April 22, 2013

Maurizio Santoro1, Carsten Pathe2 ,Julian Schwilk2 , Ina Burjack2 ,Kerstin Traut2

1 Gamma Remote Sensing, Gümligen, Switzerland 2 Department of Earth Observation, Friedrich-Schiller University, Jena, Germany

Forest DRAGON-2 GSV Change Map for Northeast China (2005 – 2010)

Continuous change values Derived from Envisat ASAR GSV maps based on BIOMASAR algorithm (Santoro et al. 2011) Total coverage of forest and shrubland: ~ 420 500 km²

Coherence 1998 vs. GSV Classes 1998

Continuous GSV 2010 vs GSV Classes 1998.

Second project year, KO+12 / KO+24 II-i) Finalised Production of validated forest GSV change maps 1995-2005-2010 for Northeast China. II-ii) Finalised Production of a validated forest structure maps 1995-2005-2010 for Northeast China. II-iii) Publication of the validated 15-year change maps for Northeast China. II-iv) Using ERS and Envisat SAR data for forest GSV and coverage mapping in Eastern Russia and Continental Southeast Asia in 1995 and 2005. II-v) Publication of the forest changes detection and analysis for Eastern Russia and Continental Southeast Asia.

• Utilization of SRTM DEM at 1 arcsec resolution (ca. 30 m) • Mapping of continuous GSV values instead of discrete classes • Replacement of MODIS Vegetation Continuous Fields (VCF) product

with Landsat VCF Tree Cover (30 m resolution) (Sexton et al. 2013)

Major changes in GSV retrieval workflow

Fig. 1: Landsat 2000 Tree Cover example scene (UTM Zone 51N, WGS-84)

Fig. 3: Old GSV classification. Projection: Albers Datum WGS-84 Spatial resolution: 50 m

Fig. 4: New GSV classification. Projection: UTM Zone 51N Datum WGS-84 Spatial resolution: 30 m

Comparison of old and new GSV maps: discrete classes

Fig. 5: Continuous approach for old SAR products Projection: Albers, Datum WGS-84, Spatial res: 50 m

Fig. 6: Continuous approach for new SAR products Projection: UTM Zone 51N, Datum WGS-84.Res 30m

Comparison of old and new GSV maps: continuous

Third project year, KO+24 / KO+36 III-i) Investigation of synergies from using existing land cover and/or fire products for the 2005- and 2010 maps. III-ii) Investigation of synergies from using optical satellite data at different temporal and spatial resolutions for the 2005- and 2010 maps. III-iii) Development of forest GSV estimation and coverage mapping algorithms using Sentinel-1/2 data.

1. Determine a potential (!) Forest/Non-Forest threshold for the BIOMASAR GSV maps Non-forest < 80 m³/ha < Forest

Why take a GSV of 80 m³/ha? • Cartus et al. 2008 • Reiche et al. 2010

• BSc Thesis Schratz (2014): Forest/Non-Forest comparison of BIOMASAR GSV with Landsat & SPOT data resulted in best threshold of 80.12 m³/ha

0-20 [m³/ha] 20-50 [m³/ha] 50-80 [m³/ha] >80 [m³/ha]

4 classes

BIOMASAR change 2005-2010

• We assume 80 m³/ha as a threshold for Forest/Non-Forest

• Map shows areas which – Feature a GSV value of 80m³/ha

and more in 2005 (=Forest) – Feature a GSV value of 80m³/ha

and less in 2010 (=Non-Forest)

Change from Forest (2005) to Non-Forest (2010)

Comparison to Global LC Products

1. Comparison to Forest/Non-Forest product from GLCF: Forest Cover Change (Hansen et al. 2013)

• 30 m • Based on Landsat time series

• Forest loss was defined as a stand-replacement disturbance or the complete removal of tree cover canopy at the Landsat pixel scale.

• Forest gain was defined as the inverse of loss, or the establishment of tree canopy from a nonforest state. (Hansen et al. 2013)

Intermediate step

1. Detect areas where only BIOMASAR shows change:

1. Areas of change (BIOMASAR) 2. Areas of change (GLCF) 3. Erase GLCF areas from

BIOMASAR

areas where only BIOMASAR shows a change

Why this? • To compare these areas

with global LC products to explore potential patterns

– MODIS 2010 – GlobCover 2009 – CCI LC 2010 (2008-2012)

1. Comparison of „BIOMASAR only“ change areas with

1. MODIS LC 2010 (500m) 2. GlobCover 2009 (300m)

3. CCI LC 2010 (300m)

Let´s look on the class statistics (next slide)

Class statistics: „BIOMASAR only“ change areas (= BIOMASAR change – GLCF Change) compared to MODIS LC 2010

0

10000

20000

30000

40000

50000

60000

70000

km²

MODIS class

Cropland/Natural

vegetation mosaic

25%

Mixed forest 24%

Croplands 15%

Deciduous Needleleaf

forest 13%

Woody savannas

7%

Grasslands 6%

Open shrublands

5%

Deciduous Broadleaf

forest 2%

Savannas 2%

Some thoughts on these results (focusing on the 4 biggest classes) 1) 40 % of the areas are croplands (25% + 15%) 2) 37% of the areas are forest areas (24% + 13%)

#1) Supports BIOMASAR map (change from Forest 2005 to croplands 2010) #2) supports GLCF FCC (no change; forest 2005 = forest 2010) unclear result, more research needed

1. Comparison of „BIOMASAR only“ change areas with

1. MODIS LC 2010 (500m) 2. GlobCover 2009 (300m)

3. CCI LC 2010 (300m)

Let´s look on the class statistics (next slide)

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

km²

CCI LC class

Tree cover, needleleaved,

deciduous, closed to open (>15%)

37%

Tree cover, broadleaved,

deciduous, closed to open (>15%)

25%

Grassland 11%

Cropland, rainfed 8%

Tree cover, needleleaved,

evergreen, closed to open (>15%)

4%

Sparse vegetation (tree, shrub,

herbaceous cover) (<15%)

3%

Cropland, irrigated or post-flooding

2%

Mosaic tree and shrub (>50%) / herbaceous

cover (<50%) 2%

Class statistics: „BIOMASAR only“ change areas compared to CCI LC 2010

Some thoughts on the results (focusing on the 4 biggest classes) 1) 62 % of the areas are forest areas (37% + 25%) 2) 19% of the areas are „grassland“ and “cropland“ (11% + 8%)

#1) supports GLCF FCC (no change; forest 2005 = forest 2010) #2) Supports BIOMASAR map (change from Forest 2005 to croplands 2010) supports the assumption of falsely detected forest change of the BIOMASAR map for threshold = 80m³/ha

Comparison to LC CCI 2010

Closer look on the GSV values for the selected

areas of change 1. To what GSV values have the selected

areas changed to?

See statistics on the next slide

Thoughts:

• Class 70-80 m³/ha highly influenced by the variance of the BIOMASAR map production (multitemporal ASAR values)

• drop of GSV values can be very marginal

• can be caused by BIOMASAR product variance

• All other classes (60 and less) seem to have a (significant?) drop

Explanation?

0

10000

20000

30000

40000

50000

60000

70000

0-40 40-50 50-60 60-70 70-80

km²

GSV value [m³/ha]

GSV values (2010) of areas with BIOMASAR 2005 > 80 and BIOMASAR 2010 <80

GSV in MODIS LC classes for 2005 and 10

Value

Label

0 Water 1 Evergreen Needleleaf forest 2 Evergreen Broadleaf forest 3 Deciduous Needleleaf forest 4 Deciduous Boradleaf forest 5 Mixed forest 6 Closed shrublands 7 Open shrublands 8 Woody savannas 9 Savannas

10 Grasslands 11 Permanent wetlands 12 Croplands 13 Urban and built-up

14 Cropland/Natural vegetation mosaic

15 Snow and ice

16 Barren or sparsely

vegetated

GSV in GlobCover classes, 05 + 09 Value

GlobCover legend

11 Post-flooding or irrigated cropla

14 Rainfed croplands

20 Mosaic Cropland (50-70%)/Veget

(20-50%)

30 Mosaic Vegetation (50-70%)/Crop

(20-50%)

40 Closed to open (>15%) broadlea evergreen and/or semi-deciduo

forest

50 Closed (>40%) broadleaved decid

forest

60 Open (15-40%) broadleaved decid

forest

70 Closed (>40%) needleleaved everg

forest

90 Open (15-40%) needleleaved

deciduous or evergreen fores

100 Closed to open (>15%) mixed

broadleaved and needleleaved fo

110 Mosaic Forest/Shrubland (50-70

Grassland (20-50%)

120 Mosaic Grassland (50-70%) / Forest/Shrubland (20-50%)

130 Closed to open (>15%) shrubla

140 Closed to open (>15%) grasslan

150 Sparse (>15%) vegetation

160 Closed (>40%) broadleaved for

regularly flooded - Fresh wate

170 Closed (>40%) broadleaved sem

deciduous and/or evergreen for regularly flooded - Saline wate

180 Closed to open (>15%) vegetatio

regularly flooded or waterlogged Fresh, brackish or saline wate

190 Artificial surfaces and associated

(urban areas >50%)

200 Bare areas

210 Water bodies

220 Permanent snow and ice

230 No Data

1.4 GlobCover 05 vs GlobCover 09

Fourth project year, KO+36 / KO+48 IV-i) Investigation of the suitability of fire emission data for forest GSV change estimation. IV-ii) Investigation of synergies from using Sentinel-1/2 data. IV-iii) Production of an ASAR GSV map for 2015 based on the BIOMASAR algorithm for Northeast China. IV-iv) Production of a forest GSV change map 1995-2005-2010-2015. IV-v) Update forest GSV and coverage maps for Northeast and South of China, Eastern Russia and Continental Southeast Asia to 2015 using algorithms developed in “III-iii”.

DLR – ESA Consultation | 5/7/2013 | Slide 34 ESA UNCLASSIFIED – For Official Use

DUE GlobBiomass

Objective:

Provide the user communities with a better characteristic of the distribution and changes, and an improved quantification of regional and global biomass

User Consultation in Jena, October 2012: User Requirements from: • Science: Carbon Cycle Science Community • Policy: National Forest Inventory and REDD • Forest Industry: timber production and certification

Project Activities: 1. Improve above ground biomass maps (stock and changes)

• Better geometric resolution • Improved accuracy • Validation (discrepancy map and error statistics)

2. Platform for data sharing and validation 3. Better stratification of landscape (forest types/species) 4. Standardization of maps

ITT issue: Q3 2014

KO: Q1 2015

Budget: €1,500,000

Duration: 3 years

GlobBiomass User Consultation| 9-11/10/2012 | Slide 35 ESA UNCLASSIFIED – For Official Use

GlobBiomass Regional Maps

Biomass stock and change maps with better spatial resolution than the global reference map (50 – 150 m) and with a multi temporal approach comprising three epochs: 2000 or 2005, 2010 (reference year), and 2015. The regional maps will aim for an overall error of 20% or better: • Poland: Temperate zone • Sweden: Boreal zone • Indonesia: Tropical zone • Mexico Tropical-woodland transition • Northern Congo forest-savanna mosaic

GlobBiomass User Consultation| 9-11/10/2012 | Slide 36 ESA UNCLASSIFIED – For Official Use

GlobBiomass Global Map

One of the key requirements is the generation of a global biomass map with specified requirements to spatial resolution (150-500 m) and accuracy (expected 70%) and time frame = year 2010.

Distribution of aboveground forest biomass (GEOCARBON 2013)

Thank you for your attention!

GlobBiomass User Consultation| 9-11/10/2012 | Slide 38 ESA UNCLASSIFIED – For Official Use