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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
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