Globbiomass: new products and approaches

Transcription

1 Globbiomass: new products and approaches Global mapping of forest biomass: status-quo Maurizio Santoro 1, Oliver Cartus 1, GAMMA Remote Sensing Thuy Le Toan 2, Stephane Mermoz 2, Alexandre Bouvet 2, Ludovic Villard 2 CESBIO GlobBiomass project team 1

2 Some initial considerations EO widely exploited to generate biomass maps (nation-wide, ecosystem-wide) However, hardly any global data product of forest variables exists AGB: Hu et al. (2016) and GEO-CARBON 1km, Kindermann et al. 0.5 Forest canopy height: Lefsky (2010) and Simard et al. (2011), 1 km Carbon stocks: 0.1, Ruesch and 0.01 Most datasets use data from around year 2000 Errors and uncertainties often not fully described Significant discrepancies between maps difficult to understand what is right and what is wrong 2

3 EO data and global biomass mapping There is no remote sensing observable that can give us biomass directly need to derive models that adapt what is available to the desired result the strategy of GlobBiomass is to use the information on biomass in each of the available datasets and complement these to get an improved estimate with respect to what could be obtained by taking each input layer singularly 3

4 Key features of algorithm design Development of multiple global biomass algorithms Algorithms shall lead to an improved global map of AGB (*) Take into account regional approaches and data products Take into account the Product Specification by users Take into account available ground and space data for (*) spatial resolution < 500 m and an error expected of max 30%. 4

5 Selected algorithms Method Input EO Model What does it do and how BIOMASAR-C Envisat ASAR Water Cloud Model Physically-based model relating SAR observable to biomass CUBIST Landsat Landsat+PALSAR Non-parametric Model calibration NOT requiring in situ data Hyper-temporal combination of biomass estimates from individual SAR backscatter observations. Estimates GSV, requires conversion to AGB Rescales BIOMASAR-C biomass estimates BIOMASAR-L PALSAR Water Cloud Model Mimics BIOMASAR-C (i.e. no in situ required for training) CESBIO PALSAR Water Cloud Model Model parameters estimated from in situ observations and E.M. model theory. Retrieval based on a Bayesian approach. (*) 5

6 The GlobBiomass global biomass mapping concept 6

7 Summary on selected algorithms Method Strengths Weaknesses BIOMASAR-C CUBIST BIOMASAR-L CESBIO Reproduces spatial distribution of biomass Straightforward imputation of GSV Reasonable performance even in high biomass forests Precise estimation of model parameters at training samples Underestimation in high biomass forest and fragmented landscapes Relies heavily on input BIOMASAR-C -> propagates large scale errors to final product Large errors possible due to the availability of a single L-band image in the form of a mosaic Representativeness of model parameters estimates to be assessed across larger scales Biomass estimated up to 100 Mg/ha, Method evaluated in the tropics test on low biomass in other biomes + High biomass algorithm to be tested 7

8 Global GSV of 2010 BIOMASAR-C (as of Oct. 2016) Spat. Resolution: 1,000 m Spat. Resolution: 1,000 m; GSV scaled between 0 and 300 m 3 /ha Hyper-temporal combination of ASAR data acquired between Oct and Feb not everywhere large amount of data need to revise the temporal window 8

9 Global GSV of 2010 BIOMASAR-L (as of Oct. 2016) Spat. Resolution: 25 m GSV scaled between 0 and 300 m 3 /ha Single-image retrieval (ALOS PALSAR HV mosaic of 2010) 9

10 Comparing L- and C-band estimates Currently, trying to identify major bottlenecks in algorithms looking at macroscale level r = 0.88 RMSD = 32.5 m 3 /ha RMSD% = 58.1% r = 0.94 RMSD = 45.2 m 3 /ha RMSD% = 49.1% 10

11 Spat. Resolution: 1,000 m BIOMASAR-C, 1,000 m Along Angara River, Central Siberia - GSV scaled between 0 and 300 m 3 /ha 11

12 Spat. Resolution: 1,000 m BIOMASAR-C, BIOMASAR-L, 25 1,000 m m Along Angara River, Central Siberia - GSV scaled between 0 and 300 m 3 /ha 12

13 Rondonia, AGB, 1 km, Hu et al. 13

14 Rondonia, AGB, 1km, Saatchi et al. Rondonia, Brazil - Values scaled between 0 and 300 m 3 /ha or t/ha 14

15 Rondonia, AGB, 1 km, GEO-CARBON 15

16 Rondonia, GSV, 1 km, BIOMASAR-C Santoro et al., GlobBiomass: New products and approaches GFOI Workshop, The Hague, 31 October

17 Rondonia, AGB, 30 m, Baccini et al. Santoro et al., GlobBiomass: New products and approaches GFOI Workshop, The Hague, 31 October

18 Rondonia, GSV, 30 m, BIOMASAR-L 18

19 Some initial observation Macro-scale distribution of biomass appears to be well captured with the approaches here developed BIOMASAR-C appears to give a more homogeneous picture but underestimates high biomass BIOMASAR-L is more suited above 200 m 3 /ha. Question is what is reliably estimated Ahead: determine rules to merge maps convert GSV to AGB validate estimates 19

20 Some final notes The next steps need support from project validation team and external experts Interested in supporting map assessment, validation etc.? Contact Interested in evaluating the global AGB map ahead of public release? Register (and commit) as User by contacting Chris Schmullius Intended release of GlobBiomass AGB map + uncertainties: end of 2017 For more information, visit 20

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22 The BIOMASAR algorithm Water Cloud Model (SAR backscatter=f(gsv)) trained without in situ training data Auxiliary datasets required to define the slope of the model and the max GSV Model estimated for each pixel and each observation I i ASAR images Training with: -MODIS VCF - CCI land cover - Dataset of max GSVs V i GSV maps GSV map (Santoro et al., 2011, 2013, 2015), Cartus et al. (2012) -Support by regional GSV/AGB maps to estimate one model parameter: forest transmissivity AGBmap 22