BIOMASS. * US Observers

Transcription

1 1 BIOMASS Malcolm Davidson, A. Thompson, C. Lin, P. Benzi, F. Heliere (ESA-ESTEC) and the BIOMASS MAG (T. Le Toan, S. Quegan, H. Baltzer, P. Paillou, K. Papathanassiou, F. Rocca, L. Ulander, S. Plummer, S. Saatchi*, H. Shugart* ) * US Observers

2 Uncertainties in biomass estimates 2 Spatial distribution of biomass in the Amazon: Comparison of current methods (Houghton et al., 2001)

3 Potential of P-Band for biomass retrieval Sigma (db) L - HV P - HV HF - VHF Above-ground biomass (tons/ha)

4 4 Biomass mapping: temperate conifer plantations Image optique P-band SAR Image CESBIO Forêt de Nézer Biomasse (T/ha) Beaudoin et al., 94

5 Bayesian inversion with a priori knowledge 5

6 6

7 Biomass mapping i boreal forest 7 Field Stem Biomass (tons/ha) P-band Algorithm R=0.954 Saatchi & Moghaddam SAR Predicted Stem Biomass Biomass map for BOREAS test site from P-Band SAR (Saatchi et al., 1997)

8 Contribution to stem biomass Dynamic Coefficient Contribution of HH Contribution of VV 8

9 Airsar Landes Above ground biomass Crown biomass Stem biomass 9

10 Landes AIRSAR RMSE= 10 tons/ha 10

11 Colombia Above ground Biomass Crown Stem 11

12 12 Slide from Ake Rosenqvist, JAXA

13 Inversion Results: Multi-baseline Forest Height 13 P-band L-band May April March April 0m

14 H100 from LIDAR Height Data The h 100 as a reference forest height: 30 Typical forestry standard parameter for the upper canopy height; tallest trees per hectare; LIDAR Height H m Estimated from Lidar data by taking the highest measurement within a 10 x 10m window.

15 Validation against. LIDAR H P-Band May P-Band March P-Band April L-Band April

16 Inventory Data & Ground Measurements 16 A14 P-Band May A07 A08 A06 A01 A02 A05 A13 P-Band April A11 A03 A15 80 by 80 meters areas 20 by 50 meters areas

17 Inversion Results: Stem Volume 17 Area 01 Area 05 Area 14 Area 15 May 600 Area 01 Area Area 14 March 0 m3/ha Area 15 April

18 Forest wetland extent 18 Methane strong contributor to global warming Extent and temporal evolution of floodplain under forest canopy not well known Varzea Dry Season Varzea Wet Season P-band backscatter P-band backscatter

19 DALEC A f L f C f R h R a A r L r GPP C r C lit D A w L r C w C som/cwd

20 Multi-parameter estimation PDF without biomass PDF with biomass p3 p4 p10 p3 = allocation to leaves p4 = allocation to woody biomass p10 = temp-dependent rate parameter

21 Illustration of the impact of a Kalman filter on the estimate of the woody biomass pool of the DALEC model. Model estimate of woody biomass: thick brown line, Model uncertainty: yellow regions. The biomass observations with error bars: vertical black lines. The assumed true value for the woody biomass is the thin black line.

22 Mission requirements 22 Information Product Forest Biomass (above ground) Forest Disturbance Forest Regrowth Forest seasonal floods Mission Requirements 20% accuracy m resolution/16 looks 2 biomass maps/year Polarimetric Interferometric mode Global coverage of forests Maps of disturbed area with 10% classification accuracy 100m resolution/16 looks 1-2 forest disturbance maps every 2 months Global coverage of forests Biomass information 20% accuracy Biomass rate of change 20% accuracy m resolution/16 looks 2 revisits/year Global coverage with focus on tropical forests Inundation area information 10% classification accuracy 100m resolution/16 looks 1 revisit/month during flood season tropical forests (main target) + boreal wetlands (secondary target) for methane emission

23 Biomass information within carbon models Biomass predicted by Global Dynamic Vegetation Model (gc/m2) 23 Biomass map derived from SAR measurements 1 2 Le Toan et al, 2004

24 Need to reduce uncertainties 24 Land to atmosphere emissions resulting from land use changes during the 1990s and the 1980s (GtC yr 1 ) from the IPCC Fourth Assessment Report (AR4)