Scaling Up Above Ground Live Biomass From Plot Data to Amazon Landscape Sassan S. Saatchi NASA/Jet Propulsion Laboratory California Institute of Technology.

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Scaling Up Above Ground Live Biomass From Plot Data to Amazon Landscape Sassan S. Saatchi NASA/Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive Pasadena, California Tel: Fax:

Houghton et al. 2000

Houghton et al Conclusion: 1. Estimates of biomass for Brazilian Amazon vary by more than a factor of Disagreements on regions of high & low biomass 3. Methods disagree on spatial patterns of distribution of biomass 4. Unknown spatial variations in biomass makes accurate calculations of flux impossible Question: What is the spatial distribution of vegetation biomass in the Amazon basin?

What is the Regional Distribution of Biomass? Methods: Assign average biomass values to vegetation map 2. Interpolate biomass plots to the region 3. Model structural distribution and biomass using environmental variables such as climate, topography, vegetation, soil, etc. 4. Use of ecosystem models for carbon allocations and/or assimilation 5. Extrapolate plot data to the region using remote sensing metrics. Questions: 1.Which environmental variables or RS metrics? 2.Which models or extrapolation approaches? 3.How errors and uncertainties are propagated?

544 plots used for this study

Methodology Collect most recent ground measurements of above ground forest biomass in the basin. Acquire and develop a series of remote sensing data and products: Radar backscatter and texture MODIS continuous field product (% forest cover) Canopy roughness derived from RS data fusion Digital Elevation, Slope, and Ruggedness factor (SRTM) NDVI metrics Scatterometer metrics (surface moisture) Vegetation map from data fusion Develop a cover specific semi-empirical algorithm between remote sensing data and above ground biomass Use ground data in a bootstrapping technique to iteratively improve algorithm and verify results Produce a 1km resolution above ground vegetation biomass over the Amazon basin

Ground Biomass Measurements Measurements are performed on small plots Allometric equations are species specific Geographical locations are not accurate Detailed structural parameters are not always available

Surface Geomorphology & Drainage Systems TM Amazon Basin SRTM 90 m Detailed View

SRTM Derived Drainage System and Watersheds

DEM < 100 m

Dense forest Liana forest Bamboo forest Seasonal forest Montane forest Closed flood forest Open flood forest Herbaceous floodplain Mangrove Savanna Open woodland Park savanna Closed woodland Mixed forest-woodland Secondary forest Nonforest Vegetation Map of Amazon Basin

5.0<Z0< <Z0<5.0

NDVI SPOT-VEGETATION

11 km Radar Mosaic & Texture Maps Segmentation of radar texture

Segmentation of Surface Ruggedness

a= c= n= Kriging Plot Data Undisturbed Forest: Disturbed Forest: Bamboo Forest: Semi-Dec: Flooded Forest: Open Flooded Forest: Woodlands:

Relationship Between Radar Backscatter & Biomass Secondary & Primary Forests Savanna Vegetation

Relationship between biomass & 1. Co-occurance Energy 2. SNR adjusted Coef. Variation

Metric 6: Max. NDVI Dry season Dry Season Radar Backscatter Biomass Estimation Approach for Undisturbed Forests

Biomass Estimation Algorithm 1. Use land cover map for algorithm implementation 2. Use the bootstrapping method and nonlinear estimation techniques to estimate the coefficients of the following function: b i = a 1i T  + a 2i Z  + a 3i A  where: b i = biomass of class i T = SNR corrected coefficient of variation Z = Canopy Roughness A = Backscatter Amplitude  power coefficients 3. The optimum coefficients from the bootstrapping technique is used in final algorithm

Mg/ht Vegetation Above Ground Live Biomass

1. Biomass of deforested areas & Secondary Regrowth along roads are delineated. 2. Vegetation biomass along river channels are separated from terre firme forest. 3. Mico topography can cause errors in biomass estimation. Analysis of Results

Biomass of transitional vegetation such as bamboo and liana forests are accurately estimated.

Vegetation Carbon Distribution Over Two Transects Along the Amazon Basin East-West Transect North-South Transect

Leaf Biomass Wood Biomass

Summary Bootstrapping Approach No validation has been performed except the internal error analysis of the algorithm using the ground biomass. Estimation Error increases with biomass because: 1. Lack of sensitivity of existing radar and optical instruments to high biomass values. 2. Quality of biomass data over dense forest Spatial distribution of Amazon biomass/Carbon can be improved as more data becomes available. Similar to RADAMBRAZIL & Brown interpolation map high biomass runs east-west through central Amazonia with some high values in Peruvian Amazon