Dengsheng Lu: dlu@indiana Indiana University Mateus Batistella: mb@cnpm.embrapa.br

1. A Comparative Analysis of Satellite-based Approaches for Aboveground Biomass Estimation in the Brazilian Amazon Dengsheng Lu: dlu@indiana.edu Indiana University Mateus Batistella: mb@cnpm.embrapa.br Emilio Moran: moran@indiana.edu November 17-21, 2008 Manaus, Brazil

2. Definition • Biomassincludes the aboveground and belowground living mass, such as trees, shrubs, vines, roots, and the dead mass of fine and coarse litter associated with the soil • Due to the difficulty in collecting field data of belowground biomass, most previous research on biomass estimation focuses on aboveground biomass

3. Study Areas in the Brazilian Amazon

4. Data Sets Used in Research

5. Strategy of Vegetation Inventory in Field Data Collection Terminology: Tree: DBH >=10 cm Sapling: DBH 2 - 10 cm Seedling: DBH < 2 cm Region: the study area Site: study sampling location Plot: delimited area within the sample site, used to measure trees Subplot: smaller plot within the plot used to measure saplings and count the number of seedlings DBH: diameter of a tree trunk at breast height, usually 1.3 meters off the ground Stem Height: the height to the first major branch Region Site Plot Inventory Items inside the Subplot: All saplings were identified and measured for DBH and total height All seedlings were identified and counted If the individuals were uncountable, percent coverage was estimated Subplot Inventory Items inside the Plot: All trees were identified and measured for DBH, stem height, and total height Ten 10x15 meter plots were randomly located along a randomly-oriented transect within a forest stand (site). Inside the plot, a 5x2 meter subplot was randomly placed

6. Image Processing • Image geometric rectification and atmospheric correction • Development of vegetation indices and textures • Lu, D., Mausel, P., Brondizio, E., and Moran, E. 2004. Relationships between Forest Stand Parameters and Landsat Thematic Mapper Spectral Responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198 (1-3), 149-167. • Lu, D., and Batistella, M. 2005. Exploring TM Image Texture and its Relationships with Biomass Estimation in Rondônia, Brazilian Amazon. Acta Amazonica, 35(2), 261-268. • Development of fraction images with spectral mixture analysis of multispectral images

7. Endmember Selection • Image-based endmember selection • Minimum noise transform • Space features Three endmembers: shade, vegetation, and soil, were selected • Constrained least-squares solution

8. Development of Biomass Estimation Models • Selection of variables • Correlation analysis • Stepwise regression analysis • Selection of algorithms • Linear and nonlinear regression analysis

9. Biomass Estimation Models with TM Derived Variables in Different Biophysical Environments

10. Biomass EstimationModels with TM Spectral and Fraction Images for Successional and Mature forests in the Rondonia Site, Brazil

11. Biomass Distribution in Altamira

12. Impacts of Biophysical Environment on the Biomass Estimation Performance • Forest stand structure • Soil fertility • Land use history

13. A Comparison of Different Study Areas on the Relationships between Biomass and Age

14. A Comparison of Different Study Areas on the Relationship between Soil Fertility and Biomass Growth Rate

15. Conclusions • Biomass can be estimated using Landsat TM images, especially for the forest sites with relatively simple forest stand structures. • Incorporation of textures and spectral responses improved model performance. • Linear spectral mixture analysis is a potential method for biomass estimation. • Different biophysical environments affect development of biomass estimation models.