A Comparative Analysis of Satellite-based Approaches for Aboveground Biomass Estimation in the...
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A Comparative Analysis of Satellite-based Approaches for Aboveground Biomass
Estimation in the Brazilian Amazon
Dengsheng Lu: [email protected]
Indiana University
Mateus Batistella: [email protected]
Emilio Moran: [email protected]
November 17-21, 2008
Manaus, Brazil
Definition
• Biomass includes 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
Study Areas in the Brazilian Amazon
Data Sets Used in Research
Study area No. of
samples Biomass range
(kg/m2)
Date for field data collection
TM image acquisition
date
Altamira*§ 20* 0.828 – 51.675 1992 & 1993 7/20/1991
Bragantina§ 18 1.697 – 30.825 1994 & 1995 6/21/1994
Pedras§ 14 2.408 – 39.467 1992 & 1993 7/22/1991
Machad. (SF) 26 2.397 – 15.987
Machad. (MF) 14 11.132 – 49.470
1999 6/18/1998
Strategy of Vegetation Inventory in Field Data Collection
Region Site
PlotSubplotInventory 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
Inventory Items inside the Plot:
All trees were identified and measured for DBH, stem height, and total height
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
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
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
Endmember Selection
• Image-based endmember selection – Minimum noise transform– Space features
Three endmembers: shade, vegetation, and soil, were selected
• Constrained least-squares solution
Development of Biomass Estimation Models
• Selection of variables– Correlation analysis– Stepwise regression analysis
• Selection of algorithms– Linear and nonlinear regression analysis
Biomass Estimation Models with TM Derived Variables in Different Biophysical Environments
Study Areas Models R2 Beta Value
Altamira 122.288 - 1.078*KT1 -
128.913*VARtm2_9 0.772
-0.28 (sp), -0.72 (txt)
Pedras 65.239 - 10.189*TM7 -3.816KUtm3_5 0.748 -0.58 (sp), -0.42 (txt)
Bragantina 64.037 - 1.651*TM4 + 1.405*SKtm4_9 0.780 -0.76 (sp), 0.29 (txt)
Machad. (SF) 48.082-0.806*TM4 –
0.098*VARtm4_15 0.755
-0.71(SP), -0.24(txt)
Machad. (MF) 75.331-4.321*TM5+1.789*
CONtm5_19 0.498
-0.31(sp), 0.65(txt)
Biomass Estimation Models with TM Spectral and Fraction Images for Successional and Mature
forests in the Rondonia Site, Brazil
Type Variable Regression models R2
Biomass = 48.674 - 57.904 ∙ GV 0.785 Fraction
Biomass = 23.787 - 6.47 ∙ GV/(1-GV) 0.812 Biomass = 66.772 - 1.392 ∙ TM4 0.746
SF Spectral
Ln(biomass) = 7.853 - 0.14 ∙ TM 4 0.697 Biomass = 33.284 - 414.143 ∙ Soil fraction 0.181 Fraction
Ln(biomass) = 3.386 - 13.688 ∙ Soil fraction 0.120 Biomass = 102.414 - 5.496 ∙ TM 5 0.158
PF Spectral
Ln(biomass) = 6.626 - 0.249 ∙ TM 5 0.198
Biomass Distribution in Altamira
Impacts of Biophysical Environment on the Biomass Estimation Performance
• Forest stand structure
• Soil fertility
• Land use history
0
5
10
15
20
25
30
0 5 10 15 20 25 30 35 40
Fallow age
Bio
ma
ss
Altamira Bragantina Pedras
Bragantina Pedras Altamira
A Comparison of Different Study Areas on the Relationships between Biomass and Age
A Comparison of Different Study Areas on the Relationship between Soil Fertility and Biomass Growth Rate
4
6
8
10
12
14
16
18
20
22
0.0 0.5 1.0 1.5 2.0
Biomass growth rate
So
il f
ertilit
y
Altamira Bragnatina Pedras
Pedras Bragantina Altamira
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.