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

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.