TITLE:
Above Ground Biomass Assessment from Combined Optical and SAR Remote Sensing Data in Surat Thani Province, Thailand
AUTHORS:
Kilaparthi Kiran Kumar, Masahiko Nagai, Apichon Witayangkurn, Kunnaree Kritiyutanant, Shinichi Nakamura
KEYWORDS:
Above Ground Biomass, Spectral Indices, Backscattering, LANDSAT 8, ALOS-2
JOURNAL NAME:
Journal of Geographic Information System,
Vol.8 No.4,
August
24,
2016
ABSTRACT: Today the carbon content in the atmosphere is predominantly increasing
due to greenhouse gas emission and deforestation. Forest plays a key role in
absorbing carbon dioxide from atmosphere by process of sequestration through
photosynthesis and stores in form of wood biomass which contains nearly 70% - 80%
of global carbon. Different forms of biomass in the environment include agricultural
products, wood, renewable energy and solid waste. Therefore, it is essential to
estimate the biomass content in the environment. In olden days, biomass is
estimated by forest inventory techniques which consume lot of time and cost.
The spatial distribution of biomass cannot be obtained by traditional inventory
forest techniques so the application of remote sensing in biomass assessment is
introduced to solve the problem. Overall accuracy of classified map indicates that
land features of Surat Thani on map show an accuracy of 91.13% with different
land features on ground. Both optical (LANDSAT-8) and synthetic aperture radar
(ALOS-2) remote sensing data are used for above ground biomass (AGB)
assessment. Biomass that stores in branch and stem of tree is called as above
ground biomass. Twenty ground sample plots of 30 m × 30 m utilized for biomass
calculation from allometric equations. Optical remote sensing calculates the
biomass based on the spectral indices of Soil Adjusted Vegetation Index (SAVI)
and Ratio Vegetation Index (RVI) by regression analysis (R2 = 0.813).
Synthetic aperture radar (SAR) is an emerging technique that uses high
frequency wavelengths for biomass estimation. HV backscattering of ALOS-2 shows
good relation (R2 = 0.74) with field calculated biomass compared to
HH (R2 = 0.43) utilizes for biomass model generation by linear
regression analysis. Combination of both optical spectral indices (SAVI, RVI)
and HV (ALOS-2) SAR backscattering increases the plantation biomass accuracy to
(R2 = 0.859) compared to optical (R2 = 0.788) and SAR (R2 = 0.742).