TY - JOUR
T1 - The potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass
AU - Basuki, Tyas Mutiara
AU - Skidmore, Andrew K.
AU - van Laake, Patrick E.
AU - van Duren, Iris
AU - Hussin, Yousif A.
PY - 2012/7
Y1 - 2012/7
N2 - A main limitation of pixel-based vegetation indices or reflectance values for estimating above-ground biomass is that they do not consider the mixed spectral components on the earth's surface covered by a pixel. In this research, we decomposed mixed reflectance in each pixel before developing models to achieve higher accuracy in above-ground biomass estimation. Spectral mixture analysis was applied to decompose the mixed spectral components of Landsat-7 ETM+ imagery into fractional images. Afterwards, regression models were developed by integrating training data and fraction images. The results showed that the spectral mixture analysis improved the accuracy of biomass estimation of Dipterocarp forests. When applied to the independent validation data set, the model based on the vegetation fraction reduced 5-16% the root mean square error compared to the models using a single band 4 or 5, multiple bands 4, 5, 7 and all non-thermal bands of Landsat ETM+.
AB - A main limitation of pixel-based vegetation indices or reflectance values for estimating above-ground biomass is that they do not consider the mixed spectral components on the earth's surface covered by a pixel. In this research, we decomposed mixed reflectance in each pixel before developing models to achieve higher accuracy in above-ground biomass estimation. Spectral mixture analysis was applied to decompose the mixed spectral components of Landsat-7 ETM+ imagery into fractional images. Afterwards, regression models were developed by integrating training data and fraction images. The results showed that the spectral mixture analysis improved the accuracy of biomass estimation of Dipterocarp forests. When applied to the independent validation data set, the model based on the vegetation fraction reduced 5-16% the root mean square error compared to the models using a single band 4 or 5, multiple bands 4, 5, 7 and all non-thermal bands of Landsat ETM+.
KW - above-ground biomass
KW - spectral mixture analysis
KW - decomposition of mixed components
KW - fraction endmembers
KW - selective logging
UR - http://www.scopus.com/inward/record.url?scp=84863615576&partnerID=8YFLogxK
U2 - 10.1080/10106049.2011.634928
DO - 10.1080/10106049.2011.634928
M3 - Article
AN - SCOPUS:84863615576
VL - 27
SP - 329
EP - 345
JO - Geocarto International
JF - Geocarto International
SN - 1010-6049
IS - 4
ER -