The potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass

Tyas Mutiara Basuki, Andrew K. Skidmore, Patrick E. van Laake, Iris van Duren, Yousif A. Hussin

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

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+.

Original languageEnglish
Pages (from-to)329-345
Number of pages17
JournalGeocarto International
Volume27
Issue number4
DOIs
Publication statusPublished - Jul 2012
Externally publishedYes

Keywords

  • above-ground biomass
  • spectral mixture analysis
  • decomposition of mixed components
  • fraction endmembers
  • selective logging

Fingerprint Dive into the research topics of 'The potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass'. Together they form a unique fingerprint.

Cite this