Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 414678
Title The potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass
Author(s) Basuki, T.M.; Skidmore, A.K.; Laake, P.E. van; Duren, I.C. van; Hussin, Y.A.
Source Geocarto International 27 (2012)4. - ISSN 1010-6049 - p. 329 - 345.
Department(s) Resource Ecology
Publication type Refereed Article in a scientific journal
Publication year 2012
Keyword(s) urban vegetation abundance - landsat tm data - aboveground biomass - satellite estimation - kyoto protocol - indexes - amazon - carbon - reflectance - information
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+.
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