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Journal of Applied Remote Sensing

Estimating woody above-ground biomass in an arid zone of central Australia using Landsat imagery
Author(s): Zhihui Wang; Gary N. Bastin; Liangyun Liu; Peter A. Caccetta; Dailiang Peng
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Paper Abstract

Woody cover was found to be an easily measurable and significant variable for estimating woody above-ground biomass (AGB) based on in situ measurements in an arid zone of central Australia. In addition, the potential of woody cover to estimate woody AGB based on Landsat imagery was further tested. Linear spectral mixture analysis (LSMA) using multitemporal endmembers was employed to estimate the woody cover for Landsat imagery and the LSMA method was tested for different Landsat images under various drought conditions. The results show that the accuracy of the woody cover estimation increased as the accumulated rainfall prior to the image acquisition date decreased. Woody AGB across the study area was finally retrieved using the combination of plot-level woody AGB model and woody cover derived from dry-period Landsat images closest to the acquisition time of the field data, with an root mean square error of 0.798  t/ha. It is concluded that woody cover is able to replace tree basal area to estimate woody AGB at plot-level scale, and it is much more suitable for estimating woody AGB than the normalized difference vegetation index-AGB model for this arid zone covered with low-cover shrubs; however, woody AGB estimated using the proposed method is underestimated for some areas.

Paper Details

Date Published: 25 June 2015
PDF: 19 pages
J. Appl. Remote Sens. 9(1) 096036 doi: 10.1117/1.JRS.9.096036
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Zhihui Wang, Institute of Remote Sensing and Digital Earth (China)
Graduate Univ. of Chinese Academy of Sciences (China)
Gary N. Bastin, Commonwealth Scientific and Industrial Research Organisation (Australia)
Liangyun Liu, Institute of Remote Sensing and Digital Earth (China)
Peter A. Caccetta, Commonwealth Scientific and Industrial Research Organisation (Australia)
Dailiang Peng, Institute of Remote Sensing and Digital Earth (China)


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