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Journal of Applied Remote Sensing

Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data
Author(s): Jing-Jing Zhou; Zhong Zao; Qingxia Zhao; Jun Zhao; Haize Wang
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Paper Abstract

Optical remote sensing is the most widely used method for obtaining forest biomass information. This research investigated the potential of using topographical and high-resolution optical data from Quickbird for measurement of black locust plantation aboveground biomass (AGB) grown in the hill-gully region of the Loess Plateau. Three different processing techniques, including spectral vegetation indices (SVIs), texture, and topography were evaluated, both individually and combined. Simple linear regression and stepwise multiple-linear regression models were developed to describe the relationship between image parameters obtained using these approaches and field measurements. SVI and topography-based approaches did not yield reliable AGB estimates, accounting for at best 23 and 19% of the observed variation in AGB. Texture-based methods were better, explaining up to 70% of the observed variation. A combination of SVIs, texture, and topography yielded an even better R 2 value of 0.74 with the lowest root mean square error (17.21  t/ha ) and bias (−1.85  t/ha ). The results suggest that texture information from high-resolution optical data was more effective than SVIs and topography to estimate AGB. The performance of AGB estimation can be improved by adding SVIs and topography results to texture data; the best results can be obtained using a combination of these three data types.

Paper Details

Date Published: 5 November 2013
PDF: 18 pages
J. Appl. Rem. Sens. 7(1) 073484 doi: 10.1117/1.JRS.7.073484
Published in: Journal of Applied Remote Sensing Volume 7, Issue 1
Show Author Affiliations
Jing-Jing Zhou, Northwest A&F Univ. (China)
Zhong Zao, Northwest A&F Univ. (China)
Qingxia Zhao, Northwest A&F Univ. (China)
Jun Zhao, Northwest A&F Univ. (China)
Haize Wang, Northwest A&F Univ. (China)

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