5 November 2013 Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data
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J. of Applied Remote Sensing, 7(1), 073484 (2013). doi:10.1117/1.JRS.7.073484
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.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)
Jing-Jing Zhou, Zhong Zao, Qingxia Zhao, Jun Zhao, Haize Wang, "Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data," Journal of Applied Remote Sensing 7(1), 073484 (5 November 2013). http://dx.doi.org/10.1117/1.JRS.7.073484
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KEYWORDS
Data modeling

Vegetation

Biological research

Statistical analysis

Near infrared

Remote sensing

Spectral models

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