Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data

Jing-Jing Zhou; Zhong Zao; Qingxia Zhao; Jun Zhao; Haize Wang

doi:10.1117/1.JRS.7.073484

5 November 2013 Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data

Jing-Jing Zhou, Zhong Zao, Qingxia Zhao, Jun Zhao, Haize Wang

Author Affiliations +

Funded by: Forestry Science and Technology Support Plan, Researches and Demonstration of the Key Technology for Plantation Sustainable Management in the Loess Plateau, Program of National Silviculture Teaching Team, Ministry of Education

Journal of Applied Remote Sensing, Vol. 7, Issue 1, 073484 (November 2013). https://doi.org/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.

Citation Download Citation

Jing-Jing Zhou, Zhong Zao, Qingxia Zhao, Jun Zhao, and 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). https://doi.org/10.1117/1.JRS.7.073484

Published: 5 November 2013

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