Research Papers

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

[+] Author Affiliations
Jing-Jing Zhou

Northwest A&F University, College of Forestry, Key Laboratory of Environment and Ecology in Western China of Ministry of Education, Yangling, Shaanxi 712100, China

Zhong Zhao

Northwest A&F University, College of Forestry, Key Laboratory of Environment and Ecology in Western China of Ministry of Education, Yangling, Shaanxi 712100, China

Qingxia Zhao

Northwest A&F University, College of Forestry, Key Laboratory of Environment and Ecology in Western China of Ministry of Education, Yangling, Shaanxi 712100, China

Jun Zhao

Northwest A&F University, College of Forestry, Key Laboratory of Environment and Ecology in Western China of Ministry of Education, Yangling, Shaanxi 712100, China

Haize Wang

Northwest A&F University, College of Forestry, Key Laboratory of Environment and Ecology in Western China of Ministry of Education, Yangling, Shaanxi 712100, China

J. Appl. Remote Sens. 7(1), 073484 (Nov 05, 2013). doi:10.1117/1.JRS.7.073484
History: Received March 7, 2013; Revised September 21, 2013; Accepted October 9, 2013
Text Size: A A A

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 R2 value of 0.74 with the lowest root mean square error (17.21t/ha) and bias (1.85t/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.

Figures in this Article
© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Jing-Jing Zhou ; Zhong Zhao ; Qingxia Zhao ; Jun Zhao and Haize Wang
"Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data", J. Appl. Remote Sens. 7(1), 073484 (Nov 05, 2013). ; http://dx.doi.org/10.1117/1.JRS.7.073484


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.