Image and Signal Processing Methods

Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery

[+] Author Affiliations
Chaofan Wu, Huanhuan Shen, Jinsong Deng, Muye Gan, Hongwei Xu, Ke Wang

Zhejiang University, Institute of Applied Remote Sensing and Information Technology, Hangzhou 310058, Zhejiang, China

Aihua Shen

Zhejiang Forestry Academy, Hangzhou, Zhejiang Province 310023, China

Jinxia Zhu

Zhejiang University of Finance and Economics, Institute of Economic and Social Development, Hangzhou 310018, China

J. Appl. Remote Sens. 10(3), 035010 (Aug 08, 2016). doi:10.1117/1.JRS.10.035010
History: Received January 29, 2016; Accepted July 21, 2016
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Abstract.  Biomass is one significant biophysical parameter of a forest ecosystem, and accurate biomass estimation on the regional scale provides important information for carbon-cycle investigation and sustainable forest management. In this study, Landsat satellite imagery data combined with field-based measurements were integrated through comparisons of five regression approaches [stepwise linear regression, K-nearest neighbor, support vector regression, random forest (RF), and stochastic gradient boosting] with two different candidate variable strategies to implement the optimal spatial above-ground biomass (AGB) estimation. The results suggested that RF algorithm exhibited the best performance by 10-fold cross-validation with respect to R2 (0.63) and root-mean-square error (26.44  ton/ha). Consequently, the map of estimated AGB was generated with a mean value of 89.34  ton/ha in northwestern Zhejiang Province, China, with a similar pattern to the distribution mode of local forest species. This research indicates that machine-learning approaches associated with Landsat imagery provide an economical way for biomass estimation. Moreover, ensemble methods using all candidate variables, especially for Landsat images, provide an alternative for regional biomass simulation.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Chaofan Wu ; Huanhuan Shen ; Aihua Shen ; Jinsong Deng ; Muye Gan, et al.
"Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery", J. Appl. Remote Sens. 10(3), 035010 (Aug 08, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.035010


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