Extracting distribution and expansion of rubber plantations from Landsat imagery using the C5.0 decision tree method

Zhongchang Sun; Patrick Leinenkugel; Huadong Guo; Chong Huang; Claudia Kuenzer

doi:10.1117/1.JRS.11.026011

2 May 2017 Extracting distribution and expansion of rubber plantations from Landsat imagery using the C5.0 decision tree method

Zhongchang Sun, Patrick Leinenkugel, Huadong Guo, Chong Huang, Claudia Kuenzer

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Journal of Applied Remote Sensing, Vol. 11, Issue 2, 026011 (May 2017). https://doi.org/10.1117/1.JRS.11.026011

Abstract

Natural tropical rainforests in China’s Xishuangbanna region have undergone dramatic conversion to rubber plantations in recent decades, resulting in altering the region’s environment and ecological systems. Therefore, it is of great importance for local environmental and ecological protection agencies to research the distribution and expansion of rubber plantations. The objective of this paper is to monitor dynamic changes of rubber plantations in China’s Xishuangbanna region based on multitemporal Landsat images (acquired in 1989, 2000, and 2013) using a C5.0-based decision-tree method. A practical and semiautomatic data processing procedure for mapping rubber plantations was proposed. Especially, haze removal and deshadowing were proposed to perform atmospheric and topographic correction and reduce the effects of haze, shadow, and terrain. Our results showed that the atmospheric and topographic correction could improve the extraction accuracy of rubber plantations, especially in mountainous areas. The overall classification accuracies were 84.2%, 83.9%, and 86.5% for the Landsat images acquired in 1989, 2000, and 2013, respectively. This study also found that the Landsat-8 images could provide significant improvement in the ability to identify rubber plantations. The extracted maps showed the selected study area underwent rapid conversion of natural and seminatural forest to a rubber plantations from 1989 to 2013. The rubber plantation area increased from 2.8% in 1989 to 17.8% in 2013, while the forest/woodland area decreased from 75.6% in 1989 to 44.8% in 2013. The proposed data processing procedure is a promising approach to mapping the spatial distribution and temporal dynamics of rubber plantations on a regional scale.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Zhongchang Sun, Patrick Leinenkugel, Huadong Guo, Chong Huang, and Claudia Kuenzer "Extracting distribution and expansion of rubber plantations from Landsat imagery using the C5.0 decision tree method," Journal of Applied Remote Sensing 11(2), 026011 (2 May 2017). https://doi.org/10.1117/1.JRS.11.026011

Received: 3 October 2016; Accepted: 17 April 2017; Published: 2 May 2017

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