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Remote Sens. 2016, 8(3), 201; doi:10.3390/rs8030201

Identification of Factors Influencing Locations of Tree Cover Loss and Gain and Their Spatio-Temporally-Variant Importance in the Li River Basin, China

1
,
2,3
and
1,*
1
Institute of Remote Sensing and Geographic Information Systems, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China
2
College of Geoscience and Surveying Engineering, China University of Mining and Technology, Ding No.11 Xueyuan Road, Haidian District, Beijing 100083, China
3
State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Ding No.11 Xueyuan Road, Haidian District, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Academic Editors: Parth Sarathi Roy and Prasad S. Thenkabail
Received: 6 December 2015 / Revised: 10 February 2016 / Accepted: 24 February 2016 / Published: 1 March 2016
View Full-Text   |   Download PDF [1852 KB, uploaded 1 March 2016]   |  

Abstract

Intensive tree cover loss and gain have been significantly influencing the environment and society. It is essential to identify the potential factors and to evaluate their importance. A large body of literature has investigated the factors influencing tree cover loss, usually at the global or regional scale and focusing on the quantity issue: how are the rate and extent of tree cover loss influenced by different factors? This paper has two objectives. The first is to pinpoint factors influencing the locations of tree cover loss and gain (the location issue) at the pixel level. The second is to evaluate the heterogeneous importance of factors in two periods of 1991 through 2002 and 2002 through 2013 and in four counties within the Li River Basin, Guangxi Zhuang Autonomous Region, China. The random forests technique was adopted to model the responses of tree cover loss and gain probabilities of sampled pixels to initial landscape pattern factors, biophysical factors and proximity factors. A ranking of factor importance and a set of important factors were derived for each county and time period. The partial dependence plots were generated for the most important factors to reveal how exactly tree cover loss and gain probabilities change as influenced by these factors. The results confirmed that factor importance varied across time and space, and the variability of proximity factors and initial landscape pattern factors were more pronounced. The furthered understanding of the heterogeneous importance of different factors on the locations of tree cover loss and gain can support more sustainable forest management practices and the development of more effective policies regarding ecosystem conservation and economic development. View Full-Text
Keywords: tree cover loss; tree cover gain; random forests; variable importance; tree cover change tree cover loss; tree cover gain; random forests; variable importance; tree cover change
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, Y.; Li, J.; Qin, Q. Identification of Factors Influencing Locations of Tree Cover Loss and Gain and Their Spatio-Temporally-Variant Importance in the Li River Basin, China. Remote Sens. 2016, 8, 201.

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