NDLI: Quantifying the Compound Factors of Forest Land Changes in the Pearl River Delta, China

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Quantifying the Compound Factors of Forest Land Changes in the Pearl River Delta, China

Content Provider	MDPI
Author	Chen, Xinchuang Li, Feng Li, Xiaoqian Hu, Yinhong Hu, Panpan
Copyright Year	2021
Description	Forestland has been a focus of urbanization research, yet the effect of urbanization on forest land change on an urban agglomeration scale still remains unclear. Screening and quantifying the main factors affecting forest land changes have practical significance for land planning and management. Considering the characteristics of the region and referring to related studies, 26 natural, social, and economic factors were screened in the Pearl River Delta (PRD), where land-use changes are intense. Geographically weighted regression and the relative importance were used to quantify the spatial heterogeneity of these main factors. There was still a large area of deforestation evident in the PRD with its afforestation area of 604.3 km2 (mainly converted from cropland) and a deforestation area of 1544.6 km2 (mainly converted from built-up land). The effects of socio-economic factors were the main factors for these forest land changes, especially the rural population and migration. Deforestation mainly occurs in urban growth boundaries, which will be the focus area for further land management. These main factors have the potential to provide a methodological contribution to land-use changes, and the results of this study can provide a solid theoretical basis for forest land management and urban planning (e.g., balancing expansion of built-up land and ecological protection that advances forest land protection and restoration).
Starting Page	1911
e-ISSN	20724292
DOI	10.3390/rs13101911
Journal	Remote Sensing
Issue Number	10
Volume Number	13
Language	English
Publisher	MDPI
Publisher Date	2021-05-13
Access Restriction	Open
Subject Keyword	Remote Sensing Water Science and Technology Urbanization Forest Land Change Driving Factor Geographically Weighted Regression Relative Weight
Content Type	Text
Resource Type	Article

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