Department of Atmospheric Sciences, University of Illinois, Urbana, Illinois.
Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, Maryland.
Glob Chang Biol. 2019 Jun;25(6):2137-2151. doi: 10.1111/gcb.14611. Epub 2019 Mar 25.
South and Southeast Asia (SSEA) has been a hotspot for land use and land cover change (LULCC) in the past few decades. The identification and quantification of the drivers of LULCC are crucial for improving our understanding of LULCC trends. So far, the biophysical and socioeconomic drivers of forest change have not been quantified at the regional scale, particularly for SSEA. In this study, we quantify the biophysical and socioeconomic drivers of forest change on a country-by-country basis in SSEA using an integrated quantitative methodology, which systematically accounts for previously published driver information and regional datasets. We synthesize more than 200 publications to identify the drivers of the forest change at different spatial scales in SSEA. Subsequently, we collect spatially explicit proxy data to represent the identified drivers. We quantify the dynamics of forest and agricultural land from 1992 to 2015 using the Climate Change Initiative (CCI) land cover data developed by the European Space Agency (ESA). A geographically weighted regression method is employed to quantify the spatially heterogeneous drivers of forest change. Our results show that socioeconomic drivers are more important than biophysical drivers for the conversion of forest to agricultural land in South Asia and maritime Southeast Asia. In contrast, biophysical drivers are more important than socioeconomic drivers for the conversion of agricultural land to forest in maritime Southeast Asia and less important in South Asia. Both biophysical and socioeconomic drivers contribute approximately equally to both changes in the mainland Southeast Asia region. By quantifying the dynamics of forest and agricultural land and the spatially explicit drivers of their changes in SSEA, this study provides a solid foundation for LULCC modeling and projection.
南亚和东南亚（Southeast Asia）在过去几十年一直是土地利用和土地覆被变化（LULCC）的热点地区。识别和量化土地覆被变化的驱动因素对于提高我们对土地覆被变化趋势的理解至关重要。到目前为止，森林变化的生物物理和社会经济驱动因素尚未在区域尺度上进行量化，特别是在南亚和东南亚地区。在这项研究中，我们使用一种综合的定量方法，根据国家逐个量化了南亚和东南亚森林变化的生物物理和社会经济驱动因素，该方法系统地考虑了先前发表的驱动因素信息和区域数据集。我们综合了 200 多篇出版物，以确定南亚和东南亚不同空间尺度上森林变化的驱动因素。随后，我们收集了空间显式代理数据来代表已识别的驱动因素。我们使用欧洲航天局（ESA）开发的气候变化倡议（CCI）土地覆盖数据来量化 1992 年至 2015 年森林和农业用地的动态。我们采用地理加权回归方法来量化森林变化的空间异质性驱动因素。研究结果表明，在南亚和海上东南亚，社会经济驱动因素比生物物理驱动因素对森林向农业用地的转化更为重要。相比之下，在海上东南亚，生物物理驱动因素比社会经济驱动因素对农业用地向森林的转化更为重要，而在南亚则不太重要。在东南亚大陆地区，生物物理和社会经济驱动因素对这两种变化的贡献大致相等。通过量化南亚和东南亚森林和农业用地的动态及其变化的空间显式驱动因素，本研究为土地利用和土地覆被变化建模和预测提供了坚实的基础。
