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Quantifying the biophysical and socioeconomic drivers of changes in forest and agricultural land in South and Southeast Asia

机译:量化南南亚和东南亚森林与农业土地变动的生物物理和社会经济驱动因素

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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.
机译:南和东南亚(SSEA)是过去几十年来土地利用和土地覆盖(LULCC)的热点。 LULCC驱动程序的识别和量化对于改善我们对LULCC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规模上量化,特别是对于SSEA。在这项研究中,我们使用综合定量方法量化SSEA在SSEA的森林变革的生物物理和社会经济驱动因素,这些方法系统地占先前发布的驾驶信息和区域数据集。我们综合了200多个出版物,以确定SSEA不同空间尺度的森林变化的驱动因素。随后,我们收集空间显式代理数据以表示已识别的驱动程序。我们使用欧洲航天局(ESA)开发的气候变化倡议(CCI)土地覆盖数据量化1992年至2015年森林和农业用地的动态。采用地理加权回归方法量化森林变化的空间异构驱动因素。我们的研究结果表明,社会经济司机比生物物理驱动因素更重要,以便在南亚和海上东南亚农业用地转换森林。相比之下,生物物理司机比社会经济司机更重要,以便在海上海洋东南亚的农业土地转换到森林,在南亚不太重要。生物物理和社会经济司机均致力于同样贡献大陆东南亚地区的变化。通过量化森林和农业用地的动态以及SSEA中变化的空间明确驱动程序,本研究为LULCC建模和投影提供了坚实的基础。

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