利用随机森林模型了解印度东北部森林破碎化动态并确定森林覆盖丧失的驱动因素,以制定有效的森林管理战略

IF 6 Q1 ENGINEERING, MULTIDISCIPLINARY
Soumik Mahapatra , Bishal Kumar Majhi , Mriganka Shekhar Sarkar , Debajit Datta , Arun Pratap Mishra , Upaka Rathnayake
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引用次数: 0

摘要

森林砍伐在全球范围内构成了一个重大的保护挑战,危及植物生命和依赖于它的相互联系的动物群落。这种损失主要是由人为活动造成的,强调需要有细致的监测工具,以适应影响特定区域森林损失的区域社会政治和文化动态的复杂性。本研究利用先进的遥感技术,利用谷歌地球引擎平台上的Landsat图像,生成了三十年(1991-2021)的详细土地利用和土地覆盖(LULC)分类,揭示了随时间的显著景观变化。利用FRAGSTATS得出的空间指标分析了森林破碎化模式和损失,以评估生态影响。此外,利用空间和非空间随机森林回归技术确定了景观中森林损失的关键驱动因素。对森林砍伐的评估表明,森林砍伐显著减少了9%,特别是在阿萨姆邦、曼尼普尔邦和梅加拉亚邦平原,面积、周长和形状发生了实质性变化(p <;0.05)。景观破碎化分析揭示了周边林带和森林穿孔对快速砍伐的敏感性。人口密度、森林人口比和平均温度是森林损失的主要驱动因素,气温升高会增加森林火灾风险。相反,崎岖的地形和高降雨量对该区域不易进入的地区的森林损失产生不利影响。我们的研究强调了东北印度地区迫切需要基于证据的保护战略和可持续土地利用实践。通过整合遥感和建模技术,我们的方法为全球区域分析提供了一个模板,为保护陆地森林生态系统的决策和地面管理工作提供了信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding forest fragmentation dynamics and identifying drivers for forest cover loss using random forest models to develop effective forest management strategies in North-East India
Deforestation poses a significant conservation challenge on a global scale, endangering both plant life and the interconnected animal communities reliant upon it. This loss is primarily propelled by anthropogenic activities, emphasizing the need for meticulous monitoring tools tailored to the intricacies of regional socio-political and cultural dynamics influencing forest loss within specific regions. This study utilized advanced remote sensing technologies, employing Landsat imagery on the Google Earth Engine platform, to generate detailed Land Use and Land Cover (LULC) classifications spanning three decades (1991–2021), revealing significant landscape changes over time. Forest fragmentation patterns and loss were analyzed using spatial metrics derived from FRAGSTATS to assess ecological impacts. Furthermore, spatial and non-spatial Random Forest regression techniques were employed to identify key drivers of forest loss within the landscape. The assessment of deforestation identifies a significant ∼9% reduction, particularly in the plains of Assam, Manipur, and Meghalaya, with substantial changes in AREA, PERIM, and SHAPE (p < 0.05). Landscape fragmentation analysis revealed the susceptibility of peripheral forest zones and forest perforation to rapid deforestration. Human population density, forest-to-population ratio, and mean temperature emerged as key drivers of forest loss, with elevated temperatures augmenting forest fire risks. Conversely, rugged terrain and high rainfall negatively impacted forest loss in less inaccessible areas of the region. Our study underscores the urgent need for evidence-based conservation strategies and sustainable land use practices in the North East Indian Region. By integrating remote sensing and modeling techniques, our approach offers a template for regional analysis worldwide, informing policy-making and ground-based management efforts to safeguard terrestrial forest ecosystems.
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来源期刊
Results in Engineering
Results in Engineering Engineering-Engineering (all)
CiteScore
5.80
自引率
34.00%
发文量
441
审稿时长
47 days
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