喜马拉雅山山区大规模毁林易感性绘图评估:印度 Khangchendzonga 生物圈保护区案例研究

IF 3.8 Q2 ENVIRONMENTAL SCIENCES
Karma Detsen Ongmu Bhutia , Manoranjan Mishra , Rajkumar Guria , Biswaranjan Baraj , Arun Kumar Naik , Richarde Marques da Silva , Thiago Victor Medeiros do Nascimento , Celso Augusto Guimarães Santos
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引用次数: 0

摘要

康钦宗嘎生物圈保护区(KBR)位于喜马拉雅山脉东部,是当地特有动植物物种的重要栖息地,在碳封存方面也发挥着关键作用。本研究的主要目的是绘制 KBR 山区大规模毁林易发区地图。研究区域分为三个区:(a)过渡区;(b)核心区;(c)缓冲区。本研究利用了通过谷歌地球引擎(GEE)平台获取的多个遥感数据集,包括降水、温度、海拔、森林密度、河流距离、NDVI、NDSI、居民点距离、居民点密度、道路距离以及土地利用和土地覆盖数据。此外,还采用了层次分析法(AHP)和简单加权法(SAW)来绘制毁林敏感性地图。为验证提议的毁林易发性,使用了 2001 年至 2022 年的汉森全球森林变化(HGFC)数据。此外,我们还使用接收者工作特征曲线(ROC)和曲线下面积(AUC)指标对毁林易感性进行了评估。值得注意的是,我们的研究结果表明,在关键年份(2009 年、2011 年、2019 年和 2020 年),过渡区、核心区和缓冲区的树木覆盖率分别大幅下降了 1.60%、1.27% 和 0.89%。在这些时期,森林被大量砍伐,表明保护区的森林覆盖状况正在恶化。虽然两种方法的结果略有出入,但都凸显了 KBR 东部和南部过渡区的特殊脆弱性。这项研究采用的综合方法建立了先进的空间数据基础设施,对于即时保护规划和适应性管理战略不可或缺。从这项调查中获得的启示为指导未来的恢复和保护工作带来了巨大希望,这些工作旨在丰富这一关键地区的生物多样性和加强生态系统服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Evaluation of large-scale deforestation susceptibility mapping in the mountainous region of the Himalayas: A case study of the Khangchendzonga Biosphere Reserve, India

Evaluation of large-scale deforestation susceptibility mapping in the mountainous region of the Himalayas: A case study of the Khangchendzonga Biosphere Reserve, India

The Khangchendzonga Biosphere Reserve (KBR) is located in the Eastern Himalayas and serves as a critical habitat for endemic species of flora and fauna, as well as playing a key role in carbon sequestration. The primary aim of this study was to map large-scale deforestation susceptibility zones in the mountainous region of KBR. The study area was divided into three zones: (a) Transition Zone, (b) Core Zone, and (c) Buffer Zone. This study utilized multiple remote sensing datasets acquired through the Google Earth Engine (GEE) platform, including precipitation, temperature, elevation, forest density, distance from rivers, NDVI, NDSI, distance from settlements, settlement density, distance from roads, and land use and land cover data. Additionally, the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods were employed to map deforestation susceptibility. To validate the proposed deforestation susceptibility, Hansen Global Forest Change (HGFC) data from 2001 to 2022 were used. Moreover, deforestation susceptibility was evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) metrics. Notably, our findings revealed significant declines in tree cover of 1.60%, 1.27%, and 0.89% in the Transition, Core, and Buffer Zones, respectively, during critical years (2009, 2011, 2019, 2020). These periods witnessed substantial deforestation, indicating a deteriorating condition of the reserve's forest cover. Although there were minor discrepancies in the results of the two methods, both highlighted the particular vulnerability of the transition zones in the eastern and southern regions of KBR. The comprehensive methodology employed in this research establishes an advanced spatial data infrastructure that is indispensable for immediate conservation planning and adaptive management strategies. The insights gleaned from this investigation hold substantial promise for guiding future restoration and conservation efforts aimed at enriching biodiversity and fortifying ecosystem services in this critical area.

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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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