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
{"title":"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","authors":"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","doi":"10.1016/j.rsase.2024.101285","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101285"},"PeriodicalIF":3.8000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation of large-scale deforestation susceptibility mapping in the mountainous region of the Himalayas: A case study of the Khangchendzonga Biosphere Reserve, India\",\"authors\":\"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\",\"doi\":\"10.1016/j.rsase.2024.101285\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":53227,\"journal\":{\"name\":\"Remote Sensing Applications-Society and Environment\",\"volume\":\"36 \",\"pages\":\"Article 101285\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing Applications-Society and Environment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352938524001496\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Applications-Society and Environment","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352938524001496","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
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.
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