Assessing Burned Areas in Sikkim, India through Satellite Mapping

IF 0.8 Q3 FORESTRY
Kapila Sharma, Gopal Thapa, Salghuna Nn
{"title":"Assessing Burned Areas in Sikkim, India through Satellite Mapping","authors":"Kapila Sharma, Gopal Thapa, Salghuna Nn","doi":"10.17475/kastorman.1394888","DOIUrl":null,"url":null,"abstract":"Aim of study: Fire impacts biodiversity and ecosystems, and is crucial for understanding fire causes. This paper aimed to assess burned areas and severity levels in Sikkim's forest fire incidence data from 2004-2019. Area of the study: The study area for the work is the state of Sikkim, situated in the Himalayan Mountain's North-eastern region. Material and methods: Landsat 8 and Landsat 5 satellite image were used for the study and Standard vegetation indices like Delta Normalized Burn Ratio (dNBR) and Relativized Burn Ratio (RBR) are computed. Also, a linear regression analysis was performed between weather parameters like temperature (℃), wind (Km/h), rainfall (mm) on burn severity (dNBR classes) of forest fires in Sikkim between the year 2009-2019. Main results: According to the findings, out of 557 numbers forest fire incidents in Sikkim between 2004 and 2019, 250 numbers were classified as Unburned (46.21 %), 199 numbers as Enhanced Regrowth, Low (35.72 %), and 43 numbers as Enhanced Regrowth, High (7.94 %), while 32 numbers were classified as Low Severity (5.92 %), 9 numbers as Moderate-Low Severity (1.66 %), 5 numbers as Moderate-High Severity (0.92 %), and 2 numbers as High Severity (0.36 %). It was found that the wind (r=0.80, Slope=0.57, SD=0.70) and rainfall (r=0.77, Slope=-0.18, SD=7.00) showed a strong positive and strong negative linear relationships respectively in influencing the burn severity (dNBR). While, temperature (r=0.69, Slope=0.74, SD=0.01) plays a moderate positive role in influencing the burn severity (dNBR). Highlights: The study has shown the effectiveness of burn area mapping and remote sensing data products in analyzing forest fire regions with limited resources and diverse landforms and vegetation. Researchers will be able to identify the regions affected by forest fires and those that have not recovered since the fire. Goal of this research is to improve forest fire planning and management by fostering aid to the responsible authorities to evaluate the pattern of vegetation degradation in burn regions and estimate the impact of forest fires","PeriodicalId":17816,"journal":{"name":"Kastamonu University Journal of Forestry Faculty","volume":"1 1","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Kastamonu University Journal of Forestry Faculty","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17475/kastorman.1394888","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"FORESTRY","Score":null,"Total":0}
引用次数: 0

Abstract

Aim of study: Fire impacts biodiversity and ecosystems, and is crucial for understanding fire causes. This paper aimed to assess burned areas and severity levels in Sikkim's forest fire incidence data from 2004-2019. Area of the study: The study area for the work is the state of Sikkim, situated in the Himalayan Mountain's North-eastern region. Material and methods: Landsat 8 and Landsat 5 satellite image were used for the study and Standard vegetation indices like Delta Normalized Burn Ratio (dNBR) and Relativized Burn Ratio (RBR) are computed. Also, a linear regression analysis was performed between weather parameters like temperature (℃), wind (Km/h), rainfall (mm) on burn severity (dNBR classes) of forest fires in Sikkim between the year 2009-2019. Main results: According to the findings, out of 557 numbers forest fire incidents in Sikkim between 2004 and 2019, 250 numbers were classified as Unburned (46.21 %), 199 numbers as Enhanced Regrowth, Low (35.72 %), and 43 numbers as Enhanced Regrowth, High (7.94 %), while 32 numbers were classified as Low Severity (5.92 %), 9 numbers as Moderate-Low Severity (1.66 %), 5 numbers as Moderate-High Severity (0.92 %), and 2 numbers as High Severity (0.36 %). It was found that the wind (r=0.80, Slope=0.57, SD=0.70) and rainfall (r=0.77, Slope=-0.18, SD=7.00) showed a strong positive and strong negative linear relationships respectively in influencing the burn severity (dNBR). While, temperature (r=0.69, Slope=0.74, SD=0.01) plays a moderate positive role in influencing the burn severity (dNBR). Highlights: The study has shown the effectiveness of burn area mapping and remote sensing data products in analyzing forest fire regions with limited resources and diverse landforms and vegetation. Researchers will be able to identify the regions affected by forest fires and those that have not recovered since the fire. Goal of this research is to improve forest fire planning and management by fostering aid to the responsible authorities to evaluate the pattern of vegetation degradation in burn regions and estimate the impact of forest fires
通过卫星测绘评估印度锡金的烧毁地区
研究目的:火灾影响生物多样性和生态系统,对了解火灾原因至关重要。本文旨在评估锡金 2004-2019 年森林火灾发生率数据中的烧毁面积和严重程度。 研究区域:研究区域为锡金邦,位于喜马拉雅山东北部地区。 材料和方法:研究使用了 Landsat 8 和 Landsat 5 卫星图像,并计算了标准植被指数,如三角洲归一化燃烧比(dNBR)和相对化燃烧比(RBR)。此外,还对 2009-2019 年锡金森林火灾的温度(℃)、风速(Km/h)、降雨量(毫米)等天气参数与燃烧严重程度(dNBR 等级)之间的关系进行了线性回归分析。 主要结果:根据研究结果,2004 年至 2019 年锡金发生的 557 起森林火灾中,250 起被归类为未燃烧(46.21 %),199 起被归类为低度强化再生(35.72 %),43 起被归类为高度强化再生(7.94 %),32 个数字被归类为低严重程度(5.92 %),9 个数字被归类为中低严重程度(1.66 %),5 个数字被归类为中高严重程度(0.92 %),2 个数字被归类为高严重程度(0.36 %)。研究发现,风力(r=0.80,Slope=0.57,SD=0.70)和降雨量(r=0.77,Slope=-0.18,SD=7.00)对烧伤严重程度(dNBR)的影响分别呈现出很强的正线性关系和很强的负线性关系。而温度(r=0.69,Slope=0.74,SD=0.01)对烧伤严重程度(dNBR)的影响呈中度正相关。 亮点:该研究表明,在分析资源有限、地貌和植被多样的森林火灾区域时,燃烧区绘图和遥感数据产品非常有效。研究人员将能够确定受森林火灾影响的地区以及火灾后尚未恢复的地区。这项研究的目标是改善森林火灾的规划和管理,帮助有关部门评估火灾地区的植被退化模式,并估计森林火灾的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
20.00%
发文量
18
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信