{"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