Remote sensing techniques have been used effectively for measuring the overall loss of terrestrial ecosystem production and biodiversity due to the forest fire. The current research focuses on assessing the impact of forest fire severity on terrestrial ecosystem productivity using different burn indices in Uttarakhand, India. Satellite-based land surface temperature (LST) was calculated for pre-fire (2014) and fire (2016) year using MODerate Resolution Imaging Spectroradiometer (MODIS) to identify the burn area hotspots across all eco-regions in Uttarakhand. In this study, spatial and temporal changes of different vegetation and burn area indices i.e Normalized Burn Ratio (NBR), Burnt Area Index (BAI), Normalized Multiband Drought Index (NMDI), Soil Adjusted Vegetation Index (SAVI), Global Environmental Monitoring Index (GEMI), Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI)were estimated for both pre-fire and fire years. Additionally, two Light Use Efficiency (LUE) models i.e Carnegie- Ames-Stanford-Approach (CASA) and Vegetation Photosynthesis Model (VPM) were selected to quantify the terrestrial Net Primary Productivity (NPP) in pre-fire and fire years across all biomes of the study area.The present approach appears to be promising and has a potential in quantifying the loss of ecosystem productivity due to forest fires. A detailed field observation data is required for further training, and testing of remotely sensed fire maps for future research.
翻译:目前的研究重点是利用印度Uttarakhand的不同燃烧指数评估森林火灾严重性对陆地生态系统生产力的影响。基于卫星的陆地表面温度(LST)是为火前(2014年)和火灾年份计算出来的。 使用红前(2015年)和火灾年份的强化植被指数(EVI)、标准差异植被指数(NDVI)估算的火前和火灾年份的所有生态区域燃烧面积热点。此外,两个简易使用效率(LUE)模型,即正常烧率(NBR)、燃烧地区指数(BAI)、正常化多频段干旱指数(NMDI)、土壤调整植被指数(SAVI)、全球环境监测指数(GEMI)、强化植被指数(EVI)、正常化差异植被指数(NDVI)估算的火前和火灾年份的所有生态区域。在对未来绿色温度测试中,为未来地球温度测试选择了最新的地面温度测试和绿色温度模型。