摘要翻译：
遥感技术已被有效地用于测量森林火灾造成的陆地生态系统生产和生物多样性的总体损失。目前的研究重点是在印度Uttarakhand使用不同的燃烧指数评估森林火灾严重程度对陆地生态系统生产力的影响。使用中分辨率成像光谱仪(MODIS)计算了火灾前（2014）和火灾（2016）年的卫星地表温度(LST)，以识别北阿坎德所有生态区域的烧伤区热点。本研究估算了火前和火年不同植被和烧伤面积指数（归一化烧伤比(NBR)、烧伤面积指数(BAI)、归一化多带干旱指数(NMDI)、土壤调整植被指数(SAVI)、全球环境监测指数(GEMI)、增强植被指数(EVI)和归一化植被差异指数(NDVI)的时空变化。此外，本文还选择了两个光利用效率(LUE)模型，即卡内基-艾姆斯-斯坦福-方法(CASA)和植被光合作用模型(VPM)来定量研究区所有生物群落在火灾前和火灾年的陆地净初级生产力(NPP)。需要详细的野外观测数据进行进一步的培训，并测试遥感火灾地图，以供今后的研究。
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英文标题：
《Effects of forest fire severity on terrestrial carbon emission and
  ecosystem production in the Himalayan region, India》
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作者：
Srikanta Sannigrahi, Sandeep Bhatt, Shahid Rahmat, Virendra Rana and
  Suman Chakraborti
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最新提交年份：
2018
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/1805.11680