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宫大鹏,李炳怡,刘晓东.草原火烧严重度燃烧指数的适用性比较分析.生态学报,2018,38(7):2434~2441 本文二维码信息
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草原火烧严重度燃烧指数的适用性比较分析
Comparative analysis of burn index adaptability when evaluating grassland fire severity
投稿时间:2017-03-29  修订日期:2017-11-09
DOI: 10.5846/stxb201703290545
关键词草原火  火烧严重度  Landsat8影像  燃烧指数  差分归一化燃烧率
Key Wordsgrassland fire  fire severity  landsat8 OLI  burn index  dNBR(differenced Normalized Burn Ratio)
基金项目国家重点研发计划课题(2017YFD0600106-6);国家自然科学基金项目(31270696);国家林业局野生动植物保护与自然保护区管理司项目
作者单位E-mail
宫大鹏 北京林业大学林学院, 北京 100083  
李炳怡 北京林业大学林学院, 北京 100083  
刘晓东 北京林业大学林学院, 北京 100083 xd_liu@bjfu.edu.cn 
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摘要:
基于遥感影像的燃烧指数被广泛应用于火烧严重度(fire severity)研究,选取适宜燃烧指数定量评估草原火烧严重度,对草原生态系统植被的恢复与管理具有重要意义。以呼伦贝尔草原火烧迹地为研究区域,基于landsat8 OLI影像分别构建4种燃烧指数(NBR、NSTV1、dNBR和RdNBR)与综合燃烧指数(CBI)的回归模型并进行精度验证,对比分析不同燃烧指数识别草原火烧严重度等级的能力。结果表明:在燃烧指数与CBI构建的回归模型中,dNBR指数的相关性(n=70,R2=0.856)最高;4种燃烧指数识别火烧严重度的精度存在差异,中度火烧区域(1 < CBI ≤ 2)内,NSTV1指数识别精度最高,未过火(CBI=0)、轻度火烧(0 < CBI ≤ 1)和重度火烧(2 < CBI ≤ 3)区域内,dNBR指数识别精度均表现最好,分别为80%、62.5%和100%;基于不同燃烧指数的草原火烧严重度制图中,dNBR指数的总体精度同样高于其他燃烧指数,为82.1%,Kappa系数高达0.76。综上所述,dNBR指数是草原火烧严重度分析与评价的适宜遥感指数。
Abstract:
The burn index, based on remote sensing images, is widely used to study fire severity. The quantitative evaluation of grassland fire severity by selecting a suitable burn index has great significance when studying vegetation recovery and management. A burnt area of Hulunbuir pasture land was selected as the study area. Landsat8 OLI images were used to develop a regression model of the Composite Burn Index (CBI) using four different burn indices (Normalized Burn Ratio, NBR; NIR-SWIR-Temperature Version1, NSTV1; differenced Normalized Burn Ratio, dNBR; and Relative differenced Normalized Burn Ratio, RdNBR), and the accuracy of the model was tested. The ability to detect fire severity at different levels by the four different burn indices was compared. The results showed that dNBR (n=70, R2=0.856) had the best fitting effect in the polynomial regression model; but there were some differences between the four burn indices when identifying grassland fire intensity at different levels. The NSTV1 index performed best at moderate severity (1 < CBI ≤ 2). For non-fire (CBI=0), low severity (0 < CBI ≤ 1), and high severity (2 < CBI ≤ 3), the dNBR index performed best with accuracies up to 80%, 62.5%, and 100%, respectively; A map of grassland fire severity, which was drawn using the dNBR index, had the highest overall accuracy at up to 82.1%. The Kappa coefficient was also up to 0.76. In conclusion, the dNBR index is the most suitable remote sensing index for analyzing and evaluating grassland fire severity.
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