An adaptability analysis of remote sensing indices in evaluating fire severity
TAN Liuxia1,2, ZENG Yongnian1,2, ZHENG Zhong1,2
1. School of Geosciences and Geomatics, Central South University, Changsha 410083, China;
2. Center for Geomatics and Sustainable Development Research, Central South University, Changsha 410083, China
Performing quantitative evaluation of forest fire severity scientifically and reasonably is helpful to revealing the changing of forest ecosystems under fire, and is also of great significance for studying the vegetation recovery and management. Taking the north rim of Grand Canyon National Park in USA as the study area, combined with the composite burn index (CBI) after field survey, the authors used Landsat5 TM images of Poplar Fire to analyze the applicability of NDVI, NBR, ΔNDVI and ΔNBR so as to evaluate fire severity. According to the result obtained, there is some difference between the four remote sensing indices in identifying forest fire intensity of different levels. For non-fire and light fire, indices from a uni-temporal can perform better than indices from bi-temporal (pre and post fire), and NBR has the highest accuracy up to 66.7% and 80%, respectively; on the contrary, for moderate fire and severe fire, indices from bi-temporal (pre and post fire) can perform better than indices from a uni-temporal, and ΔNBR outperformed the others, because it considers only indices difference resulting from change of vegetation situation and environmental factors caused by forest fire and not affected by surroundings; it has high accuracy of evaluating moderate fire and severe fire, with the accuracy up to 100% and 90%. In general, indices from bi-temporal (pre and post fire) have higher overall accuracy than indices from a uni-temporal, and ΔNBR has the highest overall accuracy in evaluating fire severity with the accuracy up to 86.2%, which is hence the most suitable remote sensing indices to evaluate fire severity in this study area.
谭柳霞, 曾永年, 郑忠. 林火烈度遥感评估指数适应性分析[J]. 国土资源遥感, 2016, 28(2): 84-90.
TAN Liuxia, ZENG Yongnian, ZHENG Zhong. An adaptability analysis of remote sensing indices in evaluating fire severity. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 84-90.
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2016, Vol. 28 
