1.Key Laboratory of Forestry Remote Sensing and Information Techniques, State Forestry Administration, Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China 2.Forestry Information Center of Sichuan Province, Chengdu 610081, China
Vegetation index is one of the commonly used method for adopting satellite remote sensing image to identify burned areas. Due to the disturbance of fire, vegetation becomes burned area, and its spectral characteristics are easily confused with the spectra of bare land, water body, road, shadow and arable land and some other factors. Therefore, the improvement of the accuracy of remote sensing monitoring for burned area using appropriate vegetation index remains an urgent problem. In this paper, four burned areas in Sichuan Province and Inner Mongolia where fire burning occurred in 2014 and 2017 were selected as the study areas. Based on the spectral characteristics of Gaofen-1 satellite 16 m wide width (GF-1 WFV) data and Landsat8 data, the authors chose normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), global environment monitoring index (GEMI), burned area index (BAI) and normalized burn ration (NBR) and constructed separation index M of different spectral indices to quantitatively evaluate the potential of different spectral indices for burned areas identification. The results show that NBR calculated with near-infrared and short-wave infrared band and BAI based on visible light-near infrared band have a better capability for separating burned areas, the separability of NDVI takes the second, whereas EVI and GEMI have a poor separability. For GF-1 WFV data and Landsat8 data, BAI and NBR which have a good separate capability for burned area identification were used for the burned area in Oroqen Autonomous Banner of Inner Mongolia to separate burned areas (for GF-1 WFV data, only BAI was used to identify burned area), and Gaofen-2 satellite (GF-2) data which have higher spatial resolution combined with confusion matrix method were used to verify the accuracy. The overall accuracy of both methods were higher than 87%, and the Kappa coefficients were all higher than 0.7.
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2019, Vol. 31 
), 刘树超1, 李晓彤1, 陈小中2, 钟祥清2
