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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (1) : 204-211     DOI: 10.6046/gtzyyg.2019.01.27
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Potential analysis of typical vegetation index for identifying burned area
Guifen SUN1, Xianlin QIN1(), Shuchao LIU1, Xiaotong LI1, Xiaozhong CHEN2, Xiangqing ZHONG2
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
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Abstract  

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.
Keywords GF-1 WFV data      Landsat8 data      burned area      vegetation index      separability     
:  TP79  
Corresponding Authors: Xianlin QIN     E-mail: noaags@ifrit.ac.cn
Issue Date: 14 March 2019
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Guifen SUN
Xianlin QIN
Shuchao LIU
Xiaotong LI
Xiaozhong CHEN
Xiangqing ZHONG
Cite this article:   
Guifen SUN,Xianlin QIN,Shuchao LIU, et al. Potential analysis of typical vegetation index for identifying burned area[J]. Remote Sensing for Land & Resources, 2019, 31(1): 204-211.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.01.27     OR     https://www.gtzyyg.com/EN/Y2019/V31/I1/204
编号 研究区 火烧时间 火场中心经纬度 火场中心位置海拔/m 过火面积/hm2
1 陈巴尔虎旗火烧迹地 20170517 49°55'09"N,120°51'16"E 925 8 400
2 鄂伦春自治旗火烧迹地 20170502 49°30'32"N,123°06'02"E 500 11 500
3 雅江县火烧迹地 20140125 30°04'48"N,101°10'53"E 3 800 32
4 冕宁县火烧迹地 20140214 28°33'36"N,102°13'12"E 2 539 63
Tab.1  Profile of burned area
研究区 GF-1WFV
获取时间		 Landsat8
获取时间		 GF-2
获取时间
陈巴尔虎旗火烧迹地 20170605 20170624 —
鄂伦春自治旗火烧迹地 20170605 20170509 20170613
雅江县火烧迹地 20170222 20140131 —
冕宁县火烧迹地 20140226 20140225 —
Tab.2  Acquisition information of remote sensing data
Fig.1  Spectral curves of typical features in GF-1 WFV and Landsat8 images
Fig.2  Technique flow chart
Fig.3  Vegetation index values of typical features extracted by GF-1 WFV and Landsat8 data
Fig.4  Separability index M of typical features extracted by GF-1 WFV and Landsat8 data
Fig.5  Remote sensing images in study area and identification results of burned area
GF-1 Landsat8
评价指标 BAI NBR BAI
火烧区制图精度/% 78.95 61.84 72.45
未火烧区制图精度/% 96.59 96.92 94.55
火烧区用户精度/% 91.46 92.68 86.59
未火烧区用户精度/% 90.83 86.70 87.61
总体精度/% 91.00 88.33 87.33
Kappa系数 0.78 0.73 0.70
Tab.3  Results of accuracy evaluation
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