Spectral and Index Analysis for Burned Areas Identification Using GF-6 WFV Data
LIU Qian，QIN Xian-lin*，HU Xin-yu，LI Zeng-yuan
Research Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing 100091, China
Abstract：This study aims to explore the appropriate spectral bands and indices of GF-6 WFV data in identifying burned areas. The study area is located in three burned areas in the Greater Khingan Mountains forest region of Inner Mongolia of China. 11 indexes, including Normalized Difference Vegetation Index (NDVI), Global Environment Monitoring Index (GEMI), Enhanced Vegetation Index (EVI), Burned Area Index (BAI), Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI), Normalized Difference Water Index (NDWI), MERIS Terrestrial Chlorophyll Index (MTCI), Normalized Difference Red Edge Index 1 (NDRE1), Modified Chlorophyll Absorption Ratio Index 2 (MCARI2) and Modified Normalized Difference Soil Index (MNDSI) were selected according to the channels of GF-6 WFV data. To quantitatively evaluate the ability of selected spectral indexes and modified indexes to identify burned areas, the separability M was calculated between burned areas and other typical categories based on single-temporal and bi-temporal images. Then these 11 indexes and their differenced indexes were used to identify the burned areas. The results show that (1) the near-infrared band of GF-6 WFV and the two newly added red-edge bands provided better spectral separation, indicating an ability to reflect the characteristics of burned areas. (2) In terms of distinguishing between the same area before and after burned, NDVI, GEMI, EVI, BAI, SAVI, MSAVI and NDWI improved performance. Among four modified indexes, NDRE1 and MCARI2 performed better than MNDSI and MTCI. (3) As for distinguishing burned areas from other typical categories, BAI, NDVI, MCARI2 and NDWI performed better, followed by NDRE1, GEMI, EVI, SAVI and MSAVI, while MNDSI and MTCI performing poorly. (4) In extracting burned areas using indexes and differenced indexes, GEMI, EVI, BAI, SAVI and MSAVI performed better, followed by MCARI2, NDVI and NDWI with medium performance, while MTCI, MNDSI and NDRE1 performing poorly. In summary, BAI and GEMI had the best performance in identifying burned areas, followed by NDVI, EVI, SAVI, MSAVI, NDWI and MCARI2 with medium identification ability, while three modified indices MNDSI, NDRE1 and MTCI performing poorly.
Key words：GF-6 WFV data; Spectral index; Modified index; Separability index M; Burned area
刘 倩，覃先林，胡心雨，李增元. 高分六号宽幅数据识别火烧迹地的光谱及指数分析[J]. 光谱学与光谱分析, 2021, 41(08): 2536-2542.
LIU Qian，QIN Xian-lin，HU Xin-yu，LI Zeng-yuan. Spectral and Index Analysis for Burned Areas Identification Using GF-6 WFV Data. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2536-2542.
