摘要
为探究利用高分六号卫星宽幅(GF-6 WFV)数据识别火烧迹地的适宜光谱波段和指数,选取2019年发生在我国内蒙古大兴安岭林区的三处雷击火形成的火烧迹地作为研究区,结合GF-6 WFV波段组成,选取归一化植被指数(normalized difference vegetation index,NDVI)、全球环境监测指数(global environment monitoring index,GEMI)、增强植被指数(enhanced vegetation index,EVI)、燃烧面积指数(burned area index,BAI)、土壤调节植被指数(soil-adjusted vegetation index,SAVI)、改进型土壤调节植被指数(modified soil-adjusted vegetation index,MSAVI)和归一化差异水体指数(normalized difference water index,NDWI)等7个光谱指数及地面叶绿素指数(MERIS terrestrial chlorophyll index,MTCI)、归一化差值红边指数(normalized difference red edge index 1,NDRE1)、改进的叶绿素吸收指数(modified chlorophyll absorption ratio index 2,MCARI2)和改进的归一化土壤指数(modified normalized difference soil index,MNDSI)等4个改进指数,基于同期影像和前后两期影像进行火烧迹地和其他典型类别的区分度计算,并利用上述11个指数及指数差值进行火烧迹地的识别,定量评价了GF-6 WFV各波段、所选光谱指数及改进指数识别火烧迹地的能力。结果表明:(1)GF-6 WFV的近红外波段和新增的两个红边波段区分度较高,反映火烧迹地特征的能力较强。(2)在区分火烧迹地和火烧前正常植被上,NDVI,GEMI,EVI,BAI,SAVI,MSAVI和NDWI 7个光谱指数等的区分能力较强,4个改进指数中,NDRE1和MCARI2的区分能力较好,MNDSI和MTCI的区分效果较差。(3)在区分同期影像火烧迹地和其余典型类别上,BAI,NDVI,MCARI2和NDWI区分效果较优,其次为NDRE1,GEMI,EVI,SAVI和MSAVI,而MNDSI,MTCI的区分能力较差。(4)在利用所选指数和指数差值识别火烧迹地中,GEMI,EVI,BAI,SAVI和MSAVI的识别精度均较优,其次是MCARI2,NDVI和NDWI,做差后提取精度显著上升,Kappa系数均提升到0.80以上,MTCI,
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,NDW
作者
刘倩
覃先林
胡心雨
李增元
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)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2021年第8期2536-2542,共7页
Spectroscopy and Spectral Analysis
基金
国家高分重大科技专项项目(21-Y30B02-9001-19/22-6)
民用航天预研项目(D040402)资助。
