文献类型：期刊文献

中文题名：高分一号与Landsat TM数据估算稀疏植被信息对比

英文题名：Comparison of Sparse Vegetation Information Estimation Based on GF-1 and Landsat Multi-spectral Data

作者：孙斌[1] 李增元[1] 郭中[2] 高志海[1] 王琫瑜[1]

第一作者：孙斌

机构：[1]中国林业科学研究院资源信息研究所;[2]内蒙古自治区林业科学研究院

年份：2015

卷号：0

期号：5

起止页码：48-56

中文期刊名：遥感信息

外文期刊名：Remote Sensing Information

收录：CSTPCD;;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：国家863计划项目(2012AA12A03);国家高分辨率对地观测系统重大专项(21-Y30B05-9001-13/15)

语种：中文

中文关键词：高分一号;Landsat;8;植被指数;植被覆盖度;地上生物量;回归模型

外文关键词：GF-1;;Landsat 8;;vegetation index;;vegetation coverage;;above-ground biomass;;regression model

分类号：Q948

摘要：为了分析高分一号卫星数据在稀疏植被信息提取方面的能力,该文选取浑善达克沙地及其周边为研究区,以GF-1和Landsat TM为数据源,结合地面同步实测数据,比较了两个传感器在荒漠化地区植被覆盖度和地上生物量估算方面的能力与差异。结果表明:在该区域,两种数据基于NDVI建立的对数模型可用于植被覆盖度的估测(GF-1:R2=0.7966,RMSEP=0.0908;Landsat 8:R2=0.8080,RMSEP=0.0871),GF-1基于SAVI和Landsat 8基于NDVI建立的乘幂模型进行地上生物量的估测效果最好(R2=0.4866,0.3715;RMSEP=143.46,130.71)。其次,在该区域,经过修正的土壤调节植被指数MSAVI相对于没有经过修正的土壤调节植被指数SAVI,与植被覆盖度和植被生物量的相关性并没有多大提高。第三,两种数据通过引入蓝色、绿色波段的多元回归模型估算植被覆盖度相比单一植被指数植被要好,尤其是对于Landsat影像改进效果更为明显,R2提高了0.3。总之,GF-1的16m数据具有相对较高的质量,可以代替Landsat 8多光谱数据,而且其具有更高的分辨率、重访周期和覆盖范围。
With the wide use of GF-1 data,the ability of sparse vegetation information estimation of the data needs to be further analyzed.Based on the data of domestic satellite GF-1and Landsat 8 as well as the simultaneous field survey of vegetation cover and above-ground biomass,the research was implemented on Otindag sandy land and its surrounding areas,in which abilities of two sensors to estimate vegetation physiological parameters in desertification areas were compared with each other.It was shown that,firstly,in study region,logarithmic functions which were established on NDVI of GF-1 data (R 2 =0.7966,RMSEP=0.0841 )and Landsat 8 data (R 2 = 0.8080,RMSEP = 0.0871 )could be used to estimate the vegetation coverage perfectly,the power functions which were established on NDVI of GF-1and SAVI of Landsat 8 have the best estimation effect (R 2 =0.4866,0.3715;RMSEP=143.46,130.71 ).Secondly,compared with the unrevised vegetation index SAVI,the correlation of MSAVI with vegetation cover and above-ground biomass was not significantly improved.Thirdly,blue and green band were introduced into multiple regression models,which were supposed to enhance the ability of estimating vegetation coverage,especially for Landsat,and R 2 was improve 0.3.In general,GF-1 has a relatively high data quality,it can replace Landsat 8 data in vegetation parameter inversion,and it has a higher resolution,shorter revisit cycle and wider coverage.