针对黑河流域大尺度环境下水体提取难度大、演变规律尚不明晰等问题,基于谷歌地球引擎(Google Earth Engine,GEE)处理黑河流域1986—2024年Landsat影像,采集7.8×10^(4)个水体/非水体样本并构建逐年样本数据集,通过将多波段水体指数...针对黑河流域大尺度环境下水体提取难度大、演变规律尚不明晰等问题,基于谷歌地球引擎(Google Earth Engine,GEE)处理黑河流域1986—2024年Landsat影像,采集7.8×10^(4)个水体/非水体样本并构建逐年样本数据集,通过将多波段水体指数(Multi band water index,MBWI)、增强型水体指数(Enhanced water index,EWI)、改进归一化差异水体指数(Modified normalized difference water index,MNDWI)与光谱波段进行单独与统一组合,构建并筛选出最佳融合水体指数的随机森林(Random forest,RF)水体提取方法,提取了研究区39个时相的逐年地表水体影像,采用曼-肯德尔(Mann-Kendall,M-K)法揭示了黑河流域逐年地表水体面积变化特征,基于主成分与敏感性分析探究了影响地表水体演变的主要驱动因素。结果表明:融合3种水体指数(MBWI、EWI、MNDWI)的随机森林水体提取方法对黑河流域Landsat影像的水体提取效果最佳,平均总体精度(Overall accuracy,OA)为96.16%,平均Kappa系数为0.9128;经M-K法检验,黑河流域1986—2024年地表水体面积呈波动减少态势;年降水量、人口、年蒸散量为黑河流域地表水体演变的最主要驱动因素。研究结果可为全流域地表水体的快速准确提取提供理论支持。展开更多
This study evaluates the long-term radiometric performance of the USGS new released Landsat Collection 1 archive, including the absolute calibration of each Landsat sensor as well as the relative cross-calibration amo...This study evaluates the long-term radiometric performance of the USGS new released Landsat Collection 1 archive, including the absolute calibration of each Landsat sensor as well as the relative cross-calibration among the four most popular Landsat sensors. A total of 920 Landsat Collection 1 scenes were evaluated against the corresponding Pre-Collection images over a Pseudo-Invariant Site, Railroad Valley Playa Nevada, United States (RVPN). The radiometric performance of the six Landsat solar reflective bands, in terms of both Digital Numbers (DNs) and at-sensor Top of Atmosphere (TOA) reflectance, on the sensor cross-calibration was examined. Results show that absolute radiometric calibration at DNs level was applied to the Landsat-4 and -5 TM (L4 TM and L5 TM) by –1.119% to 0.126%. For L4 TM and L5 TM, the cross-calibration decreased the radiometric measurement level by rescaling at-sensor radiance to DN values. The radiometric changes, –0.77% for L4 TM, 0.95% for L5 TM, –0.26% for L7 ETM+, and –0.01% for L8 OLI, were detected during the cross-calibration stage of converting DNs into TOA reflectance. This study has also indicated that the long-term radiometric performance for the Landsat Collection 1 archive is promising. Supports of these conclusions were demonstrated through the time-series analysis based on the Landsat Collection 1 image stack. Nevertheless, the radiometric changes across the four Landsat sensors raised concerns of the previous Landsat Pre-Collection based results. We suggest that Landsat users should pay attention to differences in results from Pre-Collection and Collection 1 time-series data sets.展开更多
文摘针对黑河流域大尺度环境下水体提取难度大、演变规律尚不明晰等问题,基于谷歌地球引擎(Google Earth Engine,GEE)处理黑河流域1986—2024年Landsat影像,采集7.8×10^(4)个水体/非水体样本并构建逐年样本数据集,通过将多波段水体指数(Multi band water index,MBWI)、增强型水体指数(Enhanced water index,EWI)、改进归一化差异水体指数(Modified normalized difference water index,MNDWI)与光谱波段进行单独与统一组合,构建并筛选出最佳融合水体指数的随机森林(Random forest,RF)水体提取方法,提取了研究区39个时相的逐年地表水体影像,采用曼-肯德尔(Mann-Kendall,M-K)法揭示了黑河流域逐年地表水体面积变化特征,基于主成分与敏感性分析探究了影响地表水体演变的主要驱动因素。结果表明:融合3种水体指数(MBWI、EWI、MNDWI)的随机森林水体提取方法对黑河流域Landsat影像的水体提取效果最佳,平均总体精度(Overall accuracy,OA)为96.16%,平均Kappa系数为0.9128;经M-K法检验,黑河流域1986—2024年地表水体面积呈波动减少态势;年降水量、人口、年蒸散量为黑河流域地表水体演变的最主要驱动因素。研究结果可为全流域地表水体的快速准确提取提供理论支持。
文摘This study evaluates the long-term radiometric performance of the USGS new released Landsat Collection 1 archive, including the absolute calibration of each Landsat sensor as well as the relative cross-calibration among the four most popular Landsat sensors. A total of 920 Landsat Collection 1 scenes were evaluated against the corresponding Pre-Collection images over a Pseudo-Invariant Site, Railroad Valley Playa Nevada, United States (RVPN). The radiometric performance of the six Landsat solar reflective bands, in terms of both Digital Numbers (DNs) and at-sensor Top of Atmosphere (TOA) reflectance, on the sensor cross-calibration was examined. Results show that absolute radiometric calibration at DNs level was applied to the Landsat-4 and -5 TM (L4 TM and L5 TM) by –1.119% to 0.126%. For L4 TM and L5 TM, the cross-calibration decreased the radiometric measurement level by rescaling at-sensor radiance to DN values. The radiometric changes, –0.77% for L4 TM, 0.95% for L5 TM, –0.26% for L7 ETM+, and –0.01% for L8 OLI, were detected during the cross-calibration stage of converting DNs into TOA reflectance. This study has also indicated that the long-term radiometric performance for the Landsat Collection 1 archive is promising. Supports of these conclusions were demonstrated through the time-series analysis based on the Landsat Collection 1 image stack. Nevertheless, the radiometric changes across the four Landsat sensors raised concerns of the previous Landsat Pre-Collection based results. We suggest that Landsat users should pay attention to differences in results from Pre-Collection and Collection 1 time-series data sets.
