新疆地区土地覆被遥感数据的一致性研究
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摘要
鉴于新疆地区对中国乃至中亚有着特殊的战略意义,本文针对不同数据源及分类系统在土地覆被数据的空间分布上缺乏互通性问题,结合2010年目视解译土地利用现状遥感监测数据、GlobeLand30和GlobCover2009共3种土地覆被数据,采用类型相似分析、类型混淆分析、混淆矩阵分析、空间一致性分析4种方法开展精度评价及一致性分析,以期对土地覆被数据在中国西北干旱区的适用性及适用范围提供有效建议。结果表明,3种土地覆被数据对新疆地区土地覆被类型构成基本一致,且对裸地类型的辨识度最高;新疆地区中高度一致区域占新疆总面积的95%;3种数据两两对比时,总体精度在64.11%～72.57%之间,其中目视解译数据/GlobeLand 30组合表现出最高水平,且仍有提高空间,反映出目前相同卫星传感器是提升精度评价结果的重要因素之一,且不同分类系统、分类方法、空间分辨率及卫星过境时间等因素对精度评价结果也会产生巨大影响。为解决此类问题,利用多源土地覆被遥感数据的融合技术提高数据精度,或是利用深度学习对遥感影像资料进行精确地解译和判读,将是今后全球土地覆被制图及应用领域的主要发展趋势。
Xinjiang region is of strategic significance to China and Central Asia. This study aimed to effectively combine different data sources and classification systems to mitigate the lack of their interoperability regarding spatial distribution of land cover data. For this purpose, three types of land cover data were included. They were the visual interpretation of land use status in 2010 remote sensing monitoring data, GlobeLand30, and GlobCover2009. Four methods including category similarity analysis, category confusion analysis, confusion matrix analysis, and spatial consistency analysis were used to evaluate their accuracies and consistencies. We expect that this study would provide recommendations for the applicability of land cover data in the arid region of northwest China. The results showed that the three types of land cover data exhibited a good consistency for describing land cover categories in Xinjiang, with similarity higher than 0.9. Particularly, bare land identification demonstrated the highest consistency, followed by grassland, cultivated land, and forest. About 95% of the land area in Xinjiang showed a relatively high consistency, and the overall accuracy for land cover data ranged from64.11% to 72.57%. Data from the group of visual interpretation/GlobCover2009 demonstrated the lowest accuracy, followed by the group of GlobeLand30/GlobCover2009. The group of visual interpretation data/GlobeLand30 had the highest accuracy, but it still had room for improvement. These results demonstrated that using the same satellite sensor plays an integral role in enhancing the accuracy of evaluation results. Moreover,classification systems, classification methods, spatial resolution, and satellite passage time used would also have a huge impact on the accuracy of evaluation results. In order to solve this problem more effectively, multi-source remote sensing data integration technology or deep learning will become more promising for accurately interpreting remote sensing image data in the near future, for further improving data accuracy in global land cover mapping and application fields. Depending on the distinctive landscape pattern of Xinjiang region, this research analyzed the accuracy of three different kinds of data for different land cover categories to provide reliable information which shall be proved to be useful in resource development, environment protection and sustainable development of Xinjiang. Additionally, it initiated a framework for providing basic data for China's significant development strategy "the Belt and Road". Moreover, the results demonstrated the better performance of GlobeLand30 in accuracy assessment. As compared to other land cover data within the same category, the GlobeLand30 data is overwhelming in spatial resolution.
Xinjiang region is of strategic significance to China and Central Asia. This study aimed to effectively combine different data sources and classification systems to mitigate the lack of their interoperability regarding spatial distribution of land cover data. For this purpose, three types of land cover data were included. They were the visual interpretation of land use status in 2010 remote sensing monitoring data, GlobeLand30, and GlobCover2009. Four methods including category similarity analysis, category confusion analysis, confusion matrix analysis, and spatial consistency analysis were used to evaluate their accuracies and consistencies. We expect that this study would provide recommendations for the applicability of land cover data in the arid region of northwest China. The results showed that the three types of land cover data exhibited a good consistency for describing land cover categories in Xinjiang, with similarity higher than 0.9. Particularly, bare land identification demonstrated the highest consistency, followed by grassland, cultivated land, and forest. About 95% of the land area in Xinjiang showed a relatively high consistency, and the overall accuracy for land cover data ranged from64.11% to 72.57%. Data from the group of visual interpretation/GlobCover2009 demonstrated the lowest accuracy, followed by the group of GlobeLand30/GlobCover2009. The group of visual interpretation data/GlobeLand30 had the highest accuracy, but it still had room for improvement. These results demonstrated that using the same satellite sensor plays an integral role in enhancing the accuracy of evaluation results. Moreover,classification systems, classification methods, spatial resolution, and satellite passage time used would also have a huge impact on the accuracy of evaluation results. In order to solve this problem more effectively, multi-source remote sensing data integration technology or deep learning will become more promising for accurately interpreting remote sensing image data in the near future, for further improving data accuracy in global land cover mapping and application fields. Depending on the distinctive landscape pattern of Xinjiang region, this research analyzed the accuracy of three different kinds of data for different land cover categories to provide reliable information which shall be proved to be useful in resource development, environment protection and sustainable development of Xinjiang. Additionally, it initiated a framework for providing basic data for China's significant development strategy "the Belt and Road". Moreover, the results demonstrated the better performance of GlobeLand30 in accuracy assessment. As compared to other land cover data within the same category, the GlobeLand30 data is overwhelming in spatial resolution.
引文
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[17]曹小敏,李爱农,雷光斌,等.尼泊尔土地覆被遥感制图及其空间格局分析[J].地球信息科学学报,2016,18(10):1384-1398.[Cao X M,Li A N,Lei G B,et al.Land Cover mapping and spatial pattern analysis with remote sensing in Nepal[J].Journal of Geo-information Science,2016,18(10):1384-1398.]
[18]刘纪远,张增祥,徐新良,等.21世纪初中国土地利用变化的空间格局与驱动力分析[J].地理学报,2009,64(12):1411-1420.[Liu J Y,Zhang Z X,Xu X L,et al.Spatial patterns and driving forces of land use change in China in the early 21stcentury[J].Acta Geographica Sinica,2009,64(12):1411-1420.]
[19]刘纪远,匡文慧,张增祥,等.20世纪80年代末以来中国土地覆被变化的基本特征与空间格局[J].地理学报,2014,69(1):3-13.[Liu J Y,Kuang W H,Zhang Z X,et al.Spatiotemporal characteristics,patterns,and causes of landuse changes in China since the late 1980s[J].Journal of Geographical Sciences,2014,69(1):3-13.]
[20]徐新良.土地利用/覆被变化时空信息分析方法及应用[M].北京:科学技术文献出版社,2014.[Xin X L.Spatialtemporal pattern analysis of land use/cover change:Methods and applications[M].Beijing:Scientific and technical documentation press,2014.]
[2]Clerici N,Paracchini M L,Maes J.Land-cover change dynamics and insights into ecosystem services in European stream riparian zones[J].Ecohydrology&Hydrobiology,2014,14(2):107-120.
[3]陈军,廖安平,陈晋,等.全球30m地表覆盖遥感数据产品-GlobeLand30[J].地理信息世界,2017,24(1):1-8.[Chen J,Liao A P,Chen J,et al.30-Meter Global land cover data product-GlobeLand30[J].Geomatics World,2017,24(1):1-8.]
[4]冯春,郭建宁,闵祥军,等.土地利用/土地覆盖遥感变化检测方法新进展[J].遥感信息,2006,58(3):81-85.[Feng C,Guo J N,Min X J,et al.New progress in land use/land cover change detection by remote sensing[J].Remote Sensing Information,2006,58(3):81-85.]
[5]Hansen M C,Defries R S,Townshend J R G,et al.Global land cover classification at 1 km spatial resolution using a classification tree approach[J].International Journal of Remote Sensing,2000,21(6-7):34.
[6]Loveland T R.Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data[J].International Journal of Remote Sensing,2000,21(6-7):1303-1330.
[7]Friedl M A,Mciver D K,Hodges J C F,et al.Global land cover mapping from MODIS:Algorithms and early results[J].Remote Sensing of Environment,2002,83(1-2):287-302.
[8]BartholoméE,Belward A S.GLC2000:A new approach to global land cover mapping from Earth observation data[J].International Journal of Remote Sensing,2005,26(9):1959-1977.
[9]Defourny P,Schouten L,Bartalev S,et al.Accuracy assessment of a 300 m global land cover map:The GlobCover experience[J].New Library World,2009,112(5-6):236-247.
[10]Jun C,Ban Y,Li S.China:Open access to earth land-cover map[J].Nature,2014,514(7523):434.
[11]Brovelli M,Molinari M,Hussein E,et al.The first comprehensive accuracy assessment of GlobeLand30 at a national level:Methodology and results[J].Remote Sensing,2015,7(4):4191-4212.
[12]Pérez-Hoyos A,García-Haro F J,San-Miguel-Ayanz J.Conventional and fuzzy comparisons of large scale land cover products:Application to Corine,GLC2000,MO-DIS and GlobCover in Europe[J].Isprs Journal of Photogrammetry&Remote Sensing,2012,74(11):185-201.
[13]Ran Y,Li X,Lu L.Evaluation of four remote sensing based land cover products over China[J].Journal of Glaciology&Geocryology,2009,31(2):391-401.
[14]马京振,孙群,肖强,等.河南省GlobeLand30数据精度评价及对比分析[J].地球信息科学学报,2016,18(11):1563-1572.[Ma J Z,Sun Q,Xiao Q,et al.Accuracy assessment and comparative analysis of GlobeLand30 dataset in Henan province[J].Journal of Geo-information Science,2016,18(11):1563-1572.]
[15]戴昭鑫,胡云锋,张千力.多源卫星遥感土地覆被产品在南美洲的一致性分析[J].遥感信息,2017,32(2):137-148.[Dai S X,Hu Y F,Zhang Q L.Agreement analysis of multi-source land cover products derived from remote sensing in South America[J].Remote Sensing Information,2017,32(2):137-148.]
[16]Kuenzer C,Leinenkugel P,Vollmuth M,et al.Comparing global land-cover products-implications for geoscience applications:An investigation for the trans-boundary Mekong basin[J].International Journal of Remote Sensing,2014,35(8):2752-2779.
[17]曹小敏,李爱农,雷光斌,等.尼泊尔土地覆被遥感制图及其空间格局分析[J].地球信息科学学报,2016,18(10):1384-1398.[Cao X M,Li A N,Lei G B,et al.Land Cover mapping and spatial pattern analysis with remote sensing in Nepal[J].Journal of Geo-information Science,2016,18(10):1384-1398.]
[18]刘纪远,张增祥,徐新良,等.21世纪初中国土地利用变化的空间格局与驱动力分析[J].地理学报,2009,64(12):1411-1420.[Liu J Y,Zhang Z X,Xu X L,et al.Spatial patterns and driving forces of land use change in China in the early 21stcentury[J].Acta Geographica Sinica,2009,64(12):1411-1420.]
[19]刘纪远,匡文慧,张增祥,等.20世纪80年代末以来中国土地覆被变化的基本特征与空间格局[J].地理学报,2014,69(1):3-13.[Liu J Y,Kuang W H,Zhang Z X,et al.Spatiotemporal characteristics,patterns,and causes of landuse changes in China since the late 1980s[J].Journal of Geographical Sciences,2014,69(1):3-13.]
[20]徐新良.土地利用/覆被变化时空信息分析方法及应用[M].北京:科学技术文献出版社,2014.[Xin X L.Spatialtemporal pattern analysis of land use/cover change:Methods and applications[M].Beijing:Scientific and technical documentation press,2014.]