2015年中国1:10万土地覆被数据河南地区精度评价
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摘要
研究目的:针对国内首套2015年中国1∶10万土地覆被数据产品,以河南省为研究区进行精度评价。研究方法:采用实地考察与格网抽样融合的样本设计方法,共采集区域全覆盖样本1894个,其中,实地样本271个,正六边形格网样本1623个;通过混淆矩阵精度评价方法,评价数据产品精度,分析影响土地覆被分类精度的原因。研究结果:耕地、建设用地、水体、林地一级地类制图精度都在90%以上,草地和其它制图精度分别为84.68%和85.7%;20个二级地类的总体精度91.34%,其中,旱地、城镇建设用地、农村居民地、交通用地、河流、水库/坑塘、裸岩二级地类精度在90%以上,除常绿阔叶林、灌丛、灌丛草地、草甸、河湖滩地、裸地几个地类分类精度较低,其余地类精度均在80%以上。研究结论:该数据产品在河南地区具有较高的精度,可为气候、水文、生态等相关科研领域提供基础数据。
The purpose of the paper is to assess the 1∶100 000 land cover data product of China in 2015 with the sample design method combining field survey and grid sampling in Henan region. A regional full-cover sample database was built with 1894 samples, including 271 field samples and 1623 regular hexagonal grid samples. The accuracy of the data product was assessed by building the confusion matrix, and several factors influencing the accuracy were also taken into account. The results are as follows. 1)The accuracy of the data product based on 1894 sample sites is high at level 1 class,the accuracy of croplands, construction land, water bodies and forests is more than 90%, and the accuracy of grassland and other land use types reaches 84.68% and 85.7% respectively. 2)The overall accuracy of data product at level 2 class reaches 91.34%. The accuracy of rainfed cropland, urban construction land, rural settlement land, traffic land, rivers,reservoirs/ponds and bare rock all reaches 90%; and except that the accuracy of evergreen broad leaf forests, shrub land,shrub-grass land, meadow, bottom land and bare land is lower, the rest of land use types is all above 80%. This data can be used in the scientific research of climate, hydrology, ecology, etc.
The purpose of the paper is to assess the 1∶100 000 land cover data product of China in 2015 with the sample design method combining field survey and grid sampling in Henan region. A regional full-cover sample database was built with 1894 samples, including 271 field samples and 1623 regular hexagonal grid samples. The accuracy of the data product was assessed by building the confusion matrix, and several factors influencing the accuracy were also taken into account. The results are as follows. 1)The accuracy of the data product based on 1894 sample sites is high at level 1 class,the accuracy of croplands, construction land, water bodies and forests is more than 90%, and the accuracy of grassland and other land use types reaches 84.68% and 85.7% respectively. 2)The overall accuracy of data product at level 2 class reaches 91.34%. The accuracy of rainfed cropland, urban construction land, rural settlement land, traffic land, rivers,reservoirs/ponds and bare rock all reaches 90%; and except that the accuracy of evergreen broad leaf forests, shrub land,shrub-grass land, meadow, bottom land and bare land is lower, the rest of land use types is all above 80%. This data can be used in the scientific research of climate, hydrology, ecology, etc.
引文
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[2]GONG P,WANG J,YU L,et al.Finer resolution observation and monitoring of global land cover:first mapping results with Landsat TM and ETM+data[J].International Journal of Remote Sensing,2013,34(7):2607-2654.
[3]DICKINSON R E,ERRICO R M,GIORGI F,et al.A regional climate model for the western United States[J].Climatic change,1989,15(3):383-422.
[4]YANG J,GONG P,FU R,et al.The role of satellite remote sensing in climate change studies[J].Nature Climate Change,2013,3(10):875-883.
[5]MATHEUSSEN B,KIRSCHBAUM R L,GOODMAN I A,et al.Effects of land cover change on streamflow in the interior Columbia River basin(USA and Canada)[J].Hydrological Processes,2015,14(5):867-885.
[6]YIGINI Y,PANAGOS P.Assessment of soil organic carbon stocks under future climate and land cover changes in Europe[J].Science of the Total Environment,2016:557-558,838-850.
[7]WANG Z,HOFFMANN T,SIX J,et al.Human-induced erosion has offset one-third of carbon emissions from land cover change[J].Nature Climate Change,2017,7(5):345-349.
[8]ALCOCK I,WHITE M,CHERRIE M,et al.Land cover and air pollution are associated with asthma hospitalisations:a cross-sectional study[J].Environment International,2017,109:29-41.
[9]王航,秦奋,朱筠,等.土地利用及景观格局演变对生态系统服务价值的影响[J].生态学报,2017,37(4):1286-1296.
[10]廖顺宝,秦耀辰.草地理论载畜量调查数据空间化方法及应用[J].地理研究,2014,33(1):179-190.
[11]HAMILTON S K,HUSSAIN M Z,BHARDWAJ A K,et al.Comparative water use by maize,perennial crops,restored prairie,and poplar trees in the US Midwest[J].Environmental Research Letters,2015,10(6):064015.
[12]HEROLD M,MAYAUX P,WOODCOCK C E,et al.Some challenges in global land cover mapping:an assessment of agreement and accuracy in existing 1 km datasets[J].Remote Sensing of Environment,2008,112(5):2538-2556.
[13]陈军,陈晋,廖安平,等.全球30 m地表覆盖遥感制图的总体技术[J].测绘学报,2014,43(6):551-557.
[14]GONG P,YU L,LI C,et al.A new research paradigm for global land cover mapping[J].Annals of Geographic Information Sciences,2016,22(2):87-102.
[15]INGLADA J,VINCENT A,ARIAS M,et al.Operational high resolution land cover map production at the country scale using satellite image time series[J].Remote Sensing,2017,9(1):95.
[16]YANG Y,XIAO P,FENG X,et al.Accuracy assessment of seven global land cover datasets over China[J].ISPRSJournal of Photogrammetry and Remote Sensing,2017,125:156-173.
[17]FOODY G M.Status of land cover classification accuracy assessment[J].Remote Sensing of Environment,2002,80(1):185-201.
[18]AN Y,ZHAO W,ZHANG Y.Accuracy assessments of the GLOBCOVER dataset using global statistical inventories and FLUXNET site data[J].Acta Ecologica Sinica,2012,32(6):314-320.
[19]BAI Y,FENG M,JIANG H,et al.Validation of land cover maps in China using a sampling-based labeling approach[J].Remote Sensing,2015,7(8):10589-10606.
[20]冉有华,李新,卢玲.四种常用的全球1 km土地覆盖数据中国区域的精度评价[J].冰川冻土,2009,31(3):490-500.
[21]黄亚博,廖顺宝.首套全球30 m分辨率土地覆被产品区域尺度精度评价--以河南省为例[J].地理研究,2016,35(8):1433-1446.
[22]FOODY G M.Assessing the accuracy of land cover change with imperfect ground reference data[J].Remote Sensing of Environment,2010,114(10):2271-2285.
[23]MAYAUX P,EVA H,GALLEGO J,et al.Validation of the global land cover 2000 map[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(7):1728-1739.
[24]MAHDAVI-AMIRI A,HARRISON E,SAMAVATIF.Hexagonal connectivity maps for Digital Earth[J].International Journal of Digital Earth,2015,8(9):750-769.
[25]LIN B,ZHOU L,XU D,et al.A discrete global grid system for earth system modeling[J].International Journal of Geographical Information Science,2018,32(4):711-737.
[26]WHITE D,KIMERLING J A,OVERTON S W.Cartographic and geometric components of a global sampling design for environmental monitoring[J].Cartography and Geographic Information Systems,1992,19(1):5-22.
[27]WOODALL C W,WALTERS B F,COULSTON J W,et al.Monitoring network confirms land use change is a substantial component of the forest carbon sink in the eastern United States[J].Scientific Reports,2015,5:17028.
[28]THEOBALD D.A general-purpose spatial survey design for collaborative science and monitoring of global environmental change:the global grid[J].Remote Sensing,2016,8(10):813.
[29]CANTERS F.Evaluating the uncertainty of area estimates derived from fuzzy land-cover classification[J].Photogrammetric Engineering&Remote Sensing,1997,63(4):403-414.