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2015年河南省土地覆被栅格数据

Grid data on land cover of Henan Province(2015)
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摘要 利用86景Landsat 8卫星OLI云量小于10%的卫星影像,以及高时间分辨率的高分一号WFV宽视角多光谱相机、无人机影像、Google Earth影像为基础数据,根据河南省的地势地貌和土地覆被特征,选取4个覆盖研究区所有土地覆被类型的样方进行全面野外调查,并获得1518个样点,参考样点和样区的土地覆被特征用ArcMap 10.3软件目视解译样区(到二级类型)。采用专家论证法评价和修正样区解译结果。在样区解译工作的基础上,再次野外调查并解译河南省剩余区域土地覆被类型,最终获取野外样点共4883个。最后采用全球随机正六边形网格(12级)的方法选取1895个地面验证点评价解译精度,结果显示河南省土地覆被解译二级类型总体精度为91.19%,Kappa系数为0.886。 This study draws sources from 86-scene Landsat 8 OLI satellite images with cloud volume less than 10%,high-temporal resolution GF-1 WFV wide-viewing angle multispectral camera,UAV(unmanned aerial vehicle)images,and Google Earth images.According to the the topographical and land cover features of Henan Province,four sample regions with a full range of land cover types were selected for conducting a comprehensive field survey,through which we obtained 1518 samples.Then ArcMap 10.3 was used to visually interpret the sample area(to the secondary type)based on the characteristics of sample points and land cover features.Experts were asked to evaluate and correct the interpretation results.On the basis of the interpretation of sample area,another field survey was conducted in the remaining land of Henan Province,and a total of 4,883 field samples were obtained at last.Finally,1,895 ground verification points were selected through the global random regular hexagon grid(12 levels)method to evaluate the interpretation accuracy.The results showed that the overall interpretation accuracy of the land cover in Henan Province was 91.19%,and the Kappa coefficient was 0.886.
作者 李宁 朱筠 秦奋 Li Ning;Zhu Jun;Qin Fen(College of Environment and Planning,Henan University,Kaifeng 475004,China;Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions,Henan University,Kaifeng 475004,China)
出处 《中国科学数据(中英文网络版)》 CSCD 2019年第4期181-202,共22页 China Scientific Data
基金 国家科技基础条件平台–地球系统科学数据共享平台项目“2015年河南地区1:10万土地覆被遥感解译制图” 国家科技基础条件平台–地球系统科学数据共享平台项目“2015年河南省区域1:10万土地覆被质量验证”
关键词 河南省 土地覆被 Landsat 8 目视解译 2015年 Henan Province land cover Landsat 8 visual interpretation 2015
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