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文章摘要
基于GF-1/WFV影像的青海祁连山地区地表覆被自动分类应用研究
Study on Automatic Classification of Land Cover in Qinghai Qilian Mountain Area Based on GF-1/WFV Images
  
DOI:
中文关键词: GF-1/WFV  地表覆被  分类规则  自动提取  祁连山地区
英文关键词: GF-1/WFV  Land cover  Classification rules  Automatic extraction  Qilian
基金项目:国家科技重大专项中国科学院“祁连山南坡矿区及周边受损生态系统植被修复技术研究与示范”基金资助项目(KFJ-EW-STS-125);国家发改委“十二五”规划《祁连山生态保护与建设综合治理规划(2012—2020)》基金资助项目
作者单位
祁佳丽 青海省生态环境监测中心青海省生态环境监测与评估重点实验室 
李飞 青海省生态环境监测与评估重点实验室青海省生态环境厅信息中心 
李志强 青海省生态环境监测中心 
薛旭东 青海省有色地质矿产勘查局 
张妹婷 青海省生态环境监测中心 
鲁子豫 青海省生态环境监测中心 
殷万玲 青海省生态环境监测中心 
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中文摘要:
      基于GF-1/WFV影像的地表覆被自动提取方法,利用青海祁连山地区复杂、独特的地表覆被垂直变化特征,建立以祁连山地区为例的水源涵养区地表覆被分类规则。结果表明,总体分类精度为9573%,Kappa值为0889 6,分类精度优于80%。尝试将网络远程视频监控系统应用在遥感解译中,选取样本点近距离验证典型地物类型,包括生态脆弱区(冰川)、生物多样性重点保护区(青海小叶杨原种保护地)等,分类精度为948%,应用潜力大。
英文摘要:
      The classification rules for land cover in water source conservation area were established based on GF-1/WFV image automatic extraction and the complex unique vertical variation characteristics in Qilian mountain area. Results showed that the total classification accuracy was 95.73%, the Kappa was 0.889 6, the classification accuracy was greater than 80%. As an attempt of applying remote network video monitoring system on remotely sensing interpretation, and carrying out close verification for the typical objects at selected sample points, including ecological fragile area(glacier)and key protection area of biodiversity (Qinghai Populus simonii origin area), the classification accuracy was 94.8%, showing it had vast potential on application.
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