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基于Landsat时间序列数据的祁连山区域 土地利用变化
引用本文:张赫林,彭代亮,邓睿,王大成,韩永欢.基于Landsat时间序列数据的祁连山区域 土地利用变化[J].北京工业大学学报,2017,43(5).
作者姓名:张赫林  彭代亮  邓睿  王大成  韩永欢
作者单位:1. 重庆交通大学建筑与城市规划学院,重庆 400074;中国科学院遥感与数字地球研究所数字地球重点实验室,北京 100094;2. 中国科学院遥感与数字地球研究所数字地球重点实验室,北京,100094;3. 重庆交通大学建筑与城市规划学院,重庆 400074;重庆交通大学水利水运工程教育部重点实验室,重庆 400074;4. 中国科学院遥感与数字地球研究所国家遥感应用工程技术研究中心,北京,100094;5. 长丰县杨庙中学,合肥,231141
基金项目:国家自然科学基金资助项目,国家重点研发计划资助项目,中国科学院青年创新促进会资助项目,国土资源部地学空间信息技术重点实验室开放基金资助项目,重庆交通大学国家内河航道整治工程技术研究中心暨水利水运工程教育部重点实验室开放基金资助项目
摘    要:为了研究祁连山地区土地利用变化情况,基于祁连山区域1986—2015年的Landsat时间序列数据,通过相对辐射校正获取时序地表反射率数据.采用光谱扩展与基于回归树的决策树分类(CART)获取规则的决策树分类方法,应用于长时间序列卫星影像,对各类土地利用类型近30 a的变化情况进行分析.结果表明:相对辐射归一化能有效减少时间序列数据之间光谱值差异,基于CART获取规则的决策树分类方法具有较高的分类精度.以2012年分类结果为例,总体分类精度为88.72%,Kappa系数为0.86,并分析了可能存在的误差.研究区耕地、林地和草地面积总体呈下降趋势发展,并且草地破碎化程度加剧,戈壁面积增多,植被退化导致土地荒漠化问题更加严重.最后,根据研究区土地利用变化情况进行讨论,并针对该情况提出建议.

关 键 词:Landsat时序数据影像  长时间序列  相对辐射归一化  CART决策树分类  土地利用/覆被变化

Dynamic Change of Land Use in Qilian Mountains Based on Time-series Landsat Image
ZHANG Helin,PENG Dailiang,DENG Rui,WANG Dacheng,HAN Yonghuan.Dynamic Change of Land Use in Qilian Mountains Based on Time-series Landsat Image[J].Journal of Beijing Polytechnic University,2017,43(5).
Authors:ZHANG Helin  PENG Dailiang  DENG Rui  WANG Dacheng  HAN Yonghuan
Abstract:To study the change of land use in Qilian Mountains, Landsat TM/ETM+ images during 1986 to 2015 in Qilian Mountains were collected, and processed for time-series ground surface reflectance data by using relative radiometric correction methods. Combining spectral extension expansion method and classification and regression tree ( CART ) classification method, land-covers were classified into eight classes including forest, grassland, cropland, water, ice snow, gobi, sand and the other. Time-series Landsat images were used to classify the land types. The result shows that relative radiometric correction methods reduce radiation difference of time-series image. The classification result in 2012 was validated by the ground data, with an overall precision of 88. 72%, and a Kappa coefficient of 0. 86. During this period, the area of cropland, forest and grassland in the study area was declining, and the fragmentation degree of grassland was increasing. The area of gobi increased, and the desertification caused by vegetation degradation was more serious. Finally, the situation for land use in the research areas was discussed and suggestions for land use were proposed.
Keywords:Landsat image  long time-series  relative radiation normalization  CART ( classification and regression tree) decision tree classification  land use change
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