中国农业科学院农业信息研究所 中国农学会
Agricultural Information Institute of CAAS China Association of Agricultural Science Societies
En
Ch
中国农业科学院农业信息研究所 中国农学会 主办

AI检索

订阅

返回顶部
当前位置: 首页 > 文章 > 基于MODIS影像大尺度森林资源信息提取方法研究 中南林业科技大学学报 2015 (11) 21-26+42
Position: Home > Articles > Based on MODIS image large-scale forest resources information extraction method Journal of Central South University of Forestry & Technology 2015 (11) 21-26+42

基于MODIS影像大尺度森林资源信息提取方法研究

作  者：
罗朝沁;林辉;孙华;吴梓尚
单  位：
中南林业科技大学林业遥感信息工程研究中心
关键词：
遥感;森林类型;概率密度;大尺度;MODIS
摘  要：
森林类型的识别对于掌握森林生态系统和自然环境变化具有重要意义。针对单一时相遥感数据提取森林植被类型信息方法的局限性,以中国东北三省为研究区,探讨了基于多时相MODIS遥感数据,实现主要森林类型识别的方法。将东三省的森林植被划分为非林地、针叶林、阔叶林、针阔混交林、灌木林5种类型,通过分析不同森林类型一年内生长差异,选取多时相NDVI第10期、NDIV第23期、EVI第10期、LAI第20期特征数据,建立了非林地、针叶林、阔叶林、针阔混交林、灌木林的决策树模型,实现了森林类型信息的识别,得出了东三省的森林覆盖率42.39%,植被类型分类总体精度为86.7%,与第八次全国森林资源清查的东三省结果对比,森林覆盖率提取精度高达95.6%。说明应用多时相的MODIS遥感影像可以实现大尺度森林资源信息的快速提取,在大范围的植被类型调查与监测方法具有较大的应用价值。
译  名：
Based on MODIS image large-scale forest resources information extraction method
作  者：
LUO Chao-qin;LIN Hui;SUN Hua;WU Zi-shang;Research Center of Forest Remote Sensing & Information Engineering, Central South University of Forestry & Technology;
关键词：
remote sense;;forest type;;probability density;;large scale;;MODIS
摘  要：
It plays an irreplaceable role that identified the forest types for control the changes of forest ecology system and the natural environment. As the limitation of single phase remote sensing data extract forest vegetation types' information, this paper choose the Chinese northeastern provinces as the study area and multi-temporal MODIS remote sensing as the study data. Find the main forest type identification method was the purpose of the paper. In addition, the forest vegetation was divided into 5 types including nonwoodland, coniferous and broadleaf forest, broadleaf forest and shrub. By analyzing the growing differences of different forest types in a year, the feature data of 10 th and 23 rd multi-temporal NDVI、10th multi-temporal EVI and 20 th multi-temporal LAI were selected to build the tree model for non-woodland, coniferous and broadleaf forest, broadleaf forest and shrub. And then it realizes the forest types information identifies. The forest cover rate is 42.39% and the overall accuracy of vegetation type classification is 86.7%. Intercomparison of the 8th national forest resources inventory results, the accuracy of Forest coverage up to 95.6%. It proved that large scale forest resources formation extraction can be achieve using multi-temporal MODIS satellite images and it has greater value in a wide range of vegetation types inventory and monitoring.

相似文章

导出引用
计量
文章访问数: 10
HTML全文浏览量: 0
PDF下载量: 0

所属期刊

推荐期刊

友情链接Friendly link
农业信息研究所Agricultural information institute 农业智能知识服务平台Agricultural intelligent knowledge service platform 农业学术服务平台Agricultural Knowledge Service System 中国知网CNKI 维普网VIP 万方WANFANG DATA
联系我们Contact us

电话Tell：010-82109652-808

邮箱E-mail：agrijournal@caas.cn

服务Service
关于我们About us 意见建议Opinions and suggest
版权说明Copyright
单位：中国农业科学院农业信息研究所 中国农学会 © 2017中国农业科学院农业信息研究所 ICP备案号：京ICP备09089781号-46