| Envisat ASAR的区域森林-非森林制图 |
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| 引用本文： | 凌飞龙,李增元,陈尔学,黄燕平,田昕,SCHMULLIUS Christin,LEITERER Reik,REICHE Johannes,SANTORO Maurizio.Envisat ASAR的区域森林-非森林制图[J].遥感学报,2012,16(5):1100-1113. |
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| 作者姓名： | 凌飞龙 李增元 陈尔学 黄燕平 田昕 SCHMULLIUS Christin LEITERER Reik REICHE Johannes SANTORO Maurizio |
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| 作者单位： | 福州大学 空间数据挖掘与信息共享教育部重点实验室,福建 福州 350002;中国林业科学研究院 资源信息研究所,北京 100091;中国林业科学研究院 资源信息研究所,北京 100091;福州大学 空间数据挖掘与信息共享教育部重点实验室,福建 福州 350002;中国林业科学研究院 资源信息研究所,北京 100091;德国耶拿大学 对地观测研究所;德国耶拿大学 对地观测研究所;德国耶拿大学 对地观测研究所;瑞士Gamma遥感公司 |
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| 基金项目： | 国家重点基础研究发展计划 (973 计划) (编号: 2007CB714404); 福建省科技计划(编号: 200910014);中欧合作“龙计划”项目(编号:5314) |
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| 摘 要： | Envisat卫星ASAR传感器的双极化数据对区域森林监测十分有效。通过分别采用SRTM DEM和Landsat TM图像对地形起伏区域和平坦区域的SAR图像进行地理编码,发展了一种SAR图像自动预处理方法。基于冬季单时相ASAR数据的HH(水平发射,水平接收)、HV(水平发射,垂直接收)极化比值和HV极化图像,提出了一种面向对象的森林-非森林分类方法。将之应用于中国东北森林/非森林制图,分类总体精度、森林用户精度和生产者精度分别为83.7%,85.6%和75.7%。结果表明,本文提出的方法十分适合区域森林-非森林制图的业务化运行。
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| 关 键 词： | Envisat ASAR 森林制图 面向对象分类 中国东北 |
| 收稿时间： | 2011/9/30 0:00:00 |
| 修稿时间： | 3/2/2012 12:00:00 AM |
Regional forest and non-forest mapping using Envisat ASAR data |
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| LING Feilong,LI Zengyuan,CHEN Erxue,HUANG Yanping,TIAN Xin,SCHMULLIUS Christin,LEITERER Reik,REICHE Johannes and SANTORO Maurizio.Regional forest and non-forest mapping using Envisat ASAR data[J].Journal of Remote Sensing,2012,16(5):1100-1113. |
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| Authors: | LING Feilong LI Zengyuan CHEN Erxue HUANG Yanping TIAN Xin SCHMULLIUS Christin LEITERER Reik REICHE Johannes and SANTORO Maurizio |
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| Institution: | Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350002, China;Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, Beijing 100091, China;Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, Beijing 100091, China;Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350002, China;Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, Beijing 100091, China;Department of Earth Observation, Friedrich-Schiller University Jena, Jena 07745, Germany;Department of Earth Observation, Friedrich-Schiller University Jena, Jena 07745, Germany;Department of Earth Observation, Friedrich-Schiller University Jena, Jena 07745, Germany;Gamma Remote Sensing, 3073 Gümligen, Switzerland |
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| Abstract: | Envisat Advanced Synthetic Aperture Radar (ASAR) dual-polarization data are shown to be effective for regional forest monitoring. To this scope, an automatic SAR image preprocessing procedure was developed using SRTM DEM and Land-sat TM image for geocoding in rugged terrain and smooth terrain areas, respectively. An object-oriented forest and non-forest classification method was then proposed based on the HH (horizontal transmit and horizontal receive) to HV (horizontal transmit and vertical receive) polarization intensity ratio and HV images ofASAR data at single acquisition time in winter. The developed method was applied to forest and non-forest mapping in Northeast China. The overall accuracy, the user’s accuracy and the producer’s accuracy of forest were 83.7%, 85.6% and 75.7%, respectively. These results indicate that the proposed method is prom- ising for operational forest mapping at regional scale. |
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| Keywords: | Envisat ASAR forest mapping object-oriented classif ication Northeast China |
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