基于ERS-1/2和Envisat ASAR数据的大区域森林制图研究

田昕; 李增元; 陈尔学; 凌飞龙; OliverCartus3MaurizioSantoro; ChristianeSchmullius

基于ERS-1/2和Envisat ASAR数据的大区域森林制图研究

1 中国林业科学研究院资源信息研究所2福州大学空间信息工程研究中心3 德国耶纳大学对地观测学院4 瑞士Gamma遥感公司

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出版历程
- 收稿日期: 1899-12-31
- 修回日期: 1899-12-31
- 发布日期: 2013-01-29

Large-scale forest mapping based on ERS-1/2 and Envisat ASAR data.

1 Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, P.R.China; 2 Spatial Information Research Center, Fuzhou University, 350002, P. R. China; 3 Department of Earth Observation, FriedrichSchiller University Jena, 07745, Germany; 4 Gamma Remote Sensing, Gümligen, 3073, Switzerland.

摘要

摘要: 分别利用1995—2000年的ERS-1/2串行数据和2005年的Envisat ASAR数据对我国东北林区进行森林制图研究。针对ERS-1/2数据相干模型，采用一种不依靠地面实况数据而是基于MODIS植被连续覆盖产品进行训练的方法，从而实现进行大区域森林蓄积量分级制图的目的。分级制图包括0～20、20～50、50～80和80 m3/hm2 4个蓄积量等级。基于Envisat ASAR数据，采用面向对象的分类方法，进行自动化森林和非森林分类处理。基于2005年Landsat TM-5分类结果的交叉验证表明：这2种传感器SAR数据均可用于大区域森林制图。2期森林制图结果为进一步的森林变化分析以及制图更新研究提供支持。
- 干涉SAR /
- 极化SAR /
- 大区域森林制图 /
- 面向对象
Abstract: In this paper, two kinds of forest map over Northeast China were produced by use of archived ERS-1/2 tandem data (1995-2000) and Envisat ASAR data (2005). As far as ERS-1/2 coherence model was concerned, a new approach was introduced, that allowed model training using the MODIS Vegetation Continuous Fields canopy cover product without further need for ground data. On basis of this method, the largescale forest stem volume map (four classes: 0-20, 20-50, 50-80 and 80 m3/hm2) was generated. As regards the Envisat ASAR data, the object-based classifier was applied to map the forest/nonforest areas automatically. The crosscomparisons were performed for these two kinds of map based on the land use map from Landsat TM-5 data acquired in 2005. The results show that these two satellite SAR data can be used for the largescale forest mapping and their maps in current case will provide supports for the further analysis of forest change detection and the map updating.
- interferometric SAR /
- polarimetric SAR /
- large-scale forest mapping /
- object-based