| 水平和垂直尺度乔、灌、草覆盖度遥感提取研究进展 |
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| 引用本文： | 黄健熙,吴炳方,曾源,田亦陈.水平和垂直尺度乔、灌、草覆盖度遥感提取研究进展[J].地球科学进展,2005,20(8):871-881. |
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| 作者姓名： | 黄健熙 吴炳方 曾源 田亦陈 |
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| 作者单位： | 1. 中国科学院遥感应用研究所,北京,100101
2. 中国科学院遥感应用研究所,北京,100101;Wageningen University and Research Centre,the Netherlands |
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| 基金项目： | 中国科学院知识创新工程项目 |
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| 摘 要： | 植被覆盖及其变化是区域生态系统环境变化的重要指示，而植被覆盖度是植物群落覆盖地表状况的一个综合量化指标，是生态模型、碳循环、水循环模型等的重要特征参量。传统的植被覆盖度是指一定尺度下所有植被(乔、灌、草)覆盖的综合反映值，当考虑植被垂直方向的异质性，垂直尺度的乔、灌、草覆盖度提取为定量化准确衡量生态环境、全球气候变化等领域提供更具有生态意义的植被参量。目前，遥感大面积估算水平尺度乔、灌、草覆盖度已有比较成熟和可靠的算法，主要方法有：植被指数法、回归分析法、分类决策树法、神经网络法、像元分解模型法、物理模型反演法等，其估算精度基本能达到应用要求。植被垂直方向的异质性给垂直尺度乔、灌、草覆盖度遥感提取带来较大挑战，垂直尺度上的乔、灌、草覆盖度遥感提取的研究在欧美等国已经有了一定规模的开展，在国内则处于起步阶段。遥感提取垂直尺度乔、灌、草覆盖度的主要手段有：激光雷达（LIDAR）、多角度遥感以及两层结构冠层反射模型反演。综述了水平尺度和垂直尺度上乔、灌、草覆盖度遥感提取的最新进展，比较和分析主要的遥感提取方法、模型和现存的一些问题，并对未来的研究发展趋势进行了展望。
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| 关 键 词： | 乔、灌、草覆盖度 遥感提取 水平尺度 垂直尺度 |
| 文章编号： | 1001-8166（2005）08-0871-11 |
| 收稿时间： | 2005-03-29 |
| 修稿时间： | 2005年3月29日 |
REVIEW OF TREE, SHRUB, GRASS COVER OF HORIZONTAL AND VERTICAL SCALE RETRIEVAL FROM REMOTELY SENSED DATA |
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| HUANG Jian-xi,WU Bing-fang,Zeng Yuan,TIAN Yi-chen.REVIEW OF TREE, SHRUB, GRASS COVER OF HORIZONTAL AND VERTICAL SCALE RETRIEVAL FROM REMOTELY SENSED DATA[J].Advance in Earth Sciences,2005,20(8):871-881. |
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| Authors: | HUANG Jian-xi WU Bing-fang Zeng Yuan TIAN Yi-chen |
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| Institution: | 1. Institute of Remote Sensing Applications, CAS, Beijing 100101, China; 2.Wageningen University and Research Centre, the Netherlands |
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| Abstract: | Vegetation cover is an important indicator of regional ecosystem change. Vegetation coverage is a synthetically quantitative index of conditions of vegetation community cover and an important characteristic variable of ecosystem models, water and carbon cycles models. Conventional vegetation coverage means the integrated results of different vegetation type, including tree, shrub and grass. When vegetation vertical heterogeneity is considered, decomposition of vegetation cover into tree and shrub/grass components using remotely sensed data is a new research field and will provide more ecological meaning parameters for quantifying the ecological environment and global climate change. Currently, a series of algorithms have been successfully used to retrieve tree, shrub and grass cover of horizontal scale from remotely sensed data, including vegetation indices, regression analysis, classification and regression tree, artificial neural networks, pixel unmixing analysis, physically-based model inversion,etc. These methods could meet the requirements of application accuracy. With the development of the new sensors,like LIDAR, multi-angle sensors as well as physically-based models, such as geometric optical and radiative transfer models, especially, two-layer canopy reflectance model, the retrieval of tree and shrub/grass cover of vertical scale in different temporal and spatial scale shows promising expectations. The paper reviews in detail the latest achievements and frontiers of the horizontal and vertical scale retrieval of tree, shrub, and grass cover from remotely sensed data, compares and analyzes main methods and models. In the end, it discusses the existing problems of various methods and gives an outlook of future research directions. |
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| Keywords: | Tree cover Shrub cover Grass cover Remotely sensed acquisition Horizontal scale Vertical scale |
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