• 资源研究方法 •

基于光谱混合分析和反照率-植被盖度特征空间的土地荒漠化遥感评价

1. 1. 西北师范大学 地理与环境科学学院, 兰州 730070;
2. 南京师范大学 地理科学学院, 南京 210046
• 收稿日期:2010-04-07 修回日期:2010-06-06 出版日期:2010-11-20 发布日期:2010-11-20
• 作者简介:潘竟虎(1974- ),男,甘肃嘉峪关人,副教授,研究方向为环境遥感与GIS应用。E-mail: panjh_nwnu@nwnu.edu.cn
• 基金资助:

国家自然科学基金(40971078,40961026,41061017);西北师范大学知识与科技创新工程项目(NWNU-KJCXGC-03-60)。

Extracting Desertification from Landsat Imagery Based on Spectral Mixture Analysis and Albedo-Vegetation Feature Space

PAN Jing-hu1, LI Tian-yu2

1. 1. College of Geographic and Environmental Science,Northwest Normal University,Lanzhou 730070,China;
2. College of Geographic Science,Nanjing Normal University,Nanjing 210046,China
• Received:2010-04-07 Revised:2010-06-06 Online:2010-11-20 Published:2010-11-20

Abstract: Land desertification has been a worldwide environmental problem. Desertification monitoring and evaluation is a very important content in desertification context. Scientific and accurate evaluation of desertification can provide scientific basis for decision-making in combating desertification. Because of the advantage of large amount of information, short cycle and broad scope of data, less restrictions on the human and material resources and so on,remote sensing has become an important technology to monitor land desertification in the past 30 years. Desertification is the most typical and serious form of desertification in China, especially in the oasis zone distributed along inland rivers or in the lower reaches of inland rivers in northwestern China. Quantitative evaluation of the current desertification remote sensing methods used are mostly obtained through the vegetation index and vegetation cover, to gain information on the extent of desertification. As the arid and semiarid sparse vegetation cover, soil and soil moisture on the most common vegetation index have a greater effect. Firstly, based on the SMA(Spectral Mixture Analysis)model,three kinds of endmember consisting of vegetation,water and bare soil were selected. The image dimensionality was reduced by the minimum noise fraction(MNF). The pixel purity index (PPI)transformation was used to narrow the range of the endmember. On the scatterplot of MNF,three kinds of endmember were selected, and relative abundance distribution of each component was obtained by using linear spectral mixture model. Secondly, a spectral feature space composed of vegetation component and land surface albedo retrieved from Landsat TM Imagery was constructed to evaluate desertification present condition and degree quantificationally. Finally, an empirical study was carried out taking the middle reaches of Heihe River as an example. Results indicated that this method makes full use of multi-dimensional remote sensing information, reflecting the desertification land cover, water, thermal environment and its changes,with a clear biophysical significance,and the index is simple, easy to obtain, high in precision, and is conducive to quantitative analysis, monitoring and assessment of desertification. It was rather ideal to assess desertification on the basis of Albedo-Vegetation feature space: correct prediction proportion of testing samples reached 90.3% and the prediction error of desertification degree was less than two grades. This method can be applied to the practical project.

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