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

采用线性光谱混合分析模型对Landsat TM遥感影像进行混合像元分解,获取植被、裸土和水体组分的相对丰度分布;在反演地表反照率的基础上,构建了基于Albedo-Vegetation特征空间的土地荒漠化遥感监测模型;以黑河中游部分区域为例,进行了实证研究,并利用实地调查数据进行精度验证。结果表明:此方法充分利用了多维遥感信息,反映了荒漠化土地地表覆盖、水热组合及其变化,具有明确的生物物理意义,而且指标简单、易于获取,精度较高,有利于荒漠化的定量分析与监测。

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

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.