基于定量遥感的绿洲土壤盐渍化研究(英文)

Study on Oasis Soil Salinization in Xinjiang Based on Quantitative Remote Sensing

土壤盐渍化是干旱、半干旱农业区主要的土地退化问题,同时也是一个重要的环境问题。选取塔里木盆地南缘克里雅河流域绿洲为研究靶区,利用Landsat-ETM+卫星图像数据和野外调查数据分析盐渍化土壤与地表反照率(Albedo)、土壤盐分指数(SI)之间的关系。回归分析发现,盐渍化土壤在SI-Albedo特征空间分布具有显著规律,即非盐渍化土壤呈团状分布;轻、中度盐渍化土壤具有线性分布特征;非盐渍化土壤与轻度盐渍化土壤分异明显。结合分异规律,编制分类算法模型,得到研究区盐渍化土壤信息提取结果,并与传统监督最大似然分类法结果进行对比分析。结果表明,在SI-Albedo特征空间中定量快速提取盐渍化土壤信息的总体效果较好,对准确且自动提取干旱区盐渍化土壤信息以及区域尺度盐渍化遥感定量监测研究具有重要意义。

Soil salinization is an important worldwide environmental problem, especially in arid and semi-arid regions. Knowledge of its temporal and spatial variability is crucial for the management of oasis agriculture. Yutian County was selected for this study because of its importance as a significant site for agricultural development. Located in the south of the Keriya oasis, it has recently been exposed to severe soil salinization. Thematic Mapper-plus（ETM＋） images dated October 7, 2002 were used against the data of soil features obtained from field investigation and analysis of typical soil information, to extract Salinization Index （SI） and land surface albedo, which are very important biophysical parameters of land surface. In this paper the relationship between salinization index （SI） and albedo was analyzed quantitatively. Through experiment and theoretical reasoning, the author proposed a conception of SI-Albedo space and discussed its biophysical characteristics. Analysis revealed that location could be used to improve the current strategies for salinization in the SI-Albedo space, and hence the strategies for salinization mapping, by defining measurements in this feature space. An information extraction model, using the decision-tree classification method, was established and applied to classifica- tion of RS images. Results indicate that the classification based on SI-Albedo space has a higher classification accuracy than the one based on maximum likelihood. Its highest overall-accuracy is about 0.921 4% higher than the x,.laximum likelihood. Although both techniques show some mix-class phenomena in the classification result, but the classification based on SI-Albedo space has less than the maximum likelihood, and thus a higher separability. Based on the salinized soil map, the salinity soil early-warning line was derived for anticipating further soil degradation. Such contrasting and complementary behavior suggests a potential synergism between salinization index and land surface albedo for mapping and monitoring of a complex soil salinization environment such as Keriya oasis.