Yao Yuan, Ding Jian-Li, Zhang Fang, Wang Gang, Jiang Hong-Nan
Ministry of Education Key Laboratory of Oasis Ecology, College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China.
Ying Yong Sheng Tai Xue Bao. 2013 Nov;24(11):3213-20.
Soil salinization is one of the most important eco-environment problems in arid area, which can not only induce land degradation, inhibit vegetation growth, but also impede regional agricultural production. To accurately and quickly obtain the information of regional saline soils by using remote sensing data is critical to monitor soil salinization and prevent its further development. Taking the Weigan-Kuqa River Delta Oasis in the northern Tarim River Basin of Xinjiang as test object, and based on the remote sensing data from Landsat-TM images of April 15, 2011 and September 22, 2011, in combining with the measured data from field survey, this paper extracted the characteristic variables modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), and the third principal component from K-L transformation (K-L-3). The decision tree method was adopted to establish the extraction models of soil salinization in the two key seasons (dry and wet seasons) of the study area, and the classification maps of soil salinization in the two seasons were drawn. The results showed that the decision tree method had a higher discrimination precision, being 87.2% in dry season and 85.3% in wet season, which was able to be used for effectively monitoring the dynamics of soil salinization and its spatial distribution, and to provide scientific basis for the comprehensive management of saline soils in arid area and the rational utilization of oasis land resources.
土壤盐渍化是干旱地区最重要的生态环境问题之一,它不仅会导致土地退化、抑制植被生长,还会阻碍区域农业生产。利用遥感数据准确快速地获取区域盐渍土信息对于监测土壤盐渍化及防止其进一步发展至关重要。以新疆塔里木河流域北部的渭干-库车河三角洲绿洲为试验对象,基于2011年4月15日和2011年9月22日的Landsat-TM影像遥感数据,结合实地调查测量数据,提取了修正归一化差异水体指数(MNDWI)、归一化差异植被指数(NDVI)以及K-L变换的第三主成分(K-L-3)等特征变量。采用决策树方法建立了研究区两个关键季节(旱季和雨季)土壤盐渍化的提取模型,并绘制了两个季节的土壤盐渍化分类图。结果表明,决策树方法具有较高的判别精度,旱季为87.2%,雨季为85.3%,能够有效地监测土壤盐渍化动态及其空间分布,为干旱地区盐渍土的综合治理和绿洲土地资源的合理利用提供科学依据。