【作者】 吐尔逊·艾山；

【作者基本信息】 新疆大学 ， 地图学与地理信息系统， 2008， 硕士

【摘要】 土壤盐碱化是干旱、半干旱农业区主要的土地退化问题,及时、精准、动态地获取盐碱土盐分信息,对治理盐碱土、防止其进一步退化以及农业可持续发展至关重要。传统土壤盐碱化监测采用野外土壤定点调查方式,不仅费时、费力,而且测点少,代表性差,无法实现大面积实时监测。遥感由于能大面积、重复获取多波段、多时相的信息,为大面积实时动态监测盐碱土状况提供了可能。本论文以渭—库三角洲绿洲作为研究区,利用盐碱土光谱数据和非遥感因子构建盐碱土盐分遥感反演模型。利用土壤溶液测定法得到土壤含盐量,利用CI700便携式野外光谱仪野外采集光谱数据,通过分析不同盐分盐碱土的光谱数据得出盐碱土光谱曲线特征。利用单波段相关分析及多元线性逐步回归分析方法,讨论了不同盐分盐碱土光谱数据与盐分的关系,精选出能够表征盐碱土盐分信息的最佳波段组合,即890nm、800nm、680nm、590nm、470nm该波段组合可以作为本模型的输入变量。除遥感因子外、地下水埋深、地下水矿化度和表层土壤矿化度三个非遥感因子也是建立盐碱土盐分遥感反演模型重要的输入变量。选用BP人工神经网络模型——智能计算的重要分支,在建模过程中经过反复试验与结果比较,不断调整隐含层数目和神经元的个数,使得模型结果不断优化。最终,选择BP神经网络反演盐分信息模型。该模型奠定了利用卫星影像反演盐碱土盐分的基础,有可能为各地区盐碱土盐分信息的提取提供方法支持。论文共分5章,第1章主要阐述了本论文的研究背景、目标、意义,盐碱土遥感监测的国内外研究进展及应用3S技术对土壤盐渍化研究存在的问题。第2章介绍了研究区——渭—库三角洲绿洲的自然、人文地理概况、土壤盐碱化现状,研究区数据源及野外考察内容。第3章介绍了本研究的主要研究方法、研究内容、工作路线并定量分析了实验采集的盐碱土光谱数据与盐分、TM影像数据的关系,利用多元线性逐步回归和相关分析方法确定了表征盐碱土盐分信息的最佳波段组合。第4章介绍了BP神经网络的基本原理、影响盐碱土盐分的关键因子,然后基于BP神经网络模型构建了盐碱土盐分遥感反演模型、并进行模型精度检验。第5章对论文所做的工作进行了简单的总结,并并讨论了本模型现存问题及改进之处。更多还原

【Abstract】 Soil salinization is a major environmental issue in the world, and it is more serious in arid and semi-arid area. Acquiring accurate salinity information timely is important for monitoring and evaluating soil salinization. Traditionally, soil salinization monitoring selects fixed points to investigate in field, which wastes not only time but also manpower and can’t show representative areas. It is impossible to realize large-area, real-time inspection. Remote sensing technique shows huge excellence in these aspects. Spectral technique is a new and effective approach in studying soil attributes. Spectral data in ground which is the base of band selection, validation and evaluation can build the links of ground, aviation and satellite remote sensing data.Taking Weigan—Kucha Oasis as the example area, this paper aims to explore saline-alkali soils salinity information remote sensing inversion model based on BP neural network in this semi-arid area. Firstly, the salinity of soil specimens is measured with soil solution method in laboratory and spectral data is gotten by CI700 in field. Analyzing the saline-alkali soils spectral data characteristic, this paper discusses the relations between saline-alkali soils spectral data and salinity, then selects the best band combination which can represent saline-alkali soils salinity spectral characteristic by means of multi-variables linear stepwise regression analysis and correlation analysis methods. That is spectral reflectance of 890nm, 800nm, 680nm, 590nm, 470nm, except remote sensing factors, the degree of groundwater mineralization, buried groundwater depth and the degree of topsoil mineralization are the three major factors which influence saline-alkali soils salinity, and these three factors are important input variables for this model.Being an important branch of intelligence computation, neural network model is a nonlinear mathematical model. This method may realize the mapping from eight dimensions variables to salinity information through training specimen data and adjusting the weights. Using neural network method to retrieve saline-alkali soils salinity is beneficial and can show the potentials of geography computation techniques in analyzing high quality data.This model contains two hidden layers, the first hidden layer including five nodes and the second hidden layer including three nodes. It has only one output layer, that is salinity information. The recycling model training makes this model approach to real mapping relation of datasets infinitely.This paper was divided into five chapters as follows: The first chapter expresses the main study background, goal, significance, then dissertates the evolvement of saline-alkali soils remote sensing inspection in the world, and some existing problems of the research on the saline-alkali soils using the 3S technology. The second chapter introduces the situations of the study area which cause soil salinization, including its nature environment, social economic situations and the status of soil salinization, The data source of the study area and the investigate in field.The third chapter dissertates the study method, content, technique route in this paper, and quantitively analyzes the relations among the collected spectral data in experiment, salinity and TM image data, and make use of multi-variables linear stepwise regression and correlation analysis method to select the best band combination which can show saline-alkali soils salinity characteristic.The forth chapter introduced the fundamental of NN model、the main factors to effecting saline-alkali soils salinity, then designs saline-alkali soils salinity information remote sensing inversion model based on back propagation neural network, and then this chapter gives the method of precision validation. In the last chapter, the results and the defects about this paper are discussed.更多还原