银川平原灌区盐渍化土壤遥感监测模型
发布时间：2019-03-22 10:02
【摘要】：土壤盐渍化作为土地荒漠化和土地退化的主要类型之一,是造成干旱、半干旱地区土壤流失的重要原因,及时快速发现和治理土地盐渍化问题显得尤为重要。为建立土壤盐渍化遥感监测模型,选取宁夏平罗县典型土壤盐渍化发生区域作为研究区,以野外原位光谱测量数据和实验室内测得的土壤含盐量与pH数据为基础,运用高光谱数据处理方法,分析不同盐渍化程度土壤的光谱特征；对实测土壤光谱反射率进行倒数、对数、均方根及其一阶微分等光谱变换,计算高光谱指数；与土壤样本含盐量进行相关性分析,筛选盐渍化土壤的光谱特征波段,利用多元线性回归分析建立土壤盐渍化监测模型。通过本文的研究,得到如下结论：(1)野外实测光谱数据与实验室测得的土壤含盐量数据相结合,将土壤类型主要分为非盐渍化土壤、轻度盐渍化土壤、中度盐渍化土壤、重度盐渍化土壤四大类型。不同程度盐渍化地区的土壤光谱特征曲线在形态上基本趋于一致；但在可见光波段,不同程度盐渍化土壤的反射率并不呈现规律性变化。(2)将实测的土壤及植被光谱反射率数据进行倒数、对数、对数倒数、均方根等四种形式的变换,再对原始光谱反射率以及四种变换形式的光谱反射率数据进行一阶导数微分变换以及原始光谱的二阶微分导数变换,共计22种变换形式的反射率数据。再同四种不同类型的土壤含盐量数据进行统计分析,计算相关系数,得到相关系数图。图中显示,各种变换形式可以有效提高光谱反射率和土壤含盐量两者之间的相关性。(3)以筛选出的特征波段为自变量与土壤含盐量进行统计回归分析,构建土壤盐分动态监测模型,结果表明构建模型的模拟值与实际测量的土壤含盐量值之间的相关性较高。尤其是反射率倒数一阶微分模型,相关系数高达0.81,土壤盐渍化遥感监测模型的预测效果较好。(4)变换后的土壤和植被光谱反射率,其中倒数对数一阶微分变换的光谱反射率与实验室测得的土壤含盐量相关性最好。选择相关性最好的特征波段450nm、685nm构建盐分指数模型及960nm和1094nm构建植被指数模型,结果表明两者和土壤含盐量的相关性较高,因此协同两指数构建区域的土壤盐渍化遥感监测模型,经验证,模拟效果很好,可以用来快速提取该区域的土壤盐渍化信息,为今后土壤盐渍化监测提供一种新的手段。
[Abstract]:Soil salinization, as one of the main types of land desertification and land degradation, is an important cause of soil erosion in arid and semi-arid areas. It is very important to find and control soil salinization in time and quickly. In order to establish the remote sensing monitoring model of soil salinization, the typical soil salinization area in Pingluo County, Ningxia, was selected as the study area, based on the in-situ spectral data measured in the field and the soil salinity and pH data measured in the laboratory. The spectral characteristics of soils with different degree of salinization were analyzed by using hyperspectral data processing method. The spectral reflectance of the measured soil was calculated by inverse logarithm root mean square and its first order differential isospectral transformation to calculate the hyperspectral index. The spectral characteristic band of salinized soil was selected and the monitoring model of soil salinization was established by multiple linear regression analysis. The results are as follows: (1) the soil types are divided into non-salinized soil, mild salinized soil and moderate salinized soil according to the combination of field measured spectral data and soil salt content data measured in laboratory. Four types of severely salinized soil. The spectral characteristic curves of soil in different degree salinized areas tend to be consistent in morphology. However, in visible light band, the reflectivity of salinized soil in different degrees does not change regularly. (2) the measured spectral reflectance data of soil and vegetation are inversed, logarithmic reciprocal, root mean square and other four forms of transformation. Then the first derivative differential transformation and the second order differential derivative transformation of the original spectral reflectivity and four kinds of spectral reflectivity data are carried out. The reflectivity data of 22 kinds of transformation forms are made up of the first derivative differential transformation and the second order differential derivative transformation of the original spectrum. Then four different types of soil salt content data were statistically analyzed, the correlation coefficient was calculated and the correlation coefficient diagram was obtained. The results show that the correlation between spectral reflectivity and soil salt content can be effectively improved by various transformation forms. (3) Statistical regression analysis is carried out with selected characteristic bands as independent variables and soil salt content as independent variables. The dynamic monitoring model of soil salinity was constructed, and the results showed that the correlation between the simulated value of the model and the measured soil salt content was high. The correlation coefficient is as high as 0.81, and the prediction effect of the remote sensing monitoring model for soil salinization is better. (4) the spectral reflectivity of soil and vegetation after the transformation, the correlation coefficient is 0.81, and the correlation coefficient is 0.81. The spectral reflectivity of the reciprocal logarithm first order differential transformation is the best correlation with the soil salt content measured in the laboratory. The best correlation band was 450 nm, the salt index model was constructed at 685nm, and the vegetation index model was constructed by 960nm and 1094nm. The results showed that the correlation between them and soil salt content was high. Therefore, the remote sensing monitoring model of soil salinization is constructed in collaboration with two indices. The simulation result is very good, which can be used to extract the soil salinization information quickly and provide a new method for the monitoring of soil salinization in the future.
【学位授予单位】：宁夏大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：S156.41
本文编号：2445497
[Abstract]:Soil salinization, as one of the main types of land desertification and land degradation, is an important cause of soil erosion in arid and semi-arid areas. It is very important to find and control soil salinization in time and quickly. In order to establish the remote sensing monitoring model of soil salinization, the typical soil salinization area in Pingluo County, Ningxia, was selected as the study area, based on the in-situ spectral data measured in the field and the soil salinity and pH data measured in the laboratory. The spectral characteristics of soils with different degree of salinization were analyzed by using hyperspectral data processing method. The spectral reflectance of the measured soil was calculated by inverse logarithm root mean square and its first order differential isospectral transformation to calculate the hyperspectral index. The spectral characteristic band of salinized soil was selected and the monitoring model of soil salinization was established by multiple linear regression analysis. The results are as follows: (1) the soil types are divided into non-salinized soil, mild salinized soil and moderate salinized soil according to the combination of field measured spectral data and soil salt content data measured in laboratory. Four types of severely salinized soil. The spectral characteristic curves of soil in different degree salinized areas tend to be consistent in morphology. However, in visible light band, the reflectivity of salinized soil in different degrees does not change regularly. (2) the measured spectral reflectance data of soil and vegetation are inversed, logarithmic reciprocal, root mean square and other four forms of transformation. Then the first derivative differential transformation and the second order differential derivative transformation of the original spectral reflectivity and four kinds of spectral reflectivity data are carried out. The reflectivity data of 22 kinds of transformation forms are made up of the first derivative differential transformation and the second order differential derivative transformation of the original spectrum. Then four different types of soil salt content data were statistically analyzed, the correlation coefficient was calculated and the correlation coefficient diagram was obtained. The results show that the correlation between spectral reflectivity and soil salt content can be effectively improved by various transformation forms. (3) Statistical regression analysis is carried out with selected characteristic bands as independent variables and soil salt content as independent variables. The dynamic monitoring model of soil salinity was constructed, and the results showed that the correlation between the simulated value of the model and the measured soil salt content was high. The correlation coefficient is as high as 0.81, and the prediction effect of the remote sensing monitoring model for soil salinization is better. (4) the spectral reflectivity of soil and vegetation after the transformation, the correlation coefficient is 0.81, and the correlation coefficient is 0.81. The spectral reflectivity of the reciprocal logarithm first order differential transformation is the best correlation with the soil salt content measured in the laboratory. The best correlation band was 450 nm, the salt index model was constructed at 685nm, and the vegetation index model was constructed by 960nm and 1094nm. The results showed that the correlation between them and soil salt content was high. Therefore, the remote sensing monitoring model of soil salinization is constructed in collaboration with two indices. The simulation result is very good, which can be used to extract the soil salinization information quickly and provide a new method for the monitoring of soil salinization in the future.
【学位授予单位】：宁夏大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：S156.41
【引证文献】
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1 陈建军;张树文;;基于MODIS数据的东北地区土地覆盖分类的精度评价研究[A];第十四届全国遥感技术学术交流会论文选集[C];2003年
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