基于多元成土因素的土壤有机质空间分布分析

Abstract

Abstract: Based on setting 667 pieces of soil organic matters as object from Lantian County, Shaanxi Province, China in 2013, utilizing GIS spatial analysis and remote sensing digital image to manage and collect the causes of the formation of the soil such as terain, soil quality, plants and so on which could affect the formation of nutritions in the soil, this paper integrated all the causes of the formation of the soil to predict the spartial distribution of the soil nutrition by using the multiple regression analysis. All soil factors were unified as the relative measurement with the method of classification statistical average weighting and pixel linear stretching, and appropriating factors were selected by the level of significant correlation degree between organic matter content of the soil and factors, which was favorable for the integration of various kinds of soil factors to construct multivariate linear regression model. Using qualitative analysis to predicted results showed that, there are same spatial distribution trend between the results of multiple linear regression prediction and of the Kriging method on the macrolevel. But the spatial distribution characteristics of soil organic matter predicted multivariate linear regression carry various characteristics of soil factors. From the visual effect, the method of multivariate linear regression overcome the patch shape distribution phenomenon existing in the traditional interpolation method, more details of the spatial distribution of organic matter was described in research region. Accuracy of quantitative analysis from MPE and RMS showed that, the method with integration of multiple soil factors to analyze the spatial distribution of organic matter is superior to the common kriging interpolation method, and the method can be the effective method for integrating soil factors to predicting spatial distribution of soil nutrients. The areas with high values of soil organic matter in the investigation district mainly concentrated in the piedmont alluvial plain which is flat and gentle in slope, the mountainous area with high altitude and steep slope countains have the lower organic matters.

Key words: GIS, RS, Organic matter, soil factors, Multiple regression analysis, Spatial prediction

CLC Number:

S153.6

HUANG An, YANG Lian-an, DU Ting, ZHANG Bin, SONG Ying-qiang, WANG An-le, QIN Jin. Spatial distribution of the soil organic matter based on multiple soil factors[J]., 2015, 38(5): 994-1003.

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Spatial distribution of the soil organic matter based on multiple soil factors

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