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干旱区地理 ›› 2015, Vol. 38 ›› Issue (5): 994-1003.

• 生物与土壤 • 上一篇    下一篇

基于多元成土因素的土壤有机质空间分布分析

黄安1, 杨联安1, 杜挺1, 张彬1, 宋英强1, 王安乐2, 秦进1   

  1. 1. 西北大学城市与环境学院, 陕西西安 710069;
    2. 蓝田县农业技术推广中心, 陕西西安 710500
  • 收稿日期:2014-12-30 修回日期:2015-04-06 出版日期:2015-09-25
  • 通讯作者: 杨联安(1968-),男,陕西武功人,副教授,博士,主要研究方向为3S在精准农业中应用.Email:yanglianan@163.com E-mail:yanglianan@163.com
  • 作者简介:黄安(1990-),男,四川雅安人,硕士研究生,主要研究方向为遥感与地理信息系统在精准农业中的应用.Email:hhanner@163.com
  • 基金资助:

    西北大学"211工程"研究生自主创新项目(YZZ14013);陕西省农业科技攻关项目(2011K02-11)

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

HUANG An1, YANG Lian-an1, DU Ting1, ZHANG Bin1, SONG Ying-qiang1, WANG An-le2, QIN Jin1   

  1. 1. College of Urban and Environmental Science, Northwest University, Xi an 710069, Shaanxi, China;
    2. Lantian County Agricultural Technology Popularization Center, Xi'an 710500, Shaanxi, China
  • Received:2014-12-30 Revised:2015-04-06 Online:2015-09-25

摘要: 以陕西省蓝田县2013年667份土壤有机质样本为对象,运用GIS空间分析及遥感数字图像处理收集整理土壤类型、地形、植被等成土因子,利用多元线性回归分析集成所有成土因子对土壤养分进行空间分布预测。结果表明:通过分级统计均值定权法和像元线性拉伸法将所有成土因子统一为相对度量值,并根据成土因子与有机质含量的相关性显著程度进行因子取舍,有利于集成各类成土因子构建多元线性回归模型。预测结果定性分析表明:多元线性回归预测结果与kriging法预测结果在宏观上具有一致的空间分布趋势;但多元线性回归预测结果土壤有机质空间分布特征带有各种成土因子的变化特征,从视觉效果上,克服了传统插值法中存在的斑块状分布现象,更精细的描述了本区域内有机质空间分布趋势; MPERMS定量精度分析显示,在集成多元成土因素对有机质进行空间分布分析时,本文方法优于常用kriging插值法,该法可作为集成多元成土因子对土壤养分空间分布预测的有效方法。本区域内土壤有机质高值区域主要集中在地势低平、坡度缓和、湿度适中的农耕区,地势较高、坡度陡的山区有机质含量低。

关键词: GIS, RS, 有机质, 成土因子, 多元线性回归分析, 空间预测

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

中图分类号: 

  • S153.6