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基于地统计学和遥感的岩溶区石漠化空间变异特征
引用本文:杨奇勇,蒋忠诚,马祖陆,罗为群,尹 辉,喻琦雯,李文军.基于地统计学和遥感的岩溶区石漠化空间变异特征[J].农业工程学报,2012,28(4):243-247.
作者姓名:杨奇勇  蒋忠诚  马祖陆  罗为群  尹 辉  喻琦雯  李文军
作者单位:1. 中国地质科学院岩溶地质研究所,桂林,541004
2. 湖南文理学院资源环境与旅游学院,常德,415000
基金项目:“十二五”国家科技支撑计划课题“喀斯特峰丛洼地退化生态系统适应性修复技术与示范”(2010BAE00739-02);广西科技攻关课题(“桂科转0719005-2-3”,“桂科转2010124005”);水利部公益性行业科研专项“岩溶水资源保护与调配利用研究”(201101019-2)
摘    要:为了全面掌握岩溶区石漠化的空间分布特征,解决区域内遥感影像阴影区域石漠化信息"缺失"的问题,该文以广西平果县果化镇生态重建示范区为例,借助图像处理软件ENVI(environment for visualizing images)提取研究区域石漠化指数。在GIS技术支持下,利用地统计学的方法对剔除阴影区后的石漠化指数进行了空间变异特征分析。结果表明,研究区域石漠化指数主要受内在因子的共同作用,具有强烈的空间自相关性;Kriging插值结果表明,研究区域石漠化指数较高的区域主要分布在研究区的西南部和东北部,各石漠化等级的空间分布表现为条带状和斑块状分布。岩性和地形地貌条件是影响石漠化空间变异的主要因素。研究利用地统计学的方法很好地估计了阴影区石漠化指数的"缺失"信息,为岩溶区石漠化监控、评估提供了新的思路与方法。

关 键 词:遥感  空间变异测量  地理信息系统  石漠化  半方差函数  地统计学
收稿时间:6/2/2011 12:00:00 AM
修稿时间:2011/8/28 0:00:00

Spatial variability of karst rock desertification based on geostatistics and remote sensing
Yang Qiyong,Jiang Zhongcheng,Ma Zulu,Luo Weiqun,Yin hui,Yu Qiwen and Li Wenjun.Spatial variability of karst rock desertification based on geostatistics and remote sensing[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(4):243-247.
Authors:Yang Qiyong  Jiang Zhongcheng  Ma Zulu  Luo Weiqun  Yin hui  Yu Qiwen and Li Wenjun
Institution:1. Institute of Karst Geology, Chinese Academy of Geological Sciences, Guilin 541004, China; 2. College of Resources and Environment and Tourism, Hunan University of Arts and Science, Changde 415000, China)
Abstract:In order to master the spatial variability of karst rock desertification, and solve the problem of the shadow "missing" information of remote sensing images existing in the karst area,rock desertification indices were extracted in Guohua Ecological Experimental Area,which was in Pingguo county of Guangxi province by applying image processing software of ENVI(environment for visualizing images). And the rock desertification indices of the shadow areas were excluded with the remote sensing images, then their spatial distribution were analyzed with geostatistical method. The results indicated that the rock desertification indices of the study area were impacted intrinsic factors and performed strong spatia1 autocorrelation. The spatial distribution maps of the rock desertification indices interpolated by Kriging interpolation method showed that the higher indices emerged in the northeast and southwest of the study area, and the spatial distribution of the karst rock desertification was apparently strip and block. The spatial distribution of the rock desertification levels in the study area is chiefly controlled by lithology and topography. Using geostatistical methods to predict the shadow "missing" information can provide a new idea and method for monitoring and evaluation of the karst rock desertification.
Keywords:remote sensing  spatial variables measurement  geographic-information-systems  rocky desertification  semi-variances  geostatistics
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