农业机械学报2017，Vol.48Issue(10)：134-141,8.DOI:10.6041/j.issn.1000-1298.2017.10.016
基于MAS-LCM的沙漠化空间模拟方法研究
Spatial Simulation Method of Desertification Based on MAS-LCM Model
摘要
Abstract
Dengkou County,a typical city in the arid area,was taken as study area,and the spatial distribution of desertification for every five years from 1995 to 2015 in the study area was obtained by Landsat TM images remote sensing interpretation.Spatial and temporal variation trend of desertification landscape was analyzed by using GIS spatial analysis and gravity center migration model.Based on the 2010 desertification classification data,the 2005-2010 desertification classification area transfer matrix table was used as Markov transfer matrix file.Using the Logistic CA-Markov model (LCM) and introducing the multi-agent system (MAS) model to correct the transfer rule,the desertification classification and its spatial distribution pattern were forecasted and compared to analyze the advantages and disadvantages of the two simulation methods.The results showed that the desertification area of Dengkou County had a significant reduction in severe desertification and very severe desertification over the past 20 years.Mild desertification landscape area and non-desertification area were gradually increased,of which non-desertification landscape reached 37.09％ in 2015.Various types of desertification center of gravity left away from Dengkou County,showing a good momentum.The CA-Markov prediction model with MAS model can significantly improve the simulation accuracy of the model.The predicted Kappa coefficient reached 0.62,which was higher than that of CA-Markov model.It can better predict the distribution of desertification in arid areas,and provide technical support for the current and future desertification regulation and governance.关键词
干旱区/沙漠化/CA-Markov/多智能体系统/模拟Key words
arid region/desertification/CA-Markov/multi-agent system/simulation分类
社会科学引用本文复制引用
马欢,于强,岳德鹏,张启斌,黄元,高敬雨..基于MAS-LCM的沙漠化空间模拟方法研究[J].农业机械学报,2017,48(10):134-141,8.基金项目
国家自然科学基金项目(41371189)和“十二五”国家科技支撑计划项目(2012BAD16B00) （41371189）
