Land use and land cover change prediction with the theory of evidence: a case study in an intensive agricultural region of France

Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997) Pub Date : 2002-07-08 DOI:10.1109/ICIF.2002.1021139

L. Hubert‐Moy, S. Corgne, G. Mercier, B. Solaiman

{"title":"Land use and land cover change prediction with the theory of evidence: a case study in an intensive agricultural region of France","authors":"L. Hubert‐Moy, S. Corgne, G. Mercier, B. Solaiman","doi":"10.1109/ICIF.2002.1021139","DOIUrl":null,"url":null,"abstract":"In intensive agricultural regions, accurate assessment of the spatial and temporal variation of winter vegetation covering is a key indicator of water transfer processes, essential for controlling land management and helping local decision making. Spatial prediction modeling of winter bare soils is complex and it is necessary to introduce uncertainty in modeling land use and cover changes, especially as high spatial and temporal variability are encountered. Dempster's fusion rule is used in the present study to spatially predict the location of winter bare fields for the next season on a watershed located in an intensive agricultural region. It expresses the model as a function of past-observed bare soils, field size, distance from farm buildings, agro-environmental action, and production quotas per ha. The model well predicted the presence of bare soils on 4/5 of the total area. The spatial distribution of misrepresented fields is a good indicator for identifying change factors.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIF.2002.1021139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 21

Abstract

In intensive agricultural regions, accurate assessment of the spatial and temporal variation of winter vegetation covering is a key indicator of water transfer processes, essential for controlling land management and helping local decision making. Spatial prediction modeling of winter bare soils is complex and it is necessary to introduce uncertainty in modeling land use and cover changes, especially as high spatial and temporal variability are encountered. Dempster's fusion rule is used in the present study to spatially predict the location of winter bare fields for the next season on a watershed located in an intensive agricultural region. It expresses the model as a function of past-observed bare soils, field size, distance from farm buildings, agro-environmental action, and production quotas per ha. The model well predicted the presence of bare soils on 4/5 of the total area. The spatial distribution of misrepresented fields is a good indicator for identifying change factors.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于证据理论的土地利用和土地覆盖变化预测:以法国集约化农业区为例

在集约化农区，准确评价冬季植被覆盖的时空变化是流域调水过程的重要指标，对土地管理和地方决策具有重要意义。冬季裸土的空间预测建模是复杂的，在土地利用和覆被变化的建模中需要引入不确定性，特别是在高时空变异性的情况下。本研究采用Dempster融合规则对集约化农业区流域下一季冬季裸地的位置进行空间预测。它将模型表示为过去观察到的裸露土壤、田地大小、与农场建筑物的距离、农业环境行动和每公顷生产配额的函数。该模型很好地预测了总面积的4/5存在裸土。误表示场的空间分布是识别变化因素的一个很好的指标。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)

自引率

0.00%

发文量

期刊最新文献

Approximating fuzzy measures by hierarchically decomposable ones Tracking and fusion for wireless sensor networks A dynamic communication model for loosely coupled hybrid tracking systems On platform-based sensor management An improved Bayes fusion algorithm with the Parzen window method