文献类型：会议论文

英文题名：Evenness detection method for sample distribution of agricultural heavy metal in the fragmented regions

作者：Dong, Shiwei[1] Pan, Yuchun[1] Guo, Hui[2] Gao, Bingbo[1] Gao, Yunbing[1] Li, Xiaolan[1]

第一作者：Dong, Shiwei

通信作者：Dong, SW[1]

机构：[1]Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China;[2]Chinese Acad Forestry, Forestry Expt Ctr North China, Beijing, Peoples R China

会议论文集：7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

会议日期：AUG 06-09, 2018

会议地点：George Mason Univ, Ctr Spatial Informat Sci & Syst, Hangzhou, PEOPLES R CHINA

主办单位：George Mason Univ, Ctr Spatial Informat Sci & Syst

语种：英文

外文关键词：evenness detection; sampling; data evaluation; agricultural soil; spatial distribution

年份：2018

摘要：Spatial sampling survey is the most basic work, and the even distribution of sampling sites is the important optimization target of sampling layout design and the key factor of the later data detection and evaluation of the samples. This paper presents an evenness detection method for sample distribution of agricultural heavy metal in the fragmented regions for Beijing, China. The even variation values for 171 samples of the agricultural heavy metal were calculated based on Thiessen polygons, and then the even variation index and even variation curve of samples were constructed to detect the evenness of sampling sites. The results showed that the even variation index of sample distribution of agricultural heavy metal in the fragmented regions for Beijing was 0.63, and there were four abnormal samples according to the comparisons of the even variation curve of sampling sites and the simulated standard curve based on spatial simulated annealing (SSA) and minimization of the mean of shortest distances (MMSD) criterion. Four abnormal samples will be adopted for detailed analysis to support further data refinement and mining of sampling sites. The method developed in this study can accurately and effectively detect the sampling evenness of agricultural heavy metal in the fragmented regions. This study provides a robust operational tool and technical method for the regional data quality evaluation and has broad potential in the agricultural soil pollution and prevention, the detailed investigation of soil pollution, and the monitoring of agricultural product origins. The method is effective to improve the monitoring and supervision levels of the agricultural and ecological environment.