摘要
以广西壮族自治区马山县为研究区,在野外调查、室内实验测试获取182个土壤水分含量数据的基础上,采用半方差函数和 Moran’s I 统计量对研究区域土壤水分含量的空间自相关关系、空间相关尺度和空间分布规律进行了研究。结果表明:(1)研究区域土壤水含量平均值为16.97%,受结构性因素和随机因素共同作用,土壤水分含量具有中等强度的空间异质性;(2)研究区域土壤水分含量 Moran’s I 指数为0.423,表明研究区内土壤水分含量存在空间自相关性,在0-21 km 和31-34 km 范围内土壤水分含量自相关性为正,在21.7-31 km 和34-45 km 范围内自相关性为负;(3)Lisa 聚类图表明,土壤水分含量空间聚集区和空间孤立区相伴存在,其中“高-高”空间聚集主要分布在马山县东北部,“低-低”聚集区主要分布在东南部。“低-低”聚集区和“高-低”孤立区土壤水分含量缺乏风险大。
Mashan county,located in the middle Guangxi Zhuang Autonomous Region,southwestern China, was selected as the study area.Based on the plentiful information from field surveys,soil sampling and labo-ratory analysis,we were studied the spatial autocorrelation coefficients,correlation distances and spatial pat-terns of soil water content in topsoil (0 – 20 cm)using semi-variances and Moran’s I statistics.The results show that the mean value of soil water content is 1 6.97%.Soil water content shows a moderate spatial auto-correlation within the distance of 78.8 km,which is affected by the constitutive and random factors.(2)Mo-ran index of soil water content in the study area is 0.43,suggesting that the soil water content possesses spa-tial autocorrelation.In the ranges of 0-21.7 km and 31-34 km,the values of Moran′s I of soil water con-tent are greater than 0,implying positive spatial autocorrelation;while in the ranges of 21.7-31 km and 34-45 km,the values are negative,indicating negative spatial autocorrelation.Lisa cluster maps show that there are spatial aggregation areas and spatial isolated areas of the soil water content.The “high-high”spatial aggregation areas cluster in the northeast of Mashan county and “low-low”spatial aggregation clustered in the southeast.There are bigger risk of short of soil water content in the “low-low”spatial aggregation and“high-low”spatial isolated areas.
出处
《中国岩溶》
CAS
CSCD
北大核心
2015年第3期260-265,共6页
Carsologica Sinica
基金
国家科技支撑计划项目(2012BAC16B02)
基于生态修复的石漠化土地利用适宜性评价(桂科攻1598016-11)
地质大调查项目"西南岩溶石漠化遥感调查与地面监测"(1212011220958)
关键词
土壤水分含量
半方差函数
空间自相关
空间异质性
soil water content
semi-variances
spatial autocorrelation
spatial heterogeneity