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首页> 外文期刊>Computers & geosciences >Geostatistical Model For Correlating Declining Groundwater Levels With Changes In Land Cover Detected From Analyses Of Satellite Images
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Geostatistical Model For Correlating Declining Groundwater Levels With Changes In Land Cover Detected From Analyses Of Satellite Images

机译:通过卫星图像分析检测到的地下水统计与土地覆盖变化相关的地统计学模型

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A combination of satellite-data processing and geostatistical spatial modeling is examined to investigate long-term changes in groundwater levels within a catchment basin and identify the causes of the changes. The data processing was used to obtain an accurate image classification of land covers while the geostatistical modeling was used to interpolate residual components of groundwater levels by removing identified trends from the original data. Our case study focused on the Kumamoto Plain, situated in central Kyushu, southwest Japan, where all of the water supply is derived from groundwater. A Linear Spectral Mixture was adopted to the 10 sub-scene images of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhancement Thematic Mapper Plus (ETM + ) sensors covering the study area for a 16-year period (1987-2003); this proved to be highly effective in detecting declining amounts of groundwater-recharging material. Descriptive statistical parameters from the groundwater-level data were integrated with the process of identifying, characterizing, and removing the trend components that are closely correlated with rainfall. Variogram modeling and ordinary kriging were used to construct a spatial model of the residual groundwater-level data. Finally, a multivariate model is proposed to estimate the degree of reduction in groundwater levels. The correlation coefficients between the observed and estimated residual levels by the multivariate regression model at 14 wells for the cross-validation range from 0.95 to 0.98. This demonstrates the applicability of the model to both the local areas around well sites and across the entire study area. To deepen the regression result, the Tukey honestly significantly different statistical test was applied. The test clarified that changes in land cover from rice field to urban areas have the greatest impact on reductions in groundwater within the recharge area.
机译:结合了卫星数据处理和地统计空间模型的研究,以调查集水盆地内地下水位的长期变化并找出变化的原因。数据处理用于获得准确的土地覆盖物图像分类,而地统计模型则用于通过从原始数据中删除已确定的趋势来内插地下水位的残留成分。我们的案例研究集中在位于日本西南部九州中部的熊本平原,那里的所有供水均来自地下水。线性光谱混合被用于Landsat 5主题映射器(TM)和Landsat 7增强主题映射器(ETM +)传感器的10个子场景图像,覆盖了研究区域,为期16年(1987-2003年);事实证明,这种方法在检测地下水补给量下降方面非常有效。来自地下水位数据的描述性统计参数与识别,表征和消除与降雨密切相关的趋势分量的过程集成在一起。利用方差图建模和普通克里金法来构建残留地下水位数据的空间模型。最后,提出了一个多变量模型来估算地下水位下降的程度。多元回归模型在14口井的交叉验证范围从0.95至0.98时,观察到的残留水平与估计的残留水平之间的相关系数。这证明了该模型对井场周围的局部区域以及整个研究区域的适用性。为了加深回归结果,应用了Tukey坦白地说显着不同的统计检验。该测试表明,从稻田到城市地区的土地覆盖变化对补给区内地下水减少的影响最大。

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