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首页> 外文期刊>Photogrammetric Engineering & Remote Sensing: Journal of the American Society of Photogrammetry >Mapping multiple variables for predicting soil loss by geostatistical methods with tm images and a slope map
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Mapping multiple variables for predicting soil loss by geostatistical methods with tm images and a slope map

机译：使用tm图像和坡度图通过地统计学方法映射用于预测土壤流失的多个变量

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摘要

Soil erosion is widely predicted as a function of six input factors, including rainfall erosivity, soil erodibility, slope length, slope steepness, cover management, and support practice. Because of the multiple factors, their interactions, and their spatial and temporal variability, accurately mapping the factors and further soil loss is very difficult. This paper compares two geostatistical methods and a traditional stratification to map the factors and to estimate soil loss. Soil loss is estimated by integrating a sample ground data set, TM images, and a slope map. The geostatistical methods include collocated cokriging and a joint sequential co-simulation model. With both geostatistical methods, local estimates and variances at any location where the factors and soil Iqss are unknown can be computed. The results showed that the two geostatistical methods performed significantly better than traditional stratification in terms of overall and spatially explicit estimates. Furthermore, the cokriging led to higher accuracy of mean estimates than did the co-simulation, while the latter provided decision makers with reliable uncertainties of the local estimates as useful information to assess risk when making decisions based on the prediction maps.
机译：人们广泛预测土壤侵蚀是六个输入因素的函数，包括降雨侵蚀力，土壤易蚀性，边坡长度，边坡陡度，覆盖管理和支持措施。由于多种因素，它们之间的相互作用以及它们在空间和时间上的变化，因此很难准确地绘制出这些因素并进一步造成土壤流失。本文比较了两种地统计学方法和传统分层方法，以绘制影响因子和估算土壤流失的方法。通过整合样本地面数据集，TM图像和坡度图来估算土壤流失。地统计学方法包括并置协同克里金法和联合顺序协同模拟模型。通过这两种地统计学方法，可以计算出在未知因素和土壤Iqss的任何位置的局部估计和方差。结果表明，就整体和空间显式估算而言，这两种地统计学方法的性能明显优于传统分层方法。此外，与协同模拟相比，协同克里格法可以提高平均估计的准确性，而协同模拟则为决策者提供了可靠的局部估计不确定性，可以作为基于预测图做出决策时评估风险的有用信息。

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