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A data fusion approach for soil erosion monitoring in the Upper Yangtze River Basin of China based on Universal Soil Loss Equation (USLE) model

机译:基于通用水土流失方程(USLE)模型的长江上游水土流失监测数​​据融合方法

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Soil losses and soil deposits in the Upper Yangtze River watershed have been increasing for decades, with serious negative effects on the agriculture along the Yangtze River and the Three Gorges Dam. The Chinese Government has carried out the Return Farmland into Trees (Grass) Project since the early 1990s. The effectiveness of the project has been estimated in some testing sites in the Upper Yangtze River watershed. In this paper, we carried out a test in the Minjiang River Valley, one of the largest river valleys in the Upper Yangtze River watershed. It was very difficult to calculate the amount of soil losses for the valley when a traditional sample method was used to collect the data for the Universal Soil Loss Equation (USLE model). In order to develop the capability to monitor soil losses in the Minjiang River Valley, we assessed the use of remote sensing data, DEM data, land use and land cover GIS data. The key point of the procedure was data fusion, which was based on an image pixel un-mixing technique. The values of parameters extracted from satellite sensor data for soil erosion USLE model calculation was based on pixels. In this way it improves the accuracy for every parameter calculation. Before making the final calculation, we carried out a field survey along the river valley in July 2000. There was +- 5% soil erosion difference between our calculation results from Landsat Enhanced Thematic Mapper (ETM) data in 2000 and in situ statistical results in 1999. The method used in the Minjiang River Valley can be used as an example for soil loss calculation for other rivers in the Upper Yangtze River watershed.
机译:几十年来,长江上游流域的土壤流失和土壤沉积物一直在增加,这对长江沿岸和三峡大坝的农业产生了严重的负面影响。自1990年代初以来,中国政府就开展了退耕还林(草)项目。已经在长江上游流域的一些测试点评估了该项目的有效性。在本文中,我们在Min江上游流域最大的river河流域之一the江流域进行了测试。当使用传统的采样方法来收集通用土壤流失方程(USLE模型)的数据时,计算山谷的土壤流失量非常困难。为了开发监测the江流域水土流失的能力,我们评估了遥感数据,DEM数据,土地利用和土地覆盖GIS数据的使用。该过程的关键是数据融合,它是基于图像像素非混合技术的。从卫星传感器数据中提取的用于土壤侵蚀USLE模型计算的参数值基于像素。这样,它提高了每个参数计算的准确性。在进行最终计算之前,我们于2000年7月对河谷进行了实地调查。我们从2000年Landsat Enhanced Thematic Mapper(ETM)数据得出的计算结果与2000年就地统计结果之间的土壤侵蚀差异为+-5%。 1999年,the江流域采用的方法可以作为计算长江上游流域其他河流水土流失的一个实例。

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