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Accuracy analysis on automatically extracted river network based on SRTM DEM — A case study of Oujiang Watershed in Zhejiang Province

机译:基于SRTM DEM自动提取河网的准确性分析 - 以浙江省瓯江流域为例

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To explore the possibility to automatically update the 1:250,000 river network data, this paper chooses the Oujiang Watershed in Zhejiang Province as a study case, extracts automatically the river network based on 90m SRTM DEM, and assesses its accuracy in terms of point positional accuracy, river morphology parameters, and river-network-matching-difference. Since the accuracy of extracted river network is highly correlated with the drainage area threshold, this paper proposes the average drainage branching ratio to determine this threshold, which can improve the data accuracy by 1.24%. The results show that under any topography, the positional accuracy of automatically extracted river network is high than that of 1:250,000 data, which accounts for 21.4% on plains, 15.7% on hills, and 53.0% on mountains, respectively. Furthermore, the greater the relief, the higher the accuracy of extracted data. In terms of river morphology, the river-network-matching-difference is less than 1.2%, which implies the automatically extracted river network seems to best match 1:250,000 data. Therefore, automatically extracted waterlines based on SRTM DEM can basically meet the update requirements of 1:250,000 river network data.
机译:为了探索自动更新1:250,000河网络数据的可能性,本文选择了浙江省瓯江流域作为一家学习案例,基于90米SRTM DEM自动提取河网络,并在点位置精度方面评估其准确性,河流形态参数,河流匹配差异。由于提取的河网的准确性与排水区阈值高度相关,因此本文提出了平均排水分支比以确定该阈值,这可以通过1.24%提高数据精度。结果表明,在任何形貌下,自动提取的河网的位置准确性高于1:250,000数据,占平原的21.4%,山上15.7%,山上的53.0%。此外,浮雕越大,提取数据的准确性越高。在河流形态方面,河流匹配差异小于1.2%,这意味着自动提取的河流网络似乎最佳匹配1:250,000数据。因此,自动提取基于SRTM DEM的水线可以基本满足1:250,000河网络数据的更新要求。

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