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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的河网自动提取精度分析-以浙江省jiang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1:25万河网数据的可能性,本文以浙江省the江流域为研究案例,基于90m SRTM DEM自动提取河网,并根据点位精度评估其精度。 ,河流形态参数和河流网络匹配差异。由于提取的河网的精度与流域阈值高度相关,因此本文提出了平均流域分支比来确定该阈值,从而可以将数据精度提高1.24%。结果表明,在任何地形条件下,自动提取的河网位置精度均高于1:25万的数据,分别占平原的21.4%,丘陵的15.7%和山脉的53.0%。此外,浮雕越大,提取的数据的准确性越高。在河流形态方面,河网匹配差异小于1.2%,这意味着自动提取的河网似乎最匹配1:250,000数据。因此,基于SRTM DEM的自动提取水位线基本可以满足1:250,000河网数据的更新要求。

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