多源DEM数据在平原流域河网提取中的对比

Comparison of multi-source DEM data in river network extraction in plain watershed

摘要

【目的】为研究不同数字高程模型(DEM)在平原区提取河网时的适用性，【方法】收集ALOS DEM、ASTER GDEM、SRTM DEM 3种DEM数据源，选择7个代表性平原河网流域进行研究。通过河网套合差计算，分析各DEM在平原流域的河网提取精度。【结果】结果显示：各个DEM数据在地势较平坦区、湖泊区和城市区提取河网结果均存在较大误差；基于ALOS DEM提取的数字河网精度最高；使用Arc Hydro Tools的DEM修正功能，可明显提高数字河网的精度，修正后套合差均小于3%;ASTER GDEM在丘陵区的精度高于SRTM DEM,SRTM DEM在湖泊、农田、城市等地形情况下精度高于ASTER GDEM;ALOS DEM提取河网的最佳集水面积阈值为50 000,ASTER GDEM最佳集水面积阈值则为20 000,SRTM DEM提取河网的最佳集水面积阈值则为5 000。【结论】研究选取多源DEM,从多角度分析提取数字河网的影响，可以为平原区数字河网提取提供参考。

Abstract

[Objective] To investigate the suitability of different Digital Elevation Models(DEMs) for extracting river networks in plain areas, [Methods] ALOS DEM, ASTER GDEM, and SRTM DEM data sources were collected. Seven representative watersheds were selected for the study, and the accuracy of river network extraction was analyzed using the fitting difference calculation.[Results] The result showed that all DEM data exhibited significant errors in extracting river networks in flat terrain, lake areas, and urban areas. ALOS DEM demonstrated the highest accuracy in extracting digital river networks. The precision of the digital river network can be significantly improved by using the DEM correction function of Arc Hydro Tools. ASTER GDEM exhibited higher accuracy in hilly areas compared to SRTM DEM, while SRTM DEM showed higher accuracy in lake, farmland, and urban terrain conditions compared to ASTER GDEM. The optimal threshold for watershed area extraction using ALOS DEM was 50,000, for ASTER GDEM it was 20,000, and for SRTM DEM it was 5,000.[Conclusion] By selecting multiple DEM sources and analyzing the extraction of digital river networks from different perspectives, this study provides valuable insights for extracting digital river networks in plain areas.

关键词

DEM数据 / 多源 / ArcGIS / 河网提取 / DEM修正 / 河网精度 / 影响因素

Key words

DEM data / multi-source / ArcGIS / river network extraction / DEM reconditioning / river network accuracy / influencing factors

基金资助

广西重点研发计划(AB22080093)

广西重点研发计划(AB22035075)

江苏省水利科技项目(2021033)

国家重点研发计划课题(2023YFC320900001)