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<p>Developing method for generating accurate Digital Terrain Model (DTM) of streams is necessary due to the limitations of traditional field survey methods, which are time-consuming and costly and do not provide continuous data. The objective of this study was to develop an advanced method for generating high-quality DTM of streams using Structure from Motion (SfM) data. A leveling survey was conducted on four cross-sections of the Bokha stream in Icheon City, S. Korea, and SfM-based DTM was produced using the Pix4Dmapper program and Phantom 4 multispectral drone. Two vegetation filters (NDVI and NDI) and two morphological filters (ATIN and CSF) were applied to the data, and the best filter combination was identified based on MAE and RMSE analyses. The integration of NDVI and CSF showed the best performance for the vegetation area, while a single application of NDVI showed the lowest MAE for the bare area. The effectiveness of the SfM method in eliminating waterfront vegetation was confirmed, with an overall MAE of 0.299&#xa0;m RMSE of 0.375&#xa0;m. These findings suggest that generating DTMs of riparian zones can be achieved efficiently with a limited budget and time using the proposed methodology.</p>

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Development of a stream DTM generation method using vegetation and morphology composite filters with SfM point clouds

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月4日

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  • Hyeokjin Lee,
  • Jaejun Gou,
  • Jinseok Park,
  • Seongju Jang,
  • Inhong Song

摘要

Developing method for generating accurate Digital Terrain Model (DTM) of streams is necessary due to the limitations of traditional field survey methods, which are time-consuming and costly and do not provide continuous data. The objective of this study was to develop an advanced method for generating high-quality DTM of streams using Structure from Motion (SfM) data. A leveling survey was conducted on four cross-sections of the Bokha stream in Icheon City, S. Korea, and SfM-based DTM was produced using the Pix4Dmapper program and Phantom 4 multispectral drone. Two vegetation filters (NDVI and NDI) and two morphological filters (ATIN and CSF) were applied to the data, and the best filter combination was identified based on MAE and RMSE analyses. The integration of NDVI and CSF showed the best performance for the vegetation area, while a single application of NDVI showed the lowest MAE for the bare area. The effectiveness of the SfM method in eliminating waterfront vegetation was confirmed, with an overall MAE of 0.299 m RMSE of 0.375 m. These findings suggest that generating DTMs of riparian zones can be achieved efficiently with a limited budget and time using the proposed methodology.