Semantic Scholar Open Access 2025 5 sitasi

Accuracy Assessment of iPhone LiDAR for Mapping Streambeds and Small Water Structures in Forested Terrain

Krausková Dominika Mikita Tomáš Hrůza Petr Kudrnová Barbora

Abstrak

Accurate mapping of small water structures and streambeds is essential for hydrological modeling, erosion control, and landscape management. While traditional geodetic methods such as GNSS and total stations provide high precision, they are time-consuming and require specialized equipment. Recent advances in mobile technology, particularly smartphones equipped with LiDAR sensors, offer a potential alternative for rapid and cost-effective field data collection. This study assesses the accuracy of the iPhone 14 Pro’s built-in LiDAR sensor for mapping streambeds and retention structures in challenging terrain. The test site was the Dílský stream in the Oslavany cadastral area, characterized by steep slopes, rocky surfaces, and dense vegetation. The stream channel and water structures were first surveyed using GNSS and a total station and subsequently re-measured with the iPhone. Several scanning workflows were tested to evaluate field applicability. Results show that the iPhone LiDAR sensor can capture landscape features with useful accuracy when supported by reference points spaced every 20 m, achieving a vertical RMSE of 0.16 m. Retention structures were mapped with an average positional error of 7%, with deviations of up to 0.20 m in complex or vegetated areas. The findings highlight the potential of smartphone LiDAR for rapid, small-scale mapping, while acknowledging its limitations in rugged environments.

Topik & Kata Kunci

Penulis (4)

K

Krausková Dominika

M

Mikita Tomáš

H

Hrůza Petr

K

Kudrnová Barbora

Format Sitasi

Dominika, K., Tomáš, M., Petr, H., Barbora, K. (2025). Accuracy Assessment of iPhone LiDAR for Mapping Streambeds and Small Water Structures in Forested Terrain. https://doi.org/10.3390/s25196141

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Informasi Jurnal
Tahun Terbit
2025
Bahasa
en
Total Sitasi
Sumber Database
Semantic Scholar
DOI
10.3390/s25196141
Akses
Open Access ✓