Enhancing Georeferencing and Mosaicking Techniques over Water Surfaces with High-Resolution Unmanned Aerial Vehicle (UAV) Imagery

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Aquatic ecosystems are crucial in preserving biodiversity, regulating biogeochemical cycles, and sustaining human life; however, their resilience against climate change and anthropogenic stressors remains poorly understood. Recently, unmanned aerial vehicles (UAVs) have become a vital monitoring tool, bridging the gap between satellite imagery and ground-based observations in coastal and marine environments with high spatial resolution. The dynamic nature of water surfaces poses a challenge for photogrammetric techniques due to the absence of fixed reference points. Addressing these issues, this study introduces an innovative, efficient, and accurate workflow for georeferencing and mosaicking that overcomes previous limitations. Using open-source Python libraries, this workflow employs direct georeferencing to produce a georeferenced orthomosaic that integrates multiple UAV captures, and this has been tested in multiple locations worldwide with optical RGB, thermal, and multispectral imagery. The best case achieved a Root Mean Square Error of 4.52 m and a standard deviation of 2.51 m for georeferencing accuracy, thus preserving the UAV’s centimeter-scale spatial resolution. This open-source workflow represents a significant advancement in the monitoring of marine and coastal processes, resolving a major limitation facing UAV technology in the remote observation of local-scale phenomena over water surfaces.

This research has been funded by the European Union’s Horizon 2020 research and innovation programme REWRITE project (grant number 101081357), PY20-00244 (SAT4ALGAE) PROJECT and TURISDRON (PROYEXCEL 00052) project by Junta de Andalucía, RTI2018-098048B-100 (PiMetAn), PID2021-1257830B-100 (DICHOSO), and EQC2018-004275-P and TED2021-129230B-I00 funded by MCIN/AEI/10.13039/501100011033 and by ‘ERDF A way of making Europe’. The research that led to this publication was conducted with the support of a US–Spain Fulbright grant and of the Junta de Andalucía. A.R. is supported by grant FPU19/04557 funded by Ministry of Universities of the Spanish Government. This research has been financially supported by the agreement between the Spanish Ministry for Ecological Transition and Demographic Challenge and CSIC, funded by the European Union-Next Generation Program to contribute to the MSFD. This work represents a contribution to CSIC Thematic Interdisciplinary Platform PTI TELEDETECT.

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