As Mediterranean streams are highly dynamic, reconstructing space–time water presence in such systems is particularly important for understanding the expansion and contraction phases of the flowing network and the related hydro–ecological processes. Unmanned aerial vehicles (UAVs) can support such monitoring when wide or inaccessible areas are investigated. In this study, an innovative method for water presence detection in the river network based on UAV thermal infrared remote sensing (TIR) images supported by RGB images is evaluated using data gathered in a representative catchment located in Southern Italy. Fourteen flights were performed at different times of the day in three periods, namely, October 2019, February 2020, and July 2020, at two different heights leading to ground sample distances (GSD) of 2 cm and 5 cm. A simple methodology that relies on the analysis of raw data without any calibration is proposed. The method is based on the identification of the thermal signature of water and other land surface elements targeted by the TIR sensor using specific control matrices in the image. Regardless of the GSD, the proposed methodology allows active stream identification under weather conditions that favor sufficient drying and heating of the surrounding bare soil and vegetation. In the surveys performed, ideal conditions for unambiguous water detection in the river network were found with air–water thermal differences higher than 5 °C and accumulated reference evapotranspiration before the survey time of at least 2.4 mm. Such conditions were not found during cold season surveys, which provided many false water pixel detections, even though allowing the extraction of useful information. The results achieved led to the definition of tailored strategies for flight scheduling with different levels of complexity, the simplest of them based on choosing early afternoon as the survey time. Overall, the method proved to be effective, at the same time allowing simplified monitoring with only TIR and RGB images, avoiding any photogrammetric processes, and minimizing postprocessing efforts.

UAV thermal images for water presence detection in a mediterranean headwater catchment

Micieli M.
;
Botter G.;
2022

Abstract

As Mediterranean streams are highly dynamic, reconstructing space–time water presence in such systems is particularly important for understanding the expansion and contraction phases of the flowing network and the related hydro–ecological processes. Unmanned aerial vehicles (UAVs) can support such monitoring when wide or inaccessible areas are investigated. In this study, an innovative method for water presence detection in the river network based on UAV thermal infrared remote sensing (TIR) images supported by RGB images is evaluated using data gathered in a representative catchment located in Southern Italy. Fourteen flights were performed at different times of the day in three periods, namely, October 2019, February 2020, and July 2020, at two different heights leading to ground sample distances (GSD) of 2 cm and 5 cm. A simple methodology that relies on the analysis of raw data without any calibration is proposed. The method is based on the identification of the thermal signature of water and other land surface elements targeted by the TIR sensor using specific control matrices in the image. Regardless of the GSD, the proposed methodology allows active stream identification under weather conditions that favor sufficient drying and heating of the surrounding bare soil and vegetation. In the surveys performed, ideal conditions for unambiguous water detection in the river network were found with air–water thermal differences higher than 5 °C and accumulated reference evapotranspiration before the survey time of at least 2.4 mm. Such conditions were not found during cold season surveys, which provided many false water pixel detections, even though allowing the extraction of useful information. The results achieved led to the definition of tailored strategies for flight scheduling with different levels of complexity, the simplest of them based on choosing early afternoon as the survey time. Overall, the method proved to be effective, at the same time allowing simplified monitoring with only TIR and RGB images, avoiding any photogrammetric processes, and minimizing postprocessing efforts.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3417952
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