3D thermal volume mapping to assess the biological and physical characteristics of olive crops using remote sensing and photogrammetric methods

Abstract Drones, as well as ground-based and satellite platforms, offer the possibility to carry sensors able to obtain timely and precise indications about vegetation health conditions. These systems can serve as tools for agricultural monitoring and the management of crops. Nowadays, Unmanned Aerial Vehicles (UAV) systems are equipped with sophisticated sensors, such as those operating in the Thermal InfraRed spectral range, which can provide indications about the water content of vegetation at very-high spatial resolution. This study explores the feasibility of exploiting drone-based thermal imagery and Structure-from-Motion (SfM) photogrammetry to derive 3-D representations in Precision Agriculture. The health condition of olive trees was evaluated using thermal observations collected by a UAV system over an olive orchard located in the Basilicata region (Southern Italy). Following the SfM pipeline, accurate 2-D/3-D thermal photogrammetric products have been created, and analyzed by means of the Normalized Relative Canopy Temperature (NRCT) index. The goal was to explore how 3D thermal volume analysis can enhance the detection and interpretation of early signs of water stress and related plant health descriptors. Although evident symptoms of stress were not yet visible during the survey, our preliminary results highlight the added value of 3D thermal information over traditional 2D approaches, particularly in capturing spatial variability within individual tree canopies. These findings demonstrate the potential of UAV-based 3D thermal analysis as a valuable tool for advanced monitoring in Precision Agriculture and Smart Farming practices.

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Publication Details

Journal
Applied Geomatics
Published
2025-10-25
DOI
https://doi.org/10.1007/s12518-025-00658-y
Citations
2
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
3.43

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article

3D thermal volume mapping to assess the biological and physical characteristics of olive crops using remote sensing and photogrammetric methods

Nicola Genzano, Roberto Colonna
2 citations
Applied Geomatics
Remote Sensing in Agriculture
3.43
article

3D thermal volume mapping to assess the biological and physical characteristics of olive crops using remote sensing and photogrammetric methods

Nicola Genzano, Roberto Colonna
article en
2 citations

Abstract

Abstract Drones, as well as ground-based and satellite platforms, offer the possibility to carry sensors able to obtain timely and precise indications about vegetation health conditions. These systems can serve as tools for agricultural monitoring and the management of crops. Nowadays, Unmanned Aerial Vehicles (UAV) systems are equipped with sophisticated sensors, such as those operating in the Thermal InfraRed spectral range, which can provide indications about the water content of vegetation at very-high spatial resolution. This study explores the feasibility of exploiting drone-based thermal imagery and Structure-from-Motion (SfM) photogrammetry to derive 3-D representations in Precision Agriculture. The health condition of olive trees was evaluated using thermal observations collected by a UAV system over an olive orchard located in the Basilicata region (Southern Italy). Following the SfM pipeline, accurate 2-D/3-D thermal photogrammetric products have been created, and analyzed by means of the Normalized Relative Canopy Temperature (NRCT) index. The goal was to explore how 3D thermal volume analysis can enhance the detection and interpretation of early signs of water stress and related plant health descriptors. Although evident symptoms of stress were not yet visible during the survey, our preliminary results highlight the added value of 3D thermal information over traditional 2D approaches, particularly in capturing spatial variability within individual tree canopies. These findings demonstrate the potential of UAV-based 3D thermal analysis as a valuable tool for advanced monitoring in Precision Agriculture and Smart Farming practices.

Applied GeomaticsVol. 18(1)
University of Basilicata (IT), Politecnico di Milano (IT)
Università degli Studi della Basilicata
Openalex Percentile: Top 7%
Remote Sensing in Agriculture
3.43
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