# Shifting the remote sensing paradigm for monitoring the critical zone using drones

Thomas Houet, Thibaut Peres, Emilien Alvarez-Vanhard, Herve Quenol,

Thomas Corpetti

► To cite this version:

Thomas Houet, Thibaut Peres, Emilien Alvarez-Vanhard, Herve Quenol, Thomas Corpetti. Shifting the remote sensing paradigm for monitoring the critical zone using drones. eLTER Science Conference, Jun 2025, Tampere, Finland. pp.e149204, 10.3897/aca.8.e149204. http://hal-05164863

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# Shifting the remote sensing paradigm for monitoring the critical zone using drones

Thomas Houet‡, Thibaut Peres‡, Emilien Alvarez-Vanhard‡, Herve Quenol‡, Thomas Corpetti‡

‡ UMR Littoral – Environnement – Télédétection – Géomatique (LETG) - UMR 6554 CNRS, Rennes, France

Corresponding author: Thomas Houet (thomas.houet@univ-rennes2.fr)

Received: 10 Feb 2025 | Published: 28 May 2025

Citation: Houet T, Peres T, Alvarez-Vanhard E, Quenol H, Corpetti T (2025) Shifting the remote sensing paradigm for monitoring the critical zone using drones. ARPHA Conference Abstracts 8: e149204.
https://doi.org/10.3897/aca.8.e149204

## Abstract

Remote sensing data are increasingly used to monitor the critical zone. This study argues that drones are transforming the remote sensing paradigm. While recent satellite constellations (e.g., Sentinel-1 and Sentinel-2) have revolutionized Earth observation through improved temporal resolution, most research efforts have focused on refining data processing methods to extract key environmental variables such as soil moisture (Gao et al. 2017), snow cover (Gascoin et al. 2019), and crop classification (Blickensdörfer et al. 2022).

In parallel, drone technology has become more prevalent. Their high flexibility and superior spatial, spectral, and temporal resolutions enable the acquisition of high-quality remote sensing data over limited areas. To monitor essential critical zone and biodiversity variables effectively, it is crucial to adapt acquisition protocols. These protocols must be defined through interdisciplinary collaboration, considering the processes under study before data collection. They should determine the optimal spectral and temporal resolutions based on the size of the target object (e.g., leaves, trees, landscapes), its characteristics, and the ecological, hydrological, or climatic processes influencing its behavior over time (e.g., hourly, daily, or seasonally).

Drones show great potential for applications such as viticulture, forest ecology, and habitat mapping, though their use remains exploratory and experimental. This paper
presents three case studies demonstrating the capabilities of drones in critical zone
monitoring and their role in shifting the remote sensing paradigm.

Case Studies

1. Monitoring Vineyards for Temperature Variability

The first example focuses on vineyard exposure to extreme air temperatures. The objective was to detect cold air pooling, which can cause frost damage to grapevines. We hypothesized that thermal imagery could effectively identify these cold air pockets. Drone flights were conducted every 20 minutes before and after sunrise in September 2017 in Saint-Émilion, southwestern France, under low wind conditions (<2 m/s). A fixed-wing eBee+ drone equipped with a Thermomap sensor was used, and temperature sensors were deployed within a 20-hectare vineyard to validate thermal measurements. The results (Fig. 1) indicate that cold air pools were detectable and their distribution was influenced not only by valley topography but also by anthropogenic structures such as walls and buildings, which affected airflows (dark areas in Fig. 1, left). After sunrise, air and surface temperatures diverged due to differences in emissivity.

<figure><img src="image_3.png" /><figcaption>Figure 1. doi</figcaption></figure>

Left: Surface temperature acquired at 7am (before sunrise); Right: Surface temperatures acquired at 9.40am (after sunrise).

2. Mapping Semi-Natural Habitats in Marshlands

The second example explores habitat mapping in marshlands, where vegetation heterogeneity and composition are critical factors. The study was conducted in the Sougéal marsh (France), where hydrological regimes affect vegetation distribution. We hypothesized that integrating high-resolution Digital Surface Models (DSM) with multispectral and multitemporal datasets could improve classification of mesophilic,
meso-hygrophilic, and hygrophilic habitats (Fig. 2). Monthly drone acquisitions were conducted between May 2017 and May 2018 using a fixed-wing eBeeX drone equipped with RGB SODA and Sequoia multispectral sensors under consistent atmospheric conditions. Spectral signatures from drone imagery were used as reference data to train classification models applied to Sentinel-2 imagery. The best classification results were obtained using Sentinel-2 data from April (Fig. 2, right), whereas classifications for other months were less accurate. The study also revealed that grazing activities influenced spectral responses and habitat classification (Alvarez-Vanhard et al. 2020).

<figure><img src="image_4.png" /><figcaption>Figure 2. doi</figcaption></figure>

Left: 5cm DSM extract from RGB photogrammerty; Center: classification of M – mesophilic, MH – meso-hygrophilic and H – hygrophilic habitats derived from drone data combination;

Right: Estimated abundances derived from Sentinel 2 data acquired in April using drone derived spectral signatures (derived from Alvarez-Vanhard et al. (2020)).

## 3. Detecting and Mapping Ponds in Sand Dune Ecosystems

The third case study aimed to detect and map ponds within a vegetated sand dune habitat in the Vauville Natura 2000 reserve (France), an area affected by summer droughts and declining water levels. Expanding the reserve to include nearby ponds could enhance biodiversity conservation. Leveraging the thermal inertia properties of water, we combined nighttime and daytime thermal data to distinguish water bodies from surrounding vegetation. A Trinity F90+ fixed-wing drone equipped with a Micasense ALTUM-PT thermal sensor was deployed in May 2024. One-hour flights covered 150 hectares at 9:30 PM (after sunset, minimizing emissivity effects) and at noon the following day, under cloud-free conditions. The extracted water bodies are highlighted in yellow Fig. 3.

## Conclusion

This paradigm shift in remote sensing emphasizes the need to design data acquisition protocols tailored to specific research objectives, rather than solely improving data
processing methods. While enhancing processing techniques remains important for accuracy, drones offer unique flexibility in selecting the timing, frequency, and types of sensors used for data collection. Additionally, drones facilitate the integration of multispectral, thermal, RGB, LiDAR, and other sensor-based data, broadening the scope of environmental monitoring.

<figure><img src="image_5.png" /><figcaption>Figure 3. doi</figcaption></figure>

Left: Surface temperatures acquired at 9.30pm May 7th 2024; Center: Surface temperatures acquired at noon the next day; Right: Extracted ponds in yellow.

Ongoing applications include soil moisture characterization (Wu et al. 2019), forest phenology and health assessments (Ecke et al. 2024), plant ecology studies, invasive species detection (Singh et al. 2024), and ecosystem service mapping (Schenone et al. 2021). Furthermore, drone data can be used independently or in synergy with satellite imagery. Alvarez-Vanhard et al. (2021) identified four types of drone-satellite synergies that contribute to characterizing and monitoring the critical zone.

**Keywords**

UAV flexibility, sensor complementarity, data acquisition protocols, remote sensing paradigm shift.

**Presenting author**

Thomas Houet

**Presented at**

ORAL
## Acknowledgements

This study is based on multiple projects and data acquisition efforts conducted within the D2T UAVs platform. We acknowledge all field contributors, though only principal investigators and main contributors are listed as co-authors.

## Conflicts of interest

The authors have declared that no competing interests exist.

## References

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* Alvarez-Vanhard E, Corpetti T, Houet T (2021) UAV & satellite synergies for optical remote sensing applications: A literature review. Science of Remote Sensing 3 https://doi.org/10.1016/j.srs.2021.100019

* Blickensdörfer L, Schwieder M, Pflugmacher D, Nendel C, Erasmi S, Hostert P (2022) Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment 269 https://doi.org/10.1016/j.rse.2021.112831

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* Gascoin S, Grizonnet M, Bouchet M, Salgues G, Hagolle O (2019) Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data. Earth System Science Data 11 (2): 493-514. https://doi.org/10.5194/essd-11-493-2019

* Schenone S, Azhar M, Ramírez CAV, Strozzi AG, Delmas P, Thrush S (2021) Mapping the Delivery of Ecological Functions Combining Field Collected Data and Unmanned Aerial Vehicles (UAVs). Ecosystems 25 (4): 948-959. https://doi.org/10.1007/s10021-021-00694-w

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