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Drones
Special Issues
UAVs for Nature Conservation Tasks in Complex Environments
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UAVs for Nature Conservation Tasks in Complex Environments

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Ecology".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1015

Special Issue Editors

Prof. Dr. Tom Richardson

E-Mail Website
Guest Editor
School of Civil, Aerospace and Design Engineering, Bristol University, Bristol BS8 1QU, UK
Interests: flight mechanics and control; unmanned air vehicles
Prof. Dr. Ulrik Pagh Schultz Lundquist

E-Mail Website
Guest Editor
SDU UAS Center, South Denmark University, 5000 Odense, Denmark
Interests: robot programming; programming languages and formal models; modular robotics; compilers and interpreters; software technology
Prof. Dr. Fabio Remondino

E-Mail Website
Guest Editor
3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), 38123 Trento, Italy
Interests: geomatics; mapping; UAV
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent technological advancements in unmanned aerial vehicles (UAVs) have rapidly expanded their use in complex applications such as nature conservation, including monitoring, reconnaissance, mapping, tracking, and sample collection tasks, among others. UAVs and their miniaturized versions, generally called micro aerial vehicles (MAVs), have provided a previously unimaginable, yet valuable and reliable means of data collection, from images and point clouds to acoustic or eDNA samples.

This Special Issue is a collaboration between the IMAV 2024 Conference and the EU WildDrone Project, with the aim to collect new UAV developments, methodologies, best practices, and applications in complex outdoor environments (e.g., savanna) and complex nature conservation tasks (e.g., monitoring or sampling).

The Special Issue welcomes submissions related but not limited to the following:

  • UAV/MAV sensing and perception in complex environments;
  • Collaborative systems and swarm intelligence;
  • Design of novel vehicle types (hybrids, propulsion, silent);
  • Autonomous navigation in GNSS-denied environments;
  • Beyond Visual Line of Sight (BVLOS) operations;
  • Temporal planning and rerouting;
  • On-board/real-time processing, including AI methods;
  • Evaluation of UAV/MAV technologies in applications related to nature conservation.

Prof. Dr. Tom Richardson
Prof. Dr. Ulrik Pagh Schultz Lundquist
Prof. Dr. Fabio Remondino
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAV/MAV
  • perception
  • collaborative
  • wildlife
  • complex environments
  • navigation
  • mapping
  • real-time

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Published Papers (1 paper)

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Research

23 pages, 3492 KiB  
Article
A Multimodal Deep Learning Framework for Accurate Biomass and Carbon Sequestration Estimation from UAV Imagery
by Furkat Safarov, Ugiloy Khojamuratova, Misirov Komoliddin, Xusinov Ibragim Ismailovich and Young Im Cho
Drones 2025, 9(7), 496; https://doi.org/10.3390/drones9070496 - 14 Jul 2025
Viewed by 219
Abstract
Accurate quantification of above-ground biomass (AGB) and carbon sequestration is vital for monitoring terrestrial ecosystem dynamics, informing climate policy, and supporting carbon neutrality initiatives. However, conventional methods—ranging from manual field surveys to remote sensing techniques based solely on 2D vegetation indices—often fail to [...] Read more.
Accurate quantification of above-ground biomass (AGB) and carbon sequestration is vital for monitoring terrestrial ecosystem dynamics, informing climate policy, and supporting carbon neutrality initiatives. However, conventional methods—ranging from manual field surveys to remote sensing techniques based solely on 2D vegetation indices—often fail to capture the intricate spectral and structural heterogeneity of forest canopies, particularly at fine spatial resolutions. To address these limitations, we introduce ForestIQNet, a novel end-to-end multimodal deep learning framework designed to estimate AGB and associated carbon stocks from UAV-acquired imagery with high spatial fidelity. ForestIQNet combines dual-stream encoders for processing multispectral UAV imagery and a voxelized Canopy Height Model (CHM), fused via a Cross-Attentional Feature Fusion (CAFF) module, enabling fine-grained interaction between spectral reflectance and 3D structure. A lightweight Transformer-based regression head then performs multitask prediction of AGB and CO2e, capturing long-range spatial dependencies and enhancing generalization. Proposed method achieves an R2 of 0.93 and RMSE of 6.1 kg for AGB prediction, compared to 0.78 R2 and 11.7 kg RMSE for XGBoost and 0.73 R2 and 13.2 kg RMSE for Random Forest. Despite its architectural complexity, ForestIQNet maintains a low inference cost (27 ms per patch) and generalizes well across species, terrain, and canopy structures. These results establish a new benchmark for UAV-enabled biomass estimation and provide scalable, interpretable tools for climate monitoring and forest management. Full article
(This article belongs to the Special Issue UAVs for Nature Conservation Tasks in Complex Environments)
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