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Drones
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UAVs and Satellite Data for Forest Protection: Remote Sensing, Monitoring,...
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UAVs and Satellite Data for Forest Protection: Remote Sensing, Monitoring, Fire Detection and Emergency Management

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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 23091

Special Issue Editors

Dr. Alessandro Giuseppi

E-Mail Website
Guest Editor
Department of Computer Control and Management Engineering, University of Rome "La Sapienza", 00168 Rome, Italy
Interests: intelligent systems; artificial intelligence; automation; control
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Liberati

E-Mail Website
Guest Editor
Department of Computer, Control and Management Engineering “Antonio Ruberti”, University of Rome “La Sapienza”, Via Ariosto 25, 00185 Roma, Italy
Interests: automatic control; smart grids; smart cities; cyber-physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The protection of forests is a critical challenge that needs to be faced to preserve our ecosystem and oppose climate change.

Forests are subject to several risks, spacing from illegal logging to desertification and wildfires, which require the employment of complex protection systems that use a broad spectrum of specialized technologies.

In this direction, UAVs offer the capability of remotely monitoring vast areas and dynamically responding to emergency scenarios, a feature that ground sensor networks and human operators cannot provide as effectively.

The combination of UAVs with other data sources, such as satellite imaging and weather stations, may offer rich and timely decision support to their operators, guiding their actions in the prevention, detection, and response phases of the emergency management process.

This Special Issue aims at providing an overview of the latest applications and developments in forest protection systems that benefit from aerial drones, also encouraging contributions that combine satellite information and ground data with aerial measurements.

Potential topics of interest include but are not limited to:

  • Fire detection;
  • UAV patrolling and surveillance systems;
  • Formation control;
  • Flight control systems;
  • Remote sensing hardware design;
  • UAV intelligence;
  • Data fusion techniques for forest protection;
  • Machine learning for pattern recognition and anomaly detection.

Dr. Alessandro Giuseppi
Dr. Francesco Liberati
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

  • forest protection
  • fire detection
  • emergency management
  • sensor networks
  • UAV patrolling
  • remote monitoring

Published Papers (6 papers)

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Research

21 pages, 2787 KiB  
Article
Monitoring and Cordoning Wildfires with an Autonomous Swarm of Unmanned Aerial Vehicles
by Fabrice Saffre, Hanno Hildmann, Hannu Karvonen and Timo Lind
Drones 2022, 6(10), 301; https://doi.org/10.3390/drones6100301 - 14 Oct 2022
Cited by 16 | Viewed by 4194
Abstract
Unmanned aerial vehicles, or drones, are already an integral part of the equipment used by firefighters to monitor wildfires. They are, however, still typically used only as remotely operated, mobile sensing platforms under direct real-time control of a human pilot. Meanwhile, a substantial [...] Read more.
Unmanned aerial vehicles, or drones, are already an integral part of the equipment used by firefighters to monitor wildfires. They are, however, still typically used only as remotely operated, mobile sensing platforms under direct real-time control of a human pilot. Meanwhile, a substantial body of literature exists that emphasises the potential of autonomous drone swarms in various situational awareness missions, including in the context of environmental protection. In this paper, we present the results of a systematic investigation by means of numerical methods i.e., Monte Carlo simulation. We report our insights into the influence of key parameters such as fire propagation dynamics, surface area under observation and swarm size over the performance of an autonomous drone force operating without human supervision. We limit the use of drones to perform passive sensing operations with the goal to provide real-time situational awareness to the fire fighters on the ground. Therefore, the objective is defined as being able to locate, and then establish a continuous perimeter (cordon) around, a simulated fire event to provide live data feeds such as e.g., video or infra-red. Special emphasis was put on exclusively using simple, robust and realistically implementable distributed decision functions capable of supporting the self-organisation of the swarm in the pursuit of the collective goal. Our results confirm the presence of strong nonlinear effects in the interaction between the aforementioned parameters, which can be closely approximated using an empirical law. These findings could inform the mobilisation of adequate resources on a case-by-case basis, depending on known mission characteristics and acceptable odds (chances of success). Full article
(This article belongs to the Special Issue UAVs and Satellite Data for Forest Protection: Remote Sensing, Monitoring, Fire Detection and Emergency Management)
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18 pages, 2944 KiB  
Article
Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems
by Fatma S. Alrayes, Saud S. Alotaibi, Khalid A. Alissa, Mashael Maashi, Areej Alhogail, Najm Alotaibi, Heba Mohsen and Abdelwahed Motwakel
Drones 2022, 6(9), 222; https://doi.org/10.3390/drones6090222 - 26 Aug 2022
Cited by 14 | Viewed by 3135
Abstract
Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote and complex accessing regions. The UAVs can be utilized for several application areas which can [...] Read more.
Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote and complex accessing regions. The UAVs can be utilized for several application areas which can hold sensitive data, which necessitates secure processing using image encryption approaches. At the same time, UAVs can be embedded in the latest technologies and deep learning (DL) models for disaster monitoring areas such as floods, collapsed buildings, or fires for faster mitigation of its impacts on the environment and human population. This study develops an Artificial Intelligence-based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems (AISCC-DE2MS). The proposed AISCC-DE2MS technique majorly employs encryption and classification models for emergency disaster monitoring situations. The AISCC-DE2MS model follows a two-stage process: encryption and image classification. At the initial stage, the AISCC-DE2MS model employs an artificial gorilla troops optimizer (AGTO) algorithm with an ECC-Based ElGamal Encryption technique to accomplish security. For emergency situation classification, the AISCC-DE2MS model encompasses a densely connected network (DenseNet) feature extraction, penguin search optimization (PESO) based hyperparameter tuning, and long short-term memory (LSTM)-based classification. The design of the AGTO-based optimal key generation and PESO-based hyperparameter tuning demonstrate the novelty of our work. The simulation analysis of the AISCC-DE2MS model is tested using the AIDER dataset and the results demonstrate the improved performance of the AISCC-DE2MS model in terms of different measures. Full article
(This article belongs to the Special Issue UAVs and Satellite Data for Forest Protection: Remote Sensing, Monitoring, Fire Detection and Emergency Management)
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19 pages, 4518 KiB  
Article
Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
by Salil Bharany, Sandeep Sharma, Jaroslav Frnda, Mohammed Shuaib, Muhammad Irfan Khalid, Saddam Hussain, Jawaid Iqbal and Syed Sajid Ullah
Drones 2022, 6(8), 193; https://doi.org/10.3390/drones6080193 - 2 Aug 2022
Cited by 44 | Viewed by 3522
Abstract
Forest fires are a significant threat to the ecological system’s stability. Several attempts have been made to detect forest fires using a variety of approaches, including optical fire sensors, and satellite-based technologies, all of which have been unsuccessful. In today’s world, research on [...] Read more.
Forest fires are a significant threat to the ecological system’s stability. Several attempts have been made to detect forest fires using a variety of approaches, including optical fire sensors, and satellite-based technologies, all of which have been unsuccessful. In today’s world, research on flying ad hoc networks (FANETs) is a thriving field and can be used successfully. This paper describes a unique clustering approach that identifies the presence of a fire zone in a forest and transfers all sensed data to a base station as soon as feasible via wireless communication. The fire department takes the required steps to prevent the spread of the fire. It is proposed in this study that an efficient clustering approach be used to deal with routing and energy challenges to extend the lifetime of an unmanned aerial vehicle (UAV) in case of forest fires. Due to the restricted energy and high mobility, this directly impacts the flying duration and routing of FANET nodes. As a result, it is vital to enhance the lifetime of wireless sensor networks (WSNs) to maintain high system availability. Our proposed algorithm EE-SS regulates the energy usage of nodes while taking into account the features of a disaster region and other factors. For firefighting, sensor nodes are placed throughout the forest zone to collect essential data points for identifying forest fires and dividing them into distinct clusters. All of the sensor nodes in the cluster communicate their packets to the base station continually through the cluster head. When FANET nodes communicate with one another, their transmission range is constantly adjusted to meet their operating requirements. This paper examines the existing clustering techniques for forest fire detection approaches restricted to wireless sensor networks and their limitations. Our newly designed algorithm chooses the most optimum cluster heads (CHs) based on their fitness, reducing the routing overhead and increasing the system’s efficiency. Our proposed method results from simulations are compared with the existing approaches such as LEACH, LEACH-C, PSO-HAS, and SEED. The evaluation is carried out concerning overall energy usage, residual energy, the count of live nodes, the network lifetime, and the time it takes to build a cluster compared to other approaches. As a result, our proposed EE-SS algorithm outperforms all the considered state-of-art algorithms. Full article
(This article belongs to the Special Issue UAVs and Satellite Data for Forest Protection: Remote Sensing, Monitoring, Fire Detection and Emergency Management)
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21 pages, 27426 KiB  
Article
Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
by Yao-Hua Ho and Yu-Jung Tsai
Drones 2022, 6(5), 132; https://doi.org/10.3390/drones6050132 - 20 May 2022
Cited by 15 | Viewed by 3664
Abstract
Climate-related natural disasters have affected the lives of thousands of people. Global warming creates warmer and drier conditions which increase the risk of wildfires. In large-scale disasters such as wildfires, search and rescue (SAR) operations become extremely challenging due to low visibility, difficulty [...] Read more.
Climate-related natural disasters have affected the lives of thousands of people. Global warming creates warmer and drier conditions which increase the risk of wildfires. In large-scale disasters such as wildfires, search and rescue (SAR) operations become extremely challenging due to low visibility, difficulty to breath, and high temperature from fire and smoke. Unmanned aerial vehicles (UAVs), such as drones, have been used to support such operations. In our previous work, a Krypto module is proposed to “sniff” out wireless signals from mobile phones to locate any possible survivors. With the increased popularity of drones, it is possible to allow people to volunteer in SAR operations with their drones. In this paper, we propose an Open Collaborative Platform for multiple drones to assist SAR operations. The open platform manages different searching drones that carry the Krypto module to collaborate by sharing information and planning search paths/areas. With our Open Collaborative Platform, anyone can participate in SAR operations and contribute to finding possible survivors. The novelty of this work is the openness and collaboration of the platform that “crowdsourcing” the searching operation to a large group of people who share information and contribute to finding possible survivors in a large disaster such as wildfires. Our experimental study shows that the Open Collaborative Platform is effective in reducing both the number of drones required and the search time for finding survivors. Full article
(This article belongs to the Special Issue UAVs and Satellite Data for Forest Protection: Remote Sensing, Monitoring, Fire Detection and Emergency Management)
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31 pages, 51975 KiB  
Article
Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived Imagery
by Zachary Miller, Joseph Hupy, Sarah Hubbard and Guofan Shao
Drones 2022, 6(2), 52; https://doi.org/10.3390/drones6020052 - 18 Feb 2022
Cited by 4 | Viewed by 2784
Abstract
This paper introduces a detailed procedure to utilize the high temporal and spatial resolution capabilities of an unmanned aerial system (UAS) to document vegetation at regular intervals both before and after a planned disturbance, a key component in natural disturbance-based management (NDBM), which [...] Read more.
This paper introduces a detailed procedure to utilize the high temporal and spatial resolution capabilities of an unmanned aerial system (UAS) to document vegetation at regular intervals both before and after a planned disturbance, a key component in natural disturbance-based management (NDBM), which uses treatments such as harvest and prescribed burns toward the removal of vegetation fuel loads. We developed a protocol and applied it to timber harvest and prescribed burn events. Geographic image-based analysis (GEOBIA) was used for the classification of UAS orthomosaics. The land cover classes included (1) bare ground, (2) litter, (3) green vegetation, and (4) burned vegetation for the prairie burn site, and (1) mature canopy, (2) understory vegetation, and (3) bare ground for the timber harvest site. Sample datasets for both kinds of disturbances were used to train a support vector machine (SVM) classifier algorithm, which produced four land cover classifications for each site. Statistical analysis (a two-tailed t-test) indicated there was no significant difference in image classification efficacies between the two disturbance types. This research provides a framework to use UASs to assess land cover, which is valuable for supporting effective land management practices and ensuring the sustainability of land practices along with other planned disturbances, such as construction and mining. Full article
(This article belongs to the Special Issue UAVs and Satellite Data for Forest Protection: Remote Sensing, Monitoring, Fire Detection and Emergency Management)
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19 pages, 24231 KiB  
Article
UAV Patrolling for Wildfire Monitoring by a Dynamic Voronoi Tessellation on Satellite Data
by Alessandro Giuseppi, Roberto Germanà, Federico Fiorini, Francesco Delli Priscoli and Antonio Pietrabissa
Drones 2021, 5(4), 130; https://doi.org/10.3390/drones5040130 - 3 Nov 2021
Cited by 13 | Viewed by 4228
Abstract
Fire monitoring and early detection are critical tasks in which Unmanned Aerial Vehicles (UAVs) are commonly employed. This paper presents a system to plan the drone patrolling schedule according to a real-time estimation of a fire propagation index that is derived from satellite [...] Read more.
Fire monitoring and early detection are critical tasks in which Unmanned Aerial Vehicles (UAVs) are commonly employed. This paper presents a system to plan the drone patrolling schedule according to a real-time estimation of a fire propagation index that is derived from satellite data, such as the Normalized Difference Vegetation Index (NDVI) measurement and the Digital Elevation Model (DEM) of the surveilled area. The proposed system employs a waypoint scheduling logic, derived from a dynamic Voronoi Tessellation of the area, that combines characteristics of the territory (e.g., vegetation density) with real-time measurements (e.g., wind speed and direction). The system is validated on a case study in Italy, in the municipality of the city of L’Aquila, on three different fire scenarios. In normal situations, the designed waypoint-based navigation system provided an effective monitoring of the area, enabling the early detection of starting fires. The developed solution also demonstrated good performance in tracking and anticipating the fire front advance, potentially providing a better situational awareness to emergency operators and support their response policies. Both the test environment and the simulator have been made open-source. Full article
(This article belongs to the Special Issue UAVs and Satellite Data for Forest Protection: Remote Sensing, Monitoring, Fire Detection and Emergency Management)
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