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Drones, Artificial Intelligence and Advanced Analytics for the Conservation of...
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Drones, Artificial Intelligence and Advanced Analytics for the Conservation of Threatened Species

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 7122

Special Issue Editor

Dr. Grant Hamilton

E-Mail Website
Guest Editor
Faculty of Science, School of Biology & Environmental Science, Queensland University of Technology, Brisbane, QLD, Australia
Interests: conservation; detection and abundance estimation using drones and AI; biological invasions; ecological statistics; ecological modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of drones has rapidly increased over the past 5 years to the point where they are now becoming well accepted for direct observation and data collection in conservation. There is an increasing recognition, however, that the most powerful impacts will occur when they are used in combination with machine learning (otherwise known as artificial intelligence), and the advanced techniques that are required to analyse the data collected. As with the introduction of any new methodology, there is a need to confirm the existing analytical approaches that will continue to be applicable in a new context, and also to develop new approaches when they do not.

This Special Issue aims to gather original articles and reviews showing practical applications of remote sensing using drones and AI in conservation. In particular, it aims to show innovative uses of drones and AI in different contexts together with advances in machine learning techniques for image analysis, for a range of threatened species and environments. Development and critical evaluation of techniques for the analysis of data collected using these approaches is also encouraged to provide a coherent and holistic approach to the use of technology and analytics in conservation.

You may choose our Joint Special Issue in Drones.

Prof. Dr. Grant Hamilton
Guest Editor

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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • Machine learning
  • Artificial intelligence
  • Analytics
  • Conservation
  • High resolution imagery
  • Bushfire recovery
  • Vegetation conservation
  • Endangered species
  • Thermal
  • Lidar

Published Papers (3 papers)

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Research

24 pages, 13414 KiB  
Article
A Comparison of UAV-Derived Dense Point Clouds Using LiDAR and NIR Photogrammetry in an Australian Eucalypt Forest
by Megan Winsen and Grant Hamilton
Remote Sens. 2023, 15(6), 1694; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15061694 - 21 Mar 2023
Cited by 1 | Viewed by 2010
Abstract
Light detection and ranging (LiDAR) has been a tool of choice for 3D dense point cloud reconstructions of forest canopy over the past two decades, but advances in computer vision techniques, such as structure from motion (SfM) photogrammetry, have transformed 2D digital aerial [...] Read more.
Light detection and ranging (LiDAR) has been a tool of choice for 3D dense point cloud reconstructions of forest canopy over the past two decades, but advances in computer vision techniques, such as structure from motion (SfM) photogrammetry, have transformed 2D digital aerial imagery into a powerful, inexpensive and highly available alternative. Canopy modelling is complex and affected by a wide range of inputs. While studies have found dense point cloud reconstructions to be accurate, there is no standard approach to comparing outputs or assessing accuracy. Modelling is particularly challenging in native eucalypt forests, where the canopy displays abrupt vertical changes and highly varied relief. This study first investigated whether a remotely sensed LiDAR dense point cloud reconstruction of a native eucalypt forest completely reproduced canopy cover and accurately predicted tree heights. A further comparison was made with a photogrammetric reconstruction based solely on near-infrared (NIR) imagery to gain some insight into the contribution of the NIR spectral band to the 3D SfM reconstruction of native dry eucalypt open forest. The reconstructions did not produce comparable canopy height models and neither reconstruction completely reproduced canopy cover nor accurately predicted tree heights. Nonetheless, the LiDAR product was more representative of the eucalypt canopy than SfM-NIR. The SfM-NIR results were strongly affected by an absence of data in many locations, which was related to low canopy penetration by the passive optical sensor and sub-optimal feature matching in the photogrammetric pre-processing pipeline. To further investigate the contribution of NIR, future studies could combine NIR imagery captured at multiple solar elevations. A variety of photogrammetric pre-processing settings should continue to be explored in an effort to optimise image feature matching. Full article
(This article belongs to the Special Issue Drones, Artificial Intelligence and Advanced Analytics for the Conservation of Threatened Species)
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20 pages, 6054 KiB  
Article
Deep Encoder–Decoder Network-Based Wildfire Segmentation Using Drone Images in Real-Time
by Shakhnoza Muksimova, Sevara Mardieva and Young-Im Cho
Remote Sens. 2022, 14(24), 6302; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246302 - 13 Dec 2022
Cited by 9 | Viewed by 1523
Abstract
Wildfire is a hazardous natural phenomenon that leads to significant human fatalities, catastrophic environmental damages, and economic losses. Over the past few years, the intensity and frequency of fires have increased worldwide. Studies have been conducted to develop distinctive solutions to minimize forest [...] Read more.
Wildfire is a hazardous natural phenomenon that leads to significant human fatalities, catastrophic environmental damages, and economic losses. Over the past few years, the intensity and frequency of fires have increased worldwide. Studies have been conducted to develop distinctive solutions to minimize forest fires. Systems for distant fire detection and monitoring have been established, showing improvements in data collection and fire characterization. However, wildfires cover vast areas, making other proposed ground systems unsuitable for optimal coverage. Unmanned aerial vehicles (UAVs) have become the subject of active research in recent years. Deep learning-based image-processing methods demonstrate improved performance in various tasks, including detection and segmentation, which can be utilized to develop modern forest firefighting techniques. In this study, we established a novel two-pathway encoder–decoder-based model to detect and accurately segment wildfires and smoke from the images captured using UAVs in real-time. Our proposed nested decoder uses pre-activated residual blocks and an attention-gating mechanism, thereby improving segmentation accuracy. Moreover, to facilitate robust and generalized training, we prepared a new dataset comprising actual incidences of forest fires and smoke, varying from small to large areas. In terms of practicality, the experimental results reveal that our method significantly outperforms existing detection and segmentation methods, despite being lightweight. In addition, the proposed model is reliable and robust for detecting and segmenting drone camera images from different viewpoints in the presence of wildfire and smoke. Full article
(This article belongs to the Special Issue Drones, Artificial Intelligence and Advanced Analytics for the Conservation of Threatened Species)
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15 pages, 1459 KiB  
Article
Automated Detection of Koalas with Deep Learning Ensembles
by Megan Winsen, Simon Denman, Evangeline Corcoran and Grant Hamilton
Remote Sens. 2022, 14(10), 2432; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102432 - 19 May 2022
Cited by 3 | Viewed by 2677
Abstract
Effective management of threatened and invasive species requires regular and reliable population estimates. Drones are increasingly utilised by ecologists for this purpose as they are relatively inexpensive. They enable larger areas to be surveyed than traditional methods for many species, particularly cryptic species [...] Read more.
Effective management of threatened and invasive species requires regular and reliable population estimates. Drones are increasingly utilised by ecologists for this purpose as they are relatively inexpensive. They enable larger areas to be surveyed than traditional methods for many species, particularly cryptic species such as koalas, with less disturbance. The development of robust and accurate methods for species detection is required to effectively use the large volumes of data generated by this survey method. The enhanced predictive and computational power of deep learning ensembles represents a considerable opportunity to the ecological community. In this study, we investigate the potential of deep learning ensembles built from multiple convolutional neural networks (CNNs) to detect koalas from low-altitude, drone-derived thermal data. The approach uses ensembles of detectors built from combinations of YOLOv5 and models from Detectron2. The ensembles achieved a strong balance between probability of detection and precision when tested on ground-truth data from radio-collared koalas. Our results also showed that greater diversity in ensemble composition can enhance overall performance. We found the main impediment to higher precision was false positives but expect these will continue to reduce as tools for geolocating detections are improved. The ability to construct ensembles of different sizes will allow for improved alignment between the algorithms used and the characteristics of different ecological problems. Ensembles are efficient and accurate and can be scaled to suit different settings, platforms and hardware availability, making them capable of adaption for novel applications. Full article
(This article belongs to the Special Issue Drones, Artificial Intelligence and Advanced Analytics for the Conservation of Threatened Species)
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