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Special Issue "Remote Sensing Applications to Ecology: Opportunities and Challenges"

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

Deadline for manuscript submissions: 1 November 2023 | Viewed by 2173

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Special Issue Editors

Prof. Dr. Paul Fergus

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Guest Editor

School of Computer Science and Mathematics, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
Interests: artificial intelligence; machine learning; deep learning; object detection; conservation; e-health
Special Issues, Collections and Topics in MDPI journals

Dr. Carl Chalmers

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Guest Editor

Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: artificial intelligence; machine learning; deep learning; computer vision; technology in conservation and e-health
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Serge Wich

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Guest Editor

School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: great apes; conservation; drones
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Steven Longmore
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Guest Editor

Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK
Interests: physics and astronomy; earth and planetary sciences

Special Issue Information

Dear Colleagues,

We are pleased to announce a new Special Issue entitled “Remote Sensing Applications to Ecology: Opportunities and Challenges”. We are soliciting submissions for both review and original research articles related to the novel use of data obtained from sensors (camera traps, cameras, microphones, unoccupied vehicles (aerial, terrestrial, and aquatic)) and any other sensor platforms you think would support ecology to manage and protect environments globally. We would encourage submissions with a particular focus on artificial intelligence (AI) algorithms and their use in ecological studies. The Special Issue is open to contributions ranging from systems that monitor different physical environments, combat poaching and protect wildlife, support wildlife management and conservation, enable animal counting and tracking, support biodiversity assessments, monitor forest health and quality, as well as novel approaches to sensor fusion for remote sensing. Original contributions that look at integrated sensor-based technologies and wide area communications across remote sensing platforms (land, sea, air- and spaceborne) are also encouraged.

Prof. Dr. Paul Fergus
Dr. Carl Chalmers
Prof. Dr. Serge Wich
Prof. Dr. Steven Longmore
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. 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 2500 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

land, sea, air, space-based monitoring
multi-spectral remote sensing
hyperspectral remote sensing
LiDAR
sensor fusion
time series analysis
data fusion and data assimilation
wireless (2/3/4/5G/Wifi/satellite) mesh networking in remote areas
machine learning (image processing and pattern recognition)
high performance inferencing
edge/IoT deployment and inferencing
robotics (rovers, drones)
remote sensing applications
poaching
wildlife conservation
animal counting
environment monitoring
change detection

Published Papers (3 papers)

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Research

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Open AccessArticle

Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation Using Deep Learning and 3/4G Camera Traps

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Remote Sens. 2023, 15(11), 2730; https://doi.org/10.3390/rs15112730 - 24 May 2023

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Abstract

The biodiversity of our planet is under threat, with approximately one million species expected to become extinct within decades. The reason: negative human actions, which include hunting, overfishing, pollution, and the conversion of land for urbanisation and agricultural purposes. Despite significant investment from charities and governments for activities that benefit nature, global wildlife populations continue to decline. Local wildlife guardians have historically played a critical role in global conservation efforts and have shown their ability to achieve sustainability at various levels. In 2021, COP26 recognised their contributions and pledged USD 1.7 billion per year; however this is a fraction of the global biodiversity budget available (between USD 124 billion and USD 143 billion annually) given they protect 80% of the planets biodiversity. This paper proposes a radical new solution based on “Interspecies Money”, where animals own their own money. Creating a digital twin for each species allows animals to dispense funds to their guardians for the services they provide. For example, a rhinoceros may release a payment to its guardian each time it is detected in a camera trap as long as it remains alive and well. To test the efficacy of this approach, 27 camera traps were deployed over a 400 km

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area in Welgevonden Game Reserve in Limpopo Province in South Africa. The motion-triggered camera traps were operational for ten months and, using deep learning, we managed to capture images of 12 distinct animal species. For each species, a makeshift bank account was set up and credited with GBP 100. Each time an animal was captured in a camera and successfully classified, 1 penny (an arbitrary amount—mechanisms still need to be developed to determine the real value of species) was transferred from the animal account to its associated guardian. The trial demonstrated that it is possible to achieve high animal detection accuracy across the 12 species with a sensitivity of 96.38%, specificity of 99.62%, precision of 87.14%, F1 score of 90.33%, and an accuracy of 99.31%. The successful detections facilitated the transfer of GBP 185.20 between animals and their associated guardians. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges)

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Open AccessArticle

Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning

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Remote Sens. 2023, 15(10), 2638; https://doi.org/10.3390/rs15102638 - 18 May 2023

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Abstract

Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Declines in bird populations can result in reduced ecosystem services, including seed dispersal, pollination and pest control. Accurate and long-term monitoring of birds to identify species of concern while measuring the success of conservation interventions is essential for ecologists. However, monitoring is time-consuming, costly and often difficult to manage over long durations and at meaningfully large spatial scales. Technology such as camera traps, acoustic monitors and drones provide methods for non-invasive monitoring. There are two main problems with using camera traps for monitoring: (a) cameras generate many images, making it difficult to process and analyse the data in a timely manner; and (b) the high proportion of false positives hinders the processing and analysis for reporting. In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classification of bird species and automated removal of false positives in camera trap data. Images are classified in real-time using a Faster-RCNN architecture. Images are transmitted over 3/4G cameras and processed using Graphical Processing Units (GPUs) to provide conservationists with key detection metrics, thereby removing the requirement for manual observations. Our models achieved an average sensitivity of 88.79%, a specificity of 98.16% and accuracy of 96.71%. This demonstrates the effectiveness of using deep learning for automatic bird monitoring. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges)

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Other

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Open AccessTechnical Note

Using Drones to Determine Chimpanzee Absences at the Edge of Their Distribution in Western Tanzania

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Remote Sens. 2023, 15(8), 2019; https://doi.org/10.3390/rs15082019 - 11 Apr 2023

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Abstract

Effective species conservation management relies on detailed species distribution data. For many species, such as chimpanzees (Pan troglodytes), distribution data are collected during ground surveys. For chimpanzees, such ground surveys usually focus on detection of the nests they build instead of detection of the chimpanzees themselves due to their low density. However, due to the large areas they still occur in, such surveys are very costly to conduct and repeat frequently to monitor populations over time. Species distribution models are more accurate if they include presence as well as absence data. Earlier studies used drones to determine chimpanzee presence using nests. In this study, therefore, we explored the use of drones to determine the absence of chimpanzee nests in areas we flew over on the edge of the chimpanzee distribution in western Tanzania. We conducted 13 flights with a fixed-wing drone and collected 3560 images for which manual inspection took 180 h. Flights were divided into a total of 746 25 m² plots for which we determined the absence probability of nests. In three flights, we detected nests, in eight, absence was assumed based on a 95% probability criterion, and in two flights, nest absence could not be assumed. Our study indicates that drones can be used to cover relatively large areas to determine the absence of chimpanzees. To fully benefit from the usage of drones to determine the presence and absence of chimpanzees, it is crucial that methods are developed to automate nest detection in images. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges)

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