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Remote Sensing
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Towards Biodiversity Conservation: Remote Sensing Applications in Ecological Modeling
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Towards Biodiversity Conservation: Remote Sensing Applications in Ecological Modeling

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 5762

Special Issue Editors

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
Dr. Evangeline Corcoran

E-Mail Website
Guest Editor
Corcoran, Evangeline Alan Turing Institute, London, UK
Interests: wildlife conservation; conservation biology; wildlife ecology; wildlife management; endangered species; invasive species; mammals; advanced statistical modeling; generalized linear models; advanced machine learning

Special Issue Information

Dear Colleagues,

Biodiversity conservation is one of the critical issues of our time. If the global trend of species loss continues, it could have devastating impacts on ecosystems and humanity, so urgent action is required. Combating the biodiversity crisis is complex, requiring deep knowledge of at-risk species and their interactions with other species and the environment. To understand this complexity, ecological models are invaluable to provide insight on factors that impact biodiversity from observed or simulated data, to predict future trends in wildlife populations, and identify potential strategies for intervention for species of conservation concern. Ecological modeling therefore plays an integral role in the management of species to safeguard future biodiversity.

Remote sensing technologies are being increasingly used to collect data on which to train and develop ecological models, to predict future trends in populations and ecosystems, and to monitor the impact of interventions. These technologies have the potential to increase the accuracy, coverage, and frequency of data collection so that more reliable, comprehensive, and timely management decisions can be made to conserve species. To highlight developments in this important field, this Special Issue aims to bring together new and innovative applications of remote sensing data, collected from a broad range of platforms and sensors, in the context of ecological modeling. We welcome submissions with a focus on ecological models that use remote sensing data that address previously unanswered questions or provide new insights that have the potential to enhance biodiversity conservation outcomes.

Dr. Grant Hamilton
Dr. Evangeline Corcoran
Guest Editors

Megan Winsen
Guest Editor Assistant
Queensland University of Technology, Brisbane, QLD, Australia
Email: [email protected]

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

  • biodiversity
  • conservation
  • remote sensing
  • ecological modeling
  • species distribution modeling
  • spatial ecology
  • mapping and monitoring
  • population analysis
  • threatened species
  • invasive species

Published Papers (3 papers)

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Research

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20 pages, 8182 KiB  
Article
Species Abundance Modelling of Arctic-Boreal Zone Ducks Informed by Satellite Remote Sensing
by Michael Allan Merchant, Michael J. Battaglia, Nancy French, Kevin Smith, Howard V. Singer, Llwellyn Armstrong, Vanessa B. Harriman and Stuart Slattery
Remote Sens. 2024, 16(7), 1175; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16071175 - 27 Mar 2024
Viewed by 590
Abstract
The Arctic-Boreal zone (ABZ) covers over 26 million km2 and is home to numerous duck species; however, understanding the spatiotemporal distribution of their populations across this vast landscape is challenging, in part due to extent and data scarcity. Species abundance models for [...] Read more.
The Arctic-Boreal zone (ABZ) covers over 26 million km2 and is home to numerous duck species; however, understanding the spatiotemporal distribution of their populations across this vast landscape is challenging, in part due to extent and data scarcity. Species abundance models for ducks in the ABZ commonly use static (time invariant) habitat covariates to inform predictions, such as wetland type and extent maps. For the first time in this region, we developed species abundance models using high-resolution, time-varying wetland inundation data produced using satellite remote sensing methods. This data captured metrics of surface water extent and inundated vegetation in the Peace Athabasca Delta, Canada, which is within the NASA Arctic Boreal Vulnerability Experiment core domain. We used generalized additive mixed models to demonstrate the improved predictive value of this novel data set over time-invariant data. Our findings highlight both the potential complementarity and efficacy of dynamic wetland inundation information for improving estimation of duck abundance and distribution at high latitudes. Further, these data can be an asset to spatial targeting of biodiversity conservation efforts and developing model-based metrics of their success under rapidly changing climatic conditions. Full article
(This article belongs to the Special Issue Towards Biodiversity Conservation: Remote Sensing Applications in Ecological Modeling)
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22 pages, 8592 KiB  
Article
Multi-Stage Semantic Segmentation Quantifies Fragmentation of Small Habitats at a Landscape Scale
by Thijs L. van der Plas, Simon T. Geikie, David G. Alexander and Daniel M. Simms
Remote Sens. 2023, 15(22), 5277; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15225277 - 07 Nov 2023
Viewed by 3037
Abstract
Land cover (LC) maps are used extensively for nature conservation and landscape planning, but low spatial resolution and coarse LC schemas typically limit their applicability to large, broadly defined habitats. In order to target smaller and more-specific habitats, LC maps must be developed [...] Read more.
Land cover (LC) maps are used extensively for nature conservation and landscape planning, but low spatial resolution and coarse LC schemas typically limit their applicability to large, broadly defined habitats. In order to target smaller and more-specific habitats, LC maps must be developed at high resolution and fine class detail using automated methods that can efficiently scale to large areas of interest. In this work, we present a Machine Learning approach that addresses this challenge. First, we developed a multi-stage semantic segmentation approach that uses Convolutional Neural Networks (CNNs) to classify LC across the Peak District National Park (PDNP, 1439 km2) in the UK using a detailed, hierarchical LC schema. High-level classes were predicted with 95% accuracy and were subsequently used as masks to predict low-level classes with 72% to 92% accuracy. Next, we used these predictions to analyse the degree and distribution of fragmentation of one specific habitat—wet grassland and rush pasture—at the landscape scale in the PDNP. We found that fragmentation varied across areas designated as primary habitat, highlighting the importance of high-resolution LC maps provided by CNN-powered analysis for nature conservation. Full article
(This article belongs to the Special Issue Towards Biodiversity Conservation: Remote Sensing Applications in Ecological Modeling)
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Review

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23 pages, 3468 KiB  
Review
Review of Satellite Remote Sensing and Unoccupied Aircraft Systems for Counting Wildlife on Land
by Marie R. G. Attard, Richard A. Phillips, Ellen Bowler, Penny J. Clarke, Hannah Cubaynes, David W. Johnston and Peter T. Fretwell
Remote Sens. 2024, 16(4), 627; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16040627 - 08 Feb 2024
Viewed by 1317
Abstract
Although many medium-to-large terrestrial vertebrates are still counted by ground or aerial surveys, remote-sensing technologies and image analysis have developed rapidly in recent decades, offering improved accuracy and repeatability, lower costs, speed, expanded spatial coverage and increased potential for public involvement. This review [...] Read more.
Although many medium-to-large terrestrial vertebrates are still counted by ground or aerial surveys, remote-sensing technologies and image analysis have developed rapidly in recent decades, offering improved accuracy and repeatability, lower costs, speed, expanded spatial coverage and increased potential for public involvement. This review provides an introduction for wildlife biologists and managers relatively new to the field on how to implement remote-sensing techniques (satellite and unoccupied aircraft systems) for counting large vertebrates on land, including marine predators that return to land to breed, haul out or roost, to encourage wider application of these technological solutions. We outline the entire process, including the selection of the most appropriate technology, indicative costs, procedures for image acquisition and processing, observer training and annotation, automation, and citizen science campaigns. The review considers both the potential and the challenges associated with different approaches to remote surveys of vertebrates and outlines promising avenues for future research and method development. Full article
(This article belongs to the Special Issue Towards Biodiversity Conservation: Remote Sensing Applications in Ecological Modeling)
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