Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Towards Biodiversity Conservation: Remote Sensing Applications in Ecological Modeling
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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: 15 January 2025 | Viewed by 12005

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

32 pages, 4738 KiB  
Article
Weather Radars Reveal Environmental Conditions for High Altitude Insect Movement Through the Aerosphere
by Samuel Hodges, Christopher Hassall and Ryan Neely III
Remote Sens. 2024, 16(23), 4388; https://doi.org/10.3390/rs16234388 - 24 Nov 2024
Viewed by 257
Abstract
High-flying insects that exploit tropospheric winds can disperse over far greater distances in a single generation than species restricted to below-canopy flight. However, the ecological consequences of such long-range dispersal remain poorly understood. For example, high-altitude dispersal may facilitate more rapid range shifts [...] Read more.
High-flying insects that exploit tropospheric winds can disperse over far greater distances in a single generation than species restricted to below-canopy flight. However, the ecological consequences of such long-range dispersal remain poorly understood. For example, high-altitude dispersal may facilitate more rapid range shifts in these species and reduce their sensitivity to habitat fragmentation, in contrast to low-flying insects that rely more on terrestrial patch networks. Previous studies have primarily used surface-level variables with limited spatial coverage to explore dispersal timing and movement. In this study, we introduce a novel application of niche modelling to insect aeroecology by examining the relationship between a comprehensive set of atmospheric conditions and high-flying insect activity in the troposphere, as detected by weather surveillance radars (WSRs). We reveal correlations between large-scale dispersal events and atmospheric conditions, identifying key variables that influence dispersal behaviour. By incorporating high-altitude atmospheric conditions into niche models, we achieve significantly higher predictive accuracy compared with models based solely on surface-level conditions. Key predictive factors include the proportion of arable land, altitude, temperature, and relative humidity. Full article
(This article belongs to the Special Issue Towards Biodiversity Conservation: Remote Sensing Applications in Ecological Modeling)
23 pages, 19068 KiB  
Article
Impacts of Climate Change on Forest Biodiversity Changes in Northeast China
by Xiguang Yang, Yingqiu Mu, Li Yang, Ying Yu and Zechuan Wu
Remote Sens. 2024, 16(21), 4058; https://doi.org/10.3390/rs16214058 - 31 Oct 2024
Viewed by 652
Abstract
Vegetation plays a vital role in connecting ecosystems and climate features. The biodiversity of vegetation is one of the most important features for evaluating ecosystems and it is becoming increasingly important with the threat of global warming. To clarify the effects of climate [...] Read more.
Vegetation plays a vital role in connecting ecosystems and climate features. The biodiversity of vegetation is one of the most important features for evaluating ecosystems and it is becoming increasingly important with the threat of global warming. To clarify the effects of climate change on forest biodiversity in Northeast China, time-series NDVI data, meteorological data and land cover data from 2010 to 2021 were acquired, and the forest biodiversity of Northeast China was evaluated. The effect of climate change on forest biodiversity was analyzed, and the results indicated that the forest biodiversity features increased from west to east in Northeast China. There was also an increasing trend from 2010 to 2021, but the rate at which forest biodiversity was changing varied with different forest types of Northeast China, as different climatic factors had a different impact on forest biodiversity in different forest types. Average annual temperature, annual accumulated precipitation, CO2 fertilization and solar radiation were the main factors affecting forest biodiversity changing trends. This research indicated the potential impact of climate change on forest ecosystems, as it emphasized with evidence that climate change has a catalytic effect on forest biodiversity in Northeast China. Full article
(This article belongs to the Special Issue Towards Biodiversity Conservation: Remote Sensing Applications in Ecological Modeling)
►▼ Show Figures

Figure 1

26 pages, 3609 KiB  
Article
Detection and Quantification of Arnica montana L. Inflorescences in Grassland Ecosystems Using Convolutional Neural Networks and Drone-Based Remote Sensing
by Dragomir D. Sângeorzan, Florin Păcurar, Albert Reif, Holger Weinacker, Evelyn Rușdea, Ioana Vaida and Ioan Rotar
Remote Sens. 2024, 16(11), 2012; https://doi.org/10.3390/rs16112012 - 3 Jun 2024
Viewed by 710
Abstract
Arnica montana L. is a medicinal plant with significant conservation importance. It is crucial to monitor this species, ensuring its sustainable harvesting and management. The aim of this study is to develop a practical system that can effectively detect A. montana inflorescences utilizing [...] Read more.
Arnica montana L. is a medicinal plant with significant conservation importance. It is crucial to monitor this species, ensuring its sustainable harvesting and management. The aim of this study is to develop a practical system that can effectively detect A. montana inflorescences utilizing unmanned aerial vehicles (UAVs) with RGB sensors (red–green–blue, visible light) to improve the monitoring of A. montana habitats during the harvest season. From a methodological point of view, a model was developed based on a convolutional neural network (CNN) ResNet101 architecture. The trained model offers quantitative and qualitative assessments of A. montana inflorescences detected in semi-natural grasslands using low-resolution imagery, with a correctable error rate. The developed prototype is applicable in monitoring a larger area in a short time by flying at a higher altitude, implicitly capturing lower-resolution images. Despite the challenges posed by shadow effects, fluctuating ground sampling distance (GSD), and overlapping vegetation, this approach revealed encouraging outcomes, particularly when the GSD value was less than 0.45 cm. This research highlights the importance of low-resolution image clarity, on the training data by the phenophase, and of the need for training across different photoperiods to enhance model flexibility. This innovative approach provides guidelines for mission planning in support of reaching sustainable management goals. The robustness of the model can be attributed to the fact that it has been trained with real-world imagery of semi-natural grassland, making it practical for fieldwork with accessible portable devices. This study confirms the potential of ResNet CNN models to transfer learning to new plant communities, contributing to the broader effort of using high-resolution RGB sensors, UAVs, and machine-learning technologies for sustainable management and biodiversity conservation. Full article
(This article belongs to the Special Issue Towards Biodiversity Conservation: Remote Sensing Applications in Ecological Modeling)
►▼ Show Figures

Graphical abstract

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://doi.org/10.3390/rs16071175 - 27 Mar 2024
Cited by 1 | Viewed by 1239
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)
►▼ Show Figures

Figure 1

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://doi.org/10.3390/rs15225277 - 7 Nov 2023
Viewed by 4641
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)
►▼ Show Figures

Figure 1

Review

Jump to: Research

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://doi.org/10.3390/rs16040627 - 8 Feb 2024
Cited by 2 | Viewed by 3142
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop