Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
5.0
Journals
Remote Sensing
Special Issues
Application of Remote Sensing Techniques in Wildlife Mapping and Modelling
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Application of Remote Sensing Techniques in Wildlife Mapping and Modelling

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

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 15613

Special Issue Editors

Dr. Grant Connette

E-Mail Website
Guest Editor
Working Land & Seascapes Initiative, Smithsonian Conservation Biology Institute, 1500 Remount Road, Front Royal, VA 22630, USA
Interests: landscape ecology; biodiversity conservation; ecological modeling; remote sensing
Dr. David Thau

E-Mail Website
Guest Editor
World Wildlife Fund, Inc., 131 Steuart St, San Francisco, CA 94105, USA
Interests: artificial Intelligence; big geospatial data; databases and data management; remote sensing; biodiversity monitoring; biodiversity Informatics

Special Issue Information

Dear Colleagues,

Recent decades have seen a rapid increase in the use of remote sensing technologies to improve our understanding of the natural world.  A growing suite of tools, datasets, and methods now offer ecologists, wildlife managers, and conservation practitioners an unprecedented opportunity to collect and assess spatially-explicit data over large geographic areas, at regular time intervals, and in near real-time.  Remote sensing therefore represents a powerful complement to in-situ, ground-based, biological observations by enabling researchers to model ecological processes across broad spatial extents, to generate temporal forecasts (and hind-casts) of these processes, or to provide real-time monitoring of species and ecosystems.

Modern advances in computer, software, and cloud computing technologies have also greatly improved the speed and accessibility of many remote sensing methods, leading to increased innovation and wider adoption in the fields of ecology and wildlife management.  This technology revolution comes at a pivotal moment in the Earth’s history when a rapidly expanding human footprint results in a quarter of all species being threatened with extinction (IPBES, 2019).  More than ever before, improved understanding of ecological processes, timely biodiversity monitoring, and dynamic forecasting will be critical for setting conservation priorities and planning for an environmentally sustainable future.

For this Special Issue, we welcome research papers focusing on methodology or application that combine robust use of remote sensing data with ground-based biological observations in order to improve spatial mapping and modeling of wildlife populations.  Ground-based data may come from diverse sources, such as population survey and census data, GPS tracking data, species localities, camera traps, or acoustic sensors.  We especially encourage contributions with analyses performed using open source software (e.g., R, Google Earth Engine) and where code is made available as supplementary material.  Potential topics for this Special Issue may include but are not limited to the following:

  • Habitat suitability (species abundance and distribution)
  • Population or ecosystem dynamics
  • Species movement and behavioral analysis
  • Identification of wildlife movement corridors
  • Automated detection of wildlife
  • Monitoring and/or prediction of conservation outcomes
  • Development of management decision-support tools


Dr. Grant Connette
Dr. David Thau
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 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

  • Remote Sensing
  • Big Data Analysis
  • Ecological Modeling
  • Biodiversity Monitoring
  • Ecological Forecasting
  • Landscape Ecology
  • Ecosystem Function
  • Google Earth Engine

Published Papers (3 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

25 pages, 16390 KiB  
Article
Field Testing Satellite-Derived Vegetation Health Indices for a Koala Habitat Managers Toolkit
by Michael Hewson, Flavia Santamaria and Alistair Melzer
Remote Sens. 2022, 14(9), 2119; https://doi.org/10.3390/rs14092119 - 28 Apr 2022
Cited by 2 | Viewed by 2212
Abstract
A Central Queensland University (CQU) partnership with the Queensland Government National Park management agency has developed a koala (Phascolarctos cinereus) habitat managers’ toolkit for vegetation health assessment. Private and public landholders use the field-based toolkit to assess habitat suitability or monitor [...] Read more.
A Central Queensland University (CQU) partnership with the Queensland Government National Park management agency has developed a koala (Phascolarctos cinereus) habitat managers’ toolkit for vegetation health assessment. Private and public landholders use the field-based toolkit to assess habitat suitability or monitor conservation outcomes for the koala—an iconic Australian arboreal herbivorous marsupial. The toolkit was upgraded recently with instructions to process European Space Agency (ESA) Sentinel-2 multispectral satellite-derived selected vegetation maps for areal vegetation health trend monitoring. A field campaign sought to validate the relatively coarse spatial resolution derived indices (photosynthetic health, leaf area index and leaf water content) to verify their suitability for the habitat management decision-support toolkit. Other user requirement-driven criteria for including remote sensing in the toolkit were imagery and associated processing software costs and ease of map production for habitat managers without cost-effective access to spatial science skills. Despite moderate-to-low field and image vegetation proxy correlations, discussing the results with stakeholders indicates that, at a landscape scale, the use of cost-free, suitable temporal resolution, 10-m spatial resolution imagery is satisfactory when aligned with the design outcomes of a habitat health toolkit. Full article
(This article belongs to the Special Issue Application of Remote Sensing Techniques in Wildlife Mapping and Modelling)
►▼ Show Figures

Graphical abstract

10 pages, 3835 KiB  
Communication
Enhancing Animal Movement Analyses: Spatiotemporal Matching of Animal Positions with Remotely Sensed Data Using Google Earth Engine and R
by Ramiro D. Crego, Majaliwa M. Masolele, Grant Connette and Jared A. Stabach
Remote Sens. 2021, 13(20), 4154; https://doi.org/10.3390/rs13204154 - 16 Oct 2021
Cited by 10 | Viewed by 8209
Abstract
Movement ecologists have witnessed a rapid increase in the amount of animal position data collected over the past few decades, as well as a concomitant increase in the availability of ecologically relevant remotely sensed data. Many researchers, however, lack the computing resources necessary [...] Read more.
Movement ecologists have witnessed a rapid increase in the amount of animal position data collected over the past few decades, as well as a concomitant increase in the availability of ecologically relevant remotely sensed data. Many researchers, however, lack the computing resources necessary to incorporate the vast spatiotemporal aspects of datasets available, especially in countries with less economic resources, limiting the scope of ecological inquiry. We developed an R coding workflow that bridges the gap between R and the multi-petabyte catalogue of remotely sensed data available in Google Earth Engine (GEE) to efficiently extract raster pixel values that best match the spatiotemporal aspects (i.e., spatial location and time) of each animal’s GPS position. We tested our approach using movement data freely available on Movebank (movebank.org). In a first case study, we extracted Normalized Difference Vegetation Index information from the MOD13Q1 data product for 12,344 GPS animal locations by matching the closest MODIS image in the time series to each GPS fix. Data extractions were completed in approximately 3 min. In a second case study, we extracted hourly air temperature from the ERA5-Land dataset for 33,074 GPS fixes from 12 different wildebeest (Connochaetes taurinus) in approximately 34 min. We then investigated the relationship between step length (i.e., the net distance between sequential GPS locations) and temperature and found that animals move less as temperature increases. These case studies illustrate the potential to explore novel questions in animal movement research using high-temporal-resolution, remotely sensed data products. The workflow we present is efficient and customizable, with data extractions occurring over relatively short time periods. While computing times to extract remotely sensed data from GEE will vary depending on internet speed, the approach described has the potential to facilitate access to computationally demanding processes for a greater variety of researchers and may lead to increased use of remotely sensed data in the field of movement ecology. We present a step-by-step tutorial on how to use the code and adapt it to other data products that are available in GEE. Full article
(This article belongs to the Special Issue Application of Remote Sensing Techniques in Wildlife Mapping and Modelling)
►▼ Show Figures

Graphical abstract

16 pages, 5096 KiB  
Article
Combining Tracking and Remote Sensing to Identify Critical Year-Round Site, Habitat Use and Migratory Connectivity of a Threatened Waterbird Species
by Nyambayar Batbayar, Kunpeng Yi, Junjian Zhang, Tseveenmyadag Natsagdorj, Iderbat Damba, Lei Cao and Anthony David Fox
Remote Sens. 2021, 13(20), 4049; https://doi.org/10.3390/rs13204049 - 11 Oct 2021
Cited by 12 | Viewed by 3241
Abstract
We tracked 39 western flyway white-naped cranes (Antigone vipio) throughout multiple annual cycles from June 2017 to July 2020, using GSM-GPS loggers providing positions every 10-min to describe migration routes and key staging areas used between their Mongolian breeding and wintering [...] Read more.
We tracked 39 western flyway white-naped cranes (Antigone vipio) throughout multiple annual cycles from June 2017 to July 2020, using GSM-GPS loggers providing positions every 10-min to describe migration routes and key staging areas used between their Mongolian breeding and wintering areas in China’s Yangtze River Basin. The results demonstrated that white-naped cranes migrated an average of 2556 km (±187.9 SD) in autumn and 2673 km (±342.3) in spring. We identified 86 critical stopover sites that supported individuals for more than 14 days, within a 100–800 km wide migratory corridor. This study also confirmed that Luan River catchment is the most important staging region, where white-naped cranes spent 18% of the annual cycle (in both spring and autumn) each year. Throughout the annual cycle, 69% of the tracking locations were from outside of the currently protected areas, while none of the critical staging areas enjoyed any form of site protection. We see further future potential to combine avian tracking data and remote-sensing information throughout the annual range of the white-naped crane to restore it and other such species to a more favourable conservation status. Full article
(This article belongs to the Special Issue Application of Remote Sensing Techniques in Wildlife Mapping and Modelling)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop