Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

► Journal Browser

Remote Sensing for Monitoring Wildlife and Habitat in a Changing World

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 63130

Share This Special Issue

Special Issue Editors

Prof. Dr. Andrés Viña

E-Mail Website
Guest Editor

Center for Systems Integration and Sustainability (CSIS), Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, USA
Interests: biodiversity; conservation biology; wildlife habitat modeling; land use/cover change; landscape dynamics; phenology; productivity; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Mao-Ning Tuanmu

E-Mail Website
Guest Editor

Biodiversity Research Center, Academia Sinica, 128 Academia Rd., Sec. 2, Nankang Dist., Taipei City 115, Taiwan
Interests: macroecology; ecoacoustics; remote sensing; biodiversity conservation; biodiversity informatics

Special Issue Information

Dear Colleagues,

A key problem that ecologists and evolutionary biologists have strived to understand is the abundance and distribution of species. In this age of drastic and rapid rate of species extinction, such knowledge has become an essential component for management and conservation. Successful strategies require establishing which areas constitute priorities for biodiversity conservation. Such endeavor requires frequent and spatially detailed assessments of species occurrence and distribution, which are prohibitively expensive to obtain by traditional field surveys due to their limited spatial extents or small scales. Recent advances in remote sensing have become crucial for obtaining information on wildlife species occurrence and distribution, as well as for characterizing their habitat at scales ranging from local to global. However, many of these advances, while successful, are still constrained to particular geographic locations, species, and/or species assemblages. Therefore, much more research is urgently needed to develop and test effective techniques applicable at multiple scales and in different geographic settings, together with their incorporation into ecological research and biodiversity conservation. The works presented in this Special Issue represent scientific and technological innovations in remote sensing for assessing the spatio-temporal dynamics of wildlife species and their habitat, and their incorporation into ecological research and biodiversity conservation.

Dr. Andrés Viña
Dr. Mao-Ning Tuanmu
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

Abundance
Biodiversity
Conservation
Distribution
Ecological Niche

Published Papers (12 papers)

Download All Papers

Editorial

Jump to: Research

3 pages, 625 KiB

Open AccessEditorial

Editorial for Special Issue “Remote Sensing for Monitoring Wildlife and Habitat in a Changing World”

by Andrés Viña

Remote Sens. 2021, 13(14), 2762; https://doi.org/10.3390/rs13142762 - 14 Jul 2021

Cited by 2 | Viewed by 1700

Abstract

Escalating human impacts on the Earth are creating unprecedented challenges, including the drastic degradation and loss of biodiversity worldwide [...] Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Wildlife and Habitat in a Changing World)

► Show Figures

Figure 1

Research

Jump to: Editorial

13 pages, 3195 KiB

Open AccessArticle

Habitat-Suitability Model for the Yellow Rail (Coturnicops noveboracensis) in the Northern Gulf Coast of Alabama and Mississippi, USA

by Kelly M. Morris, Eric C. Soehren, Mark S. Woodrey and Scott A. Rush

Remote Sens. 2020, 12(5), 848; https://doi.org/10.3390/rs12050848 - 06 Mar 2020

Cited by 5 | Viewed by 3164

Abstract

The yellow rail (Coturnicops noveboracensis) is a migratory bird of high conservation priority throughout its range and winters across the Atlantic and Gulf Coastal Plains regions of the southeastern United States. Although the winter ecology of this species has been recently explored, no studies have addressed their distribution and abundance in relation to suitable habitat capable of supporting this species during winter along the northern Gulf Coast of Alabama and Mississippi. The objectives of this study were to develop a habitat-suitability model for yellow rail wintering in the northern Gulf Coast of Alabama and Mississippi. We then used this model to evaluate the distribution of habitat suitable for supporting yellow rail in this geographic area. Using a multivariate approach that makes use of presence-only data through a maximum entropy framework we compared the distribution of where the focal species was observed to a reference set of the whole study area. Of the 784,657 ha over which our model was applied, only 1% (8643 ha) of this area was predicted suitable in its present condition, for supporting yellow rail in winter. Our analysis indicates that the yellow rail along the northern Gulf Coast of Alabama and Mississippi occupy a very narrow range of environmental conditions highlighting need for specific management actions to maintain and conserve suitable winter landscapes for this habitat-restricted species. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Wildlife and Habitat in a Changing World)

► Show Figures

Graphical abstract

19 pages, 21653 KiB

Open AccessArticle

Challenges and Opportunities for Terrapene carolina carolina Under Different Climate Scenarios

by Amanda K. Martin and Karen V. Root

Remote Sens. 2020, 12(5), 836; https://doi.org/10.3390/rs12050836 - 05 Mar 2020

Cited by 3 | Viewed by 3261

Abstract

An unprecedented rate of global climate change as a result of human impacts has affected both endotherms and ectotherms. This is of special concern for ectotherms, such as reptiles, as these species are suffering from large population declines and lack the dispersal ability of other taxa. There are many protected areas across the United States; however, these areas are fragmented, which hinders dispersal. We examined species distribution and dispersal capabilities for Terrapene carolina carolina, a relatively narrow range, low dispersal, and vulnerable species. We created climatic suitability models to predict changes in suitable habitat and identified important predictor variables. We modeled three time periods using MaxEnt and hypothesized that there would be an increase in northern habitat. We found that most of the suitable habitat changed at the northern end of the range and that mean temperature of driest quarter had the most influence on future predictions. Overall there were relatively moderate changes in suitable habitat, but where these changes occur affects accessibility. As an example, we examined these local scale movements within Oak Openings Region and found that individuals are capable of dispersing to new suitable habitats; however, other physical barriers will hinder movements. In conclusion, there is a critical need to protect this vulnerable reptilian species and our results suggest that T. c. carolina will expand their distribution northward. We suggest that land managers increase connectivity among protected areas to facilitate dispersal, but future studies should incorporate other dynamic ecological factors at finer spatial scale. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Wildlife and Habitat in a Changing World)

► Show Figures

Graphical abstract

17 pages, 9952 KiB

Open AccessArticle

Quantifying Intertidal Habitat Relative Coverage in a Florida Estuary Using UAS Imagery and GEOBIA

by Michael C. Espriella, Vincent Lecours, Peter C. Frederick, Edward V. Camp and Benjamin Wilkinson

Remote Sens. 2020, 12(4), 677; https://doi.org/10.3390/rs12040677 - 19 Feb 2020

Cited by 16 | Viewed by 3376

Abstract

Intertidal habitats like oyster reefs and salt marshes provide vital ecosystem services including shoreline erosion control, habitat provision, and water filtration. However, these systems face significant global change as a result of a combination of anthropogenic stressors like coastal development and environmental stressors such as sea-level rise and disease. Traditional intertidal habitat monitoring techniques are cost and time-intensive, thus limiting how frequently resources are mapped in a way that is often insufficient to make informed management decisions. Unoccupied aircraft systems (UASs) have demonstrated the potential to mitigate these costs as they provide a platform to rapidly, safely, and inexpensively collect data in coastal areas. In this study, a UAS was used to survey intertidal habitats along the Gulf of Mexico coastline in Florida, USA. The structure from motion photogrammetry techniques were used to generate an orthomosaic and a digital surface model from the UAS imagery. These products were used in a geographic object-based image analysis (GEOBIA) workflow to classify mudflat, salt marsh, and oyster reef habitats. GEOBIA allows for a more informed classification than traditional techniques by providing textural and geometric context to habitat covers. We developed a ruleset to allow for a repeatable workflow, further decreasing the temporal cost of monitoring. The classification produced an overall accuracy of 79% in classifying habitats in a coastal environment with little spectral and textural separability, indicating that GEOBIA can differentiate intertidal habitats. This method allows for effective monitoring that can inform management and restoration efforts. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Wildlife and Habitat in a Changing World)

► Show Figures

Graphical abstract

18 pages, 2558 KiB

Open AccessArticle

Synthesizing Remote Sensing and Biophysical Measures to Evaluate Human–wildlife Conflicts: The Case of Wild Boar Crop Raiding in Rural China

by Madeline Giefer and Li An

Remote Sens. 2020, 12(4), 618; https://doi.org/10.3390/rs12040618 - 13 Feb 2020

Cited by 6 | Viewed by 2864

Abstract

Crop raiding by wild boars is a growing problem worldwide with potentially damaging consequences for rural dwellers’ cooperation with conservation policies. Still, limited resources inhibit continuous monitoring, and there is uncertainty about the relationship between the biophysical realities of crop raiding and humans’ perceptions and responses. By integrating data from camera traps, remote sensors, and household surveys, this study establishes an empirical model of wild boar population density that can be applied to multiple years to estimate changes in distribution over time. It also correlates historical estimates of boar population distribution with human-reported trends to support the model’s validity and assess local perceptions of crop raiding. Although the model proved useful in coniferous and bamboo forests, it is less useful in mixed broadleaf, evergreen broadleaf, and deciduous forests. Results also show alignment between perceptions of crop raiding and actual boar populations, corroborating farmers’ perceptions which are increasingly dismissed as a less reliable source of information in human–wildlife conflict research. The modeling techniques demonstrated here may provide conservation practitioners with a cost-effective way to maintain up-to-date estimates of the spatial distribution of wild boar and resultant crop raiding. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Wildlife and Habitat in a Changing World)

► Show Figures

Figure 1

22 pages, 5219 KiB

Open AccessArticle

Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach

by Manuel González-Rivero, Oscar Beijbom, Alberto Rodriguez-Ramirez, Dominic E. P. Bryant, Anjani Ganase, Yeray Gonzalez-Marrero, Ana Herrera-Reveles, Emma V. Kennedy, Catherine J. S. Kim, Sebastian Lopez-Marcano, Kathryn Markey, Benjamin P. Neal, Kate Osborne, Catalina Reyes-Nivia, Eugenia M. Sampayo, Kristin Stolberg, Abbie Taylor, Julie Vercelloni, Mathew Wyatt and Ove Hoegh-Guldberg

Remote Sens. 2020, 12(3), 489; https://doi.org/10.3390/rs12030489 - 04 Feb 2020

Cited by 79 | Viewed by 18297

Abstract

Ecosystem monitoring is central to effective management, where rapid reporting is essential to provide timely advice. While digital imagery has greatly improved the speed of underwater data collection for monitoring benthic communities, image analysis remains a bottleneck in reporting observations. In recent years, a rapid evolution of artificial intelligence in image recognition has been evident in its broad applications in modern society, offering new opportunities for increasing the capabilities of coral reef monitoring. Here, we evaluated the performance of Deep Learning Convolutional Neural Networks for automated image analysis, using a global coral reef monitoring dataset. The study demonstrates the advantages of automated image analysis for coral reef monitoring in terms of error and repeatability of benthic abundance estimations, as well as cost and benefit. We found unbiased and high agreement between expert and automated observations (97%). Repeated surveys and comparisons against existing monitoring programs also show that automated estimation of benthic composition is equally robust in detecting change and ensuring the continuity of existing monitoring data. Using this automated approach, data analysis and reporting can be accelerated by at least 200x and at a fraction of the cost (1%). Combining commonly used underwater imagery in monitoring with automated image annotation can dramatically improve how we measure and monitor coral reefs worldwide, particularly in terms of allocating limited resources, rapid reporting and data integration within and across management areas. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Wildlife and Habitat in a Changing World)

► Show Figures

Graphical abstract

20 pages, 5756 KiB

Open AccessArticle

Spatiotemporal Distribution of Human–Elephant Conflict in Eastern Thailand: A Model-Based Assessment Using News Reports and Remotely Sensed Data

by Nuntikorn Kitratporn and Wataru Takeuchi

Remote Sens. 2020, 12(1), 90; https://doi.org/10.3390/rs12010090 - 25 Dec 2019

Cited by 17 | Viewed by 5463

Abstract

In Thailand, crop depredation by wild elephants intensified, impacting the quality of life of local communities and long-term conservation of wild elephant populations. Yet, fewer studies explore the landscape-scale spatiotemporal distribution of human–elephant conflict (HEC). In this study, we modeled the potential HEC distribution in ten provinces adjacent to protected areas in Eastern Thailand from 2009 to 2018. We applied the time-calibrated maximum entropy method and modeled the relative probability of HEC in varying scenarios of resource suitability and direct human pressure in wet and dry seasons. The environmental dynamic over the 10-year period was represented by remotely sensed vegetation, meteorological drought, topographical, and human-pressure data. Results were categorized in HEC zones using the proposed two-dimensional conflict matrix. Logistic regression was applied to determine the relevant contribution of each scenario. The results showed that although HEC probability varied across seasons, overall HEC-prone areas expanded in all provinces from 2009 to 2018. The largest HEC areas were estimated during dry seasons with Chantaburi, Chonburi, Nakhon Ratchasima, and Rayong provinces being the HEC hotspots.However, the HEC potential was reduced during severe and prolonged droughts caused by El Nino events. Direct human pressure caused a more gradual increase of HEC probability around protected areas. On the other hand, resource suitability showed large variation across seasons. We recommend zone-dependent management actions towards a fine-balance between human development and the conservation of wild elephants. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Wildlife and Habitat in a Changing World)

► Show Figures

Graphical abstract

17 pages, 4654 KiB

Open AccessArticle

Combining Multiband Remote Sensing and Hierarchical Distance Sampling to Establish Drivers of Bird Abundance

by Ronny Richter, Arend Heim, Wieland Heim, Johannes Kamp and Michael Vohland

Remote Sens. 2020, 12(1), 38; https://doi.org/10.3390/rs12010038 - 20 Dec 2019

Cited by 4 | Viewed by 4067

Abstract

Information on habitat preferences is critical for the successful conservation of endangered species. For many species, especially those living in remote areas, we currently lack this information. Time and financial resources to analyze habitat use are limited. We aimed to develop a method to describe habitat preferences based on a combination of bird surveys with remotely sensed fine-scale land cover maps. We created a blended multiband remote sensing product from SPOT 6 and Landsat 8 data with a high spatial resolution. We surveyed populations of three bird species (Yellow-breasted Bunting Emberiza aureola, Ochre-rumped Bunting Emberiza yessoensis, and Black-faced Bunting Emberiza spodocephala) at a study site in the Russian Far East using hierarchical distance sampling, a survey method that allows to correct for varying detection probability. Combining the bird survey data and land cover variables from the remote sensing product allowed us to model population density as a function of environmental variables. We found that even small-scale land cover characteristics were predictable using remote sensing data with sufficient accuracy. The overall classification accuracy with pansharpened SPOT 6 data alone amounted to 71.3%. Higher accuracies were reached via the additional integration of SWIR bands (overall accuracy = 73.21%), especially for complex small-scale land cover types such as shrubby areas. This helped to reach a high accuracy in the habitat models. Abundances of the three studied bird species were closely linked to the proportion of wetland, willow shrubs, and habitat heterogeneity. Habitat requirements and population sizes of species of interest are valuable information for stakeholders and decision-makers to maximize the potential success of habitat management measures. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Wildlife and Habitat in a Changing World)

► Show Figures

Graphical abstract

16 pages, 2192 KiB

Open AccessArticle

Exposure of Marine Turtle Nesting Grounds to Named Storms Along the Continental USA

by Mariana M. P. B. Fuentes, Matthew H. Godfrey, Donna Shaver, Simona Ceriani, Christian Gredzens, Ruth Boettcher, Dianne Ingram, Matthew Ware and Natalie Wildermann

Remote Sens. 2019, 11(24), 2996; https://doi.org/10.3390/rs11242996 - 13 Dec 2019

Cited by 10 | Viewed by 4513

Abstract

Named storms can cause substantial impacts on the habitat and reproductive output of threatened species, such as marine turtles. To determine the impacts of named storms on marine turtles and inform management, it is necessary to determine the exposure of marine turtle nesting grounds to recent storm activities. To address this, remote sensing information of named storm tracks coupled with nesting ground data were used to investigate the temporal and spatial overlap between nesting grounds for four species of marine turtles in the continental United States of America. All species of marine turtles were exposed to named storms, with variation in exposure driven by the spatial distribution of each population’s nesting ground, the temporal overlap between the storms and reproductive events, and nest placement on the beach. Loggerhead turtles were the most exposed species to named storms, with the northern management unit having significantly higher exposure levels than all other loggerhead management units. Kemp’s ridley turtles, in contrast, were found to be the least exposed species to named storms. This study establishes a valuable current baseline against which to measure and compare future impacts that result as climate change progresses and storms become more frequent and intense. Importantly, cumulative and synergetic effects from other climatic processes and anthropogenic stressors should be considered in future analysis. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Wildlife and Habitat in a Changing World)

► Show Figures

Graphical abstract

16 pages, 3500 KiB

Open AccessArticle

Projecting Mammal Distributions in Response to Future Alternative Landscapes in a Rapidly Transitioning Region

by Michael V. Cove, Craig Fergus, Iara Lacher, Thomas Akre and William J. McShea

Remote Sens. 2019, 11(21), 2482; https://doi.org/10.3390/rs11212482 - 24 Oct 2019

Cited by 12 | Viewed by 3866

Abstract

Finding balance between the needs of people and wildlife is an essential component of planning sustainable landscapes. Because mammals make up a diverse and ecologically important taxon with varying responses to human disturbance, we used representative mammal species to examine how alternative land-use policies might affect their habitats and distributions in the near future. We used wildlife detections from camera traps at 1591 locations along a large-scale urban to wild gradient in northern Virginia, to create occupancy models which determined land cover relationships and the drivers of contemporary mammal distributions. From the 15 species detected, we classified five representative species into two groups based on their responses to human development; sensitive species (American black bears and bobcats) and synanthropic species (red foxes, domestic cats, and white-tailed deer). We then used the habitat models for the representative species to predict their distributions under four future planning scenarios based on strategic versus reactive planning and high or low human population growth. The distributions of sensitive species did not shrink drastically under any scenario, whereas the distributions of synanthropic species increased in response to anthropogenic development, but the magnitude of the response varied based on the projected rate of human population growth. This is likely because most sensitive species are dependent on large, protected public lands in the region, and the majority of projected habitat losses should occur in non-protected private lands. These findings illustrate the importance of public protected lands in mitigating range loss due to land use changes, and the potential positive impact of strategic planning in further mitigating mammalian diversity loss in private lands. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Wildlife and Habitat in a Changing World)

► Show Figures

Graphical abstract

31 pages, 23234 KiB

Open AccessArticle

Spatial Dynamics of Invasive Para Grass on a Monsoonal Floodplain, Kakadu National Park, Northern Australia

by James Boyden, Penelope Wurm, Karen E. Joyce and Guy Boggs

Remote Sens. 2019, 11(18), 2090; https://doi.org/10.3390/rs11182090 - 06 Sep 2019

Cited by 4 | Viewed by 5223

Abstract

African para grass (Urochloa mutica) is an invasive weed that has become prevalent across many important freshwater wetlands of the world. In northern Australia, including the World Heritage landscape of Kakadu National Park (KNP), its dense cover can displace ecologically, genetically and culturally significant species, such as the Australian native rice (Oryza spp.). In regions under management for biodiversity conservation para grass is often beyond eradication. However, its targeted control is also necessary to manage and preserve site-specific wetland values. This requires an understanding of para grass spread-patterns and its potential impacts on valuable native vegetation. We apply a multi-scale approach to examine the spatial dynamics and impact of para grass cover across a 181 km² floodplain of KNP. First, we measure the overall displacement of different native vegetation communities across the floodplain from 1986 to 2006. Using high spatial resolution satellite imagery in conjunction with historical aerial-photo mapping, we then measure finer-scale, inter-annual, changes between successive dry seasons from 1990 to 2010 (for a 48 km² focus area); Para grass presence-absence maps from satellite imagery (2002 to 2010) were produced with an object-based machine-learning approach (stochastic gradient boosting). Changes, over time, in mapped para grass areas were then related to maps of depth-habitat and inter-annual fire histories. Para grass invasion and establishment patterns varied greatly in time and space. Wild rice communities were the most frequently invaded, but the establishment and persistence of para grass fluctuated greatly between years, even within previously invaded communities. However, these different patterns were also shown to vary with different depth-habitat and recent fire history. These dynamics have not been previously documented and this understanding presents opportunities for intensive para grass management in areas of high conservation value, such as those occupied by wild rice. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Wildlife and Habitat in a Changing World)

► Show Figures

Graphical abstract

15 pages, 2675 KiB

Open AccessEditor’s ChoiceArticle

Environmental Differences between Migratory and Resident Ungulates—Predicting Movement Strategies in Rocky Mountain Mule Deer (Odocoileus hemionus) with Remotely Sensed Plant Phenology, Snow, and Land Cover

by Benjamin Robb, Qiongyu Huang, Joseph O. Sexton, David Stoner and Peter Leimgruber

Remote Sens. 2019, 11(17), 1980; https://doi.org/10.3390/rs11171980 - 22 Aug 2019

Cited by 6 | Viewed by 4123

Abstract

Migration is a valuable life history strategy for many species because it enables individuals to exploit spatially and temporally variable resources. Globally, the prevalence of species’ migratory behavior is decreasing as individuals forgo migration to remain resident year-round, an effect hypothesized to result from anthropogenic changes to landscape dynamics. Efforts to conserve and restore migrations require an understanding of the ecological characteristics driving the behavioral tradeoff between migration and residence. We identified migratory and resident behaviors of 42 mule deer (Odocoileus hemionus) based on GPS locations and correlated their locations to remotely sensed indicators of forage quality, land cover, snow cover, and human land use. The model classified mule deer seasonal migratory and resident niches with an overall accuracy of 97.8% and cross-validated accuracy of 81.2%. The distance to development was the most important variable in discriminating in which environments these behaviors occur, with resident niche space most often closer to developed areas than migratory niches. Additionally, snow cover in December was important for discriminating summer migratory niches. This approach demonstrates the utility of niche analysis based on remotely sensed environmental datasets and provides empirical evidence of human land use impacts on large-scale wildlife migrations. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Wildlife and Habitat in a Changing World)

► Show Figures