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Remote Sensing for Applied Wildlife Ecology

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

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 13183

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Special Issue Editor

Dr. Steven E. Sesnie

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Guest Editor

Division of Biological Sciences, US. Fish and Wildlife Service, Albuquerque, NM, USA
Interests: landscape and spatial ecology; animial habitat characteriziation from passive and active remote sensing systems; machine learning; LULC change

Special Issue Information

Dear Colleagues,

Wildlife Ecology broadly defines a field of study that seeks to attain practical knowledge about the relationship between animals and their environment. Applied science aims to understand animal habitat relationships, population size, density, and dynamics; and movement and distribution are essential to making informed conservation decisions. As such, technical approaches in Wildlife Ecology have increasingly been integrated with landscape and disturbance ecology, spatial data science and other natural resource fields.

Environmental data acquired through remote sensing continues to play a vital role in Wildlife Ecology. The widening range of remote sensing tools, techniques, and sensor types creates improved opportunities for investigating spatial and temporal changes in animal habitats. The increased spatial, spectral and temporal resolution of data collected from active and passive sensors helps quantify habitat conditions in ways complementary to wildlife field observations. Moreover, three-dimensional data developed through sensors such as discrete return and waveform Light Detection and Ranging (LiDAR) and high overlap aerial imagery provide novel methods to evaluate vertical habitat structure and heterogeneity. Imagery taken at a high temporal frequency can further reveal vegetation phenology, seasonal differences, and conditions that can help distinguish critical habitat features.

This Special Issue focuses on applications that combine remotely sensed data with animal detections, locations, and other phenomena to estimate key habitat parameters that often change over time. We encourage authors to submit novel research, reviews and opinion pieces that explore aspects of Wildlife Ecology by developing data acquired through remote sensing.

Dr. Steven E. Sesnie
Guest Editor

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

Habitat structure and composition
Habitat relationships
Habitat change dynamics
Animal density
Animal distribution
Animal movement
Remote sensing

Published Papers (5 papers)

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Research

11 pages, 1758 KiB

Open AccessCommunication

Drone-Based Assessment of Marine Megafauna off Wave-Exposed Sandy Beaches

by Brendan P. Kelaher, Kim I. Monteforte, Stephen G. Morris, Thomas A. Schlacher, Duane T. March, James P. Tucker and Paul A. Butcher

Remote Sens. 2023, 15(16), 4018; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15164018 - 14 Aug 2023

Viewed by 1911

Abstract

The wave-impacted waters off exposed sandy beaches support marine megafauna, including dolphins, whales, sharks, rays and turtles. To characterise variation in megafaunal assemblages in this challenging habitat, we used drone-based remote sensing to survey marine megafauna off 23 beaches along 1050 km of the New South Wales (NSW, Australia) coast from 2017 to 2020. The surveys occurred from September to May and included 17,085 drone flights, with megafaunal abundances standardised by flight hours. In total, we identified 3838 individual animals from 16 taxa, although no megafauna was observed off 5 of the 23 beaches surveyed. Bottlenose dolphins were the most commonly sighted taxa and accounted for 82.3% of total megafaunal abundance. Cownose (6.7%) and eagle (3.4%) rays were the next most abundant taxa, with potentially dangerous sharks being rarely sighted (<1% of total megafauna). The megafaunal assemblages off wave-exposed beaches in northern NSW significantly differed from those in the central region, whereas the assemblages off the central region and southern NSW did not differ significantly. Wave exposure and water temperature were the best predictors of megafaunal assemblage structure. The richness of marine megafauna off ocean beaches was significantly greater in northern than southern NSW, and turtles were only observed off beaches in the northern region. However, variation in megafaunal richness, as well as the abundances of total megafauna, dolphins, rays, sharks and turtles were not significantly explained by water temperature, wave height, distance to estuary, or proximity to the nearest reef. Overall, drone-based surveys determined that megafaunal assemblages off wave-exposed beaches are characterised by sparse individuals or small groups of sharks, turtles and rays, punctuated by occasional large aggregations of dolphins, cownose rays and schooling sharks. The exception to this pattern was bottlenose dolphins, which routinely patrolled some beaches in northern NSW. Full article

(This article belongs to the Special Issue Remote Sensing for Applied Wildlife Ecology)

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21 pages, 4305 KiB

Open AccessArticle

Trends in Lesser Prairie-Chicken Habitat Extent and Distribution on the Southern High Plains

by Carlos Portillo-Quintero, Blake Grisham, David Haukos, Clint W. Boal, Christian Hagen, Zhanming Wan, Mukti Subedi and Nwasinachi Menkiti

Remote Sens. 2022, 14(15), 3780; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153780 - 06 Aug 2022

Viewed by 1462

Abstract

The lesser prairie-chicken (Tympanuchus pallidicinctus) is a species of prairie grouse that occupies grassland ecosystems in the Southern and Central High Plains of the Great Plains. Reduced abundance and occupied ranges have led to increased conservation efforts throughout the species’ range. Habitat loss is considered the predominant cause of these declines. In the Southern High Plains of Texas and New Mexico, lesser prairie-chicken habitat corresponds to the Sand Shinnery Oak Prairie Ecoregion, which is comprised of a mixture of sand shinnery oak (Quercus havardii)-dominated grasslands, sand sagebrush (Artemisia filifolia)-dominated grasslands, and mixed grasslands. In sand shinnery oak–grassland communities, conversion to row-crop agriculture, continuous unmanaged livestock grazing, restriction of natural fire, invasive plant species (e.g., mesquite (Prosopis spp.)), extensive use of herbicides, energy development, and a variety of other factors have also negatively affected ecosystem extent and function. We integrated historical maps and remote sensing-derived information to measure trends in the extent and geographical distribution of sand shinnery oak prairies in eastern New Mexico and northwest Texas. Potential lesser prairie-chicken habitat was reduced by 56% from a potential of 43,258 km² to 18,908 km² in ~115 years (since pre-settlement). Our assessment indicated both mixed grasslands and sand shinnery oak-dominated grasslands were transformed from large parcels of existing vegetation communities to urban settlements, row crops, roads, and industrial land uses by the 1970s. Currently, potential habitat is highly fragmented and restricted to isolated locations in Texas and New Mexico, with an increasing dominance in mixed grasslands, especially in the southeastern portion of the lesser prairie-chicken range. Sand shinnery oak-dominated grasslands have been declining rapidly, from 69% of its potential extent in 1985, 65% in 1995, 54% in 2005, to 42% in 2015. Mixed grasslands drastically declined to 50% of its potential distribution by 1985. Since then, it has been stable until the 2005–2015 period when it declined to 45% of its potential extent. Based on the 2015 assessment, the current potential habitat for lesser prairie-chicken is estimated at 18,908 km² (1,890,800 ha or 4.6 million acres), where 13,126 km² corresponds to mixed grasslands and 5782 km² corresponds to sand shinnery oak-dominated grasslands. Full article

(This article belongs to the Special Issue Remote Sensing for Applied Wildlife Ecology)

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20 pages, 32917 KiB

Open AccessArticle

Towards Automated Detection and Localization of Red Deer Cervus elaphus Using Passive Acoustic Sensors during the Rut

by Egils Avots, Alekss Vecvanags, Jevgenijs Filipovs, Agris Brauns, Gundars Skudrins, Gundega Done, Janis Ozolins, Gholamreza Anbarjafari and Dainis Jakovels

Remote Sens. 2022, 14(10), 2464; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102464 - 20 May 2022

Cited by 3 | Viewed by 2066

Abstract

Passive acoustic sensors have the potential to become a valuable complementary component in red deer Cervus elaphus monitoring providing deeper insight into the behavior of stags during the rutting period. Automation of data acquisition and processing is crucial for adaptation and wider uptake of acoustic monitoring. Therefore, an automated data processing workflow concept for red deer call detection and localization was proposed and demonstrated. The unique dataset of red deer calls during the rut in September 2021 was collected with four GPS time-synchronized microphones. Five supervised machine learning algorithms were tested and compared for the detection of red deer rutting calls where the support-vector-machine-based approach demonstrated the best performance of −96.46% detection accuracy. For sound source location, a hyperbolic localization approach was applied. A novel approach based on cross-correlation and spectral feature similarity was proposed for sound delay assessment in multiple microphones resulting in the median localization error of 16 m, thus providing a solution for automated sound source localization—the main challenge in the automation of the data processing workflow. The automated approach outperformed manual sound delay assessment by a human expert where the median localization error was 43 m. Artificial sound records with a known location in the pilot territory were used for localization performance testing. Full article

(This article belongs to the Special Issue Remote Sensing for Applied Wildlife Ecology)

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18 pages, 1635 KiB

Open AccessArticle

Evaluating the Use of Lidar to Discern Snag Characteristics Important for Wildlife

by Jessica M. Stitt, Andrew T. Hudak, Carlos A. Silva, Lee A. Vierling and Kerri T. Vierling

Remote Sens. 2022, 14(3), 720; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030720 - 03 Feb 2022

Cited by 5 | Viewed by 2895

Abstract

Standing dead trees (known as snags) are historically difficult to map and model using airborne laser scanning (ALS), or lidar. Specific snag characteristics are important for wildlife; for instance, a larger snag with a broken top can serve as a nesting platform for raptors. The objective of this study was to evaluate whether characteristics such as top intactness could be inferred from discrete-return ALS data. We collected structural information for 198 snags in closed-canopy conifer forest plots in Idaho. We selected 13 lidar metrics within 5 m diameter point clouds to serve as predictor variables in random forest (RF) models to classify snags into four groups by size (small (<40 cm diameter) or large (≥40 cm diameter)) and intactness (intact or broken top) across multiple iterations. We conducted these models first with all snags combined, and then ran the same models with only small or large snags. Overall accuracies were highest in RF models with large snags only (77%), but kappa statistics for all models were low (0.29–0.49). ALS data alone were not sufficient to identify top intactness for large snags; future studies combining ALS data with other remotely sensed data to improve classification of snag characteristics important for wildlife is encouraged. Full article

(This article belongs to the Special Issue Remote Sensing for Applied Wildlife Ecology)

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19 pages, 3508 KiB

Open AccessArticle

Landscape Structure and Seasonality: Effects on Wildlife Species Richness and Occupancy in a Fragmented Dry Forest in Coastal Ecuador

by Xavier Haro-Carrión, Jon Johnston and María Juliana Bedoya-Durán

Remote Sens. 2021, 13(18), 3762; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183762 - 19 Sep 2021

Cited by 2 | Viewed by 2488

Abstract

Despite high fragmentation and deforestation, little is known about wildlife species richness and occurrence probabilities in tropical dry forest (TDF) landscapes. To fill this gap in knowledge, we used a Sentinel-2-derived land-cover map, Normalized Difference Vegetation Index (NDVI) data and a multi-species occupancy model to correct for detectability to assess the effect of landscape characteristics on medium and large mammal occurrence and richness in three TDF areas that differ in disturbance and seasonality in Ecuador. We recorded 15 species of medium and large mammals, distributed in 12 families; 1 species is critically Endangered, and 2 are Near-Threatened. The results indicate that species occupancy is related to low forest cover and high vegetation seasonality (i.e., high difference in NDVI between the wet and dry seasons). We believe that the apparent negative effect of forest cover is an indicator of species tolerance for disturbance. The three sampling areas varied from 98% to 40% forest cover, yet species richness and occupancy were not significantly different among them. Vegetation seasonality indicates that more seasonal forests (i.e., those where most tree species lose their leaves during the dry season) tend to have higher mammal species occupancy compared to less seasonal, semi-deciduous forests. Overall, occupancy did not vary between the dry and wet seasons, but species-specific data indicate that some species exhibit higher occupancy during the wet season. This research offers a good understanding of mammal species’ responses to habitat disturbance and fragmentation in TDFs and provides insights to promote their conservation. Full article

(This article belongs to the Special Issue Remote Sensing for Applied Wildlife Ecology)

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