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Use of Remote Sensing Techniques for Wildlife Habitat Assessment

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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 (31 July 2022) | Viewed by 27492

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Special Issue Editor

Dr. Glen S. Brown

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Guest Editor

Wildlife Research and Monitoring Section, Ministry of Natural Resources and Forestry, c/o Trent University, DNA Building, 2140 East Bank Drive, Peterborough, ON K9L 1Z8, Canada
Interests: biodiversity conservation; wildlife habitat modeling; animal ecology; remote sensing; UAV; landscape dynamics

Special Issue Information

Habitat loss or alteration is considered one of the greatest threats to biodiversity. Remote sensing has a wide range of applications in wildlife habitat assessment, such as mapping availability and distribution at multiple scales, informing conservation and resource management decisions, and tracking changes through time for assessment of threats to wildlife or the effectiveness of management. Studies of wildlife habitat have benefited greatly from the widespread availability of multipurpose land cover classifications derived from satellite imagery. However, such approaches may fail to characterise important fine scale or structural aspects of habitat that affect wildlife behaviour or population response. Significant advances in high resolution data capture for a range of sensors (e.g., optical, LiDAR, radar) have great potential to provide fine scale mapping of wildlife habitat at large spatial extents. However, additional research is needed to make better use of these emerging remote sensing technologies to support applications to wildlife habitat assessment, conservation, and management.

The works presented in this Special Issue describe advances in the integration of emerging remote sensing technologies to support wildlife habitat assessment at multiple scales. Contributions are encouraged that can demonstrate applications of high-resolution sensor information (on the ground, in the air, in space) to quantify structural components of habitat influential to wildlife behaviour or population response. Studies that describe integrated approaches to monitoring and management evaluation are also welcome.

Contributions in one or more of the following areas are encouraged:

Applications of high-resolution remote sensing that enable comparison of fine and course-scale habitat assessments;
Development of quantitative indices of habitat structure (e.g., vertical or horizontal vegetation structure, snags, downed logs);
Novel or modern quantitative approaches to data integration and habitat modelling;
Studies that evaluate or consider appropriate matching of scales and consideration of error or uncertainty when combining wildlife and remote sensing data;
Validation of remote sensing habitat information (e.g., using in situ measurements, manned or unmanned aerial vehicle);

Studies that demonstrate approaches to link remote sensing with habitat mapping and change detection for applications in effectiveness monitoring of management and conservation.

Dr. Glen S. Brown
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.

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Published Papers (6 papers)

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Research

22 pages, 8178 KiB

Open AccessArticle

Development of Semantic Maps of Vegetation Cover from UAV Images to Support Planning and Management in Fine-Grained Fire-Prone Landscapes

by Bianka Trenčanová, Vânia Proença and Alexandre Bernardino

Remote Sens. 2022, 14(5), 1262; https://doi.org/10.3390/rs14051262 - 4 Mar 2022

Cited by 28 | Viewed by 3088

Abstract

In Mediterranean landscapes, the encroachment of pyrophytic shrubs is a driver of more frequent and larger wildfires. The high-resolution mapping of vegetation cover is essential for sustainable land planning and the management for wildfire prevention. Here, we propose methods to simplify and automate the segmentation of shrub cover in high-resolution RGB images acquired by UAVs. The main contribution is a systematic exploration of the best practices to train a convolutional neural network (CNN) with a segmentation network architecture (U-Net) to detect shrubs in heterogeneous landscapes. Several semantic segmentation models were trained and tested in partitions of the provided data with alternative methods of data augmentation, patch cropping, rescaling and hyperparameter tuning (the number of filters, dropout rate and batch size). The most effective practices were data augmentation, patch cropping and rescaling. The developed classification model achieved an average F1 score of 0.72 on three separate test datasets even though it was trained on a relatively small training dataset. This study demonstrates the ability of state-of-the-art CNNs to map fine-grained land cover patterns from RGB remote sensing data. Because model performance is affected by the quality of data and labeling, an optimal selection of pre-processing practices is a requisite to improve the results. Full article

(This article belongs to the Special Issue Use of Remote Sensing Techniques for Wildlife Habitat Assessment)

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22 pages, 14607 KiB

Open AccessArticle

Functional Analysis for Habitat Mapping in a Special Area of Conservation Using Sentinel-2 Time-Series Data

by Simone Pesaresi, Adriano Mancini, Giacomo Quattrini and Simona Casavecchia

Remote Sens. 2022, 14(5), 1179; https://doi.org/10.3390/rs14051179 - 27 Feb 2022

Cited by 8 | Viewed by 4026

Abstract

The mapping and monitoring of natural and semi-natural habitats are crucial activities and are regulated by European policies and regulations, such as the 92/43/EEC. In the Mediterranean area, which is characterized by high vegetational and environmental diversity, the mapping and monitoring of habitats are particularly difficult and often exclusively based on in situ observations. In this scenario, it is necessary to automate the generation of updated maps to support the decisions of policy makers. At present, the availability of high spatiotemporal resolution data provides new possibilities for improving the mapping and monitoring of habitats. In this work, we present a methodology that, starting from remotely sensed time-series data, generates habitat maps using supervised classification supported by Functional Data Analysis. We constructed the methodology using Sentinel-2 data in the Mediterranean Special Area of Conservation “Gola di Frasassi” (Code: IT5320003). In particular, the training set uses 308 field plots with 11 target classes (five forests, two shrubs, one grassland, one mosaic, one extensive crop, and one urban land). Starting from vegetation index time-series data, Functional Principal Component Analysis was applied to derive FPCA scores and components. In particular, in the classification stage, the FPCA scores are considered as features. The obtained results out-performed a previous map derived from photo-interpretation by domain experts. We obtained an overall accuracy of 85.58% using vegetation index time-series, topography, and lithology data. The main advantages of the proposed approach are the capability to efficiently compress high dimensional data (dense remote-sensing time series) providing results in a compact way (e.g., FPCA scores and mean seasonal time profiles) that: (i) facilitate the link between remote sensing with habitat mapping and monitoring and their ecological interpretation and (ii) could be complementary to species-based approaches in plant community ecology and phytosociology. Full article

(This article belongs to the Special Issue Use of Remote Sensing Techniques for Wildlife Habitat Assessment)

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13 pages, 3573 KiB

Open AccessArticle

Quantification of Foraging Areas for the Northern Bald Ibis (Geronticus eremita) in the Northern Alpine Foothills: A Random Forest Model Fitted with Optical and Actively Sensed Earth Observation Data

by Helena Wehner, Katharina Huchler and Johannes Fritz

Remote Sens. 2022, 14(4), 1015; https://doi.org/10.3390/rs14041015 - 19 Feb 2022

Cited by 3 | Viewed by 5040

Abstract

The Northern Bald Ibis (Geronticus eremita, NBI) is an endangered migratory species, which went extinct in Europe in the 17th century. Currently, a translocation project in the frame of the European LIFE program is carried out, to reintroduce a migratory population with breeding colonies in the northern and southern Alpine foothills and a common wintering area in southern Tuscany. The population meanwhile consists of about 200 individuals, with about 90% of them carrying a GPS device on their back. We used biologging data from 2021 to model the habitat suitability for the species in the northern Alpine foothills. To set up a species distribution model, indices describing environmental conditions were calculated from satellite images of Landsat-8, and in addition to the well-proven use of optical remote sensing data, we also included Sentinel-1 actively sensed observation data, as well as climate and urbanization data. A random forest model was fitted on NBI GPS positions, which we used to identify regions with high predicted foraging suitability within the northern Alpine foothills. The model resulted in 84.5% overall accuracy. Elevation and slope had the highest predictive power, followed by grass cover and VV intensity of Sentinel-1 radar data. The map resulting from the model predicts the highest foraging suitability for valley floors, especially of Inn, Rhine, and Salzach-Valley as well as flatlands, like the Swiss Plateau and the agricultural areas surrounding Lake Constance. Areas with a high suitability index largely overlap with known historic breeding sites. This is particularly noteworthy because the model only refers to foraging habitats without considering the availability of suitable breeding cliffs. Detailed analyses identify the transition zone from extensive grassland management to intensive arable farming as the northern range limit. The modeling outcome allows for defining suitable areas for further translocation and management measures in the frame of the European NBI reintroduction program. Although required in the international IUCN translocation guidelines, the use of models in the context of translocation projects is still not common and in the case of the Northern Bald Ibis not considered in the present Single Species Action Plan of the African-Eurasian Migratory Water bird Agreement. Our species distribution model represents a contemporary snapshot, but sustainability is essential for conservation planning, especially in times of climate change. In this regard, a further model could be optimized by investigating sustainable land use, temporal dynamics, and climate change scenarios. Full article

(This article belongs to the Special Issue Use of Remote Sensing Techniques for Wildlife Habitat Assessment)

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16 pages, 4217 KiB

Open AccessArticle

Dolines and Cats: Remote Detection of Karst Depressions and Their Application to Study Wild Felid Ecology

by Špela Čonč, Teresa Oliveira, Ruben Portas, Rok Černe, Mateja Breg Valjavec and Miha Krofel

Remote Sens. 2022, 14(3), 656; https://doi.org/10.3390/rs14030656 - 29 Jan 2022

Cited by 13 | Viewed by 4515

Abstract

Automatic methods for detecting and delineating relief features allow remote and low-cost mapping, which has an outstanding potential for wildlife ecology and similar research. We applied a filled-DEM (digital elevation model) method using LiDAR (Light Detection and Ranging) data to automatically detect dolines and other karst depressions in a rugged terrain of the Dinaric Mountains, Slovenia. Using this approach, we detected 9711 karst depressions in a 137 km² study area and provided their basic morphometric characteristics, such as perimeter length, area, diameter, depth, and slope. We performed visual validation based on shaded relief, which indicated 83.5% accordance in detecting depressions. Although the method has some drawbacks, it proved suitable for detection, general spatial analysis, and calculation of morphometric characteristics of depressions over a large scale in remote and forested areas. To demonstrate its applicability for wildlife research, we applied it in a preliminary study in combination with GPS-telemetry data to assess the selection of these features by two wild felids, the Eurasian lynx (Lynx lynx) and the European wildcat (Felis silvestris). Both species selected for vicinity of karst depressions, among which they selected for larger karst depressions. Lynx also regularly killed ungulate prey near these features, as we found more than half of lynx prey remains inside or in close vicinity of karst depressions. These results illustrate that karstic features could play an important role in the ecology of wild felids and warrant further research, which could be considerably assisted with the use of remote detection of relief features. Full article

(This article belongs to the Special Issue Use of Remote Sensing Techniques for Wildlife Habitat Assessment)

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21 pages, 4486 KiB

Open AccessArticle

Characterizing Off-Highway Road Use with Remote-Sensing, Social Media and Crowd-Sourced Data: An Application to Grizzly Bear (Ursus Arctos) Habitat

by Sean P. Kearney, Terrence A. Larsen, Tristan R. H. Goodbody, Nicholas C. Coops and Gordon B. Stenhouse

Remote Sens. 2021, 13(13), 2547; https://doi.org/10.3390/rs13132547 - 29 Jun 2021

Cited by 3 | Viewed by 2587

Abstract

Characterizing roads is important for conservation since the relationship between road use and ecological impact can vary across species. However, road use is challenging to monitor due to limited data and high spatial-temporal variability, especially for unpaved roads, which often coincide with critical habitats. In this study, we developed and evaluated two methods to characterize off-highway road use across a large management area of grizzly bear (Ursus arctos) habitat using: (1) a ‘network-based’ approach to connect human activity hotspots identified from social media posts and remotely detected disturbances and (2) an ‘image-based’ approach, in which we modeled road surface conditions and travel speed from high spatial resolution satellite imagery trained with crowd-sourced smartphone data. To assess the differences between these approaches and their utility for characterizing roads in the context of habitat integrity, we evaluated how behavioural patterns of global positioning system (GPS)-collared grizzly bears were related to road use characterized by these methods compared to (a) assuming all roads have equal human activity and (b) using a ‘reference’ road classification from a government database. The network- and image-based methods showed similar patterns of road use and grizzly bear response compared to the reference, and all three revealed nocturnal behaviour near high-use roads and better predicted grizzly bear habitat selection compared to assuming all roads had equal human activity. The network- and image-based methods show promise as cost-effective approaches to characterize road use for conservation applications where data is not available. Full article

(This article belongs to the Special Issue Use of Remote Sensing Techniques for Wildlife Habitat Assessment)

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16 pages, 3079 KiB

Open AccessArticle

Handheld Laser Scanning Detects Spatiotemporal Differences in the Development of Structural Traits among Species in Restoration Plantings

by Nicolò Camarretta, Peter A. Harrison, Arko Lucieer, Brad M. Potts, Neil Davidson and Mark Hunt

Remote Sens. 2021, 13(9), 1706; https://doi.org/10.3390/rs13091706 - 28 Apr 2021

Cited by 10 | Viewed by 3280

Abstract

A major challenge in ecological restoration is assessing the success of restoration plantings in producing habitats that provide the desired ecosystem functions and services. Forest structural complexity and biomass accumulation are key measures used to monitor restoration success and are important factors determining animal habitat availability and carbon sequestration. Monitoring their development through time using traditional field measurements can be costly and impractical, particularly at the landscape-scale, which is a common requirement in ecological restoration. We explored the application of proximal sensing technology as an alternative to traditional field surveys to capture the development of key forest structural traits in a restoration planting in the Midlands of Tasmania, Australia. We report the use of a hand-held laser scanner (ZEB1) to measure annual changes in structural traits at the tree-level, in a mixed species common-garden experiment from seven- to nine-years after planting. Using very dense point clouds, we derived estimates of multiple structural traits, including above ground biomass, tree height, stem diameter, crown dimensions, and crown properties. We detected annual increases in most LiDAR-derived traits, with individual crowns becoming increasingly interconnected. Time by species interaction were detected, and were associated with differences in productivity between species. We show the potential for remote sensing technology to monitor temporal changes in forest structural traits, as well as to provide base-line measures from which to assess the restoration trajectory towards a desired state. Full article

(This article belongs to the Special Issue Use of Remote Sensing Techniques for Wildlife Habitat Assessment)

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