Assessing Greater Sage-Grouse Breeding Habitat With Aerial and Ground Imagery

Beck, Jeffrey L.; Booth, D. Terrance; Kennedy, Carmen L.

doi:10.2111/rem-d-12-00141.1

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…Hugenholtz et al (2013) compared topographical models of dry grasslands derived from imagery acquired by small fixed-wing UAS to conventional LiDAR-derived models, while McGwire et al (2013) compared high-resolution (2 cm) imagery collected by an unmanned helicopter to much coarser (30 m) Landsat imagery for estimating green leaf cover in a semi-arid rangeland. Beck et al (2014) assessed endangered greater sage grouse (Centrocercus urophasianus) grassland breeding habitat with the aid of ultra-high-resolution (down to 1 mm) imagery collected by a small fixed-wing UAS.…”

Section: Rangelandsmentioning

confidence: 99%

Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?

Chabot

Bird

2015

J. Unmanned Veh. Sys.

218

183

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Since the turn of the century, emerging unmanned aircraft systems (UAS) have found increasingly diverse applications in wildlife science as convenient, very highresolution remote sensing devices. Achieved or conceptualized applications include optical surveying and observation of animals, autonomous wildlife telemetry tracking, and habitat research and monitoring. As the technology continues to progress and interest from the wildlife science community grows, there may yet be much untapped potential for UAS to contribute to the discipline. We present a review of the published primary literature on the application of UAS in wildlife science and related fields. This is followed by a systematic review of the broader wildlife science literature published since the turn of the century to assess where UAS are likely to make important contributions going forward based on the trends that have emerged thus far. UAS, in particular small lightweight models, are generally well suited for collecting data at an intermediate spatial scale between what is easily coverable on the ground and what is economically coverable with conventional aircraft. They are particularly useful for monitoring wildlife and habitats in places that are difficult to access or navigate from the ground, as well as approaching sensitive or aggressive species.Key words: aerial survey, conservation, remotely piloted aircraft system, unmanned aerial vehicle, drone, wildlife monitoring.Résumé : Depuis le début du siècle, les nouveaux systèmes d'aéronefs sans pilote (UAS), en tant qu'appareils de télédétection de haute résolution pratiques, ont donné lieu à de plus en plus d'applications diverses en science de la faune. Les applications réalisées ou conceptualisées comprennent l'inventaire et l'observation optiques des animaux, la localisation autonome de la faune par télémétrie et la recherche et la surveillance des habitats. À mesure que la technologie continue de progresser et que l'intérêt de la part du milieu scientifique du domaine de la faune croît, la contribution des UAS à la discipline pourra augmenter, car il peut y avoir encore beaucoup de potentiel à exploiter. Nous présentons un examen de la littérature primaire publiée portant sur l'application des UAS dans les domaines de la science de la faune et connexes. Un examen systématique de l'ensemble de la littérature de la science de la faune publiée depuis le début du siècle s'ensuit dans le but d'évaluer, d'après les tendances qui ont émergé jusqu'à maintenant, où les UAS vont possiblement faire des contributions à l'avenir. Les UAS, en particulier les petits modèles légers, sont bien adaptés à la collecte de données à une échelle spatiale intermédiaire entre ce qui est facilement saisi sur le terrain et ce qui est saisi à l'aide d'un aéronef traditionnel. Ils sont particulièrement utiles pour la surveillance de la faune et des habitats dans les endroits difficiles à accéder ou à naviguer à partir du sol, ainsi que pour s'approcher des espèces sensibles ou agressives.

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Section: Rangelandsmentioning

confidence: 99%

Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?

Chabot

Bird

2015

J. Unmanned Veh. Sys.

218

183

View full text Add to dashboard Cite

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“…In the past, ground-derived microhabitat data were used to assess habitat selection, but its utility was limited [ 42 ]. Technology now permits the combination of high-resolution aerial imagery and ground verification, high-resolution images combined together [ 43 ], or the combination of high-resolution imagery and ground-based imagery [ 44 ] to map habitat and assess habitat selection.…”

Section: Introductionmentioning

confidence: 99%

Assessing Greater Sage-Grouse Selection of Brood-Rearing Habitat Using Remotely-Sensed Imagery: Can Readily Available High-Resolution Imagery Be Used to Identify Brood-Rearing Habitat Across a Broad Landscape?

Westover

Baxter

et al. 2016

PLoS ONE

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Greater sage-grouse populations have decreased steadily since European settlement in western North America. Reduced availability of brood-rearing habitat has been identified as a limiting factor for many populations. We used radio-telemetry to acquire locations of sage-grouse broods from 1998 to 2012 in Strawberry Valley, Utah. Using these locations and remotely-sensed NAIP (National Agricultural Imagery Program) imagery, we 1) determined which characteristics of brood-rearing habitat could be used in widely available, high resolution imagery 2) assessed the spatial extent at which sage-grouse selected brood-rearing habitat, and 3) created a predictive habitat model to identify areas of preferred brood-rearing habitat. We used AIC model selection to evaluate support for a list of variables derived from remotely-sensed imagery. We examined the relationship of these explanatory variables at three spatial extents (45, 200, and 795 meter radii). Our top model included 10 variables (percent shrub, percent grass, percent tree, percent paved road, percent riparian, meters of sage/tree edge, meters of riparian/tree edge, distance to tree, distance to transmission lines, and distance to permanent structures). Variables from each spatial extent were represented in our top model with the majority being associated with the larger (795 meter) spatial extent. When applied to our study area, our top model predicted 75% of naïve brood locations suggesting reasonable success using this method and widely available NAIP imagery. We encourage application of our methodology to other sage-grouse populations and species of conservation concern.

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“…Reliable information about the quality of habitat features is critical to predict the performance of occupying animals for their conservation (Beck et al., 2014). Studies on animal behaviors and interactions among individual species can provide answers to many questions in wildlife ecology and conservation.…”

Section: Discussionmentioning

confidence: 99%

Construct social‐behavioral association network to study management impact on waterbirds community ecology using digital video recording cameras

Rasool

Zhang

Hassan

et al. 2021

Ecology and Evolution

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Studying social‐behavior and species associations in ecological communities is challenging because it is difficult to observe the interactions in the field. Animal behavior is especially difficult to observe when selection of habitat and activities are linked to energy costs of long‐distance movement. Migrating communities tend to be resource specific and prefer environments that offer more suitability for coexisting in a shared space and time. Given the recent advances in digital technologies, digital video recording systems are gaining popularity in wildlife research and management. We used digital video recording cameras to study social interactions and species–habitat linkages for wintering waterbirds communities in shared habitats. Examining over 8,640 hr of video footages, we built tetrapartite social‐behavioral association network of wintering waterbirds over habitat (n = 5) selection events in sites with distinct management regimes. We analyzed these networks to identify hub species and species role in activity persistence, and to explore the effects of hydrological regime on these network characteristics. Although the differences in network attributes were not significant at treatment level (p = .297) in terms of network composition and keystone species composition, our results indicated that network attributes were significantly different (p = .000, r2 = .278) at habitat level. There were evidences suggesting that the habitat quality was better at the managed sites, where the formed networks had more species, more network nodes and edges, higher edge density, and stronger intra‐ and inter‐species interactions. In addition, we also calculated the species interaction preference scores (SIPS) and behavioral interaction preference scores (BIPS) of each network. The results showed that species synchronize activities in shared space for temporal niche partitioning in order to avoid or minimize any potential competition for shared space. Our social network analysis (SNA) approach is likely to provide a practical use for ecosystem management and biodiversity conservation.

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Assessing Greater Sage-Grouse Breeding Habitat With Aerial and Ground Imagery

Cited by 10 publications

References 19 publications

Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?

Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?

Assessing Greater Sage-Grouse Selection of Brood-Rearing Habitat Using Remotely-Sensed Imagery: Can Readily Available High-Resolution Imagery Be Used to Identify Brood-Rearing Habitat Across a Broad Landscape?

Construct social‐behavioral association network to study management impact on waterbirds community ecology using digital video recording cameras

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