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Increasing citizen scientist accuracy with artificial intelligence on UK camera‐trap data Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-05-19 C. R. Sharpe, R. A. Hill, H. M. Chappell, S. E. Green, K. Holden, P. Fergus, C. Chalmers, P. A. Stephens
As camera traps have become more widely used, extracting information from images at the pace they are acquired has become challenging, resulting in backlogs that delay the communication of results and the use of data for conservation and management. To ameliorate this, artificial intelligence (AI), crowdsourcing to citizen scientists and combined approaches have surfaced as solutions. Using data from
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Night lights from space: potential of SDGSAT‐1 for ecological applications Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-05-16 Dominique Weber, Janine Bolliger, Klaus Ecker, Claude Fischer, Christian Ginzler, Martin M. Gossner, Laurent Huber, Martin K. Obrist, Florian Zellweger, Noam Levin
Light pollution affects biodiversity at all levels, from genes to ecosystems, and improved monitoring and research is needed to better assess its various ecological impacts. Here, we review the current contribution of night‐time satellites to ecological applications and elaborate on the potential value of the Glimmer sensor onboard the Chinese Sustainable Development Goals Science Satellite 1 (SDGSAT‐1)
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A scalable transfer learning workflow for extracting biological and behavioural insights from forest elephant vocalizations Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-04-25 Alastair Pickering, Santiago Martinez Balvanera, Kate E. Jones, Daniela Hedwig
Animal vocalizations encode rich biological information—such as age, sex, behavioural context and emotional state—making bioacoustic analysis a promising non‐invasive method for assessing welfare and population demography. However, traditional bioacoustic approaches, which rely on manually defined acoustic features, are time‐consuming, require specialized expertise and may introduce subjective bias
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Advancing the mapping of vegetation structure in savannas using Sentinel‐1 imagery Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-04-22 Vera Thijssen, Marianthi Tangili, Ruth A. Howison, Han Olff
Vegetation structure monitoring is important for the understanding and conservation of savanna ecosystems. Optical satellite imagery can be used to estimate canopy cover, but provides limited information about the structure of savannas, and is restricted to daytime and clear‐sky captures. Active remote sensing can potentially overcome this. We explore the utility of C‐band synthetic aperture radar
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Object detection‐assisted workflow facilitates cryptic snake monitoring Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-04-21 Storm Miller, Michael Kirkland, Kristen M. Hart, Robert A. McCleery
Camera traps are an important tool used to study rare and cryptic animals, including snakes. Time‐lapse photography can be particularly useful for studying snakes that often fail to trigger a camera's infrared motion sensor due to their ectothermic nature. However, the large datasets produced by time‐lapse photography require labor‐intensive classification, limiting their use in large‐scale studies
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Towards edge processing of images from insect camera traps Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-04-17 Kim Bjerge, Henrik Karstoft, Toke T. Høye
Insects represent nearly half of all known multicellular species, but knowledge about them lags behind for most vertebrate species. In part for this reason, they are often neglected in biodiversity conservation policies and practice. Computer vision tools, such as insect camera traps, for automated monitoring have the potential to revolutionize insect study and conservation. To further advance insect
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Application of computer vision for off‐highway vehicle route detection: A case study in Mojave desert tortoise habitat Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-04-07 Alexander J. Robillard, Madeline Standen, Noah Giebink, Mark Spangler, Amy C. Collins, Brian Folt, Andrew Maguire, Elissa M. Olimpi, Brett G. Dickson
Driving off‐highway vehicles (OHVs), which contributes to habitat degradation and fragmentation, is a common recreational activity in the United States and other parts of the world, particularly in desert environments with fragile ecosystems. Although habitat degradation and mortality from the expansion of OHV networks are thought to have major impacts on desert species, comprehensive maps of OHV route
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Woody cover and geology as regional‐scale determinants of semi‐arid savanna stability Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-03-28 Liezl Mari Vermeulen, Koenraad Van Meerbeek, Paulo Negri Bernardino, Jasper Slingsby, Bruno Verbist, Ben Somers
Savannas, defined by a balance of woody and herbaceous vegetation, are vital for global biodiversity and carbon sequestration. Yet, their stability is increasingly at risk due to climate change and human impacts. The responses of these ecosystems to extreme drought events remain poorly understood, especially in relation to the regional variations in soil, terrain, climate history and disturbance legacy
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How to achieve accurate wildlife detection by using vehicle‐mounted mobile monitoring images and deep learning? Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-03-14 Leilei Shi, Jixi Gao, Fei Cao, Wenming Shen, Yue Wu, Kai Liu, Zheng Zhang
With the advancement of artificial intelligence (AI) technologies, vehicle‐mounted mobile monitoring systems have become increasingly integrated into wildlife monitoring practices. However, images captured through these systems often present challenges such as low resolution, small target sizes, and partial occlusions. Consequently, detecting animal targets using conventional deep‐learning networks
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Bridging the gap in deep seafloor management: Ultra fine‐scale ecological habitat characterization of large seascapes Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-03-12 Ole Johannes Ringnander Sørensen, Itai van Rijn, Shai Einbinder, Hagai Nativ, Aviad Scheinin, Ziv Zemah‐Shamir, Eyal Bigal, Leigh Livne, Anat Tsemel, Or M. Bialik, Gleb Papeer, Dan Tchernov, Yizhaq Makovsky
The United Nations' sustainable development goal to designate 30% of the oceans as marine protected areas by 2030 requires practical management tools, and in turn ecologically meaningful mapping of the seafloor. Particularly challenging is the mesophotic zone, a critical component of the marine system, a biodiversity hotspot, and a potential refuge. Here, we introduce a novel seafloor habitat management
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Automated extraction of right whale morphometric data from drone aerial photographs Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-03-12 Chhandak Bagchi, Josh Medina, Duncan J. Irschick, Subhransu Maji, Fredrik Christiansen
Aerial photogrammetry is a popular non‐invasive tool to measure the size, body morphometrics and body condition of wild animals. While the method can generate large datasets quickly, the lack of efficient processing tools can create bottlenecks that delay management actions. We developed a machine learning algorithm to automatically measure body morphometrics (body length and widths) of southern right
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Quantifying nocturnal bird migration using acoustics: opportunities and challenges Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-03-11 Siméon Béasse, Louis Sallé, Paul Coiffard, Birgen Haest
Acoustic recordings have emerged as a promising tool to monitor nocturnal bird migration, as it can uniquely provide species‐level detection of migratory movements under the darkness of the night sky. This study explores the use of acoustics to quantify nocturnal bird migration across Europe, a region where research on the topic remains relatively sparse. We examine three migration intensity measures
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Remotely sensing coral bleaching in the Red Sea Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-03-11 Elamurugu Alias Gokul, Dionysios E. Raitsos, Robert J. W. Brewin, Susana Carvalho, Khaled Asfahani, Ibrahim Hoteit
Coral bleaching, often triggered by oceanic warming, has a devastating impact on coral reef systems, resulting in substantial alterations to biodiversity and ecosystem services. For conservation management, an effective technique is needed to not only detect and monitor coral bleaching events but also to predict their severity levels. By combining high‐resolution satellite measurements (Sentinel‐2
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Impacts of fire on canopy structure and its resilience depend on successional stage in Amazonian secondary forests Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-02-15 Laura B. Vedovato, Luiz E. O. C. Aragão, Danilo R. A. Almeida, David C. Bartholomew, Mauro Assis, Ricardo Dalagnol, Eric B. Gorgens, Celso H. L. Silva‐Junior, Jean P. Ometto, Aline Pontes‐Lopes, Carlos A. Silva, Ruben Valbuena, Ted R. Feldpausch
Secondary forests in the Amazon are important carbon sinks, biodiversity reservoirs, and connections between forest fragments. However, their regrowth is highly threatened by fire. Using airborne laser scanning (ALS), surveyed between 2016 and 2018, we analyzed canopy metrics in burned (fires occurred between 2001 and 2018) and unburned secondary forests across different successional stages and their
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Automated identification of hedgerows and hedgerow gaps using deep learning Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-02-15 J. M. Wolstenholme, F. Cooper, R. E. Thomas, J. Ahmed, K. J. Parsons, D. R. Parsons
Hedgerows are a key component of the UK landscape that form boundaries, borders and limits of land whilst providing vital landscape‐scale ecological connectivity for a range of organisms. They are diverse habitats in the agricultural landscape providing a range of ecosystem services. Poorly managed hedgerows often present with gaps, reducing their ecological connectivity, resulting in fragmented habitats
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Alpine greening deciphered by forest stand and structure dynamics in advancing treelines of the southwestern European Alps Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2025-01-02 Arthur Bayle, Baptiste Nicoud, Jérôme Mansons, Loïc Francon, Christophe Corona, Philippe Choler
Multidecadal time series of satellite observations, such as those from Landsat, offer the possibility to study trends in vegetation greenness at unprecedented spatial and temporal scales. Alpine ecosystems have exhibited large increases in vegetation greenness as seen from space; nevertheless, the ecological processes underlying alpine greening have rarely been investigated. Here, we used a unique
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The secret acoustic world of leopards: A paired camera trap and bioacoustics survey facilitates the individual identification of leopards via their roars Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-12-23 Jonathan Growcott, Alex Lobora, Andrew Markham, Charlotte E. Searle, Johan Wahlström, Matthew Wijers, Benno I. Simmons
Conservation requires accurate information about species occupancy, populations and behaviour. However, gathering these data for elusive, solitary species, such as leopards (Panthera pardus), is often challenging. Utilizing novel technologies that augment data collection by exploiting different species' traits could enable monitoring at larger spatiotemporal scales. Here, we conducted the first, large‐scale
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Mapping oil palm plantations and their implications on forest and great ape habitat loss in Central Africa Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-12-16 Mohammed S. Ozigis, Serge Wich, Adrià Descals, Zoltan Szantoi, Erik Meijaard
Oil palm (Elaeis guineensis) cultivation in Central Africa (CA) has become important because of the increased global demand for vegetable oils. The region is highly suitable for the cultivation of oil palm and this increases pressure on forest biodiversity in the region. Accurate maps are therefore needed to understand trends in oil palm expansion for landscape‐level planning, conservation management
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The untapped potential of camera traps for farmland biodiversity monitoring: current practice and outstanding agroecological questions Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-12-13 Stephanie Roilo, Tim R. Hofmeester, Magali Frauendorf, Anna Widén, Anna F. Cord
Agroecosystems are experiencing a biodiversity crisis. Biodiversity monitoring is needed to inform conservation, but existing monitoring schemes lack standardisation and are biased towards birds, insects and plants. Automated monitoring techniques offer a promising solution, but while passive acoustic monitoring and remote sensing are increasingly used, the potential of camera traps (CTs) in farmland
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Quantifying range‐ and topographical biases in weather surveillance radar measures of migratory bird activity Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-12-13 Miguel F. Jimenez, Birgen Haest, Ali Khalighifar, Annika L. Abbott, Abigail Feuka, Aitao Liu, Kyle G. Horton
Weather radar systems have become a central tool in the study of nocturnal bird migration. Yet, while studies have sought to validate weather radar data through comparison to other sampling techniques, few have explicitly examined the impact of range and topographical blockage on sampling detection—critical dimensions that can bias broader inferences. Here, we assess these biases with relation to the
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A random encounter model for wildlife density estimation with vertically oriented camera traps Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-12-02 Shuiqing He, J. Marcus Rowcliffe, Hanzhe Lin, Chris Carbone, Yorick Liefting, Shyam K. Thapa, Bishnu P. Shrestha, Patrick A. Jansen
The random encounter model (REM) estimates animal densities from camera‐trap data by correcting capture rates for a set of biological variables of the animals (average group size, speed and activity level) and characteristics of camera sensors. The REM has been widely used for setups in which cameras are mounted on trees or other structures aimed parallel to the ground. Here, we modify the REM formula
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A comparison of established and digital surface model (DSM)‐based methods to determine population estimates and densities for king penguin colonies, using fixed‐wing drone and satellite imagery Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-11-29 J. Coleman, N. Fenney, P.N. Trathan, A. Fox, E. Fox, A. Bennison, L. Ireland, M.A. Collins, P.R. Hollyman
Drones are being increasingly used to monitor wildlife populations; their large spatial coverage and minimal disturbance make them ideal for use in remote environments where access and time are limited. The methods used to count resulting imagery need consideration as they can be time‐consuming and costly. In this study, we used a fixed‐wing drone and Beyond Visual Line of Sight flying to create high‐resolution
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Illuminating the Arctic: Unveiling seabird responses to artificial light during polar darkness through citizen science and remote sensing Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-11-24 Kaja Balazy, Dariusz Jakubas, Andrzej Kotarba, Katarzyna Wojczulanis‐Jakubas
Artificial light at night (ALAN) has global impacts on animals, often negative, yet its effects in polar regions remains largely underexplored. These regions experience prolonged darkness during the polar night, while human activity and artificial lighting are rapidly increasing. In this study, we analyzed a decade of citizen science data on light‐sensitive seabird occurrences in Longyearbyen, a High‐Arctic
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Near real‐time monitoring of wading birds using uncrewed aircraft systems and computer vision Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-11-08 Ethan P. White, Lindsey Garner, Ben G. Weinstein, Henry Senyondo, Andrew Ortega, Ashley Steinkraus, Glenda M. Yenni, Peter Frederick, S. K. Morgan Ernest
Wildlife population monitoring over large geographic areas is increasingly feasible due to developments in aerial survey methods coupled with the use of computer vision models for identifying and classifying individual organisms. However, aerial surveys still occur infrequently, and there are often long delays between the acquisition of airborne imagery and its conversion into population monitoring
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Examining wildfire dynamics using ECOSTRESS data with machine learning approaches: the case of South‐Eastern Australia's black summer Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-11-05 Yuanhui Zhu, Shakthi B. Murugesan, Ivone K. Masara, Soe W. Myint, Joshua B. Fisher
Wildfires are increasing in risk and prevalence. The most destructive wildfires in decades in Australia occurred in 2019–2020. However, there is still a challenge in developing effective models to understand the likelihood of wildfire spread (susceptibility) and pre‐fire vegetation conditions. The recent launch of NASA's ECOSTRESS presents an opportunity to monitor fire dynamics with a high resolution
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Amazonian manatee critical habitat revealed by artificial intelligence‐based passive acoustic techniques Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-10-31 Florence Erbs, Mike van der Schaar, Miriam Marmontel, Marina Gaona, Emiliano Ramalho, Michel André
For many species at risk, monitoring challenges related to low visual detectability and elusive behavior limit the use of traditional visual surveys to collect critical information, hindering the development of sound conservation strategies. Passive acoustics can cost‐effectively acquire terrestrial and underwater long‐term data. However, to extract valuable information from large datasets, automatic
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Combining satellite and field data reveals Congo's forest types structure, functioning and composition Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-10-12 Juliette Picard, Maïalicah M. Nungi‐Pambu Dembi, Nicolas Barbier, Guillaume Cornu, Pierre Couteron, Eric Forni, Gwili Gibbon, Felix Lim, Pierre Ploton, Robin Pouteau, Paul Tresson, Tom van Loon, Gaëlle Viennois, Maxime Réjou‐Méchain
Tropical moist forests are not the homogeneous green carpet often illustrated in maps or considered by global models. They harbour a complex mixture of forest types organized at different spatial scales that can now be more accurately mapped thanks to remote sensing products and artificial intelligence. In this study, we built a large‐scale vegetation map of the North of Congo and assessed the environmental
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Early spectral dynamics are indicative of distinct growth patterns in post‐wildfire forests Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-09-18 Sarah Smith‐Tripp, Nicholas C. Coops, Christopher Mulverhill, Joanne C. White, Sarah Gergel
Western North America has seen a recent dramatic increase in large and often high‐severity wildfires. After forest fire, understanding patterns of structural recovery is important, as recovery patterns impact critical ecosystem services. Continuous forest monitoring provided by satellite observations is particularly beneficial to capture the pivotal post‐fire period when forest recovery begins. However
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Leveraging the next generation of spaceborne Earth observations for fuel monitoring and wildland fire management Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-08-17 Rodrigo V. Leite, Cibele Amaral, Christopher S. R. Neigh, Diogo N. Cosenza, Carine Klauberg, Andrew T. Hudak, Luiz Aragão, Douglas C. Morton, Shane Coffield, Tempest McCabe, Carlos A. Silva
Managing fuels is a key strategy for mitigating the negative impacts of wildfires on people and the environment. The use of satellite‐based Earth observation data has become an important tool for managers to optimize fuel treatment planning at regional scales. Fortunately, several new sensors have been launched in the last few years, providing novel opportunities to enhance fuel characterization. Herein
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The application of unoccupied aerial systems (UAS) for monitoring intertidal oyster density and abundance Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-08-13 Jenny Bueno, Sarah E. Lester, Joshua L. Breithaupt, Sandra Brooke
The eastern oyster (Crassostrea virginica) is a coastal foundation species currently under threat from anthropogenic activities both globally and in the Apalachicola Bay region of north Florida. Oysters provide numerous ecosystem services, and it is important to establish efficient and reliable methods for their effective monitoring and management. Traditional monitoring techniques, such as quadrat
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Detecting selective logging in tropical forests with optical satellite data: an experiment in Peru shows texture at 3 m gives the best results Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-07-31 Chiara Aquino, Edward T. A. Mitchard, Iain M. McNicol, Harry Carstairs, Andrew Burt, Beisit L. P. Vilca, Sylvia Mayta, Mathias Disney
Selective logging is known to be widespread in the tropics, but is currently very poorly mapped, in part because there is little quantitative data on which satellite sensor characteristics and analysis methods are best at detecting it. To improve this, we used data from the Tropical Forest Degradation Experiment (FODEX) plots in the southern Peruvian Amazon, where different numbers of trees had been
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Quantifying vegetation cover on coastal active dunes using nationwide aerial image analysis Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-07-16 Cate Ryan, Hannah L. Buckley, Craig D. Bishop, Graham Hinchliffe, Bradley C. Case
Coastal active dunes provide vital biodiversity, habitat, and ecosystem services, yet they are one of the most endangered and understudied ecosystems worldwide. Therefore, monitoring the status of these systems is essential, but field vegetation surveys are time‐consuming and expensive. Remotely sensed aerial imagery offers spatially continuous, low‐cost, high‐resolution coverage, allowing for vegetation
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Highly precise community science annotations of video camera‐trapped fauna in challenging environments Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-06-25 Mimi Arandjelovic, Colleen R. Stephens, Paula Dieguez, Nuria Maldonado, Gaëlle Bocksberger, Marie‐Lyne Després‐Einspenner, Benjamin Debetencourt, Vittoria Estienne, Ammie K. Kalan, Maureen S. McCarthy, Anne‐Céline Granjon, Veronika Städele, Briana Harder, Lucia Hacker, Anja Landsmann, Laura K. Lynn, Heidi Pfund, Zuzana Ročkaiová, Kristeena Sigler, Jane Widness, Heike Wilken, Antonio Buzharevski, Adeelia
As camera trapping grows in popularity and application, some analytical limitations persist including processing time and accuracy of data annotation. Typically images are recorded by camera traps although videos are becoming increasingly collected even though they require much more time for annotation. To overcome limitations with image annotation, camera trap studies are increasingly linked to community
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Approaching a population‐level assessment of body size in pinnipeds using drones, an early warning of environmental degradation Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-06-25 Daire Carroll, Eduardo Infantes, Eva V. Pagan, Karin C. Harding
Body mass is a fundamental indicator of animal health closely linked to survival and reproductive success. Systematic assessment of body mass for a large proportion of a population can allow early detection of changes likely to impact population growth, facilitating responsive management and a mechanistic understanding of ecological trends. One challenge with integrating body mass assessment into monitoring
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Quantifying nocturnal thrush migration using sensor data fusion between acoustics and vertical‐looking radar Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-06-20 Silvia Giuntini, Juha Saari, Adriano Martinoli, Damiano G. Preatoni, Birgen Haest, Baptiste Schmid, Nadja Weisshaupt
Studying nocturnal bird migration is challenging because direct visual observations are difficult during darkness. Radar has been the means of choice to study nocturnal bird migration for several decades, but provides limited taxonomic information. Here, to ascertain the feasibility of enhancing the taxonomic resolution of radar data, we combined acoustic data with vertical‐looking radar measurements
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Mapping emergent coral reefs: a comparison of pixel‐ and object‐based methods Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-05-29 Amy Stone, Sharyn Hickey, Ben Radford, Mary Wakeford
Although emergent coral reefs represent a significant proportion of overall reef habitat, they are often excluded from monitoring projects due to their shallow and exposed setting that makes them challenging to access. Using drones to survey emergent reefs overcomes issues around access to this habitat type; however, methods for deriving robust monitoring metrics, such as coral cover, are not well
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Uncovering mangrove range limits using very high resolution satellite imagery to detect fine‐scale mangrove and saltmarsh habitats in dynamic coastal ecotones Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-05-24 Cheryl L. Doughty, Kyle C. Cavanaugh, Samantha Chapman, Lola Fatoyinbo
Mangroves are important ecosystems for coastal biodiversity, resilience and carbon dynamics that are being threatened globally by human pressures and the impacts of climate change. Yet, at several geographic range limits in tropical–temperate transition zones, mangrove ecosystems are expanding poleward in response to changing macroclimatic drivers. Mangroves near range limits often grow to smaller
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Walruses from space: walrus counts in simultaneous remotely piloted aircraft system versus very high‐resolution satellite imagery Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-05-22 Hannah C. Cubaynes, Jaume Forcada, Kit M. Kovacs, Christian Lydersen, Rod Downie, Peter T. Fretwell
Regular counts of walruses (Odobenus rosmarus) across their pan‐Arctic range are necessary to determine accurate population trends and in turn understand how current rapid changes in their habitat, such as sea ice loss, are impacting them. However, surveying a region as vast and remote as the Arctic with vessels or aircraft is a formidable logistical challenge, limiting the frequency and spatial coverage
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Robust retrieval of forest canopy structural attributes using multi‐platform airborne LiDAR Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-05-17 Beibei Zhang, Fabian J. Fischer, Suzanne M. Prober, Paul B. Yeoh, Carl R. Gosper, Katherine Zdunic, Tommaso Jucker
LiDAR data acquired from airplanes and helicopters – known as airborne laser scanning (ALS) – are widely regarded as the gold standard for characterizing the 3D structure of forests at scale. But in the last decade, advances in unoccupied aerial vehicle (UAV) technologies have led to a rapid rise in the use of UAV laser scanning (ULS) for mapping forest structure. As both ALS and ULS data become increasingly
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Estimating beluga whale abundance from space: using drones to ground‐validate VHR satellite imagery Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-05-08 Jordan B. Stewart, Justine M. Hudson, Bryanna A. H. Sherbo, Cortney A. Watt
Routine monitoring of cetaceans is imperative for understanding their population trends and making informed management decisions. However, the inherent nature of cetaceans and the marine ecosystems they inhabit make annual population surveys logistically and economically challenging with current survey methods. One emerging solution is utilizing very high‐resolution (VHR) satellite imagery, which is
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Using multiscale lidar to determine variation in canopy structure from African forest elephant trails Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-05-08 Jenna M. Keany, Patrick Burns, Andrew J. Abraham, Patrick Jantz, Loic Makaga, Sassan Saatchi, Fiona Maisels, Katharine Abernethy, Christopher E. Doughty
Recently classified as a unique species by the IUCN, African forest elephants (Loxodonta cyclotis) are critically endangered due to severe poaching. With limited knowledge about their ecological role due to the dense tropical forests they inhabit in central Africa, it is unclear how the Afrotropics are influenced by elephants. Although their role as seed dispersers is well known, they may also drive
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Annual extent of prescribed burning on moorland in Great Britain and overlap with ecosystem services Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-04-29 Mike P. Shewring, Nicholas I. Wilkinson, Emma L. Teuten, Graeme M. Buchanan, Patrick Thompson, David J. T. Douglas
In the UK uplands, prescribed burning of unenclosed heath, grass and blanket bog (‘moorland’) is used to support game shooting and grazing. Burning on moorland is contentious due to its impact on peat soils, hydrology and habitat condition. There is little information on spatial and temporal patterns of burning, the overlap with soil carbon and sensitive habitats and, importantly, whether these patterns
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Unoccupied aerial vehicles as a tool to map lizard operative temperature in tropical environments Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-04-26 Emma A. Higgins, Doreen S. Boyd, Tom W. Brown, Sarah C. Owen, Geertje M. F. van der Heijden, Adam C. Algar
To understand how ectotherms will respond to warming temperatures, we require information on thermal habitat quality at spatial resolutions and extents relevant to the organism. Measuring thermal habitat quality is either limited to small spatial extents, such as with ground‐based 3D operative temperature (Te) replicas, representing the temperature of the animal at equilibrium with its environment
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Mapping artificial drains in peatlands—A national‐scale assessment of Irish raised bogs using sub‐meter aerial imagery and deep learning methods Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-04-23 Wahaj Habib, Rémi Cresson, Kevin McGuinness, John Connolly
Peatlands, constituting over half of terrestrial wetland ecosystems across the globe, hold critical ecological significance and are large stores of carbon (C). Irish oceanic raised bogs are a rare peatland ecosystem offering numerous ecosystem services, including C storage, biodiversity support and water regulation. However, they have been degraded over the centuries due to artificial drainage, followed
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Using spatiotemporal information in weather radar data to detect and track communal roosts Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-04-17 Gustavo Perez, Wenlong Zhao, Zezhou Cheng, Maria Carolina T. D. Belotti, Yuting Deng, Victoria F. Simons, Elske Tielens, Jeffrey F. Kelly, Kyle G. Horton, Subhransu Maji, Daniel Sheldon
The exodus of flying animals from their roosting locations is often visible as expanding ring‐shaped patterns in weather radar data. The NEXRAD network, for example, archives more than 25 years of data across 143 contiguous US radar stations, providing opportunities to study roosting locations and times and the ecosystems of birds and bats. However, access to this information is limited by the cost
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Deep learning in marine bioacoustics: a benchmark for baleen whale detection Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-04-17 Elena Schall, Idil Ilgaz Kaya, Elisabeth Debusschere, Paul Devos, Clea Parcerisas
Passive acoustic monitoring (PAM) is commonly used to obtain year‐round continuous data on marine soundscapes harboring valuable information on species distributions or ecosystem dynamics. This continuously increasing amount of data requires highly efficient automated analysis techniques in order to exploit the full potential of the available data. Here, we propose a benchmark, which consists of a
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Coherence of recurring fires and land use change in South America Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-04-11 Shulin Ren, Xiyan Xu, Gensuo Jia, Anqi Huang, Wei Ma
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Assessing experimental silvicultural treatments enhancing structural complexity in a central European forest – BEAST time-series analysis based on Sentinel-1 and Sentinel-2 Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-04-03 Patrick Kacic, Ursula Gessner, Stefanie Holzwarth, Frank Thonfeld, Claudia Kuenzer
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A hierarchical, multi‐sensor framework for peatland sub‐class and vegetation mapping throughout the Canadian boreal forest Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-02-25 Nicholas Pontone, Koreen Millard, Dan K. Thompson, Luc Guindon, André Beaudoin
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Aggregated time‐series features boost species‐specific differentiation of true and false positives in passive acoustic monitoring of bird assemblages Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-02-25 David Singer, Jonas Hagge, Johannes Kamp, Hermann Hondong, Andreas Schuldt
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Tree species diversity mapping from spaceborne optical images: The effects of spectral and spatial resolution Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-02-19 Xiang Liu, Julian Frey, Catalina Munteanu, Martin Denter, Barbara Koch
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Using photographs and deep neural networks to understand flowering phenology and diversity in mountain meadows Remote Sens. Ecol. Conserv. (IF 3.9) Pub Date : 2024-02-13 Aji John, Elli J. Theobald, Nicoleta Cristea, Amanda Tan, Janneke Hille Ris Lambers