@article{MTMT:34785389,
	title = {Assessing experimental silvicultural treatments enhancing structural complexity in a central European forest – BEAST time‐series analysis based on Sentinel‐1 and Sentinel‐2},
	url = {https://m2.mtmt.hu/api/publication/34785389},
	author = {Kacic, Patrick and Gessner, Ursula and Holzwarth, Stefanie and Thonfeld, Frank and Kuenzer, Claudia},
	doi = {10.1002/rse2.386},
	journal-iso = {REMOTE SENS ECOL CON},
	journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION},
	unique-id = {34785389},
	year = {2024},
	eissn = {2056-3485},
	orcid-numbers = {Kacic, Patrick/0000-0002-4538-8286; Gessner, Ursula/0000-0002-8221-2554; Holzwarth, Stefanie/0000-0001-7364-7006; Thonfeld, Frank/0000-0002-3371-7206}
}

@article{MTMT:34485995,
	title = {Using multiplatform LiDAR to identify relationships between vegetation structure and the abundance and diversity of woodland reptiles and amphibians},
	url = {https://m2.mtmt.hu/api/publication/34485995},
	author = {Shokirov, Shukhrat and Jucker, Tommaso and Levick, Shaun R. and Manning, Adrian D. and Youngentob, Kara N.},
	doi = {10.1002/rse2.381},
	journal-iso = {REMOTE SENS ECOL CON},
	journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION},
	unique-id = {34485995},
	abstract = {Remotely sensed measures of vegetation structure have been shown to explain patterns in the occurrence and diversity of several animal taxa, including birds, mammals, and invertebrates. However, very little research in this area has focused on reptiles and amphibians (herpetofauna). Moreover, most remote sensing studies on animal–habitat associations have relied on airborne or satellite data that provide coverage over relatively large areas but may not have the resolution or viewing angle necessary to measure vegetation features at scales that are meaningful to herpetofauna. Here, we combined terrestrial laser scanning (TLS), unmanned aerial vehicle laser scanning (ULS), and fused (FLS) data to provide the first test of whether vegetation structural attributes can help explain variation in herpetofauna abundance, species richness, and diversity across a woodland landscape. We identified relationships between the abundance and diversity of herpetofauna and several vegetation metrics, including canopy height, skewedness, vertical complexity, volume of vegetation, and coarse woody debris. These relationships varied across species, groups, and sensors. ULS models tended to perform as well or better than TLS or FLS models based on the methods we used in this study. In open woodland landscapes, ULS data may have some benefits over TLS data for modeling relationships between herpetofauna and vegetation structure, which we discuss. However, for some species, only TLS data identified significant predictor variables among the LiDAR‐derived structural metrics. While the overall predictive power of models was relatively low (i.e., at most R 2  = 0.32 for ULS overall abundance and R 2  = 0.32 for abundance at the individual species level [three‐toed skink ( Chalcides striatus )]), the ability to identify relationships between specific LiDAR structural metrics and the abundance and diversity of herpetofauna could be useful for understanding their habitat associations and managing reptile and amphibian populations.},
	year = {2024},
	eissn = {2056-3485},
	orcid-numbers = {Shokirov, Shukhrat/0000-0002-2927-4115}
}

@article{MTMT:34092358,
	title = {Deep learning‐based training data augmentation combined with post‐classification improves the classification accuracy for dominant and scattered invasive forest tree species},
	url = {https://m2.mtmt.hu/api/publication/34092358},
	author = {Likó, Szilárd Balázs and Holb, Imre and Oláh, Viktor and Burai, Péter and Szabó, Szilárd},
	doi = {10.1002/rse2.365},
	journal-iso = {REMOTE SENS ECOL CON},
	journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION},
	unique-id = {34092358},
	abstract = {Species composition of forests is a very important component from the point of view of nature conservation and forestry. We aimed to identify 10 tree species in a hilly forest stand using a hyperspectral aerial image with a particular focus on two invasive species, namely Ailanthus tree and black locust. Deep learning‐based training data augmentation (TDA) and post‐classification techniques were tested with Random Forest and Support Vector Machine (SVM) classifiers. SVM had better performance with 81.6% overall accuracy (OA). TDA increased the OA to 82.5% and post‐classification with segmentation improved the total accuracy to 86.2%. The class‐level performance was more convincing: the invasive Ailanthus trees were identified with 40% higher producer's and user's accuracies (PA and UA) to 70% related to the common technique (using a training dataset and classifying the trees). The PA and UA did not change in the case of the other invasive species, black locust. Accordingly, this new method identifies well Ailanthus, a sparsely distributed species in the area; while it was less efficient with black locust that dominates larger patches in the stand. The combination of the two ancillary steps of hyperspectral image classification proved to be reasonable and can support forest management planning and nature conservation in the future.},
	keywords = {Black locust; Multiresolution segmentation; Convolutional-Neural-Network; RANDOM-FOREST; Support-vector-machine; ailanthus},
	year = {2024},
	eissn = {2056-3485},
	orcid-numbers = {Oláh, Viktor/0000-0001-5410-5914; Szabó, Szilárd/0000-0002-2670-7384}
}

@article{MTMT:34304859,
	title = {Global disparity of camera trap research allocation and defaunation risk of terrestrial mammals},
	url = {https://m2.mtmt.hu/api/publication/34304859},
	author = {Mugerwa, Badru and Niedballa, Juergen and Planillo, Aimara and Sheil, Douglas and Kramer-Schadt, Stephanie and Wilting, Andreas},
	doi = {10.1002/rse2.360},
	journal-iso = {REMOTE SENS ECOL CON},
	journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION},
	volume = {10},
	unique-id = {34304859},
	abstract = {Quantifying and monitoring the risk of defaunation and extinction require assessing and monitoring biodiversity in impacted regions. Camera traps that photograph animals as they pass sensors have revolutionized wildlife assessment and monitoring globally. We conducted a global review of camera trap research on terrestrial mammals over the last two decades. We assessed if the spatial distribution of 3395 camera trap research locations from 2324 studies overlapped areas with high defaunation risk. We used a geospatial distribution modeling approach to predict the spatial allocation of camera trap research on terrestrial mammals and to identify its key correlates. We show that camera trap research over the past two decades has not targeted areas where defaunation risk is highest and that 76.8% of the global research allocation can be attributed to country income, biome, terrestrial mammal richness, and accessibility. The lowest probabilities of camera trap research allocation occurred in low-income countries. The Amazon and Congo Forest basins - two highly biodiverse ecosystems facing unprecedented anthropogenic alteration - received inadequate camera trap research attention. Even within the best covered regions, most of the research (64.2%) was located outside the top 20% areas where defaunation risk was greatest. To monitor terrestrial mammal populations and assess the risk of extinction, more research should be extended to regions with high defaunation risk but have received low camera trap research allocation.},
	keywords = {MONITORING; CONSERVATION; defaunation; terrestrial mammals; Camera trap research; research geographic bias},
	year = {2024},
	eissn = {2056-3485},
	pages = {121-136},
	orcid-numbers = {Planillo, Aimara/0000-0001-6763-9923; Sheil, Douglas/0000-0002-1166-6591; Kramer-Schadt, Stephanie/0000-0002-9269-4446}
}

@article{MTMT:34661676,
	title = {Use of an unmanned aerial-aquatic vehicle for acoustic sensing in freshwater ecosystems},
	url = {https://m2.mtmt.hu/api/publication/34661676},
	author = {Lawson, Jenna and Farinha, Andre and Romanello, Luca and Pang, Oscar and Zufferey, Raphael and Kovac, Mirko},
	doi = {10.1002/rse2.373},
	journal-iso = {REMOTE SENS ECOL CON},
	journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION},
	unique-id = {34661676},
	keywords = {Robotics; freshwater ecosystem; Passive acoustic monitoring; biodiversity monitoring; unmanned aerial-aquatic vehicle},
	year = {2023},
	eissn = {2056-3485}
}

@article{MTMT:34617720,
	title = {Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data},
	url = {https://m2.mtmt.hu/api/publication/34617720},
	author = {Bubnicki, Jakub and Norton, Ben and Baskauf, Steven and Bruce, Tom and Cagnacci, Francesca and Casaer, Jim and Churski, Marcin and Cromsigt, Joris and Farra, Simone Dal and Fiderer, Christian and Forrester, Tavis and Hendry, Heidi and Heurich, Marco and Hofmeester, Tim and Jansen, Patrick and Kays, Roland and Kuijper, Dries and Liefting, Yorick and Linnell, John and Luskin, Matthew and Mann, Christopher and Milotic, Tanja and Newman, Peggy and Niedballa, Juergen and Oldoni, Damiano and Ossi, Federico and Robertson, Tim and Rovero, Francesco and Rowcliffe, Marcus and Seidenari, Lorenzo and Stachowicz, Izabela and Stowell, Dan and Tobler, Mathias and Wieczorek, John and Zimmermann, Fridolin and Desmet, Peter},
	doi = {10.1002/rse2.374},
	journal-iso = {REMOTE SENS ECOL CON},
	journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION},
	unique-id = {34617720},
	abstract = {Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap-derived Big Data are becoming increasingly solvable with the help of scalable cyber-infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, "Camera Trap Metadata Standard" (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media-based and event-based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in-depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard.We present a new data exchange format for camera trap data, the Camera Trap Data Package (Camtrap DP; ), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP is being developed under the umbrella of the Biodiversity Information Standards (TDWG), and through outreach and collaboration, it is now supported by GBIF. Importantly, Camtrap DP is the consensus of a long, in depth consultation process among the main existing camera trap data management platforms, as well as some of the major global players in the field of camera trapping. As an open, evolving standard for the FAIR exchange and archive of camera trap data, Camtrap DP represents an important step towards a global data sharing workflow with rapid results and thus more timely science based wildlife management recommendations.image},
	keywords = {Data sharing; data exchange; camera traps; biodiversity data; Information standards},
	year = {2023},
	eissn = {2056-3485},
	orcid-numbers = {Norton, Ben/0000-0002-5819-9134; Bruce, Tom/0000-0001-6958-1657; Casaer, Jim/0000-0001-6788-5876; Kays, Roland/0000-0002-2947-6665; Milotic, Tanja/0000-0002-3129-6196; Oldoni, Damiano/0000-0003-3445-7562; Stachowicz, Izabela/0000-0002-9721-7067; Zimmermann, Fridolin/0000-0003-3861-246X; Desmet, Peter/0000-0002-8442-8025}
}

@article{MTMT:34322507,
	title = {Use of Airborne Laser Scanning to assess effects of understorey vegetation structure on nest-site selection and breeding performance in an Australian passerine bird},
	url = {https://m2.mtmt.hu/api/publication/34322507},
	author = {Turner, Richard S. and Lasne, Ophelie J. D. and Youngentob, Kara N. and Shokirov, Shukhrat and Osmond, Helen L. and Kruuk, Loeske E. B.},
	doi = {10.1002/rse2.342},
	journal-iso = {REMOTE SENS ECOL CON},
	journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION},
	unique-id = {34322507},
	abstract = {In wild bird populations, the structure of vegetation around nest-sites can influence the risk of predation of dependent offspring, generating selection for nest-sites with vegetation characteristics associated with lower predation rates. However, vegetation structure can be difficult to quantify objectively in the field, which might explain why there remains a general lack of understanding of which characteristics are most important in determining predation rates. Airborne laser scanning (ALS) offers a powerful means of measuring vegetation structure at unprecedented resolution. Here, we combined ALS with 11 years of breeding data from a wild population of superb fairy-wrens Malurus cyaneus in southeastern Australia, a species which nests relatively close to the ground and has high rates of nest and fledgling predation. We derived structural measurements of understorey (0-8 m) vegetation from a contiguous grid of 30 x 30 m resolution cells across our c. 65 hectares study area. We found that cells with nests (nest-cells) differed in their understorey vegetation structure characteristics compared to unused cells, primarily in having denser vegetation in the lowest layer of the understorey (0-2 m; the 'groundstorey' layer). The average height of understorey vegetation was also lower in cells with nests than in those without nests. However, relationships between understorey vegetation structure characteristics and breeding performance were mixed. Nest success rates decreased with higher volumes of groundstorey vegetation, as did fledgling survival rates, though only in nest-cells with lower height vegetation. Our results indicate that ALS can identify vegetation characteristics relevant for superb fairy-wren nest-site selection, but that nesting preferences are not beneficial under current predation pressures. The study illustrates the potential for using ALS to investigate how ecological conditions affect behaviour and life-histories in wild animal populations.},
	keywords = {LIDAR; vegetation structure; NEST-SITE SELECTION; Airborne Laser Scanning; Nest predation; avian breeding performance},
	year = {2023},
	eissn = {2056-3485},
	orcid-numbers = {Shokirov, Shukhrat/0000-0002-2927-4115}
}

@article{MTMT:34312086,
	title = {High-resolution thermal imagery reveals how interactions between crown structure and genetics shape plant temperature},
	url = {https://m2.mtmt.hu/api/publication/34312086},
	author = {Olsoy, Peter J. and Zaiats, Andrii and Delparte, Donna M. and Germino, Matthew J. and Richardson, Bryce A. and Roop, Spencer and Roser, Anna V. and Forbey, Jennifer S. and Cattau, Megan E. and Buerki, Sven and Reinhardt, Keith and Caughlin, T. Trevor},
	doi = {10.1002/rse2.359},
	journal-iso = {REMOTE SENS ECOL CON},
	journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION},
	unique-id = {34312086},
	abstract = {Understanding interactions between environmental stress and genetic variation is crucial to predict the adaptive capacity of species to climate change. Leaf temperature is both a driver and a responsive indicator of plant physiological response to thermal stress, and methods to monitor it are needed. Foliar temperatures vary across leaf to canopy scales and are influenced by genetic factors, challenging efforts to map and model this critical variable. Thermal imagery collected using unoccupied aerial systems (UAS) offers an innovative way to measure thermal variation in plants across landscapes at leaf-level resolutions. We used a UAS equipped with a thermal camera to assess temperature variation among genetically distinct populations of big sagebrush (Artemisia tridentata), a keystone plant species that is the focus of intensive restoration efforts throughout much of western North America. We completed flights across a growing season in a sagebrush common garden to map leaf temperature relative to subspecies and cytotype, physiological phenotypes of plants, and summer heat stress. Our objectives were to (1) determine whether leaf-level stomatal conductance corresponds with changes in crown temperature; (2) quantify genetic (i.e., subspecies and cytotype) contributions to variation in leaf and crown temperatures; and (3) identify how crown structure, solar radiation, and subspecies-cytotype relate to leaf-level temperature. When considered across the whole season, stomatal conductance was negatively, non-linearly correlated with crown-level temperature derived from UAS. Subspecies identity best explained crown-level temperature with no difference observed between cytotypes. However, structural phenotypes and microclimate best explained leaf-level temperature. These results show how fine-scale thermal mapping can decouple the contribution of genetic, phenotypic, and microclimate factors on leaf temperature dynamics. As climate-change-induced heat stress becomes prevalent, thermal UAS represents a promising way to track plant phenotypes that emerge from gene-by-environment interactions.},
	keywords = {stomatal conductance; Leaf temperature; Artemisia tridentata; adaptive capacity; Sagebrush; common garden experiment},
	year = {2023},
	eissn = {2056-3485},
	orcid-numbers = {Buerki, Sven/0000-0002-8299-6539}
}

@article{MTMT:34303915,
	title = {Remote monitoring of short-term body mass variation in savanna ungulates},
	url = {https://m2.mtmt.hu/api/publication/34303915},
	author = {Fuentes-Allende, Nicolas and Stephens, Philip A. and MacTavish, Lynne M. and MacTavish, Dougal and Willis, Stephen G.},
	doi = {10.1002/rse2.338},
	journal-iso = {REMOTE SENS ECOL CON},
	journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION},
	unique-id = {34303915},
	abstract = {Large herbivores in seasonal environments often experience mass variation due to temporal changes in the availability of critical resources like water and forage, as well as due to breeding events. Yet the documentation of mass variation in mammals of highly seasonal savanna habitats, which host the highest densities of grazing ungulates globally, has rarely been explored. Here, we showcase a method to evaluate seasonal mass variation in bovids. Our method used mineral-baited scales and camera traps to enable us to track the body mass of three species through a period of wet and dry seasons in a South African savanna ecosystem. To illustrate one potential application of the method, we related body mass data to time, weather and resource availability. This showed that individuals altered their body masses markedly between seasons with, for example, female Kudu (Tragelaphus strepsiceros) gaining, on average, >21 kg over the 15-week wet-season period in 1 year. These changes were positively related to factors such as vegetation productivity (assessed using NDVI) and the frequency of rains. This method enables easy, non-lethal and non-invasive acquisition of mass data. The equipment is easy to deploy concurrently over large areas. Monitoring by this method has a variety of possible applications, potentially providing a useful early-warning indicator of body condition to inform management, or providing information about ecological states, such as parturition or the reproductive effort of males. Given the longer and harsher dry seasons experienced in many arid systems in recent decades, and projected in future, this method may provide a straightforward means of monitoring long-term body condition in animals as a result of environmental change.},
	keywords = {body mass; Seasonal changes; bovids; camera traps; savanna ungulate},
	year = {2023},
	eissn = {2056-3485},
	orcid-numbers = {Fuentes-Allende, Nicolas/0000-0001-8487-1943}
}

@article{MTMT:34272390,
	title = {Grassland-use intensity maps for Switzerland based on satellite time series: Challenges and opportunities for ecological applications},
	url = {https://m2.mtmt.hu/api/publication/34272390},
	author = {Weber, Dominique and Schwieder, Marcel and Ritter, Lukas and Koch, Tiziana and Psomas, Achilleas and Huber, Nica and Ginzler, Christian and Boch, Steffen},
	doi = {10.1002/rse2.372},
	journal-iso = {REMOTE SENS ECOL CON},
	journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION},
	unique-id = {34272390},
	abstract = {Land-use intensification in grassland ecosystems (i.e. increased mowing frequency, intensified grazing) has a strong negative effect on biodiversity and ecosystem services. However, accurate information on grassland-use intensity is difficult to acquire and restricted to the local or regional level. Recent studies have shown that mowing events can be mapped for large areas using satellite image time series. The transferability of such approaches, especially to mountain areas, has been little explored, however, and the relevance for ecological applications in biodiversity and conservation has hardly been investigated. Here, we used a rule-based algorithm to produce annual maps for 2018-2021 of grassland-management events, that is, mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite data. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further examined the relationships between the generated grassland-use intensity measures and plant species richness and ecological indicator values derived from a nationwide field survey. The webcam-based verification for 2020 and 2021 revealed that most detected management events were actual mowing/grazing events (>= 78%), but that a substantial number of events were not detected (up to 57%), particularly grazing events at higher elevations. We found lower plant species richness and higher mean ecological indicator values for nutrients and mowing tolerance with more frequent management events and those starting earlier in the year. A large proportion of the variance was explained by our use-intensity measures. Our findings therefore highlight that remotely assessed management events can characterise land-use intensity at fine spatial and temporal resolutions across broad scales and can explain plant biodiversity patterns in grasslands.We used a newly developed rule-based algorithm to produce annual maps of grassland-management events, i.e. mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite data. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. The derived maps confirmed anticipated spatial patterns of grassland-use intensities and explained biodiversity patterns in Swiss grasslands well, demonstrating their great potential for diverse ecological applications and in biodiversity research.image},
	keywords = {Biodiversity; Sentinel-2; LANDSAT 8; Webcam; grassland-use intensity; mowing detection},
	year = {2023},
	eissn = {2056-3485}
}