@article{MTMT:35309081, title = {UAV data and deep learning: efficient tools to map ant mounds and their ecological impact}, url = {https://m2.mtmt.hu/api/publication/35309081}, author = {Monsimet, Jeremy and Sjogersten, Sofie and Sanders, Nathan J. and Jonsson, Micael and Olofsson, Johan and Siewert, Matthias}, doi = {10.1002/rse2.400}, journal-iso = {REMOTE SENS ECOL CON}, journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION}, unique-id = {35309081}, keywords = {treeline; UAV; Object Detection; Ant mounds; Formica sp.}, year = {2024}, eissn = {2056-3485}, orcid-numbers = {Monsimet, Jeremy/0000-0001-9153-8401; Jonsson, Micael/0000-0002-1618-2617; Siewert, Matthias/0000-0003-2890-8873} } @article{MTMT:35138881, title = {Quantifying vegetation cover on coastal active dunes using nationwide aerial image analysis}, url = {https://m2.mtmt.hu/api/publication/35138881}, author = {Ryan, Cate and Buckley, Hannah L. and Bishop, Craig D. and Hinchliffe, Graham and Case, Bradley C.}, doi = {10.1002/rse2.410}, journal-iso = {REMOTE SENS ECOL CON}, journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION}, unique-id = {35138881}, abstract = {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 mapping across larger areas than traditional field surveys. Taking Aotearoa New Zealand as a case study, we used a nationally representative sample of coastal active dunes to classify vegetation from red‐green‐blue (RGB) high‐resolution (0.075–0.75 m) aerial imagery with object‐based image analysis. The mean overall accuracy was 0.76 across 21 beaches for aggregated classes, and key cover classes, such as sand, sandbinders, and woody vegetation, were discerned. However, differentiation among woody vegetation species on semi‐stable and stable dunes posed a challenge. We developed a national cover typology from the classification, comprising seven vegetation types. Classification tree models showed that where human activity was higher, it was more important than geomorphic factors in influencing the relative percent cover of the different active dune cover classes. Our methods provide a quantitative approach to characterizing the cover classes on active dunes at a national scale, which are relevant for conservation management, including habitat mapping, determining species occupancy, indigenous dominance, and the representativeness of remaining active dunes.}, year = {2024}, eissn = {2056-3485}, orcid-numbers = {Ryan, Cate/0009-0005-8999-9459; Hinchliffe, Graham/0000-0002-9369-857X} } @article{MTMT:34990964, title = {Uncovering mangrove range limits using very high resolution satellite imagery to detect fine-scale mangrove and saltmarsh habitats in dynamic coastal ecotones}, url = {https://m2.mtmt.hu/api/publication/34990964}, author = {Doughty, Cheryl L. and Cavanaugh, Kyle C. and Chapman, Samantha and Fatoyinbo, Lola}, doi = {10.1002/rse2.394}, journal-iso = {REMOTE SENS ECOL CON}, journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION}, unique-id = {34990964}, keywords = {random forest; mangroves; coastal wetland; Global climate change; WorldView; landcover classification}, year = {2024}, eissn = {2056-3485} } @article{MTMT:34977540, title = {Increased habitat availability as revealed by LiDAR contributes to the tropicalization of a subtropical butterfly community}, url = {https://m2.mtmt.hu/api/publication/34977540}, author = {Ledger, Martha J. and Li, Qiaosi and Ling, Yuet Fung and Jones, Emily E. and Lee, Kit W. K. and Wu, Jin and Bonebrake, Timothy C.}, doi = {10.1002/rse2.409}, journal-iso = {REMOTE SENS ECOL CON}, journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION}, unique-id = {34977540}, keywords = {habitat change; Species distribution models; Microclimate; TROPICAL FORESTS; AIRBORNE LIDAR; Species redistribution}, year = {2024}, eissn = {2056-3485} } @article{MTMT:34881069, title = {Aggregated time-series features boost species-specific differentiation of true and false positives in passive acoustic monitoring of bird assemblages}, url = {https://m2.mtmt.hu/api/publication/34881069}, author = {Singer, D. and Hagge, J. and Kamp, J. and Hondong, H. and Schuldt, A.}, doi = {10.1002/rse2.385}, journal-iso = {REMOTE SENS ECOL CON}, journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION}, unique-id = {34881069}, year = {2024}, eissn = {2056-3485} } @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}, volume = {10}, 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}, pages = {203-219}, 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} }