TY - JOUR AU - Monsimet, Jeremy AU - Sjogersten, Sofie AU - Sanders, Nathan J. AU - Jonsson, Micael AU - Olofsson, Johan AU - Siewert, Matthias TI - UAV data and deep learning: efficient tools to map ant mounds and their ecological impact JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON PY - 2024 PG - 15 SN - 2056-3485 DO - 10.1002/rse2.400 UR - https://m2.mtmt.hu/api/publication/35309081 ID - 35309081 LA - English DB - MTMT ER - TY - JOUR AU - Ryan, Cate AU - Buckley, Hannah L. AU - Bishop, Craig D. AU - Hinchliffe, Graham AU - Case, Bradley C. TI - Quantifying vegetation cover on coastal active dunes using nationwide aerial image analysis JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON PY - 2024 SN - 2056-3485 DO - 10.1002/rse2.410 UR - https://m2.mtmt.hu/api/publication/35138881 ID - 35138881 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Doughty, Cheryl L. AU - Cavanaugh, Kyle C. AU - Chapman, Samantha AU - Fatoyinbo, Lola TI - Uncovering mangrove range limits using very high resolution satellite imagery to detect fine-scale mangrove and saltmarsh habitats in dynamic coastal ecotones JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON PY - 2024 PG - 16 SN - 2056-3485 DO - 10.1002/rse2.394 UR - https://m2.mtmt.hu/api/publication/34990964 ID - 34990964 N1 - Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, United States Oak Ridge Associated Universities, Oak Ridge, TN 37831, United States Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, United States Department of Geography, University of California, Los Angeles, CA 90095, United States Department of Biology and Center for Biodiversity and Ecosystem Stewardship, Villanova University, Villanova, PA 19085, United States Export Date: 21 June 2024 Correspondence Address: Doughty, C.L.; Biospheric Sciences Laboratory, United States; email: cheryl.l.doughty@nasa.gov LA - English DB - MTMT ER - TY - JOUR AU - Ledger, Martha J. AU - Li, Qiaosi AU - Ling, Yuet Fung AU - Jones, Emily E. AU - Lee, Kit W. K. AU - Wu, Jin AU - Bonebrake, Timothy C. TI - Increased habitat availability as revealed by LiDAR contributes to the tropicalization of a subtropical butterfly community JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON PY - 2024 PG - 16 SN - 2056-3485 DO - 10.1002/rse2.409 UR - https://m2.mtmt.hu/api/publication/34977540 ID - 34977540 N1 - School of Biological Sciences, The University of Hong Kong, Hong Kong Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow, United Kingdom Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong Export Date: 4 July 2024 Correspondence Address: Ledger, M.J.; School of Biological Sciences, Hong Kong; email: mledger@hku.hk LA - English DB - MTMT ER - TY - JOUR AU - Singer, D. AU - Hagge, J. AU - Kamp, J. AU - Hondong, H. AU - Schuldt, A. TI - Aggregated time-series features boost species-specific differentiation of true and false positives in passive acoustic monitoring of bird assemblages JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON PY - 2024 SN - 2056-3485 DO - 10.1002/rse2.385 UR - https://m2.mtmt.hu/api/publication/34881069 ID - 34881069 N1 - Department of Forest Nature Conservation, University of Göttingen, Göttingen, Germany Department of Forest Nature Conservation, Northwest German Forest Research Institute, Hann. Münden, Germany Department of Conservation Biology, University of Göttingen, Göttingen, Germany Cited By :1 Export Date: 27 May 2024 Correspondence Address: Singer, D.; Department of Forest Nature Conservation, Büsgenweg 3, Germany; email: d.singer@posteo.de LA - English DB - MTMT ER - TY - JOUR AU - Kacic, Patrick AU - Gessner, Ursula AU - Holzwarth, Stefanie AU - Thonfeld, Frank AU - Kuenzer, Claudia TI - Assessing experimental silvicultural treatments enhancing structural complexity in a central European forest – BEAST time‐series analysis based on Sentinel‐1 and Sentinel‐2 JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON PY - 2024 SN - 2056-3485 DO - 10.1002/rse2.386 UR - https://m2.mtmt.hu/api/publication/34785389 ID - 34785389 N1 - Funding Agency and Grant Number: Bundesministerium fr Ernhrung und Landwirtschaft und Bundesministerium fr Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz [5375/1, 459717468, FKZ 2220WK81A4]; DFG (Deutsche Forschungsgemeinschaft); Federal Ministry of Food and Agriculture and Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection; Waldklimafonds through the Fachagentur Nachwachsende Rohstoffe e.V. (FNR) Funding text: All authors on the paper have seen and approved the submitted version of the manuscript. Furthermore, all authors have substantially contributed to the work, and all persons entitled to co-authorship have been included. The manuscript has been submitted solely to Remote Sensing in Ecology and Conservation and it has not been published elsewhere, either in part or whole, nor is it in press or under consideration for publication in another journal. PK acknowledges funding from the DFG (Deutsche Forschungsgemeinschaft) within the framework of the Research Unit BETA-FOR (Enhancing the structural diversity between patches for improving multidiversity and multifunctionality in production forests) (grant no. FOR 5375/1, project number 459717468). FT acknowledges funding from the ForstEO project (FKZ 2220WK81A4), funded by the Federal Ministry of Food and Agriculture and Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection based on a decision of the German Bundestag from Waldklimafonds through the Fachagentur Nachwachsende Rohstoffe e.V. (FNR) Open Access funding enabled and organized by Projekt DEAL. LA - English DB - MTMT ER - TY - JOUR AU - Shokirov, Shukhrat AU - Jucker, Tommaso AU - Levick, Shaun R. AU - Manning, Adrian D. AU - Youngentob, Kara N. TI - Using multiplatform LiDAR to identify relationships between vegetation structure and the abundance and diversity of woodland reptiles and amphibians JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON PY - 2024 SN - 2056-3485 DO - 10.1002/rse2.381 UR - https://m2.mtmt.hu/api/publication/34485995 ID - 34485995 N1 - Export Date: 11 July 2024 Correspondence Address: Jucker, T.; School of Biological Sciences, United Kingdom; email: t.jucker@bristol.ac.uk AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Likó, Szilárd Balázs AU - Holb, Imre AU - Oláh, Viktor AU - Burai, Péter AU - Szabó, Szilárd TI - Deep learning‐based training data augmentation combined with post‐classification improves the classification accuracy for dominant and scattered invasive forest tree species JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON VL - 10 PY - 2024 IS - 2 SP - 203 EP - 219 PG - 17 SN - 2056-3485 DO - 10.1002/rse2.365 UR - https://m2.mtmt.hu/api/publication/34092358 ID - 34092358 N1 - Early Access: AUG 2023 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Mugerwa, Badru AU - Niedballa, Juergen AU - Planillo, Aimara AU - Sheil, Douglas AU - Kramer-Schadt, Stephanie AU - Wilting, Andreas TI - Global disparity of camera trap research allocation and defaunation risk of terrestrial mammals JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON VL - 10 PY - 2024 IS - 1 SP - 121 EP - 136 PG - 16 SN - 2056-3485 DO - 10.1002/rse2.360 UR - https://m2.mtmt.hu/api/publication/34304859 ID - 34304859 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Lawson, Jenna AU - Farinha, Andre AU - Romanello, Luca AU - Pang, Oscar AU - Zufferey, Raphael AU - Kovac, Mirko TI - Use of an unmanned aerial-aquatic vehicle for acoustic sensing in freshwater ecosystems JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON PY - 2023 PG - 17 SN - 2056-3485 DO - 10.1002/rse2.373 UR - https://m2.mtmt.hu/api/publication/34661676 ID - 34661676 LA - English DB - MTMT ER -