Remote Sensing in Ecology and Conservation最新文献

{"title":"Drone‐mounted audio‐visual deterrence of bats: implications for reducing aerial wildlife mortality by wind turbines","authors":"Yuval Werber, Gadi Hareli, Omer Yinon, Nir Sapir, Y. Yovel","doi":"10.1002/rse2.316","DOIUrl":"https://doi.org/10.1002/rse2.316","url":null,"abstract":"Wind energy is a major and rapidly expanding renewable energy source. Horizontal‐axis wind turbines, the main tool in this industry, induce mortality in flying animals and consequently bring about conservation concerns and regulatory restrictions. We utilized a unique combination of RADAR, LIDAR and ultrasonic acoustic recorders to test the utility of a novel technology meant to prevent wind turbine‐related mortality in bats. Our drone‐mounted deterrent device produces a pulsating combination of strong auditory and visual signals while moving through the air. LIDAR was used to assess the device's impact below its flight altitude and RADAR to assess its influence above its flight altitude. Continuous acoustic recordings from ground level to ~400 m above‐ground‐level were used to monitor bat activity in the research site. We recorded the nightly altitudinal distributions of multiple bat species throughout the experiment. Analysis revealed a significant change in activity while the deterrent was flying compared to baseline conditions. We also recorded a significant ~40% decrease below and a significant ~50% increase above the deterrent's flight altitude during its operation compared to the post‐flight control. The tested technology is independent of wind farm activities and does not require modifying wind turbine form or operation procedures. The device differs from previously proposed solutions by being dynamic – moving in the airspace and emitting constantly changing signals – thus decreasing the probability of animal habituation. Our findings suggest that the deterrent could dramatically decrease wind turbine‐related mortality by deterring bats from approaching rotor‐swept airspace. Focused implementation in conditions where bat activity and energy production are in conflict may provide a practical, cost‐effective mortality mitigation solution compared to current alternatives. Thus, our results should be considered by the wind‐turbine industry and environmental monitoring and animal conservation organizations, as well as by regulatory agencies, when pursuing alleviation of wind turbine‐related mortality.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47960510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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","authors":"Richard Turner, Ophélie J. D. Lasne, Kara N. Youngentob, S. Shokirov, Helen L. Osmond, L. Kruuk","doi":"10.1002/rse2.342","DOIUrl":"https://doi.org/10.1002/rse2.342","url":null,"abstract":"1 In wild bird populations, the structure of vegetation around nest-sites can influence the risk of predation 2 of dependent young offspring, generating selection for breeding birds to choose nest-sites with 3 vegetation characteristics associated with lower predation rates. However, for researchers, vegetation 4 structure can be difficult to quantify objectively in the field, which might explain why there remains a 5 general lack of understanding of which characteristics are most important in determining rates of 6 predation. Airborne Laser Scanning (ALS) offers a powerful means of measuring vegetation structure 7 at unprecedented resolution across different spatial scales. Here, we combined ALS with 11 years of 8 breeding data from a wild population of superb fairy-wrens Malurus cyaneus in south-east Australia, a 9 species which nests relatively close to the ground and has high rates of nest and fledgling predation. We 10 derived structural measurements of understorey (0-8 m) vegetation from a contiguous grid of 30 x 30 11 m resolution cells across our c. 65 hectare study area. We tested whether: (i) cells with nests differed in 12 their understorey vegetation structure characteristics compared to those without nests; and (ii) the 13 selection of these sites for nesting was adaptive, by assessing the effects of vegetation characteristics on 14 rates of nest success and fledgling survival, and the subsequent probability of a breeding female having 15 any reproductive success. We found that nest-cells differed from unused cells primarily in having denser 16 vegetation in the lowest layer of the understorey (0-2 m; the ‘groundstorey’ layer). Understorey 17 vegetation was also on average lower in height in nest-cells. However, relationships between 18 understorey vegetation structure characteristics and breeding performance were mixed. Nest success 19 rates decreased with higher volumes of groundstorey vegetation; as did fledgling survival rates, though 20 only in nest-cells with lower height vegetation. Reproductive success was not influenced by any of the 21","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44996843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote monitoring of short‐term body mass variation in savanna ungulates","authors":"Nicolás Fuentes‐Allende, P. Stephens, Lynne M. MacTavish, Dougal MacTavish, S. G. Willis","doi":"10.1002/rse2.338","DOIUrl":"https://doi.org/10.1002/rse2.338","url":null,"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.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41840743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The vulnerability and resilience of seagrass ecosystems to marine heatwaves in New Zealand: a remote sensing analysis of seascape metrics using\u0000 PlanetScope\u0000 imagery","authors":"K. J. Clemente, M. Thomsen, R. Zimmerman","doi":"10.1002/rse2.343","DOIUrl":"https://doi.org/10.1002/rse2.343","url":null,"abstract":"","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44879370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global monitoring of soil multifunctionality in drylands using satellite imagery and field data","authors":"R. Hernández-Clemente, A. Hornero, V. González-Dugo, M. Berdugo, J. Quero, J. Jiménez, F. Maestre","doi":"10.1002/rse2.340","DOIUrl":"https://doi.org/10.1002/rse2.340","url":null,"abstract":"","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45709191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate delineation of individual tree crowns in tropical forests from aerial <scp>RGB</scp> imagery using Mask <scp>R‐CNN</scp>","authors":"James G. C. Ball, Sebastian H. M. Hickman, Tobias D. Jackson, Xian Jing Koay, James Hirst, William Jay, Matthew Archer, Mélaine Aubry‐Kientz, Grégoire Vincent, David A. Coomes","doi":"10.1002/rse2.332","DOIUrl":"https://doi.org/10.1002/rse2.332","url":null,"abstract":"Abstract Tropical forests are a major component of the global carbon cycle and home to two‐thirds of terrestrial species. Upper‐canopy trees store the majority of forest carbon and can be vulnerable to drought events and storms. Monitoring their growth and mortality is essential to understanding forest resilience to climate change, but in the context of forest carbon storage, large trees are underrepresented in traditional field surveys, so estimates are poorly constrained. Aerial photographs provide spectral and textural information to discriminate between tree crowns in diverse, complex tropical canopies, potentially opening the door to landscape monitoring of large trees. Here we describe a new deep convolutional neural network method, Detectree2 , which builds on the Mask R‐CNN computer vision framework to recognize the irregular edges of individual tree crowns from airborne RGB imagery. We trained and evaluated this model with 3797 manually delineated tree crowns at three sites in Malaysian Borneo and one site in French Guiana. As an example application, we combined the delineations with repeat lidar surveys (taken between 3 and 6 years apart) of the four sites to estimate the growth and mortality of upper‐canopy trees. Detectree2 delineated 65 000 upper‐canopy trees across 14 km 2 of aerial images. The skill of the automatic method in delineating unseen test trees was good ( F 1 score = 0.64) and for the tallest category of trees was excellent ( F 1 score = 0.74). As predicted from previous field studies, we found that growth rate declined with tree height and tall trees had higher mortality rates than intermediate‐size trees. Our approach demonstrates that deep learning methods can automatically segment trees in widely accessible RGB imagery. This tool (provided as an open‐source Python package) has many potential applications in forest ecology and conservation, from estimating carbon stocks to monitoring forest phenology and restoration. Python package available to install at https://github.com/PatBall1/Detectree2 .","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135239414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capturing long‐tailed individual tree diversity using an airborne imaging and a multi‐temporal hierarchical model","authors":"Ben. G. Weinstein, S. Marconi, Sarah J. Graves, Alina Zare, Aditya Singh, Stephanie A. Bohlman, L. Magee, Daniel J. Johnson, P. Townsend, E. White","doi":"10.1002/rse2.335","DOIUrl":"https://doi.org/10.1002/rse2.335","url":null,"abstract":"Measuring forest biodiversity using terrestrial surveys is expensive and can only capture common species abundance in large heterogeneous landscapes. In contrast, combining airborne imagery with computer vision can generate individual tree data at the scales of hundreds of thousands of trees. To train computer vision models, ground‐based species labels are combined with airborne reflectance data. Due to the difficulty of finding rare species in a large landscape, many classification models only include the most abundant species, leading to biased predictions at broad scales. For example, if only common species are used to train the model, this assumes that these samples are representative across the entire landscape. Extending classification models to include rare species requires targeted data collection and algorithmic improvements to overcome large data imbalances between dominant and rare taxa. We use a targeted sampling workflow to the Ordway Swisher Biological Station within the US National Ecological Observatory Network (NEON), where traditional forestry plots had identified six canopy tree species with more than 10 individuals at the site. Combining iterative model development with rare species sampling, we extend a training dataset to include 14 species. Using a multi‐temporal hierarchical model, we demonstrate the ability to include species predicted at <1% frequency in landscape without losing performance on the dominant species. The final model has over 75% accuracy for 14 species with improved rare species classification compared to 61% accuracy of a baseline deep learning model. After filtering out dead trees, we generate landscape species maps of individual crowns for over 670 000 individual trees. We find distinct patches of forest composed of rarer species at the full‐site scale, highlighting the importance of capturing species diversity in training data. We estimate the relative abundance of 14 species within the landscape and provide three measures of uncertainty to generate a range of counts for each species. For example, we estimate that the dominant species, Pinus palustris accounts for c. 28% of predicted stems, with models predicting a range of counts between 160 000 and 210 000 individuals. These maps provide the first estimates of canopy tree diversity within a NEON site to include rare species and provide a blueprint for capturing tree diversity using airborne computer vision at broad scales.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42181383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BatNet\u0000 : a deep learning‐based tool for automated bat species identification from camera trap images","authors":"G. Krivek, Alexander Gillert, Martin Harder, M. Fritze, Karina Frankowski, Luisa Timm, Liska Meyer‐Olbersleben, Uwe Freiherr von Lukas, G. Kerth, J. van Schaik","doi":"10.1002/rse2.339","DOIUrl":"https://doi.org/10.1002/rse2.339","url":null,"abstract":"","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43714566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reindeer control over shrubification in subarctic wetlands: spatial analysis based on unoccupied aerial vehicle imagery","authors":"M. Villoslada, H. Ylänne, S. Juutinen, T. Kolari, Pasi Korpelainen, T. Tahvanainen, Franziska Wolff, T. Kumpula","doi":"10.1002/rse2.337","DOIUrl":"https://doi.org/10.1002/rse2.337","url":null,"abstract":"Herbivores can exert a controlling effect on the reproduction and growth of shrubs, thereby counter‐acting the climate‐driven encroachment of shrubs in the Arctic and the potential consequences. This control is particularly evident in the case of abundant herbivores, such as reindeer (Rangifer tarandus tarandus), whose grazing patterns are affected by management. Here, we tested how different reindeer grazing practices on the border between Finland and Norway impact the occurrence of willow (Salix spp.) dominated patches, their above‐ground biomass (AGB) and the ability of willows to form dense thickets. We used a combination of multispectral and RGB imagery obtained from unoccupied aerial vehicles field data and an ensemble of machine‐learning models, which allowed us to model the occurrence of plant community types (Overall accuracy = 0.80), AGB fractions (maximum R2 = 0.90) and topsoil moisture (maximum R2 = 0.89). With this combination of approaches, we show that willows are kept in a browsing‐trap under spring and early summer grazing by reindeer, growing mostly small and scattered in the landscape. In contrast, willows under the winter grazing regime formed dense stands, particularly within riparian areas. We confirm this pattern using a random forest willow habitat distribution model based on topographical parameters. The model shows that willow biomass correlated with parameters of optimal habitat quality only in the winter grazing regime and did not respond to the same parameters under spring and summer grazing of reindeer.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41812984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forest edge structure from terrestrial laser scanning to explain bird biophony characteristics from acoustic indices","authors":"Tom E. Verhelst, P. Vangansbeke, P. De Frenne, Barbara D'hont, Q. Ponette, Luc Willems, H. Verbeeck, K. Calders","doi":"10.1002/rse2.334","DOIUrl":"https://doi.org/10.1002/rse2.334","url":null,"abstract":"Forest edges can be important strongholds for biodiversity and play a crucial role in the protection of forest interiors against edge effects. However, their potential to host biodiversity is dependent on the structure of the forest: Abrupt edges often fail to realise this potential. Yet, methods to accurately characterise and quantify forest edge abruptness are currently lacking. Here, we combine three‐dimensional forest structural data with biodiversity monitoring to assess the influence of forest edge structure on habitat suitability. We derived several structural metrics to determine forest edge abruptness using terrestrial laser scanning and applied these to six forest edge transects in Belgium. The local soundscapes were captured using audio recording devices (Audiomoths) and quantified using acoustic indices (AIs) (metrics on the soundscape characteristics). In each transect, the dawn choruses were recorded over a period of a week, both at the edge and the interior of the forest. No correlation between the AIs and bird species richness was found. There were clear differences between transects in the structural metrics and the recorded soundscapes. Some possible relations between both were found. In this proof of concept, we demonstrated innovative techniques to semi‐automatically classify forest structure and rapidly quantify soundscape characteristics and found a weak effect of forest edge structure on bird biophony.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41886742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}