Remote Sensing in Ecology and Conservation最新文献

{"title":"A semi‐automated camera trap distance sampling approach for population density estimation","authors":"Maik Henrich, Mercedes Burgueño, J. Hoyer, T. Haucke, V. Steinhage, H. Kühl, M. Heurich","doi":"10.1002/rse2.362","DOIUrl":"https://doi.org/10.1002/rse2.362","url":null,"abstract":"Camera traps have become important tools for the monitoring of animal populations. However, the study‐specific estimation of animal detection probabilities is key if unbiased abundance estimates of unmarked species are to be obtained. Since this process can be very time‐consuming, we developed the first semi‐automated workflow for animals of any size and shape to estimate detection probabilities and population densities. In order to obtain observation distances, a deep learning algorithm is used to create relative depth images that are calibrated with a small set of reference photos for each location, with distances then extracted for animals automatically detected by MegaDetector 4.0. Animal detection by MegaDetector was generally independent of the distance to the camera trap for 10 animal species at two different study sites. If an animal was detected both manually and automatically, the difference in the distance estimates was often minimal at a distance about 4 m from the camera trap. The difference increased approximately linearly for larger distances. Nonetheless, population density estimates based on manual and semi‐automated camera trap distance sampling workflows did not differ significantly. Our results show that a readily available software for semi‐automated distance estimation can reliably be used within a camera trap distance sampling workflow, reducing the time required for data processing, by >13‐fold. This greatly improves the accessibility of camera trap distance sampling for wildlife research and management.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49207353","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":"Quantifying wetness variability in aapa mires with Sentinel‐2: towards improved monitoring of an EU priority habitat","authors":"Tytti Jussila, R. Heikkinen, S. Anttila, K. Aapala, M. Kervinen, J. Aalto, P. Vihervaara","doi":"10.1002/rse2.363","DOIUrl":"https://doi.org/10.1002/rse2.363","url":null,"abstract":"Aapa mires are waterlogged northern peatland ecosystems characterized by a patterned surface structure where water‐filled depressions (‘flarks’) alternate with drier hummock strings. As one of the EU Habitat Directive priority habitats, aapa mires are important for biodiversity and carbon cycling, harbouring several red‐listed species and supporting unique species communities. Due to their sensitivity to hydrological disturbances, reliable, up‐to‐date and systematic information on the hydrological condition and responses of mires is crucial and required for multiple purposes ranging from carbon exchange modelling to EU Habitats Directive reporting and conservation and ecosystem restoration planning. Here, we demonstrate the usability of Sentinel‐2 satellite data in a semi‐automatic cloud‐based approach to retrieve large‐scale information on aapa mire hydrological variability. Two satellite‐derived metrics, soil moisture index and the extent of water‐saturated surfaces based on pixel‐wise classification, are used to quantify monthly and interannual wetness variation between 2017 and 2020 across Natura 2000 aapa mires in Finland, including responses to the extreme drought of 2018. The results revealed high temporal variability in wetness, particularly in the southern parts of the aapa mire zone and generally in the late summer months interannually. Observations from the drought summer showed that one third of usually year‐round wet flark surfaces may be exposed to drying during climatic extremes. Responses varied between sites and regions, implicating the significance of environmental factors for drought resistance: some sites maintained high levels of moisture, whereas others lost wet surfaces completely. Our study provides the first comprehensive national‐level representation of seasonal and interannual wetness variability and drought‐sensitivity of pristine aapa mire sites. The approach and methods used here can be directly upscaled outside protected areas and to other EU countries. Thus, they provide a means for harmonized, systematic large‐scale monitoring of this priority habitat, as well as valuable information for other applications supporting peatland conservation and research.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48626698","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":"Combining environmental DNA with remote sensing variables to map fish species distributions along a large river","authors":"Shuo Zong, Jeanine Brantschen, Xiaowei Zhang, C. Albouy, A. Valentini, Heng Zhang, F. Altermatt, L. Pellissier","doi":"10.1002/rse2.366","DOIUrl":"https://doi.org/10.1002/rse2.366","url":null,"abstract":"Biodiversity loss in river ecosystems is much faster and more severe than in terrestrial systems, and spatial conservation and restoration plans are needed to halt this erosion. Reliable and highly resolved data on the state of and change in biodiversity and species distributions are critical for effective measures. However, high‐resolution maps of fish distribution remain limited for large riverine systems. Coupling data from global satellite sensors with broad‐scale environmental DNA (eDNA) and machine learning could enable rapid and precise mapping of the distribution of river organisms. Here, we investigated the potential for combining these methods using a fish eDNA dataset from 110 sites sampled along the full length of the Rhone River in Switzerland and France. Using Sentinel 2 and Landsat 8 images, we generated a set of ecological variables describing both the aquatic and the terrestrial habitats surrounding the river corridor. We combined these variables with eDNA‐based presence and absence data on 29 fish species and used three machine‐learning models to assess environmental suitability for these species. Most models showed good performance, indicating that ecological variables derived from remote sensing can approximate the ecological determinants of fish species distributions, but water‐derived variables had stronger associations than the terrestrial variables surrounding the river. The species range mapping indicated a significant transition in the species occupancy along the Rhone, from its source in the Swiss Alps to outlet into the Mediterranean Sea in southern France. Our study demonstrates the feasibility of combining remote sensing and eDNA to map species distributions in a large river. This method can be expanded to any large river to support conservation schemes.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"1 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41338444","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 disparity of camera trap research allocation and defaunation risk of terrestrial mammals","authors":"B. Mugerwa, Jürgen Niedballa, A. Planillo, D. Sheil, S. Kramer‐Schadt, A. Wilting","doi":"10.1002/rse2.360","DOIUrl":"https://doi.org/10.1002/rse2.360","url":null,"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.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47332556","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":"Automatically drawing vegetation classification maps using digital time‐lapse cameras in alpine ecosystems","authors":"Ryotaro Okamoto, R. Ide, H. Oguma","doi":"10.1002/rse2.364","DOIUrl":"https://doi.org/10.1002/rse2.364","url":null,"abstract":"Alpine ecosystems are particularly vulnerable to climate change. Monitoring the distribution of alpine vegetation is required to plan practical conservation activities. However, conventional field observations, airborne and satellite remote sensing are difficult in terms of coverage, cost and resolution in alpine areas. Ground‐based time‐lapse cameras have been used to observe the regions' snowmelt and vegetation phenology and offer significant advantages in terms of cost, resolution and frequency. However, they have not been used in research monitoring of vegetation distribution patterns. This study proposes a novel method for drawing georeferenced vegetation classification maps from ground‐based imagery of alpine regions. Our approach had two components: vegetation classification and georectification. The proposed vegetation classification method uses a pixel time series acquired from fall images, utilizing the fall leaf color patterns. We demonstrated that the performance of the vegetation classification could be improved using time‐lapse imagery and a Recurrent Neural Network. We also developed a novel method to accurately transform ground‐based images into georeferenced data. We propose the following approaches: (1) an automated procedure to acquire Ground Control Points and (2) a camera model that considers lens distortions for accurate georectification. We demonstrated that the proposed approach outperforms conventional methods, in addition to achieving sufficient accuracy to observe the vegetation distribution on a plant‐community scale. The evaluation revealed an F1 score and root‐mean‐square error of 0.937 and 3.4 m in the vegetation classification and georectification, respectively. Our results highlight the potential of inexpensive time‐lapse cameras to monitor the distribution of alpine vegetation. The proposed method can significantly contribute to the effective conservation planning of alpine ecosystems.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44575993","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":"Deep learning‐based training data augmentation combined with post‐classification improves the classification accuracy for dominant and scattered invasive forest tree species","authors":"Szilárd Balázs Likó, I. Holb, Viktor Oláh, P. Burai, Szilárd Szabó","doi":"10.1002/rse2.365","DOIUrl":"https://doi.org/10.1002/rse2.365","url":null,"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.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41461848","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 sensing of Antarctic polychaete reefs (Serpula narconensis): reproducible workflows for quantifying benthic structural complexity with action cameras, remotely operated vehicles and structure‐from‐motion photogrammetry","authors":"Juan C. Montes‐Herrera, N. Hill, V. Cummings, G. Johnstone, J. Stark, V. Lucieer","doi":"10.1002/rse2.358","DOIUrl":"https://doi.org/10.1002/rse2.358","url":null,"abstract":"","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48540560","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":"Estimating animal density using the Space‐to‐Event model and bootstrap resampling with motion‐triggered camera‐trap data","authors":"A. Lyet, Scott Waller, T. Chambert, P. Acevedo, E. Howe, H. Kühl, R. Naidoo, T. O'Brien, Pablo Palencia, Svetlana V. Soutyrina, J. Vicente, Oliver R. Wearn, T. Gray","doi":"10.1002/rse2.361","DOIUrl":"https://doi.org/10.1002/rse2.361","url":null,"abstract":"","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43471405","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":"High‐resolution thermal imagery reveals how interactions between crown structure and genetics shape plant temperature","authors":"Peter J. Olsoy, A. Zaiats, D. Delparte, M. Germino, B. Richardson, Spencer Roop, A. Roser, J. Forbey, M. Cattau, S. Buerki, K. Reinhardt, T. T. Caughlin","doi":"10.1002/rse2.359","DOIUrl":"https://doi.org/10.1002/rse2.359","url":null,"abstract":"","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47705544","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":"Mapping tree cover expansion in Montana, U.S.A. rangelands using high-resolution historical aerial imagery","authors":"Scott L. Morford, Brady W. Allred, Eric R. Jensen, Jeremy D. Maestas, Kristopher R. Mueller, Catherine L. Pacholski, Joseph T. Smith, Jason D. Tack, Kyle N. Tackett, David E. Naugle","doi":"10.1002/rse2.357","DOIUrl":"https://doi.org/10.1002/rse2.357","url":null,"abstract":"Worldwide, trees are colonizing rangelands with high conservation value. The introduction of trees into grasslands and shrublands causes large-scale changes in ecosystem structure and function, which have cascading impacts on ecosystem services, biodiversity, and agricultural economies. Satellites are increasingly being used to track tree cover at continental to global scales, but these methods can only provide reliable estimates of change over recent decades. Given the slow pace of tree cover expansion, remote sensing techniques that can extend this historical record provide critical insights into the magnitude of environmental change. Here, we estimate conifer expansion in rangelands of the northern Great Plains, United States, North America, using historical aerial imagery from the mid-20th century and modern aerial imagery. We analyzed 19.3 million hectares of rangelands in Montana, USA, using a convolutional neural network (U-Net architecture) and cloud computing to detect tree features and tree cover change. Our bias-corrected results estimate 3.0 ± 0.2 million hectares of conifer tree cover expansion in Montana rangelands, which accounts for 15.4% of the total study area. Overall accuracy was >91%, but the producer's accuracy was lower than the user's accuracy (0.60 vs. 0.88) for areas of tree cover expansion. Nonetheless, the omission errors were not spatially clustered, suggesting that the method is reliable for identifying the regions of Montana where substantial tree expansion has occurred. Using the model results in conjunction with historical and modern imagery allows for effective communication of the scale of tree expansion while overcoming the recency effect caused by shifting environmental baselines.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"157 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138532768","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}