Remote Sensing in Ecology and Conservation最新文献

{"title":"Fine‐scale landscape phenology revealed through time‐lapse imagery: implications for conservation and management of an endangered migratory herbivore","authors":"C. John, Jeffrey T. Kerby, T. Stephenson, E. Post","doi":"10.1002/rse2.331","DOIUrl":"https://doi.org/10.1002/rse2.331","url":null,"abstract":"Climate change modifies plant phenology through shifts in seasonal temperature and precipitation. Because the timing of plant growth can limit herbivore population dynamics, climatic alteration of historical patterns of vegetation seasonality may alter population trajectories in such taxa. Thus, sound management decisions may depend on understanding how plant growth varies across a landscape within and among distinct management units or protected areas. Here, we examine spatial variation in the timing of spring plant growth, measured using a network of automated time‐lapse cameras distributed across the range of endangered Sierra Nevada bighorn sheep (Ovis canadensis sierrae) in California, USA. We tracked greenness of individual plants across 2 years to compare spatial patterns of forage phenology in snowy and drought years. Green‐up timing was derived for individual plants across the camera network and compared with local estimates of green‐up timing from satellite data. Satellite‐derived estimates of green‐up timing showed strong correspondence with camera‐derived estimates in areas with dense vegetation cover and weak correspondence in areas with sparse vegetation cover. Daily time‐lapse imagery revealed consistent variation in green‐up timing across elevation, both among latitudinal zones and among individual plant species. Green‐up timing was earlier in 2020 than in 2019, reflecting differences in the end of the snowy season. Because bighorn forage seasonally on alpine species with a brief growing period, spring migration of bighorn may be linked to variation in snowmelt and plant growth across elevational gradients.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44936886","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":"Issue Information","authors":"","doi":"10.1002/rse2.280","DOIUrl":"https://doi.org/10.1002/rse2.280","url":null,"abstract":"","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43277761","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":"Spaceborne\u0000 LiDAR\u0000 for characterizing forest structure across scales in the European Alps","authors":"Lisa Mandl, A. Stritih, R. Seidl, C. Ginzler, Cornelius Senf","doi":"10.1002/rse2.330","DOIUrl":"https://doi.org/10.1002/rse2.330","url":null,"abstract":"The launch of NASA's Global Ecosystem Dynamics Investigation (GEDI) mission in 2018 opens new opportunities to quantitatively describe forest ecosystems across large scales. While GEDI's height‐related metrics have already been extensively evaluated, the utility of GEDI for assessing the full spectrum of structural variability—particularly in topographically complex terrain—remains incompletely understood. Here, we quantified GEDI's potential to estimate forest structure in mountain landscapes at the plot and landscape level, with a focus on variables of high relevance in ecological applications. We compared five GEDI metrics including relative height percentiles, plant area index, cover and understory cover to airborne laser scanning (ALS) data in two contrasting mountain landscapes in the European Alps. At the plot level, we investigated the impact of leaf phenology and topography on GEDI's accuracy. At the landscape‐scale, we evaluated the ability of GEDIs sample‐based approach to characterize complex mountain landscapes by comparing it to wall‐to‐wall ALS estimates and evaluated the capacity of GEDI to quantify important indicators of ecosystem functions and services (i.e., avalanche protection, habitat provision, carbon storage). Our results revealed only weak to moderate agreement between GEDI and ALS at the plot level (R2 from 0.03 to 0.61), with GEDI uncertainties increasing with slope. At the landscape‐level, however, the agreement between GEDI and ALS was generally high, with R2 values ranging between 0.51 and 0.79. Both GEDI and ALS agreed in identifying areas of high avalanche protection, habitat provision, and carbon storage, highlighting the potential of GEDI for landscape‐scale analyses in the context of ecosystem dynamics and management.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44322751","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 unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy‐forming macroalgae","authors":"Pippa H. Lewis, B. Roberts, P. Moore, Samuel Pike, A. Scarth, K. Medcalf, I. Cameron","doi":"10.1002/rse2.327","DOIUrl":"https://doi.org/10.1002/rse2.327","url":null,"abstract":"Brown macroalgae habitats provide a range of ecosystem services, offering coastal protection, supporting and increasing biodiversity, and more recently have been recognized for their potential role as blue carbon habitats. Consequently, accurate areal estimates of these habitats are vitally important. Satellite imagery is often utilized for areal estimates of vegetated habitats due to their ability to capture vast areas but are disadvantaged by their lower resolution. In contrast, imagery collected by unmanned aerial vehicles (UAV) provide high‐resolution datasets but are unable to cover the necessary spatial scale required for calculating areal estimates at regional, national or international scales. This study successfully and accurately corrects the outputs from low‐resolution Sentinel 2 imagery to the standard of high‐resolution UAV imagery by using a novel brown algae index and a simple regression model to provide accurate spatial estimates. This model was applied to rocky shores across Wales, UK to predict a spatial extent of 6.2 km2 for three fucoid macroalgae species; Ascophyllum nodosum, Fucus vesiculosus and F. serratus. The regression model was validated in two ways. First, the data used to create the regression model was split to train and test (50:50) the model, with a root mean square error of ~8%–14%. Secondly, spatial estimates of fucoids in independent aerial imagery were assessed using aerial photography interpretation and compared to that of the regression model (7% difference). The carbon standing stock of fucoids calculated from the spatial estimate (6.2 km2) was found to be significantly lower than that of other marine carbon stores, indicating that fucoids do not significantly contribute as a blue carbon habitat based on biomass alone. This study produces a robust and accurate remote sensing technique to estimate spatial extent of macroalgae at large spatial scales, with possible worldwide applicability.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47364746","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":"Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research","authors":"J. Hentati‐Sundberg, Agnes B. Olin, Sheetal Reddy, Per‐Arvid Berglund, Erik Svensson, M. Reddy, Siddharta Kasarareni, A. Carlsen, Matilda Hanes, Shreyash Kad, O. Olsson","doi":"10.1002/rse2.329","DOIUrl":"https://doi.org/10.1002/rse2.329","url":null,"abstract":"Ecological research and monitoring need to be able to rapidly convey information that can form the basis of scientifically sound management. Automated sensor systems, especially if combined with artificial intelligence, can contribute to such rapid high‐resolution data retrieval. Here, we explore the prospects of automated methods to generate insights for seabirds, which are often monitored for their high conservation value and for being sentinels for marine ecosystem changes. We have developed a system of video surveillance combined with automated image processing, which we apply to common murres Uria aalge. The system uses a deep learning algorithm for object detection (YOLOv5) that has been trained on annotated images of adult birds, chicks and eggs, and outputs time, location, size and confidence level of all detections, frame‐by‐frame, in the supplied video material. A total of 144 million bird detections were generated from a breeding cliff over three complete breeding seasons (2019–2021). We demonstrate how object detection can be used to accurately monitor breeding phenology and chick growth. Our automated monitoring approach can also identify and quantify rare events that are easily missed in traditional monitoring, such as disturbances from predators. Further, combining automated video analysis with continuous measurements from a temperature logger allows us to study impacts of heat waves on nest attendance in high detail. Our automated system thus produces comparable, and in several cases significantly more detailed, data than those generated from observational field studies. By running in real time on the camera streams, it has the potential to supply researchers and managers with high‐resolution up‐to‐date information on seabird population status. We describe how the system can be modified to fit various types of ecological research and monitoring goals and thereby provide up‐to‐date support for conservation and ecosystem management.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43254246","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":"Modeling approach for coastal dune habitat detection on coastal ecosystems combining very high‐resolution UAV imagery and field survey","authors":"E. Agrillo, F. Filipponi, R. Salvati, Alice Pezzarossa, L. Casella","doi":"10.1002/rse2.308","DOIUrl":"https://doi.org/10.1002/rse2.308","url":null,"abstract":"Earth observation (EO) data, derived from remote sensing and unmanned aerial vehicle (UAV), have been recently demonstrated to be essential tools for the ecosystem monitoring and habitat mapping, combining high technological and methodological procedures for applied ecology. However, research based on EO data analyses often tend to focus on image processing techniques, neglecting the development of a detailed sampling design scheme needed for an exhaustive habitat detection. This paper shows the results of a novel approach for mapping coastal dune habitats at a fine scale, using a supervised machine learning model, through the combination of vegetation plot sampling scheme, synergic use of multi‐sensor spectral imagery (UAV‐VHR) and environmental predictors (e.g., LiDAR), object‐based image analysis, and landscape metrics analysis. Proposed approach was tested in a protected area, established to preserve notable habitats along the Italian Tyrrhenian coast. A detailed sampling scheme was designed and carried out during spring and summer of 2019, combining simultaneously UAV flight acquisition and field vegetation survey data, collected at high precision positioning. The calibrated classification model achieved an overall accuracy of 78.6% (standard error 4.33), allowing us to accurately classify and map five coastal habitats, according to EUNIS (European Nature Information System) classification, which were further verified through a fully independent validation field survey. Results demonstrate that VHR imageries, combined with specific field survey schemes, can be exploited to train classification models used for the detection of plant communities (i.e., meso‐habitat) and plant species at local scale. Our findings demonstrate that UAV‐VHR data is a valid tool to produce high spatial resolution information in sand beach ecosystems, giving ecology research a new way for responsive, timely, and cost‐effective ecosystem monitoring.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43827404","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":"Colony‐nesting gulls restrict activity levels of a native top carnivore during the breeding season","authors":"Steven Guidos, J. van Dijk, Geir H. R. Systad, A. Landa","doi":"10.1002/rse2.326","DOIUrl":"https://doi.org/10.1002/rse2.326","url":null,"abstract":"Although nesting in colonies can offer substantial reproductive benefits for many seabird species, increased visibility to predators remains a significant disadvantage for most colony‐breeders. To counteract this, some seabird species have evolved aggressive nest defense strategies to protect vulnerable eggs and chicks. Here, we used an experimental approach to test whether colony inhabitance by breeding gulls Larus spp. in western Norway impacts visitation rates of a native, mammalian predator, the Eurasian otter Lutra lutra during the breeding season. Camera traps were placed inside of and on the periphery of seabird colonies prior to the breeding season and left to run for one continuous year. Sighting frequency of otters on these cameras was compared to a control region free of gull nesting. We found that otter activity was significantly reduced in the colonies when gulls were incubating and rearing chicks, compared to time periods when gulls were building nests and absent from the colonies. Rhythmic activity patterns did not seem to be significantly impacted by the presence of gulls. This study provides clear evidence that certain colony‐nesting species can have a direct, negative impact on visitation rates of a native carnivore. Seasonal carnivore activity patterns are likely to be highly dependent on differing nesting strategies and level of nest defense by seabirds.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42176626","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":"Issue Information","authors":"","doi":"10.1002/rse2.279","DOIUrl":"https://doi.org/10.1002/rse2.279","url":null,"abstract":"","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44787996","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":"Long‐term analysis of persistence and size of swallow and martin roosts in the US Great Lakes","authors":"M. Belotti, Yuting Deng, Wenlong Zhao, Victoria F. Simons, Zezhou Cheng, Gustavo Perez, Elske K. Tielens, Subhransu Maji, D. Sheldon, Jeffrey F. Kelly, K. Horton","doi":"10.1002/rse2.323","DOIUrl":"https://doi.org/10.1002/rse2.323","url":null,"abstract":"In this study, we combined a machine learning pipeline and human supervision to identify and label swallow and martin roost locations on data captured from 2000 to 2020 by 12 Weather Surveillance Radars in the Great Lakes region of the US. We employed radar theory to extract the number of birds in each roost detected by our technique. With these data, we set out to investigate whether roosts formed consistently in the same geographic area over two decades and whether consistency was also predictive of roost size. We used a clustering algorithm to group individual roost locations into 104 high‐density regions and extracted the number of years when each of these regions was used by birds to roost. In addition, we calculated the overall population size and analyzed the daily roost size distributions. Our results support the hypothesis that more persistent roosts are also gathering more birds, but we found that on average, most individuals congregate in roosts of smaller size. Given the concentrations and consistency of roosting of swallows and martins in specific areas throughout the Great Lakes, future changes in these patterns should be monitored because they may have important ecosystem and conservation implications.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"9 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41792496","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":"Beyond presence mapping: predicting fractional cover of non‐native vegetation in Sentinel‐2 imagery using an ensemble of MaxEnt models","authors":"T. Preston, Aaron N. Johnston, Kyle G. Ebenhoch, Robert H. Diehl","doi":"10.1002/rse2.325","DOIUrl":"https://doi.org/10.1002/rse2.325","url":null,"abstract":"Non‐native species maps are important tools for understanding and managing biological invasions. We demonstrate a novel approach to extend presence modeling to map fractional cover (FC) of non‐native yellow sweet clover Melilotus officinalis in the Northern Great Plains, USA. We used ensembles of MaxEnt models to map FC across landscapes from satellite imagery trained from regional aerial imagery that was trained by local unmanned aerial vehicle (UAV) imagery. Clover cover from field surveys and classified UAV imagery were nearly identical (n = 22, R2 = 0.99). Two classified UAV images provided training data to map clover presence with MaxEnt and National Agricultural Imagery Program (NAIP) aerial imagery. We binned cover predictions from NAIP imagery within each Sentinel‐2 pixel into eight cover classes to create pure (100%) and FC (20%–95%) training data and modeled each class separately using MaxEnt and Sentinel‐2 imagery. We mapped pure clover with one classification threshold and compared its performance to 15 candidate maps that included FC predictions outside pure predictions. Each FC map represented alternative combinations of five MaxEnt thresholds and three approaches to assign cover to pixels with multiple predictions from the FC ensemble. Evaluations of performance with independent datasets revealed maps including FC corresponded to field (n = 32, R2 range: 0.39–0.68) and UAV (n = 20, R2 range: 0.61–0.84) data better than pure clover maps (R2 = 0.15 and 0.31, respectively). Overall, the pure clover map predicted 3.2% cover, whereas the three best performing FC maps predicted 6.6%–8.0% cover. Including FC predictions increased accuracy and cover predictions which can improve ecological understanding of invasions. Our method allows efficient FC mapping for vegetative species discernible in UAV imagery and may be especially useful for mapping rare, irruptive or patchily distributed species with poor representation in field data, which challenges landscape‐level mapping.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49586282","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}