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
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.
{"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}
E. Agrillo, F. Filipponi, R. Salvati, Alice Pezzarossa, L. Casella
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.
{"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}
Steven Guidos, J. van Dijk, Geir H. R. Systad, A. Landa
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.
{"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}
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
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.
{"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}
T. Preston, Aaron N. Johnston, Kyle G. Ebenhoch, Robert H. Diehl
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.
{"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}
J. Ridge, Alexandra E. DiGiacomo, Antonio B. Rodriguez, Joshua D. Himmelstein, D. Johnston
Physical structures generated from ecosystem engineers can have a cascade of impacts on the ecological community and the surrounding landscape. The Eastern oyster Crassostrea virginica can form extensive intertidal reefs, whose three‐dimensional structures provide ecosystem services like nursery and foraging habitat for fishes and invertebrates and shoreline stabilization. Measurements of the structural properties of these reefs provide opportunities to quantitatively assess associated services. There is a growing variety of tools available for measuring three‐dimensional (3D) properties of intertidal habitats, including two remote sensing methods that capture 3D structural metrics in a number of environments. We surveyed reefs using a terrestrial laser scanner (TLS, LiDAR) and imagery from unoccupied aircraft systems (UAS, or drones) processed through Structure from Motion photogrammetry. Comparisons of digital elevation models from repetitive flights over an oyster reef to checkpoints yielded mean horizontal and vertical root mean square errors (RMSE) of −0.54 ± 0.47 cm and 0.97 ± 1.0 cm (Mean ± SD), respectively, indicating high accuracy among UAS surveys. Compared to TLS products, point cloud densities from UAS‐derived products were more consistent across the reef elevation gradient and much denser overall except in the low reef zone, which was proximal to most of the TLS scan locations. Comparisons of structural metrics between UAS and TLS showed similarities in metrics like profile and planform curvatures, yet indicated UAS surveys produced higher values of surface complexity and slope. Results indicate that UAS photogrammetry can produce robust oyster reef structural metrics that can be highly useful in oyster conservation and restoration.
由生态系统工程师产生的物理结构可以对生态群落和周围景观产生一连串的影响。东方牡蛎(Crassostrea virginica)可以形成广泛的潮间带礁，其三维结构为鱼类和无脊椎动物提供了苗圃和觅食栖息地，并为海岸线稳定提供了生态系统服务。对这些珊瑚礁结构特性的测量为定量评估相关服务提供了机会。有越来越多的工具可用于测量潮间带栖息地的三维(3D)特性，包括在许多环境中捕获三维结构度量的两种遥感方法。我们使用陆地激光扫描仪(TLS, LiDAR)和通过运动摄影测量处理的无人飞机系统(UAS或无人机)的图像来调查珊瑚礁。将重复飞越牡蛎礁的数字高程模型与检查站进行比较，平均水平和垂直均方根误差(RMSE)分别为- 0.54±0.47 cm和0.97±1.0 cm (mean±SD)，表明UAS调查的精度很高。与TLS产品相比，来自UAS衍生产品的点云密度在整个珊瑚礁高程梯度上更加一致，除了靠近大多数TLS扫描位置的低珊瑚礁区域外，总体密度更高。通过比较UAS和TLS的结构指标，可以发现在剖面和平台曲率等指标上存在相似之处，但也表明UAS测量的表面复杂性和坡度值更高。结果表明，UAS摄影测量可以产生稳健的牡蛎礁结构指标，对牡蛎保护和恢复具有重要意义。
{"title":"Comparison of 3D structural metrics on oyster reefs using unoccupied aircraft photogrammetry and terrestrial LiDAR across a tidal elevation gradient","authors":"J. Ridge, Alexandra E. DiGiacomo, Antonio B. Rodriguez, Joshua D. Himmelstein, D. Johnston","doi":"10.1002/rse2.324","DOIUrl":"https://doi.org/10.1002/rse2.324","url":null,"abstract":"Physical structures generated from ecosystem engineers can have a cascade of impacts on the ecological community and the surrounding landscape. The Eastern oyster Crassostrea virginica can form extensive intertidal reefs, whose three‐dimensional structures provide ecosystem services like nursery and foraging habitat for fishes and invertebrates and shoreline stabilization. Measurements of the structural properties of these reefs provide opportunities to quantitatively assess associated services. There is a growing variety of tools available for measuring three‐dimensional (3D) properties of intertidal habitats, including two remote sensing methods that capture 3D structural metrics in a number of environments. We surveyed reefs using a terrestrial laser scanner (TLS, LiDAR) and imagery from unoccupied aircraft systems (UAS, or drones) processed through Structure from Motion photogrammetry. Comparisons of digital elevation models from repetitive flights over an oyster reef to checkpoints yielded mean horizontal and vertical root mean square errors (RMSE) of −0.54 ± 0.47 cm and 0.97 ± 1.0 cm (Mean ± SD), respectively, indicating high accuracy among UAS surveys. Compared to TLS products, point cloud densities from UAS‐derived products were more consistent across the reef elevation gradient and much denser overall except in the low reef zone, which was proximal to most of the TLS scan locations. Comparisons of structural metrics between UAS and TLS showed similarities in metrics like profile and planform curvatures, yet indicated UAS surveys produced higher values of surface complexity and slope. Results indicate that UAS photogrammetry can produce robust oyster reef structural metrics that can be highly useful in oyster conservation and restoration.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45443639","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}
M. Zoffoli, P. Gernez, S. Oiry, L. Godet, S. Dalloyau, B. F. Davies, L. Barillé
Taking into account trophic relationships in seagrass meadows is crucial to explain and predict seagrass temporal trajectories, as well as for implementing and evaluating seagrass conservation policies. However, this type of interaction has been rarely investigated over the long term and at the scale of the whole seagrass habitat. In this work, reciprocal links between an intertidal seagrass species, Zostera noltei, and a herbivorous bird feeding on this seagrass species, the migratory goose Branta bernicla bernicla, were investigated using an original combination of long‐term Earth Observation (EO) and bird census data. Seagrass Essential Biodiversity Variables (EBVs) such as seagrass abundance and phenology were measured from 1985 to 2020 using high‐resolution satellite remote sensing over Bourgneuf Bay (France), and cross‐analysed with in situ measurements of bird population size during the goose wintering season. Our results showed a mutual relationship between seagrass and Brent geese over the four last decades, suggesting that the relationship between the two species extends beyond a simple grass—herbivore consumptive effect. We provided evidence of two types of interactions: (i) a bottom‐up control where the late‐summer seagrass abundance drives the wintering population of herbivorous geese and (ii) an indirect top‐down effect of Brent goose on seagrass habitat, where seagrass development is positively influenced by the bird population during the previous wintering season. Such a mutualistic relationship has strong implications for biodiversity conservation because protecting one species is beneficial to the other one, as demonstrated here by the positive trajectories observed from 1985 to 2020 in both seagrass and bird populations. Importantly, we also demonstrated here that exploring the synergy between EO and in situ bird data can benefit seagrass ecology and ecosystem management.
{"title":"Remote sensing in seagrass ecology: coupled dynamics between migratory herbivorous birds and intertidal meadows observed by satellite during four decades","authors":"M. Zoffoli, P. Gernez, S. Oiry, L. Godet, S. Dalloyau, B. F. Davies, L. Barillé","doi":"10.1002/rse2.319","DOIUrl":"https://doi.org/10.1002/rse2.319","url":null,"abstract":"Taking into account trophic relationships in seagrass meadows is crucial to explain and predict seagrass temporal trajectories, as well as for implementing and evaluating seagrass conservation policies. However, this type of interaction has been rarely investigated over the long term and at the scale of the whole seagrass habitat. In this work, reciprocal links between an intertidal seagrass species, Zostera noltei, and a herbivorous bird feeding on this seagrass species, the migratory goose Branta bernicla bernicla, were investigated using an original combination of long‐term Earth Observation (EO) and bird census data. Seagrass Essential Biodiversity Variables (EBVs) such as seagrass abundance and phenology were measured from 1985 to 2020 using high‐resolution satellite remote sensing over Bourgneuf Bay (France), and cross‐analysed with in situ measurements of bird population size during the goose wintering season. Our results showed a mutual relationship between seagrass and Brent geese over the four last decades, suggesting that the relationship between the two species extends beyond a simple grass—herbivore consumptive effect. We provided evidence of two types of interactions: (i) a bottom‐up control where the late‐summer seagrass abundance drives the wintering population of herbivorous geese and (ii) an indirect top‐down effect of Brent goose on seagrass habitat, where seagrass development is positively influenced by the bird population during the previous wintering season. Such a mutualistic relationship has strong implications for biodiversity conservation because protecting one species is beneficial to the other one, as demonstrated here by the positive trajectories observed from 1985 to 2020 in both seagrass and bird populations. Importantly, we also demonstrated here that exploring the synergy between EO and in situ bird data can benefit seagrass ecology and ecosystem management.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42839478","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}
Rewilding has been suggested as an effective strategy for addressing environmental challenges such as the intertwined biodiversity and climate change crises, but there is little information to guide the monitoring of rewilding projects. Since rewilding focuses on enhancing ecosystem functionality, with no defined endpoint, monitoring strategies used in restoration are often inappropriate, as they typically focus on assessing species composition, or the ecological transition of an ecosystem towards a defined desired state. We here discuss how satellite remote sensing can provide an opportunity to address existing knowledge and data gaps in rewilding science. We first discuss how satellite remote sensing is currently being used to inform rewilding initiatives and highlight current barriers to the adoption of this type of technology by practitioners and scientists involved with rewilding. We then identify opportunities for satellite remote sensing to help address current knowledge gaps in rewilding, including gaining a better understanding of the role of animals in ecosystem functioning; improving the monitoring of landscape‐scale connectivity; and assessing the impacts of rewilding on the conservation status of rewilded sites. Though significant barriers remain to the widespread use of satellite remote sensing to monitor rewilding projects, we argue that decisions on monitoring approaches and priorities need to be part of implementation plans from the start, involving both remote sensing experts and ecologists. Making use of the full potential of satellite remote sensing for rewilding ultimately requires integrating species and ecosystem perspectives at the monitoring, knowledge‐producing and decision‐making levels. Such an integration will require a change in know‐how, necessitating increased inter‐disciplinary interactions and collaborations, as well as conceptual shifts in communities and organizations traditionally involved in biodiversity conservation.
{"title":"Current and future opportunities for satellite remote sensing to inform rewilding","authors":"N. Pettorelli, Henrike Schulte to Bühne","doi":"10.1002/rse2.321","DOIUrl":"https://doi.org/10.1002/rse2.321","url":null,"abstract":"Rewilding has been suggested as an effective strategy for addressing environmental challenges such as the intertwined biodiversity and climate change crises, but there is little information to guide the monitoring of rewilding projects. Since rewilding focuses on enhancing ecosystem functionality, with no defined endpoint, monitoring strategies used in restoration are often inappropriate, as they typically focus on assessing species composition, or the ecological transition of an ecosystem towards a defined desired state. We here discuss how satellite remote sensing can provide an opportunity to address existing knowledge and data gaps in rewilding science. We first discuss how satellite remote sensing is currently being used to inform rewilding initiatives and highlight current barriers to the adoption of this type of technology by practitioners and scientists involved with rewilding. We then identify opportunities for satellite remote sensing to help address current knowledge gaps in rewilding, including gaining a better understanding of the role of animals in ecosystem functioning; improving the monitoring of landscape‐scale connectivity; and assessing the impacts of rewilding on the conservation status of rewilded sites. Though significant barriers remain to the widespread use of satellite remote sensing to monitor rewilding projects, we argue that decisions on monitoring approaches and priorities need to be part of implementation plans from the start, involving both remote sensing experts and ecologists. Making use of the full potential of satellite remote sensing for rewilding ultimately requires integrating species and ecosystem perspectives at the monitoring, knowledge‐producing and decision‐making levels. Such an integration will require a change in know‐how, necessitating increased inter‐disciplinary interactions and collaborations, as well as conceptual shifts in communities and organizations traditionally involved in biodiversity conservation.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47075770","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}
E. Kleiven, Pedro G. Nicolau, S. Sørbye, J. Aars, N. Yoccoz, R. Ims
Camera traps have become popular labor‐efficient and non‐invasive tools to study animal populations. The use of camera trap methods has largely focused on large animals and/or animals with identifiable features, with less attention being paid to small mammals, including rodents. Here we investigate the suitability of camera‐trap‐based abundance indices to monitor population dynamics in two species of voles with key functions in boreal and Arctic ecosystems, known for their high‐amplitude population cycles. The targeted species—gray‐sided vole (Myodes rufocanus) and tundra vole (Microtus oeconomus)—differ with respect to habitat use and spatial‐social organization, which allow us to assess whether such species traits influence the accuracy of the abundance indices. For both species, multiple live‐trapping grids yielding capture‐mark‐recapture (CMR) abundance estimates were matched with single tunnel‐based camera traps (CT) continuously recording passing animals. The sampling encompassed 3 years with contrasting abundances and phases of the population cycles. We used linear regressions to calibrate CT indices, based on species‐specific photo counts over different time windows, as a function of CMR‐abundance estimates. We then performed inverse regression to predict CMR abundances from CT indices and assess prediction accuracy. We found that CT indices (for windows maximizing goodness‐of‐fit of the calibration models) predicted adequately the CMR‐based estimates for the gray‐sided vole, but performed poorly for the tundra vole. However, spatially aggregating CT indices over nearby camera traps enabled reliable abundance indices also for the tundra vole. Such species differences imply that the design of camera trap studies of rodent population dynamics should be adapted to the species in focus, and adequate spatial replication must be considered. Overall, tunnel‐based camera traps yield much more temporally resolved abundance metrics than alternative methods, with a large potential for revealing new aspects of the multi‐annual population cycles of voles and other small mammal species they interact with.
{"title":"Using camera traps to monitor cyclic vole populations","authors":"E. Kleiven, Pedro G. Nicolau, S. Sørbye, J. Aars, N. Yoccoz, R. Ims","doi":"10.1002/rse2.317","DOIUrl":"https://doi.org/10.1002/rse2.317","url":null,"abstract":"Camera traps have become popular labor‐efficient and non‐invasive tools to study animal populations. The use of camera trap methods has largely focused on large animals and/or animals with identifiable features, with less attention being paid to small mammals, including rodents. Here we investigate the suitability of camera‐trap‐based abundance indices to monitor population dynamics in two species of voles with key functions in boreal and Arctic ecosystems, known for their high‐amplitude population cycles. The targeted species—gray‐sided vole (Myodes rufocanus) and tundra vole (Microtus oeconomus)—differ with respect to habitat use and spatial‐social organization, which allow us to assess whether such species traits influence the accuracy of the abundance indices. For both species, multiple live‐trapping grids yielding capture‐mark‐recapture (CMR) abundance estimates were matched with single tunnel‐based camera traps (CT) continuously recording passing animals. The sampling encompassed 3 years with contrasting abundances and phases of the population cycles. We used linear regressions to calibrate CT indices, based on species‐specific photo counts over different time windows, as a function of CMR‐abundance estimates. We then performed inverse regression to predict CMR abundances from CT indices and assess prediction accuracy. We found that CT indices (for windows maximizing goodness‐of‐fit of the calibration models) predicted adequately the CMR‐based estimates for the gray‐sided vole, but performed poorly for the tundra vole. However, spatially aggregating CT indices over nearby camera traps enabled reliable abundance indices also for the tundra vole. Such species differences imply that the design of camera trap studies of rodent population dynamics should be adapted to the species in focus, and adequate spatial replication must be considered. Overall, tunnel‐based camera traps yield much more temporally resolved abundance metrics than alternative methods, with a large potential for revealing new aspects of the multi‐annual population cycles of voles and other small mammal species they interact with.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48441928","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}
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