Remote Sensing in Ecology and Conservation最新文献

{"title":"Quantifying Migratory Bat Movements in Central Europe Across Seasons and Years Using a Vertical‐Looking Radar","authors":"Silvia Giuntini, Janine Aschwanden, Damiano G. Preatoni, Fabian Hertner, Birgen Haest, Baptiste Schmid","doi":"10.1002/rse2.70076","DOIUrl":"https://doi.org/10.1002/rse2.70076","url":null,"abstract":"Bat migration is an ecologically important yet poorly understood phenomenon. This is in part because monitoring these migrations is challenging, due to bats' nocturnal behaviors and their sometimes high‐altitude migratory flights. This study presents the first radar‐based examination of multi‐annual migratory bat phenology in Europe, utilizing vertical‐looking radar data collected on the Swabian Plateau in Germany between September 2019 and December 2022. Bat activity was consistently low in winter and increased gradually from March onwards to a peak between July and September. Across all years, pre‐maternity migration began between late February and mid‐March, while post‐maternity migration ended between late October and mid‐November. We estimated peak radar‐based migration traffic rates between 1159 and 2473 bats per km, with the highest peak recorded on 4 July 2022. Correlations between radar‐derived nightly bat numbers and simultaneously acquired acoustic recordings ranged from 0.47 to 0.70 for the pre‐maternity season, and from 0.14 to 0.71 during post‐maternity migration. Both monitoring techniques showed peak bat activity during the summer, with smaller surges in September and October. The radar, however, detected significantly more bats overall. These findings showcase how vertical‐looking radars can be used to quantify and characterize seasonal variability in high‐altitude bat movements. Through strategic future radar deployments and the analysis of available historical datasets, our current understanding of migratory bat seasonality, routes, and intensity could increase drastically, and underpin the development of effective protocols for biodiversity conservation.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"26 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147751682","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":"Ground‐based robotic remote sensing for standardized biodiversity monitoring in coastal habitats","authors":"Giovanni Di Lorenzo, Simonetta Bagella, Micaela del Valle Rasino, Maria Laura Carranza, Manolo Garabini, Franco Angelini","doi":"10.1002/rse2.70074","DOIUrl":"https://doi.org/10.1002/rse2.70074","url":null,"abstract":"Autonomous remote‐sensing technologies are increasingly contributing to biodiversity monitoring by enabling scalable, repeatable, and minimally invasive data collection. We present a ground‐based robotic remote‐sensing framework that integrates artificial intelligence and standardized quality assurance to support the derivation of decision‐ready ecological indicators. Using European coastal dunes as a case study, we deployed an AI‐enabled quadruped robot equipped with near‐ground imaging sensors to monitor the host–herbivore interaction between <jats:italic>Pancratium maritimum</jats:italic> and <jats:italic>Brithys crini</jats:italic> . In this citizen‐to‐robot pipeline, expert‐verified citizen‐science imagery was used to train lightweight detection models for on‐board inference and higher‐capacity models for offline auditing, ensuring reproducibility and transparency across missions. Field trials demonstrated that the system achieved consistent image quality, accurate detections, and low‐disturbance operation under natural conditions, capturing spatially explicit evidence of herbivory and host condition. By coupling standardized protocols with robotic autonomy, this approach implements a proximal remote‐sensing layer that complements aerial and satellite observations. The workflow is designed to support transferable quantification of species interactions and habitat condition across sites and seasons, contributing to the integration of robotics and ecological remote sensing for biodiversity assessment and conservation management.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"83 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147681802","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":"Modelling forest dynamics using integral projection models and repeat lidar","authors":"Alice Rosen, Robin Battison, Christina M. Hernández, Oliver G. Spacey, Jessica McLean, Suzanne M. Prober, Samuel J. L. Gascoigne, Sean McMahon, Tommaso Jucker, Roberto Salguero‐Gómez","doi":"10.1002/rse2.70050","DOIUrl":"https://doi.org/10.1002/rse2.70050","url":null,"abstract":"Estimating tree life histories and population dynamics is key to predicting how forests respond to climate change and disturbance. However, linking individual tree trajectories to whole‐forest outcomes (e.g. structural, compositional, and functional health) remains challenging. Stage‐structured demographic models offer a promising solution, but they typically require extensive field data on individual‐level vital rates (e.g. survival and growth), limiting their application at scale. Here, we demonstrate an approach that integrates repeat airborne lidar data with a structured demographic model (an integral projection model, IPM) to examine forest‐wide demography in response to environmental drivers. Using Australia's Great Western Woodlands as a case study, we model the survival, growth, and life expectancy of ~40 000 eucalypt trees over a decade. Vital rates were modelled using height for small trees and crown area for large trees, reflecting a shift in growth strategy with size. Our results indicate distinct responses of small and large trees to proxies for competition and soil moisture (local canopy density and topographic wetness index, respectively). A reduction in topographic wetness index—reflecting drier conditions—led to lower life expectancy, particularly for larger trees, which may be more vulnerable to drought. This framework enables demographic analysis at scale, using widely available lidar data, offering a scalable tool for forest monitoring, modelling, and management. We identify three priorities for broader application, including (1) mixed species stands and multilayered canopies, (2) full life cycle modelling including reproduction and early life stages, and (3) long‐term or comparative studies using high‐quality repeat lidar. By combining remote sensing data with detailed insights from field‐based studies, our study provides a scalable approach for guiding forest management and conservation decisions.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"33 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147655661","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":"Scalable low‐cost seabed landers: the missing link for sustained, integrated, long‐term observations in dynamic shallow seas","authors":"Arienne Calonge, Roeland Develter, Carlota Muñiz, Clea Parcerisas, Jan Reubens, Wieter Boone, Klaas Deneudt, Elisabeth Debusschere","doi":"10.1002/rse2.70072","DOIUrl":"https://doi.org/10.1002/rse2.70072","url":null,"abstract":"Shallow coastal seas are increasingly affected by accelerating climate change and widespread anthropogenic pressures, yet consistent, long‐term monitoring data remain scarce and fragmented. Many existing observations are restricted to project‐specific objectives, limiting their potential contribution to broader‐scale assessments. Seabed landers, when equipped with a range of biotic and abiotic sensors, offer a non‐invasive and cost‐effective solution for ecosystem‐scale monitoring of Essential Biodiversity Variables (EBVs) and Essential Ocean Variables (EOVs). Examples of EBVs and EOVs include marine mammal distribution, fish movement, underwater sound, plankton biomass and diversity, sea surface height, currents, and temperature. The integration of datasets improves our ability to address ecological questions across larger spatial and temporal scales. Incorporating biodiversity monitoring data into the Digital Twin of the Ocean, a high‐resolution multi‐dimensional virtual representation of the ocean, marks a critical step toward large‐scale, standardized ecosystem‐based assessments of ocean health. Strengthening infrastructure, data management capabilities, and protocols will further unlock the potential of seabed observatories to inform conservation efforts and policy development, particularly through the application of artificial intelligence. In this article, a perspective on the capacity of subsea observatories is presented, specifically on multi‐purpose seabed landers, to deliver scalable, sustained, and high‐resolution observations of EOVs and EBVs over large areas.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"18 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147586192","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":"Accounting for animal movement during aerial imaging surveys","authors":"Rowan L. Converse, Christopher D. Lippitt, Steven E. Sesnie, David R. Stewart, Matthew J. Butler, Grant M. Harris","doi":"10.1002/rse2.70073","DOIUrl":"https://doi.org/10.1002/rse2.70073","url":null,"abstract":"The response of wildlife to the movement of aircraft during low‐altitude image collection can lead to double‐ or undercounting animals, thereby biasing abundance estimates. The preservation of spatial arrangements of animals, particularly in spatially overlapped images, provides new opportunities for measuring such bias compared with observer surveys. We present a novel method to assess count error and platform bias in aerial imaging surveys by comparing counts and relative positions of animals in spatially overlapped image pairs. We analyzed drone imagery of overwintering migratory waterbirds at state and federal wildlife conservation areas in New Mexico, USA. To assess count error, we compared counts of animals in spatially overlapping images along transects and between adjacent transects. To detect the movement of animals in response to the movement of fixed‐wing and rotary‐wing drones in grid sampling surveys, we calculated vectors between the geographic median of the animal locations in spatially overlapping image areas, normalized these vectors across surveys relative to the heading of the drone, and used circular statistics to test for directional bias. We found that ducks, geese, and cranes showed low but differing degrees of count bias throughout surveys, and that the rotary‐wing drone generated more directionally biased movement than the fixed‐wing drone. Cranes and geese showed decreases in aggregate counts of −9.9% and −9.5% between adjacent transects, indicating movement out of the imaging area, whereas ducks showed a small net increase in the aggregate count of 2.5% between adjacent transects, indicating that they may be “pushed” slightly by the movement of the drone. We present a broadly applicable method for quantitatively assessing animal responses to aircraft, to subsequently improve the credibility of abundance estimates.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"191 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147524311","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":"Retrospective image analysis for long‐term demography using Google Earth imagery","authors":"Erola Fenollosa, Garrett Cohen, Roberto Salguero‐Gómez","doi":"10.1002/rse2.70071","DOIUrl":"https://doi.org/10.1002/rse2.70071","url":null,"abstract":"Ecosystems worldwide are undergoing rapid degradation, yet effective monitoring remains costly and time‐consuming. The proliferation of open‐access imagery from satellites, Google Earth, and citizen‐science platforms offers unprecedented opportunities to improve ecological monitoring, yet, their potential for species detection and demographic tracking remains underexplored. Here, we demonstrate a cost‐effective approach to retrospective image analysis by combining current ground truth data with historical Google Earth RGB imagery to extract long‐term demographic information. We apply this approach to two invasive plant taxa with contrasting growth forms in Mediterranean ecosystems. First, we use deep learning to detect individuals of prickly pear ( <jats:italic>Opuntia</jats:italic> spp.) across diverse habitats and image resolutions, and reconstruct 10 years of spatially explicit recruitment rates along a climatic gradient. Second, we quantify nearly 20 years of growth dynamics for the clonal invader <jats:italic>Carpobrotus</jats:italic> spp. in two contrasting environments. Our object detection model achieves 60%–80% accuracy in identifying <jats:italic>Opuntia</jats:italic> individuals, with performance enhanced by colour consistency and contrast. While detection is limited for individuals &lt;4 m <jats:sup>2</jats:sup> , the approach captures ~80% of the population. For <jats:italic>Carpobrotus</jats:italic> spp., area‐based analysis mapped &gt;7,900 m <jats:sup>2</jats:sup> across two sites and revealed a mean genet expansion of 12.95 ± 5.32 m <jats:sup>2</jats:sup> yr. <jats:sup>−1</jats:sup> . Beyond detection, time‐series analysis of Google Earth imagery allows the estimation of recruitment, growth rates, climatic sensitivity, population structure, size–age relationships, and recruitment hotspots. With image series spanning a decade for Spain, Greece, and the UK, and two decades for Portugal, we provide spatially explicit demographic reconstructions at unprecedented scales. By harnessing publicly available imagery, our pipeline expands the capacity for long‐term, large‐scale demographic monitoring. Although demonstrated here with invasive plants, the approach is broadly applicable across taxa and ecosystems. Retrospective image analysis has the potential to accelerate conservation, guide restoration, and support robust ecological forecasting in the Anthropocene.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"88 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147524310","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":"Time‐series digital camera photos combined with machine learning algorithms can realize accurate observation of flowering phenology","authors":"Chuangye Song, Yuan Jia, Lin Zhang, Dongxiu Wu","doi":"10.1002/rse2.70069","DOIUrl":"https://doi.org/10.1002/rse2.70069","url":null,"abstract":"Digital cameras are widely used for documenting phenological observations, and numerous images have been collected. However, intelligent approaches are required to extract valuable phenological information from time‐series images. In this study, we used machine learning (ML) algorithms, including convolutional neural network (CNN)‐based You Only Look Once (YOLO) object detection and semantic segmentation methods to identify flowers in images, establish curves of flower count and flower cover, and extract the phenophases of first, peak and end flowering. Random forests (RF) was performed to recognize flower pixels to calculate the flower cover, construct the flower cover curve and extract the same phenophases as those of the YOLO methods. Furthermore, flowering phenophases were also extracted through manual visual identification. We used a generalized additive model (GAM) to fit curves for flower count and flower cover, and extracted flowering phenophases by calculating the inflection points of the fitted curves. We found that (1) YOLO‐based methods could effectively identify flowers, and the variation in flower count and flower cover obtained from the YOLO object detection and semantic segmentation models reflected the trend of flowering phenology. The flower count and flower cover curves effectively supported the extraction of first and peak flowering. The difference between the YOLO‐identified and manually identified flowering phenophases ranged from 1 day to 3 days using the optimal thresholds. For end flowering, except for the end flowering identified based on flower count derived from YOLO object detection, the date difference in phenophases between the YOLO‐identified and manually identified ranged from 1 day to 8 days. (2) There are apparent outliers in the RF‐calculated flower cover values, particularly during the post‐peak‐flowering period. However, the identified flowering phenophases based on the RF‐derived flower cover curve after omitting outliers were consistent with those of manual visual identification and YOLO‐based methods (except end flowering identified based on flower count derived from YOLO object detection), with the date difference in phenophases ranging from 0 to 8 days. (3) The GAM performed well in fitting the trends of the normalized cumulative flower count and flower cover. Using the threshold generated by second derivate method, the identified end flowering was close to that of “late flowering” stage identified by manual visual identification, and the date difference ranged from 0 to 6 days. (4) Due to the variation in flowering rhythm and progression across different plant species, fixed thresholds are not fully optimal for all plants, and the thresholds used to extract flowering phenology require targeted adjustments based on specific observed species. Our study showed that a time‐lapse digital camera combined with ML algorithms can help improve the objectivity of phenology observations, indicating the possibility ","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"313 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147492810","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":"Scale dependence in remotely sensed biodiversity: Leveraging continental‐scale imaging spectroscopy from the National Ecological Observatory Network","authors":"Meghan T. Hayden, Matthew W. Rossi, Laura E. Dee, Kyle Kovach, Cibele H. Amaral, Jacob Nesslage, Madeline Slimp, Rachel S. Meyer, E. Natasha Stavros","doi":"10.1002/rse2.70068","DOIUrl":"https://doi.org/10.1002/rse2.70068","url":null,"abstract":"Biodiversity is under threat globally, with significant implications for the ecosystem processes that underpin human well‐being. Effective conservation efforts require scalable, replicable metrics to detect and monitor changes in biodiversity. However, a persistent challenge is deciding on the spatial scale over which to quantify biodiversity—including when using metrics derived from remote sensing—which is inherently scale‐dependent. Understanding the scaling properties of remote sensing metrics is thus important for biodiversity change detection and assessment. We address this challenge by investigating the scale dependence of two remotely sensed vegetation diversity metrics, spectral richness and divergence, across 15 diverse ecosystems that are part of the United States National Ecological Observatory Network (NEON). Our continental‐scale analysis builds on the success of similar studies that have shown scale dependence of spectral richness in select forest ecosystems. Our results corroborate prior findings that show that spectral richness follows well‐established ecological scaling laws by adhering to the sub‐linear scaling expected for species–area and functional diversity area relationships. We compare these scaling relationships to the null expectation of randomly distributed pixel values, demonstrating that empirical scaling relationships are non‐random. Comparing diverse ecosystems using the same data and methods, we show how scaling parameters encode important information on the relative roles of climate, geomorphology, and ecosystem structure on vegetation‐based biodiversity metrics. By advancing our understanding of the scale dependence of remotely sensed biodiversity metrics, this study lays a foundation for leveraging remote sensing data in global biodiversity monitoring and conservation.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"12 11 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147470950","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":"Classification of tree species and standing dead trees in Boreal forests using UAV‐based RGB, multispectral, and LiDAR point clouds","authors":"Anton Kuzmin, Lauri Korhonen, Topi Tanhuanpää, Mikko Kukkonen, Matti Maltamo, Timo Kumpula","doi":"10.1002/rse2.70070","DOIUrl":"https://doi.org/10.1002/rse2.70070","url":null,"abstract":"In boreal forests, old deciduous trees, particularly European Aspen ( <jats:italic>Populus tremula</jats:italic> L.), play a crucial role in supporting biodiversity by providing unique habitats for cavity‐nesting birds, insects, and mammals. Despite their ecological importance, the low economic value and sparse distribution of aspen limit knowledge of their spatial and temporal distribution, hindering effective forest management and conservation. Similarly, standing dead trees are vital for biodiversity, offering habitats for numerous species. Accurate identification of tree species and standing dead trees is essential for forest mapping and biodiversity monitoring. Unmanned aerial vehicles (UAVs) have proven effective for detailed forest assessments, offering imagery with ultra‐high spatial resolution at relatively low costs. Their flexibility and customizable sensor payloads enable rapid data acquisition in challenging forest regions, making them a cost‐efficient alternative to manned aircraft. This study assessed the accuracy of different UAV‐based sensors and their combinations in classifying Scots pine ( <jats:italic>Pinus sylvestris</jats:italic> L.), Norway spruce ( <jats:italic>Picea abies</jats:italic> (L.) Karst.), birches ( <jats:italic>Betula pendula</jats:italic> Roth and <jats:italic>Betula pubescens</jats:italic> Ehrh.), European aspen, and standing dead trees. Spectral and structural features from true‐color (RGB) and multispectral (MSP) photogrammetric point clouds, as well as LiDAR data, were used as predictors. A total of 1,205 field‐measured trees (approx. 250 per class) were analyzed, with 70% used for training and 30% for validation. Our results showed that the LiDAR + MSP approach achieved the highest accuracy (78%) and kappa value (0.72), effectively leveraging LiDAR's structural detail and MSP's spectral richness. Among single sensors, MSP performed best (75% accuracy), while RGB and LiDAR achieved 71% and 60%, respectively. These findings highlight that while single‐sensor datasets can perform well, fusing spectral and structural data is essential for maximizing classification accuracy. UAV‐based multi‐sensor approaches offer significant potential for advancing assessments of biodiversity indicators and sustainable forest management.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"6 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147470949","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 super‐resolution reconstruction and improved YOLOv9 for efficient benthos detection: a case study at Lake Hamana, Japan","authors":"Fan Zhao, Bangzhang Ma, Dianhan Xi, Jiaqi Wang, Yijia Chen, Yongying Liu, Xinlei Shao, Mowen Zhang, Guocheng Zhang, Jundong Chen, Katsunori Mizuno","doi":"10.1002/rse2.70066","DOIUrl":"https://doi.org/10.1002/rse2.70066","url":null,"abstract":"The development of remote sensing and object detection technologies has advanced benthos surveys. However, challenges remain in accuracy and cost‐efficiency due to environmental interference. A practical method combining drone‐based image acquisition and deep learning techniques for benthos monitoring is presented. Field experiments objecting hermit crabs were conducted at Lake Hamana using drones at altitudes of 2 m, 5 m and 10 m. Super‐resolution reconstruction (SRR) was applied to enhance image quality, followed by small‐object detection using the self‐built V9‐BENTHOS. With a magnification factor × 4, Residual Dense Network (RDN) achieved optimal SRR performance (PSNR: 38.15 dB, SSIM: 88.51%) and V9‐BENTHOS reached a mean average precision of 95.5%. The effects of SRR algorithms and magnification factors on hermit crab detection were discussed. This case study provides a new approach to support benthos ecological monitoring.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"9 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147448046","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}