Remote Sensing in Ecology and Conservation最新文献

{"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}

{"title":"Rhyming in the cold: first evidence of soniferous fishes in the Southern Ocean","authors":"Fannie W. Shabangu, Grant van der Heever, Charles von der Meden, Hannah Truter, Stephen J. Lamberth, Ofer Gon","doi":"10.1002/rse2.70065","DOIUrl":"https://doi.org/10.1002/rse2.70065","url":null,"abstract":"Acoustic ecology of Southern Ocean fishes is currently unknown due to lack of dedicated fish acoustic research from those remote/inaccessible areas. The objective of this study was to investigate the monthly and diel acoustic occurrence pattern of benthic fishes relative to environmental conditions at the sub‐Antarctic Prince Edward Islands (PEIs) in the Southern Ocean. To collect our passive acoustic data, we used an autonomous recorder deployed at ~167 m water depth on an oceanographic mooring over 21 months (April 2021 to December 2022). Benthic Ski‐Monkey III towed camera was deployed around the PEIs to identify potential sources of recorded underwater fish sounds. Three types of sounds (pops, grunts and drum sounds) were detected and validated using random forest models based on their characteristics. Pops and grunts were produced in series and as singlets. Pops were the most frequently detected sounds and were detected in December 2021 through May 2022, whereas grunts were detected in January through March 2022. Drum sounds were rare and were detected as singlets on a few occasions in December 2021 through March 2022. These monthly fish occurrences correspond to the breeding season of fishes in the Southern Ocean, suggesting the use of acoustic cues during breeding. From camera footage, <jats:italic>Nototheniops larseni</jats:italic> (painted notothen) was the only fish species found around the acoustic recorder location, and pops were putatively attributed to this abundant species, whereas other sounds were attributed to other observed species. Fish sound occurrence increased around sunrise and sunset. Sea surface temperatures between 5.2°C and 8°C were the primary predictor of fish acoustic occurrence, underscoring the potential vulnerability of these fish to environmental change. This study provides the first evidence of monthly and diel acoustic occurrence of soniferous fishes and demonstrates that bioacoustics can monitor fish biodiversity and breeding phenology in the Southern Ocean.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"412 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447947","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":"Identification of initial vegetation and habitat changes in small temperate fens using remote sensing","authors":"Lubomír Tichý, Patricia Singh, Petra Hájková, Anna Müllerová, Tomáš Peterka, Zuzana Plesková, Karel Prach, Adéla Široká, Kamila Vítovcová, Michal Hájek","doi":"10.1002/rse2.70067","DOIUrl":"https://doi.org/10.1002/rse2.70067","url":null,"abstract":"Small temperate fens rank among the most endangered habitats in temperate Europe. In agricultural landscapes, they are highly vulnerable to eutrophication and desiccation, which accelerate biodiversity loss and shifts in the carbon balance due to peat mineralization. The initial signs of habitat change are commonly manifested by shifts in vegetation structure and dominance, accompanied by increasing productivity, which precede major qualitative changes in species composition. The in‐time monitoring of vegetation productivity and site wetness at large areas is essential for guiding conservation management strategies for fens to slow down or reverse undesired changes. Here, we evaluated the ability of satellite (Sentinel‐2) and high‐resolution aerial imagery to detect early, structure‐ and productivity‐related signals of fen deterioration. We compared multispectral and optical imagery with ground‐based data, including both direct measurements and indicators derived from the species composition of the vegetation plots. At the landscape scale where both the acidic poor fens and the base‐rich fens occurred, MSAVI and NGRDI indices performed best, indicating primarily the vascular plant cover, species richness and representation of nutrient‐demanding species. At the within‐site scale, where the differences among plots were largely driven by habitat deterioration, NDVI, NDWI and RENDVI well captured differences in vascular plant productivity estimates and moss biomass measurements. Our results indicate that remote sensing is applicable for the identification of individual fen habitats and their nutrient status at the landscape scale and is even effective in detecting incipient habitat deterioration associated with increasing productivity. We demonstrate that remote sensing also performs well for small, island‐like fen patches. Its wider integration into the mire research would improve monitoring and enhance the amount of available ecological data.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"1143 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447951","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":"Historical remote sensing highlights long‐term persistence of Emperor Penguin ( Aptenodytes forsteri ) colonies","authors":"Martynas Bielinis, Michelle LaRue, Benjamin M. Kraemer, Catalina Munteanu","doi":"10.1002/rse2.70064","DOIUrl":"https://doi.org/10.1002/rse2.70064","url":null,"abstract":"Satellite imagery extending as far back as the 1960's has the potential to inform Antarctic conservation by providing insights into habitat and population dynamics that are otherwise difficult to observe. Here we demonstrate the detection of Emperor Penguin ( <jats:italic>Aptenodytes forsteri</jats:italic> ) guano stains on sea ice using Keyhole, Landsat, and Sentinel‐2 imagery from the 1960s to 2024. For 18 of the 66 known emperor penguin colonies, we confirmed colony presence in images that predate their earliest published records. Beyond presence detection, we examined the colony with the densest available imagery (Cape Washington) to quantify change in guano area over time. The guano area detected with satellites was correlated with observed chick counts from ground surveys (Spearman's ρ = 0.59, <jats:italic>P</jats:italic> ‐value = 0.017), and showed no strong evidence for a long‐term trend ( <jats:italic>P</jats:italic> = 0.61). Taken together, our results indicate substantial interannual and intra‐annual variability in colony size, but no evidence for a consistent long‐term directional trend and highlight that the use of remote sensing imagery across the Antarctic could inform conservation efforts and benefit the ongoing historical studies of penguin colony dynamics.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"12 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447954","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":"Semi‐automated seal detection on the Western Antarctic Peninsula: an unsupervised machine learning approach for detecting ice seals in aerial survey data","authors":"Claire McGinnity, Connor C.G. Bamford, Nathan Fenney, Andrew Fleming, Jaume Forcada, Michael S. Tift, Luis A. Hückstädt, Daniel P. Costa, Peter T. Fretwell","doi":"10.1002/rse2.70060","DOIUrl":"https://doi.org/10.1002/rse2.70060","url":null,"abstract":"Over the past 25 years, the Western Antarctic Peninsula (WAP) has experienced dramatic shifts in sea ice extent. This change has coincided with rapid alterations in ice‐dependent ecosystems, including those supporting crabeater seals—the most abundant Antarctic seal and one of the largest mammalian consumers of krill. Despite their ecological importance, population estimates for ice seals remain scarce due to the difficulty of surveying large‐scale, remote, ice‐covered habitats. In 2023, during an abnormally low sea ice year, we conducted aerial surveys over Crystal Sound and Marguerite Bay during the end of the breeding season, flying over 1000 km of transects. Seals were extremely sparse in the resulting imagery—occupying less than 1% of the surveyed area. This posed a significant challenge for both manual annotation and automated detection. Here, we present a semi‐automated, rule‐based image analysis pipeline to substantially reduce human annotation time. Our method leverages hierarchical clustering with just two tuneable parameters, avoiding the computational burden and opacity of deep learning models. Using this method, we identified 758 seals within an ~350 km <jats:sup>2</jats:sup> survey subset, achieving a test recall of 79% ± 9.1%. In the absence of concurrent tagging data to estimate haul‐out corrections, we refrain from extrapolating to a population estimate. However, the low observed densities highlight the urgent need for continued monitoring. Our improved data processing pipeline is a key step in facilitating the large‐scale analysis required to inform conservation strategies for this key species.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"253 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147373924","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}