Remote Sensing in Ecology and Conservation最新文献

{"title":"Evaluating land–sea linkages using land cover change and coral reef monitoring data: A case study from northeastern Puerto Rico","authors":"Pirta Palola, Sasha Hills, Simon J. Pittman, Edwin A. Hernández‐Delgado, Antoine Collin, Lisa M. Wedding","doi":"10.1002/rse2.70054","DOIUrl":"https://doi.org/10.1002/rse2.70054","url":null,"abstract":"Land cover change that leads to increased nutrient and sediment runoff is an important driver of change in coral reef ecosystems. Linking landscape change to seascape change is necessary for integrated land–sea management of coral reefs. This study explored the use of freely available satellite products to examine long‐term patterns of change across the land–sea continuum. We focused on northeastern Puerto Rico, where a widespread decline in live coral cover has occurred despite concomitant watershed reforestation that was expected to reduce land‐based threats. The aims of this study were (1) to examine whether these land–sea trends continued in 2000–2015 and (2) to assess the opportunities and limitations associated with using satellite data to inform land–sea management. We applied a Random Forest classifier on Landsat‐7 satellite imagery to assess changes in land cover and landscape development intensity, a spatial index to estimate land‐based pressure on nearshore marine ecosystems. We used field monitoring data to quantify benthic community change. We found that reforestation continued in 2000–2015 (+11%), suggesting reduced land‐based pressure on adjacent reefs in both northern (Luquillo) and eastern (Ceiba‐Fajardo) watersheds. Concomitantly, coral cover continued to decline, and a new aggressive expansion of peyssonnelid algal crust was recorded. Clustering analysis indicated that benthic monitoring sites in the same geographic regions (nearshore/offshore, north/east) followed similar community composition trajectories over time. Our results suggest that continued reforestation and the expected reduction in land‐based pressure have not been sufficient to halt coral cover decline in northeastern Puerto Rico. To improve the characterization and monitoring of the full causal chain from changes in land cover to water quality to benthic communities, advances in satellite‐based water quality mapping in optically shallow waters are needed. A strategic combination of remote sensing and targeted field surveys is required to monitor and mitigate land‐based stressors on coral reefs.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"18 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902907","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":"Using time‐series remote sensing to identify and track individual bird nests at large scales","authors":"S. K. Morgan Ernest, Lindsey A. Garner, Ben G. Weinstein, Peter Frederick, Henry Senyondo, Glenda M. Yenni, Ethan P. White","doi":"10.1002/rse2.70046","DOIUrl":"https://doi.org/10.1002/rse2.70046","url":null,"abstract":"The challenges of monitoring wildlife often limit the scales and intensity of the data that can be collected. New technologies—such as remote sensing using unoccupied aircraft systems (UASs)—can collect information more quickly, over larger areas, and more frequently than is feasible using ground‐based methods. While airborne imaging is increasingly used to produce data on the location and counts of individuals, its ability to produce individual‐based demographic information is less explored. Repeat airborne imagery to generate an imagery time series provides the potential to track individuals over time to collect information beyond one‐off counts, but doing so necessitates automated approaches to handle the resulting high‐frequency large‐spatial scale imagery. We developed an automated time‐series remote sensing approach to identifying wading bird nests in the Everglades ecosystem of Florida, USA to explore the feasibility and challenges of conducting time‐series based remote sensing on mobile animals at large spatial scales. We combine a computer vision model for detecting birds in weekly UAS imagery of colonies with biology‐informed algorithmic rules to generate an automated approach that identifies likely nests. Comparing the performance of these automated approaches to human review of the same imagery shows that our primary approach identifies nests with comparable performance to human review, and that a secondary approach designed to find quick‐fail nests resulted in high false‐positive rates. We also assessed the ability of both human review and our primary algorithm to find ground‐verified nests in UAS imagery and again found comparable performance, with the exception of nests that fail quickly. Our results showed that automating nest detection, a key first step toward estimating nest success, is possible in complex environments like the Everglades and we discuss a number of challenges and possible uses for these types of approaches.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"16 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801312","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":"On the compatibility of single‐scan terrestrial LiDAR with digital photogrammetry and field inventory metrics of vegetation structure in forest and agroforestry landscapes","authors":"Magnus Onyiriagwu, Nereoh Leley, Caleb W. T. Ngaba, Anthony Macharia, Henry Muchiri, Abdalla Kisiwa, Martin Ehbrecht, Delphine Clara Zemp","doi":"10.1002/rse2.70047","DOIUrl":"https://doi.org/10.1002/rse2.70047","url":null,"abstract":"In tropical ecosystems, accurately quantifying vegetation structure is crucial to determining their capacity to deliver ecosystem services. Terrestrial laser scanning (TLS) and UAV‐based digital aerial photogrammetry (DAP) are remote sensing tools used to assess vegetation structure, but are challenging to use with conventional methods. Single‐Scan TLS and DTM‐independent DAPs are alternative scanning approaches used to describe vegetation structure; however, it remains unclear to what extent they relate to each other and how accurately they can distinguish forest structural characteristics, including vertical structure, horizontal structure, vegetation density, and structural heterogeneity. First, we quantified bivariate and multivariate correlations between equivalent/analogous structural metrics from these data sources using principal component and Procrustes analysis. We then evaluated their ability to characterize the forest and agroforestry landscapes. DAP, TLS, and Field metrics were moderately aligned for vegetation density, canopy top height, and gap dynamics, but differed in height variability and surface heterogeneity, reflecting differences in data structure. DAP and TLS achieved the highest accuracy in classifying forests and agroforestry plots, with overall accuracies of 89% and 78%, respectively. Though the field metrics were unable to resolve 3D characteristics related to heterogeneity, their capacity to distinguish the stand structure at 69% accuracy was driven by the relative pattern of its suite of metrics. The results indicate that the single‐scan TLS and DTM‐independent DAP yield meaningful descriptors of vegetation structure, which, when combined, can provide a comprehensive representation of the structure in these tropical landscapes.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"93 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145746672","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":"Using phenology to improve invasive plant detection in fine‐scale hyperspectral drone‐based images","authors":"Kelsey S. Huelsman, Howard E. Epstein, Xi Yang, Roderick Walker","doi":"10.1002/rse2.70049","DOIUrl":"https://doi.org/10.1002/rse2.70049","url":null,"abstract":"Mapping and managing invasive plants are top priorities for land managers, but traditional approaches are time and labor‐intensive. To improve detection efforts, we explored the effectiveness of hyperspectral, drone‐based detection algorithms that incorporate phenology. We collected fine‐resolution (3 cm) hyperspectral images using a drone equipped with a Nano‐Hyperspec imager on seven dates from April to November, 2020 and then used a subsample of pixels from the images to develop multitemporal detection algorithms for three invasive plant species within heterogeneous vegetation communities. The three species are invasive in much of the U.S. and in Virginia, where the data were collected: <jats:italic>Ailanthus altissima</jats:italic> (tree of heaven), <jats:italic>Elaeagnus umbellata</jats:italic> (autumn olive), and <jats:italic>Rhamnus davurica</jats:italic> (Dahurian buckthorn). We determined when each species could be accurately detected, what spectral features allowed for detection, and the consistency of those features over a growing season. All three species could be detected in June. Only <jats:italic>E. umbellata</jats:italic> had consistently accurate algorithms and used consistent features in the visible and red edge across the growing season. Its most accurate detection algorithms in the summer included features in the yellow‐orange spectral region. <jats:italic>A. altissima</jats:italic> and <jats:italic>R. davurica</jats:italic> were both detectable in the mid‐ and late‐growing seasons, with little overlap in key spectral features across dates. Our results indicate that even a small subset of data from hyperspectral imagery can be used to accurately detect invasive plants in heterogeneous plant communities, and that incorporating species‐specific phenological traits into detection algorithms improves detection, laying methodological and theoretical groundwork for the future of invasive species management.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"17 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145704601","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":"Cameras do not always take a full picture: wolf activity patterns revealed by accelerometers versus road‐positioned camera traps","authors":"Katarzyna Bojarska, Michał Żmihorski, Morteza Naderi, J. David Blount, Mark Chynoweth, Emrah Coban, Çağan H. Şekercioğlu, Josip Kusak","doi":"10.1002/rse2.70045","DOIUrl":"https://doi.org/10.1002/rse2.70045","url":null,"abstract":"While animal‐attached devices provide the most detailed information on animal behaviour, camera traps have become an increasingly popular non‐invasive alternative in wildlife ecology. Here, we compared activity patterns of wolves ( <jats:italic>Canis lupus</jats:italic> ) assessed with accelerometers and road‐positioned camera traps in two study areas in Croatia and north‐eastern Türkiye. We used accelerometer data from 37 wolves and camera trap data from 82,375 camera trap days at 358 road locations from 2010 to 2021. We fitted generalised additive mixed models to determine the times of day and parts of the year with the highest and lowest wolf activity and correlated the predictions between accelerometer‐ and camera‐based models. Wolf activity patterns predicted from road‐positioned camera traps and accelerometer data were significantly positively correlated, but the strength of the correlation varied among areas, times of day and seasons. The lowest and highest activity periods showed little overlap between the two methods. In both study areas, camera trap data failed to detect the increase in daylight activity during the pup‐rearing season evident in accelerometer data. Overall, camera traps proved adequate for describing general daily and seasonal wolf activity patterns, while discrepancies between the two methods may largely be attributed to camera placement on roads. In light of the increasing use of camera traps in ecological research, our results highlight the value of animal‐attached devices for tracking individuals and recommend caution when interpreting activity patterns from road‐mounted cameras.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"29 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673785","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":"Impact of parameterization in multiple acoustic index comparisons: practical cases in terrestrial and underwater soundscapes","authors":"Juan C. Azofeifa‐Solano, Miles J. G. Parsons, James Kemp, Rohan M. Brooker, Robert D. McCauley, Shyam Madhusudhana, Mathew Wyatt, Stephen D. Simpson, Christine Erbe","doi":"10.1002/rse2.70044","DOIUrl":"https://doi.org/10.1002/rse2.70044","url":null,"abstract":"Acoustic indices are increasingly used to characterize soundscapes and infer biodiversity patterns in terrestrial and marine environments. However, methodological choices during data collection and signal processing—particularly the selection of sampling frequency, Fourier transform number of points and window overlap—can influence the output of acoustic indices, multivariate analysis and their ecological interpretations. Here, we evaluated the effects of these parameters on multivariate soundscape separation with two example environment comparisons: terrestrial (Bushland vs. Urban) and underwater ( <jats:italic>Pocillopora</jats:italic> dominated vs. Non‐ <jats:italic>Pocillopora</jats:italic> dominated). We assessed the influence of parameterization by computing 432 spectrogram configurations per recording across five commonly used acoustic indices. Using non‐metric multidimensional scaling, multivariate descriptors and Bayesian models, we found that parameter selection influenced soundscape separation in each environment example with data‐specific interactions. For instance, greater NFFT values increased centroid distance between habitats in terrestrial soundscapes but decreased it in underwater soundscapes. Our results confirm earlier findings that acoustic indices can be sensitive to spectrogram parameterization, and extend these by demonstrating, with a systematic multivariate framework, how interactions among sampling frequency, NFFT and window overlap affect soundscape separation across environments. This approach emphasizes the need for parameter sensitivity testing, transparent reporting and careful interpretation when comparing soundscapes. Code: <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"https://github.com/juancarlosazofeifasolano/acousticindices_parametrisation.git\">https://github.com/juancarlosazofeifasolano/acousticindices_parametrisation.git</jats:ext-link> .","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"21 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673784","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":"Programmed unmanned aerial vehicles show great potential for monitoring marine megafauna in specific areas of interest","authors":"Dinah Hartmann, Valdemar Palmqvist, Johanna Stedt","doi":"10.1002/rse2.70043","DOIUrl":"https://doi.org/10.1002/rse2.70043","url":null,"abstract":"Targeted conservation measures are contingent on robust knowledge of spatio‐temporal animal distribution in areas of interest. We explore unmanned aerial vehicle (UAV) transect monitoring as a novel method for standardized digital aerial surveys of marine megafauna by investigating the fine‐resolution spatio‐temporal distribution of harbour porpoises ( <jats:italic>Phocoena phocoena</jats:italic> ) in a Swedish nature reserve along with drivers of this distribution and potential biases. Biweekly UAV video data were collected along pre‐programmed strip transects over 17 weeks from June to September 2023, totalling a survey area of 3.37 km <jats:sup>2</jats:sup> , thereby providing porpoise monitoring data covering 89% of a special area of conservation for the species. All UAV video data were manually reviewed by a primary observer, and 25% of the UAV footage was also reviewed by a second, unexperienced observer to identify observer bias and learning effects. No significant observer bias or learning effect was found, but increased sea state affected porpoise density negatively. From the monitoring data, we were able to calculate relative density estimates, identify small‐scale spatio‐temporal differences and detect negative effects of recreational boat activity on porpoise presence. We further demonstrate that within this restricted area, porpoises are found in higher relative densities outside a designated conservation area, compared to within the conservation area, providing important knowledge to guide fine‐scale local conservation actions. We highlight advantages and areas of improvement of UAV transect monitoring as an accessible, versatile and adaptable method to survey marine megafauna in spatially restricted specific areas of interest. We conclude that this method constitutes a promising and valuable tool for wildlife monitoring, especially as it can be easily adapted and modified for specific contexts and species.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"121 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593542","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":"Improving forest age estimation to understand subtropical forest regrowth dynamics using deep learning image segmentation of time‐series historical aerial photographs","authors":"Ying Ki Law, Yi‐Fei Gu, Shuwen Liu, Guangqin Song, Aland H. Y. Chan, Cham Man Tse, Zhonghua Liu, Martha J. Ledger, Billy C. H. Hau, Sawaid Abbas, Jin Wu","doi":"10.1002/rse2.70042","DOIUrl":"https://doi.org/10.1002/rse2.70042","url":null,"abstract":"Accurate forest age estimation is essential for understanding forest recovery trajectories and evaluating the efficacy of restoration strategies. While field‐based methods for forest age estimation offer high accuracy, they are spatially constrained and challenging to apply retrospectively. In contrast, satellite‐based approaches provide extensive regional coverage but may lack precision at the local landscape level. Historical aerial photographs can bridge this gap by delivering fine‐scale land cover information. However, challenges such as limited spectral bands and topographic shadows in hilly terrains introduce uncertainty in land cover segmentation and temporal dynamics, complicating accurate forest age determination. To address these challenges, we developed a two‐step deep learning approach for image segmentation using historical aerial photographs. The method involves using a pre‐trained deep learning model with open‐source forest labels, followed by fine‐tuning based on localized forest data. This approach achieved accurate forest segmentation, with our highest accuracy model (mean IoU of 0.859) utilizing a combined U‐Net and ResNet50 architecture. Our forest age estimates demonstrated superior agreement, significantly outperforming existing national forest age products for China in terms of both temporal coverage and accuracy. By overlaying our age product with LiDAR structural metrics, we uncovered strong yet distinct recovery trajectories across forest structure attributes. Collectively, our study demonstrates the effectiveness of deep learning algorithms for forest age monitoring using greyscale historical aerial photographs, while pinpointing the limitations of existing national‐scale forest age products for local monitoring. Enhanced fine‐scale forest age mapping provides an essential technique and dataset to advance our understanding of forest regrowth and structural dynamics, and this improved knowledge of forest dynamics will aid in assessing carbon sequestration potential and informing targeted forest management and restoration strategies.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"17 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525203","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":"Wall‐to‐wall Amazon forest height mapping with planet NICFI , Aerial LiDAR , and a U‐Net regression model","authors":"Fabien H. Wagner, Ricardo Dalagnol, Griffin Carter, Mayumi C. M. Hirye, Shivraj Gill, Le Bienfaiteur Sagang Takougoum, Samuel Favrichon, Michael Keller, Jean P. H. B. Ometto, Lorena Alves, Cynthia Creze, Stephanie P. George‐Chacon, Shuang Li, Zhihua Liu, Adugna Mullissa, Yan Yang, Erone G. Santos, Sarah R. Worden, Martin Brandt, Philippe Ciais, Stephen C. Hagen, Sassan Saatchi","doi":"10.1002/rse2.70041","DOIUrl":"https://doi.org/10.1002/rse2.70041","url":null,"abstract":"Tree canopy height is a key indicator of forest biomass, productivity and structure, yet measuring it accurately at regional or larger scales, whether from the ground or remotely, remains challenging. The objective of this study is to generate the first complete canopy height map of the Amazon forest at ~4.78 m resolution using Planet NICFI imagery and deep learning. Specifically, we (i) trained a U‐Net regression model with canopy height models (CHMs) derived from tropical airborne LiDAR and their corresponding Planet NICFI images to estimate canopy height, (ii) evaluated the accuracy of our map against existing global products based on Sentinel‐2/1 and Maxar Vivid2 imagery and (iii) assessed its capacity to capture small‐scale canopy height changes. Tree height predictions on the validation sample had a mean absolute error of 3.68 m, with minimal systematic bias across the full range of tree heights in the Amazon forest. The main biases are a slight overestimation (up to 5 m) for heights of 5–15 m and an underestimation for most trees above 50 m. Outperforming existing global model‐based canopy height products in this region, the model accurately estimated canopy heights up to 40–50 m with minimal saturation. We determined that the Amazon forest has an average canopy height of ~22 m (standard deviation ~5.3 m) and exhibits large‐scale patterns, ranging from the tallest forests of the Guiana Shield to shorter forests along wetlands, rivers, rocky outcrops, savannas and high elevations. Events such as logging or deforestation could be detected from changes in tree height, and the results demonstrated a first success in monitoring the height of regenerating forests. Finally, the map of the Amazon forest canopy height is displayed.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"92 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525202","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":"Comparing convolutional neural network and random forest for benthic habitat mapping in Apollo Marine Park","authors":"Henry Simmons, Dang Nguyen, Benjamin Misiuk, Daniel Ierodiaconou, Sunil Gupta, Oli Dalby, Mary Young","doi":"10.1002/rse2.70038","DOIUrl":"https://doi.org/10.1002/rse2.70038","url":null,"abstract":"Marine habitat maps are essential tools for marine spatial planning, providing information for decision‐making in conservation and resource management. Accurate classification of benthic habitats supports their sustainable use and identifies key areas for protection. Convolutional neural networks (CNNs) are powerful deep learning algorithms that have shown promise for advancing habitat classification tasks and mapping complex marine environments. This study compares the performance of a CNN and a Random Forest (RF) model in classifying benthic habitats within Apollo Marine Park, Victoria, Australia. Models were trained to classify three distinct habitat types using bathymetry, multibeam backscatter, wave height and positioning data; however, the RF model had access to 100 additional bathymetric derivatives, of which 10 were selected as predictors. The CNN achieved an overall accuracy of 67.32%, while the RF model achieved 62.57%. For individual habitats, the CNN obtained F1‐scores of 0.664 for <jats:italic>high energy circalittoral rock with seabed‐covering sponges</jats:italic> , 0.538 for <jats:italic>low complexity circalittoral rock with non‐crowded erect sponges</jats:italic> and 0.774 for <jats:italic>infralittoral sand and shell mixes</jats:italic> . The corresponding RF scores were 0.598, 0.506 and 0.739. Both models encountered challenges in classifying transitional habitat zones, where diffuse boundaries between habitat types led to overlaps and shared acoustic properties. However, the CNN demonstrated an advantage due to its ability to automatically analyse spatial patterns across multiple scales. In contrast, while the RF model incorporated terrain attributes that capture local variation, its ability to utilize spatial context was constrained to predefined scales of the derived features. The CNN's ability to leverage spatial relationships resulted in clearer and more coherent habitat maps, reducing the salt‐and‐pepper effect commonly observed in pixel‐based classifications. This study highlights the potential of CNNs for marine habitat mapping through their ability to classify data derived from multibeam bathymetry, while also identifying avenues for further refinement to enhance their utility in marine spatial planning tasks.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"29 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525204","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}