Remote Sensing in Ecology and Conservation

Reliable demographic data are critical for seal population assessment and management, yet remain challenging to obtain from drone surveys without automated tools. Using data from harbor seal (Phoca vitulina) haulout surveys conducted in British Columbia, Canada, in 2023-2024, we developed and tested an automated pipeline that integrates detection, morphometric extraction, and size-class classification of seals into a single system. The pipeline combines cascaded DeepLabV3+ segmentation (water removal, colony-level seal detection, and individual body-contour refinement), principal component analysis (PCA)-based extraction of body length and maximum width, and three-class size-class determination based on body length (pups, juveniles, subadults/adults). We evaluated the pipeline on 2,483 manually annotated seals across four drone platforms (DJI Mavic 3 Thermal, Mavic 2 Pro, Mini 3 Pro and Mini 2), five substrate types, and two sensor modalities (RGB, thermal infrared). Aggregate detection F1 was 0.914. Orthomosaic geometric accuracy, quantified as the RMSE of calibration-rod length on the orthomosaic, ranged from 1.0 cm to 18.0 cm depending on the platform-altitude-substrate combination, a spread that aggregate metrics would mask. Relative to manual annotation on the same orthomosaics, automated RGB body lengths showed near-zero bias (-0.6 cm; RMSE = 12.5 cm), whereas automated thermal lengths were systematically shorter (bias -19.9 cm, RMSE = 24.1 cm), establishing the unsuitability of thermal imagery for automated quantitative pinniped morphometry. For RGB imagery, threshold classification by body length reached an overall accuracy (OA) of 78.7%; for individuals outside the uncertainty zones (defined as {+/-}2 x the geometric RMSE on calibration rods around each class boundary), OA reached 90.9%. Within the RGB subset, OA was 78.9% at 20 m altitude, declining to 75.0% at 40 m where geometric error dominated. The pipeline enables automated demographic monitoring of pinniped haulouts with quantitatively characterized uncertainty across survey conditions.

Wildlife re-identification (re-ID) is a widely used and powerful tool with diverse applications in animal ecology and conservation. Current automated methods typically operate on single images of a single body part of the animal. However, a single encounter may contain multiple images capturing different body regions, each providing complementary individual-specific information. In contrast to automated approaches, researchers often manually select the most suitable images and regions for identification based on factors like visibility, occlusion and image quality. This creates a mismatch between automated methods and field practice, limiting the practical adoption of current automated re-ID pipelines. Here, we address this by introducing an encounter-based, multi-body-part re-ID framework, using sea turtles as a model taxon. Our framework combines three elements: (1) An orientation-aware deep learning model, TurtleDetector, that in addition to the full bodies, it also automatically segments key body regions, i.e. heads, front and hind flippers, from images within an encounter; (2) a hybrid body-part-specific retrieval method, that sequentially combines a fast global-feature model (MiewID or DINOv3) with a more accurate but costlier local-feature model (ALIKED with LightGlue); and (3) a merged identity-prediction strategy that selects the highest calibrated similarity score across all available body parts and images of an encounter. We evaluate the framework on three long-term re-ID datasets spanning three species, loggerheads, greens, and hawksbill turtles, under an evaluation protocol that mirrors real-world, time-aware re-ID workflows. Across datasets, combining multiple body regions consistently improved identification performance over the best-performing single body region, resulting to an increase of 4-6% in top-1 accuracy. Interestingly, body regions traditionally underused in sea turtle re-ID, such as the hind flippers and carapaces, provided complementary identifying information that improved encounter-level re-ID when integrated through the hybrid retrieval method. Our findings demonstrate that automated wildlife re-ID can benefit from moving beyond single-image, single-body-part identification towards encounter-level integration of all available visual evidence. Our work further suggests that, where feasible, field photo-acquisition protocols should aim to capture multiple informative views of an individual during each encounter. Importantly, many species and taxa, including elephants, primates, cetaceans, and other large vertebrates, possess such individual-specific features across multiple body regions, highlighting the broad potential applicability of our framework.

(1) Kelp forests support ecologically, commercially, and culturally important species, yet their benthic communities remain difficult to monitor consistently across space and time. Scientific SCUBA surveys provide valuable observations but can be constrained by safety, bottom time, depth, and the spatial extent that divers can efficiently cover, motivating complementary approaches for collecting standardized benthic imagery. (2) We developed and field-tested a standardized, open-source framework for benthic visual-imaging surveys using a small, modular remotely operated vehicle (ROV). Across four cost-scalable hardware configurations, we integrated navigation sensors, custom BlueOS Extensions, and our surftrak contribution to ArduSub, which enables fixed-altitude flight above the seafloor, and developed two open-source desktop applications that streamline field operations (rov_flight_ops) and imagery processing (rov_imagery_processing). We also evaluated power supplies, benthic lighting, camera settings, field protocols, and telemetry-processing workflows over 303 transects conducted on 64 survey days from 2022 to 2026. (3) The ROV operated across soft-sediment and rocky habitats, currents of 0-3 kt, and underwater visibility of 0-10 m. surftrak held the ROV within 0.2 m of its target altitude for 96% of 21 h of survey transects, producing an approximately 1 m^2 standardized image footprint. Sufficient illumination and optimized camera settings yielded high-resolution imagery in visibility below 2 m, while topside power increased endurance and operational flexibility. Combining Doppler velocity log and underwater acoustic positioning data through the vehicle's extended Kalman filter improved navigation and post hoc transect reconstruction. During 2026 surveys, rov_flight_ops and rov_imagery_processing automated flight logging, log retrieval, fault diagnosis, and the sorting, development, and telemetry annotation of survey imagery. Dense kelp canopies nevertheless constrained vehicle operation, and downward-facing imagery provided limited observations of cryptic or obscured organisms. (4) We provide configuration-specific bills of materials, hardware and wiring documentation, software and firmware inventories, camera settings, open-source field and imagery-processing software, step-by-step field protocols, and pre- and post-dive checklists to support replication and adaptation of the framework. Together, these resources provide a field-tested starting point for expanding standardized benthic imaging while complementing diver surveys across kelp forests and other shallow coastal ecosystems.

Trail cameras have become ubiquitous tools for ecological data collection over recent decades. Despite progress in the development of automated algorithms and artificial intelligence for image classification, our ability to process large volumes of data remain limited by the need for trained human observers to make refined judgements. We provide guidance on placement of trail cameras for observing Asian elephants (Elephas maximus) and outline a protocol for training and testing naive human observers in performing image classifications (age/sex class and group composition) that cannot yet be automated. This process can be used to develop a high-throughput workflow capable of extracting useful data from large volumes of images. Our training material consisted of 14,007 images collected from 6 trail cameras around Udawalawe National Park in Sri Lanka from 2017-2019. In the first stage, expert observers (n=3) trained a group of inexperienced participants (n=4), who engaged in an iterative process to develop a protocol document. The document was then tested on a second set of subjects (n=6) each of whom classified 350 test images in four separate sequential batches using quantitative measures of precision and accuracy. The test set was sampled from 54,435 images from an additional 25 cameras. When compared to expert observers, they achieved a fair level of precision (Fleiss' kappa = 0.247) and 82.6% accuracy. Our approach can usefully be extended to other species and contexts.

Forest structure and density dynamics drive ecosystem processes including carbon sequestration, microclimate regulation, and photosynthetic capacity. While LiDAR can capture these properties, high-frequency monitoring remains constrained by scarce multi-temporal datasets, inconsistent acquisitions, and occlusion biases confounding true structural change with methodological artifacts. This study investigates spatio-temporal forest structure dynamics through voxel transmittance in a European mixed temperate forest. We acquired an extensive UAV LiDAR dataset of 38 flights with identical sensor settings over more than two years. With a custom ray-tracing framework, we estimated voxel transmittance and mapped occlusion at 0.25m resolution. Multi-temporal comparability was ensured through consistency masking and leaf-off baseline initialization of empty space. Voxel transmittance and attenuation dynamics were analyzed across three areas of interest, covering a mixed plot, a beech and a Douglas fir patch. The results revealed distinct phenological signatures: European beech showed high-amplitude seasonal variation (70% attenuation volume reduction from summer to winter) with rapid spring foliation and stable winter baselines, whereas Douglas fir exhibited 12.7% variance with delayed growth onset and no stable winter baseline. Inter-annual comparisons indicated increasing blocking biomass with upward vertical shifts in transmittance profiles from tree growth and crown expansion. Two-dimensional attenuation mapping enabled the detection of individual tree dynamics and discrete structural disturbances. We demonstrate that consistent high-frequency UAV LiDAR acquisitions enable forest morphology monitoring through relative voxel transmittance. Our occlusion-aware framework differentiates structural changes from sampling biases, providing insights into forest dynamics at high spatio-temporal resolution.

Protecting deep-sea sessile communities as Marine Protected Areas (MPAs) requires accurate estimates of their spatial distribution and abundance. Existing computer-vision detection methods require careful calibration to avoid counting errors; multi-object tracking (MOT) theoretically offers a more robust alternative. In practice, however, poor lighting and turbidity limit the efficacy of purely kinematic MOT algorithms in deep-sea environments. At the same time, the intra-species visual homogeneity typical of deep-sea scenes means traditional per-frame appearance features are often too weak to maintain stable identities in deep-association MOT algorithms. In this paper we introduce JEPA-SORT, an MOT algorithm that injects V-JEPA 2.1 embeddings into detection-to-track association, to show that video world-model embeddings - which capture not only physical appearance but spatiotemporal trajectory across viewpoints - can help maintain stable identities on visually homogeneous specimens. We provide a benchmark of twelve ROV transects surveying deep-sea sessile communities and evaluate JEPA-SORT against standard MOT algorithms across a range of simulated optical environments. We also propose the Biodensity Utility Metric (BDU), which measures tracker performance in the context of downstream ecological modeling. JEPA-SORT outperforms all benchmarked trackers on IDF1, MOTA, and BDU and is competitive on specimen-count accuracy. Ablations indicate its advantage stems primarily from the discriminative power of world-model embeddings on visually homogeneous footage.

Early detection of invasive species is critical to efficiently managing biological invasions. Passive acoustic monitoring combined with deep learning has become an effective tool for identifying invasive anurans from their species-specific mating calls. In practice, however, these systems still depend on manually retrieving recordings and running inference on a dedicated workstation, which delays detection at invasion fronts, especially on remote islands where frequent travel is impractical. Here, we present a lightweight convolutional neural network for detecting Rhinella marina and Polypedates leucomystax that can be used with low-power microcontrollers, enabling continuous, real-time monitoring of these invasive species. Our model achieved a mean invasive-species F1 of 0.87 with precision higher than 0.88 for both species, comparable to the performance of substantially larger models at a fraction of the model size. Cross-domain evaluation confirmed reliable detection on two out-of-training locations: Iriomote Island, Japan, and on an independent dataset from Australia, demonstrating generalization across recording conditions and biogeographic contexts, albeit with local threshold calibration for P. leucomystax at Iriomote. Our results demonstrate that effective acoustic detection of invasive species is feasible on self-contained, battery-powered devices, removing the dependence on server infrastructure and enabling autonomous early warning systems in remote protected areas.

Forest restoration has become a widespread response to deforestation and biodiversity loss, particularly across the tropics. Monitoring restoration is essential for tracking progress and requires accurate knowledge of baseline communities, with threatened species often used as recovery indicators. Passive Acoustic Monitoring (PAM) combined with AI classifiers is enabling this at scale. However, such tools remain immature and may underperform for rare, threatened species underrepresented in training data. Here, we demonstrate the potential of PAM assisted by the AI classifier BirdNET+ V3.0, a January 2026 beta release, to support monitoring of threatened tropical birds. By validating 995 detections from 8,940 hours of audio across a reforestation landscape in Para in the Brazilian Amazon, we found that BirdNET+ confirmed the presence of ~43% of the IUCN Red List threatened species possible in the region, five of which were the first confirmed records for the study site. We also found that this method outperformed traditional survey methods. However, BirdNET+ identified species with mixed reliability, and we could also not determine whether restoration has had any impact on them. Our results demonstrate that PAM with BirdNET+ is a viable, practical method for detecting threatened birds in tropical forest restoration landscapes, but that conventional validation approaches are not appropriate for such species, for which extensive validation effort is an essential cost. Under circumstances where this is not possible, we demonstrate validating the single highest-confidence detection per occasion as a practical alternative.

High-resolution monitoring of forest structure and productivity is essential for effective natural resource management. However, monitoring approaches such as field-based forest inventories or extensive lidar campaigns are costly, time-intensive, and spatially limited. Therefore, inexpensive and accessible methods are needed. SatCHM (Satellite Canopy Height Model) was developed to be an accessible and open-source tool for researchers, allowing for site-specific and temporally flexible predictions of canopy height with limited computational resources. SatCHM requires four inputs: panchromatic satellite imagery, solar and sensor angle metadata of satellite imagery, digital elevation models (DEMs), and lidar-produced CHMs for an area of interest. After SatCHM pre-processes inputs, data is loaded into a collection of convolutional neural networks (CNNs) for image-to-image regression. This ensemble cooperates to yield high-resolution predictions (up to 0.5-meter) of three-dimensional tree structure with discernible tree crowns across a broader defined area of interest. After calculating the mean absolute error for each prediction output, the median of these mean absolute errors was 6.06 meters.

Ecoacoustic monitoring is enabling scientists and land managers to monitor and manage biodiversity more effectively and cost-efficiently in the face of human pressures and rapidly changing climates. Currently, most ecoacoustic surveys use manually deployed static sensors to record data, limiting the scale and reach of surveying efforts. Here we present a proof-of-concept autonomous drone platform that can use intermittent locomotion to conduct ecoacoustic surveys using an onboard sensor. Our custom prototype is able to fly, navigate, and avoid obstacles autonomously, land at a pre-determined location, record audio from an onboard microphone whilst static, before taking off and moving to the next sampling site. Autonomous navigation and operation enable greater sampling flexibility, reach, and scalability. Furthermore, by recording audio only whilst landed, noise from the drone's rotors does not mask signals or disturb animals, simplifying signal processing and downstream ecological analyses. We conducted trials in a scrubland habitat at the Knepp Estate in West Sussex, where our prototype demonstrated successful autonomous navigation and obstacle avoidance. Furthermore, we found that avian biodiversity data collected from the drone platform was comparable to that from traditional static acoustic sensor deployments, and that vocalisation patterns were not significantly impacted by the noise of the drone arriving or leaving a site. While scaled deployments of our technology would require further technical and regulatory challenges to be solved, our first demonstration of autonomous intermittent robotics-assisted ecoacoustic surveys lays the foundations for more cost-effective and far-reaching biodiversity surveys, with transformative potential for conservation, agricultural management, biosecurity, and more.

Coral reef fish assemblages are widely used as indicators of ecosystem condition, yet manual annotation of underwater video remains a major bottleneck for scalable biodiversity monitoring. Despite rapid progress in automated detection, ecologically realistic and publicly available datasets remain scarce, particularly for the Western Indian Ocean. Here, we present WIO-ReefFish, a reef fish detection dataset derived from diver-operated line-intercept transects and designed for ecological monitoring under natural survey conditions. WIO-ReefFish comprises 1,000 ultra-high-definition images (3840 $\times$ 2160 pixels) and 6,768 exhaustive bounding-box annotations spanning 24 taxonomic categories, thereby preserving full-frame assemblage structure in complex reef scenes. We also establish a standardized benchmark across nine object detection models under two complementary protocols: class-aware detection and class-agnostic fish localization. Detection performance was consistently higher under the class-agnostic protocol. The best-performing model (RT-DETR) improved from 0.48 mAP50 in the class-aware setting to 0.70 mAP50 when taxonomic constraints were removed, indicating that taxonomic discrimination remains substantially more challenging than fish localisation in reef imagery. Spatially independent evaluation revealed a pronounced generalisation gap, particularly for taxonomic detection, whereas class-agnostic fish localisation remained substantially more robust across transects and countries. Together, these results establish WIO-ReefFish as a realistic benchmark for automated reef fish detection and provide a foundation for more robust computer-vision tools in coral reef biodiversity monitoring. The WIO-ReefFish dataset and associated benchmarking resources are publicly available.

Global biodiversity loss is a consequence of habitat degradation and fragmentation caused by the expansion of the Anthropocene and the overexploitation of natural resources. Passive acoustic monitoring enables longitudinal assessment of biodiversity but generates massive datasets. Advancements in machine learning have enabled the development of convolutional neural networks capable of automating this process. We developed a lightweight 2D convolutional neural network capable of detecting echolocation calls of the greater sac-winged bat, Saccopteryx bilineata, an insectivorous aerial edge-foraging bat, across a tropical restoration gradient in Para, Brazil. The 2D-CNN model achieved a 97.84% accuracy and 100% precision. Deploying this model across 157,335 field recordings across 29 sampling points resampled over 3 years (2023-2025) yielded 2,098 positive detections of S. bilineata. Generalised linear mixed models revealed that time since reforestation significantly increased the odds of detecting S. bilineata ({beta} = 0.22, p < 0.001), and that detection probability was 2.14 times higher in restoration plots than in forest habitats ({beta} = 0.76, p = 0.0701). Acoustic hardware had a significant covariate influence in both fitted models ({beta} = 0.52, {beta} = 0.72, both p < 0.001). These findings demonstrate the practical applications of deep learning and bioacoustics to quantify habitat recovery and the value of restoration in regenerating functional ecological niches for aerial insectivores during early successional stages.

Individual encounter histories are central to capture-recapture models, but fisheries monitoring is often reduced to counts that do not account for variation in detection probability. This study presents a machine-vision workflow for converting long-term video surveillance of Atlantic salmon (Salmo salar) and sea trout (Salmo trutta) spawning runs into individual-level data for capture-recapture analysis. The workflow detects fish-positive frames, saves video clips, extracts fish-head regions of interest, and organizes cropped images for re-identification. A binary EfficientNetB0 fish detector achieved validation accuracy of 0.9918 and PR AUC of 0.9992; at a conservative threshold of 0.98, validation false positives were eliminated while retaining 94.3% recall. A YOLOv8n model localized fish-head regions, and an EfficientNetB0 ArcFace model trained on head images from 700 identities achieved 99.80% accuracy among accepted known matches and an image-weighted false-accept rate of 0.81% when non-training identities were treated as unknown. Zero-shot closed-set retrieval achieved 99.17% top-1 accuracy across 1,416 identities excluded from model training, demonstrating strong generalization to previously unseen identities.

Understanding the consequences of global change for our ecosystems requires reliable, accurate, and scalable tools for detecting ecological change. To this end, recent efforts have focused on scaling up the quantification of demographic processes from individual organisms (the fundamental ecological unit) to the larger spatial scales they inhabit to yield more precise and informative estimations of ecological change, especially in response to disturbance. Here, we describe a transferable workflow that combines robust underwater photogrammetry techniques with machine-learning-assisted image analysis to quantify demographic changes in coral colonies (growth, stasis, partial mortality, and complete mortality) through time on the reef landscape. We then demonstrate several analytical applications of this workflow that can be broadly applied to sessile and/or clonal organisms in other ecosystems. First, we tracked the fates of 3,162 coral colonies through a marine heatwave that caused substantial mortality. Second, our approach enabled detection of size- and taxon-dependent patterns in demographic changes from before to after the heatwave. Finally, for a subset (425) of these colonies, we further demonstrated how our method can be used to track fates across multiple years, including a year of change before and after the heatwave took place, revealing nuanced patterns of survival and mortality, as well as changes in size structure that occurred before and after the disturbance. Our workflow offers an important advance for increasing the accuracy of change detection on coral reefs and other ecosystems by linking demographic changes in individual organisms with spatially extensive image-based surveys through time.

Motion-activated camera traps are an effective tool for monitoring animals, but a single study can produce millions of images, which must be analyzed before they yield data. The burden of analyzing them has led many teams to automate the work with computer vision. These models lose accuracy on images unlike those they were trained on, however, and a general model may not use the categories a project needs. Adapting a model to a project usually requires at least one of the following: many labeled images per species, programming expertise, a graphics processing unit (GPU), or uploading images to a remote server. I describe WildObserve, a desktop application that adapts to each project without any of these requirements. SpeciesNet suggests a species for each animal that MegaDetector finds, and WildObserve keeps SpeciesNet's feature vector for each one. A project model, a logistic regression on those features, then retrains in about a second as the reviewer labels, learning the project's own species and names. From the reviewed records, and optionally the suggestions, WildObserve also fits occupancy and abundance models. Images stay on the user's computer, and every record says which model suggested its species and who confirmed it. WildObserve processed 8.1 images/s on an Apple M1 Pro laptop. On Oregon Critters, a dataset released after SpeciesNet was trained, the project model's accuracy passed that of SpeciesNet with ten labels per species (balanced accuracy 0.783 against 0.751). I also tested occupancy on 63 cameras in Malawi that SpeciesNet had not seen. With 5% of the images reviewed and the project model's suggestions counted for the rest, species' occupancy estimates differed from those of a full review by 0.11 on average. From the reviewed images alone, they differed by 0.41. WildObserve takes a project from images to species records in its own categories and on to occupancy estimates, without writing code, needing a GPU, or sending images elsewhere.

Flowering time is a trait of broad interest and importance to both plant breeders and plant biologists. Unlike many other traits, flowering time cannot be scored accurately from a single observation; its measurement requires repeated observations of the same experiments over time. Both classification- and object-detection-based approaches have demonstrated the potential to estimate flowering time from Unmanned aerial vehicle (UAV) imagery rather than by manual observation, but they still require data collection at many time points. Here, we train and deploy a regression-based framework for scoring flowering time. Using a dataset of more than 200,000 UAV images and associated flowering-time records collected across 27 environments, we demonstrate that this approach enables the prediction of flowering time from as few as one observation per field experiment and successfully generalizes to field experiments in environments not represented in the training dataset. These results indicate that regression-based approaches for predicting flowering time from sparse UAV observations have the potential to substantially reduce the data-collection burden in field experiments

Fine-grained localisation of plumage regions is a prerequisite for computational analyses of avian colouration, patterning and visual traits in ecological and evolutionary research. Progress is limited by the scarcity of image resources with annotations aligned to biologically meaningful anatomical units: existing avian benchmarks provide either landmark points or coarse part categories that do not capture ornithologically defined plumage regions. We present a curated dataset of 4,705 bird images annotated for nine plumage regions: head, throat, breast, belly, vent, back, coverts, remiges and tail. Spanning 39 avian orders and 222 families, the dataset provides a taxonomically broad resource for fine-grained avian image analysis. The dataset was built through an iterative model-assisted annotation workflow, in which model predictions were reviewed and corrected rather than drawn from scratch, improving the efficiency of region-level annotation. We benchmark classical segmentation architectures, SAM-based models and self-supervised foundation-model encoders on this task. A frozen DINOv3 encoder with a lightweight decoder achieved the highest performance on the held-out test set, reaching 84.01% mean Intersection over Union while requiring substantially less memory than end-to-end fine-tuning. The model generalised to external avian benchmarks, including the bird subset of PartImageNet and CUB-200-2011, and achieved competitive performance on the full PartImageNet part-segmentation benchmark, which includes diverse animal taxa. We provide a modular detect-track-segment pipeline as a proof-of-concept extension to video data. Together, these results show that anatomically grounded avian annotations can serve both as a resource for plumage phenotyping and as a benchmark for efficient, transferable biological part segmentation. Author SummaryBirds vary enormously in colour and pattern, but studying this variation at large scales requires more than identifying the bird in a photograph. Researchers often need to know where each colour or pattern occurs on the body, such as on the head, throat, breast, wing or tail. We created PlumageParts to make this kind of region-level analysis easier. The dataset contains 4,705 bird images annotated into nine biologically meaningful plumage regions, covering a wide range of bird families and orders. To build the dataset efficiently, we used a model-assisted workflow in which computer-generated masks were checked and corrected by researchers rather than drawn entirely by hand. We then tested several image-segmentation approaches and found that a frozen self-supervised vision model, combined with a lightweight decoder, provided accurate plumage-region predictions while requiring relatively modest computing resources. The same approach also performed well on a broader animal part-segmentation benchmark, suggesting that it may be useful beyond birds when suitable annotations are available. By releasing the annotations, code and trained model, we aim to support future studies of bird plumage and biologically meaningful image segmentation.

Coastal forests are increasingly threatened by saturated soil and elevated salinity levels resulting from sea level rise, saltwater intrusion, and storm surges. In response to rising salinization and flooding, healthy coastal forests that rely on freshwater (both wetland forests and low-elevation upland forests) are transitioning into landscapes dominated by dead or dying trees, known as ghost forests. Situated among salt-tolerant shrubs and grasses, ghost forests eventually become marshes or open water. Here, our main objective was to quantify the dynamics and pathways of these forest landscape conversions, as well as the factors contributing to the changes, which is vital for understanding the progression of coastal ecosystem degradation and forecasting future changes. We focused first on identifying the best method to track forest landscape change by exploring the role of multiple remote sensing indices (i.e., multispectral, bi-seasonal, topographical, and phenological metrics) in enhancing the performance of deep learning models (convolutional neural networks, CNNs) for land cover classification in the coastal plain of North Carolina using surface reflectance of Landsat 8 and Sentinel-2 images. Then, we used the best available data (Landsat 8) to understand long-term change and identify patterns of land cover change from 1985 to 2021. Our study reveals that incorporating phenology and topographical indices enhances the separability of the ghost forests class from all other vegetation classes. In our assessment, the higher-resolution Sentinel-2 data (F1 Score = 96.3) outperformed Landsat images (F1 score = 93.4) for the 2021 co-available year. However, Landsat remains an important tool used due to its long-term data record. Therefore, we used Landsat to determine that 21% of forests were lost between 1985 and 2021, and that the rate of loss is increasing. Between 2010 and 2021, 23,876 ha of forest were converted to marsh, ghost forest, and shrub, which is 1.5 times higher than the 16,968 ha lost between 1985 and 2010. These conversions from forest to ghost forest and marshes were driven primarily by proximity to the channel, salinity, and the increasing rate of relative sea level rise (RSLR), which are the key environmental drivers of observed changes. By quantifying these changes, we highlight regions most vulnerable to environmental stressors, providing a basis for targeted conservation strategies.

Insects are a key group in ecosystems and economics, but are increasingly under threat, making ongoing monitoring essential to preserving them. Traditional monitoring approaches are time consuming, among other limitations. Automated acoustic recognition can be a good solution for monitoring arboreal, soniferous insects, such as cicadas, and the acoustic biology of cicadas in Australia is understudied, especially using automated methods. To evaluate the applicability of automated approaches for detecting cicadas, we used the deep-learning acoustic model BirdNET (v2.4) to perform embedding searches for Cystosoma schmeltzi (lesser bladder), Illyria burkei (eastern rattler), Macrotristria intersecta (corroboree cicada), Thopha sessiliba (northern double-drummer), and Pauropsalta opaca (fairy dust squawker), using audio recorded by Passive Acoustic Monitoring over 15-months at 18 sites in north Queensland. All classifiers achieved high area under the precision-recall curve (AUPRC) ranging from 0.753 to 0.982. We used a precision-prioritised threshold ([&ge;] 0.85) to constrain the false positive rate while retaining adequate outputs, which were consistent with the documented species ecology and life history. This approach was readily applicable to the Cicadidae, offering a scalable solution for biodiversity monitoring in complex acoustic environments.

Red crown rot (RCR), caused by Calonectria ilicicola, is an emerging soybean disease in the U.S. Midwest for which scalable approaches to characterize within-field disease distribution are lacking. This study evaluated high-resolution PlanetScope satellite imagery for mapping RCR-affected soybean canopies across 15 commercial fields in Illinois surveyed during the 2024 and 2025 growing seasons. A total of 2,921 georeferenced canopy plots were classified as asymptomatic or RCR-affected and paired with six multispectral bands and seven vegetation indices. Spectral differences between classes were evaluated using linear mixed-effects models, and seven machine-learning classifiers representing linear, tree-based, neural-network, kernel, and probabilistic approaches were compared using spatially independent leave-one-field-out cross-validation. RCR-affected canopies exhibited increased reflectance in the visible and red-edge regions, reduced near-infrared reflectance, and lower vegetation-index values relative to asymptomatic canopies. All classifiers showed strong discrimination, with ROC-AUC values ranging from 0.963 to 0.982. Regularized logistic regression achieved the highest overall performance, with an accuracy of 0.945, balanced accuracy of 0.945, F1-score of 0.948, and ROC-AUC of 0.982 at the optimized decision threshold. Permutation analysis identified EVI, NDVI, and red reflectance as the most influential predictors across representative model architectures. Satellite-derived probability and classification maps generally corresponded with symptomatic canopy patterns observed in high-resolution UAV imagery, although mixed pixels reduced precision near disease-patch boundaries. These results demonstrate the potential of high-resolution satellite imagery for within-field mapping of RCR-associated canopy symptoms across independent commercial soybean fields.