Remote Sensing in Ecology and Conservation

With bird populations across the world being impacted by ever-growing anthropogenic pressures, reliable monitoring is essential to help halt or reverse declines. Existing visual bird monitoring approaches, which employ cameras or radars to deliver automated and large-scale monitoring data, face a variety of issues. Image-based species classification is only possible if the fine-scale features of a bird are clear, which can be difficult to achieve in real monitoring contexts without expensive, high-resolution cameras due to occlusion and lighting. Radar and video-based approaches which analyse longer-term flight behaviour over the course of seconds can achieve more reliable results in real monitoring contexts, particularly from greater distances, but still require expensive equipment and do not account for all the possible types of flight patterns. Here we present a novel approach to track a wide range of bird flight patterns using inexpensive equipment. As a proof-of-concept, we demonstrate how our approach can be used to classify birds between four species, Red Kite, Kestrel, Black-Headed Gull and Sparrowhawk, which represent four different types of flight patterns. The balanced accuracy of the classification is 0.5583, with a recall and precision per species that range from 0.2640-0.7750 and 0.4583-0.5962, respectively. Our proof-of-concept study demonstrates how new and existing visual bird monitoring systems can leverage flight patterns to deliver species-level insights at lower costs and on larger scales than before.

Waterbirds serve as important indicators of both aquatic and terrestrial ecosystem health, making effective monitoring essential for tracking population health and identifying potential causes of decline. Drones have provided opportunities to overcome historic waterbird monitoring challenges, but the expertise and time required for manual image analysis creates a major bottleneck. Recent advances in deep learning-based object detection have enabled rapid, automatic detection of features in complex ecological imagery, though applications have largely been limited to single-species colonies, and practitioners lack quantitative comparisons of annotation time and accuracy across different levels of automation. We systematically compared four waterbird monitoring approaches using identical survey areas from Chester Island, a mixed-species colony in Matagorda Bay, Texas, in 2025: (1) traditional ground-based counts, (2) manual drone imagery-based counts, (3) computer-assisted counts using pre-annotations from an object detector with manual human verification (Human+ML), and (4) fully automated counts using object detector annotations (ML-only). We trained a YOLOv10 object detection model on manually annotated imagery of Chester Island in 2021 and applied it to the 2025 imagery. Manual drone annotation detected 6,530 birds in 40.5 hr and served as the primary reference standard. Human+ML detected 5,826 birds (89% of manual) in 7.7 hr, an 81% reduction in annotation time. ML-only detected 5,679 birds (87% of manual) in approximately 46 min, a 98% reduction. Ground counts recorded 5,868 birds (90% of manual). Detection generalized well across species while classification depended heavily on training data and morphological distinctiveness. The Human+ML workflow emerged as a practical middle ground, providing practitioners with empirical data to evaluate partial versus full automation strategies based on monitoring objectives.

Tropical forest canopies contain many tree and liana species, and foliar and reproductive characteristics useful for taxonomic identification are often difficult to see from the forest floor. As such, taxonomic identification often becomes a bottleneck in tropical forest inventories. Here we present a drone-based workflow to automatically acquire large volumes of close-up, ultra-high resolution photos of selected tree crowns (or specific locations over the canopy) to support tropical botanical and ecological studies (https://youtu.be/80goMEifpc4). Our workflow is built around the small, easy-to-use DJI Mavic 3 Enterprise (M3E) drone, which is equipped with a wide-angle and a telephoto camera. On day one, the pilot maps a forest area of up to [~]200 ha with the wide-angle camera to generate a high-resolution digital surface model (DSM) and orthomosaic using structure-from-motion (SfM) photogrammetry. On subsequent days, the pilot acquires close-up photos with the telephoto camera from up to 300 selected canopy trees per day. These close-up photos are acquired from 6 m above the canopy and contain a high level of visual detail that allows botanists to reliably identify many tree and liana species. The photos are geolocated with survey-grade accuracy using RTK GNSS, thus facilitating spatial co-registration with other data sources, including the photogrammetry products. The primary operational challenge of our workflow is the need to maintain RTK corrections with the drone to ensure that close-up photos are acquired exactly at the predefined locations. The maximum operational range we achieved was 3 km, which would allow the pilot to reach any tree within a [~]2800 ha area from the take-off point. Although our workflow was developed to support taxonomic identification of tropical trees and lianas, it could be extended to any other forest or vegetation type to support botanical, phenological, and ecological studies. We provide harpia, an open-source Python library to program these automatic close-up photo missions with the M3E drone (https://github.com/traitlab/harpia). Data/code for peer review statementWe provide harpia, an open-source Python library to program these automatic close-up photo missions (https://github.com/traitlab/harpia). Drone imagery and labelled close-up photo data are not yet publicly available because they were acquired with the goal of publishing benchmark machine learning datasets and models for tree and liana species classification and prior publication of the data would jeopardize this future publication.

Unmanned aerial vehicles (UAVs) are increasingly used for high throughput phenotyping. In principle, freely flown vehicles would permit real-time flexibility in identifying and scouting regions of interest. Mosaicking multiple images provides a high resolution global image and consumer-grade UAVs offer low cost, ease of flying, and excellent RGB cameras. The vehicles inaccurate telemetry complicates estimating the homographies between pairs of frames, the standard mosaicking approach. Moreover, errors accumulate during computation, distorting later portions of the mosaic. Finally, crop fields are particularly challenging to mosaic because their planting is so regular and the plants are so similar, eliminating distinctive features that could guide mosaicking. We propose MaiZaic, an end-to-end pipeline that dynamically samples video frames using optical flow, automates camera and gimbal calibration, estimates homographies with an unsupervised convolutional neural network, detects shots among frames, and generates mini-mosaics. Together, these techniques significantly reduce errors in the output mosaics. Our deep learning model is trained on a comprehensive video dataset comprising different flight trajectories, maize lines, growth stages, and augmented illumination data. MaiZaic is more accurate and faster than ASIFT and more robust than our earlier CorNet and CorNetv2. We demonstrate MaiZaics effectiveness in generating accurate mosaics of imagery captured by freely-flown UAVs and explore its generalizability. Core ideasO_LIMaiZaic is an end-to-end pipeline to mosaic freely flown agricultural imagery captured with consumer-grade UAVs. C_LIO_LIMaiZaic introduces novel algorithms that efficiently choose video frames, calibrate, and mosaic the imagery. C_LIO_LIOur unsupervised deep homography estimator, CorNetv3, is 14 times faster and 8.59% more accurare than ASIFT. C_LIO_LIMaiZaic generalizes well and mosaicks maize at different growth stages, objects, trajectories, cameras, and pilots. C_LIO_LIThe mini-mosaicking algorithm improves mosaic accuracy by interrupting error accumulation. C_LI

Thermal drones offer significant advantages for monitoring wildlife in low-light conditions; however, detection performance is influenced by technical settings and environmental factors. This study evaluates the use of nocturnal drone surveys to detect macropods in Tasmania, with the aim of determining optimal flight parameters for balancing detection accuracy and survey efficiency. Field surveys were conducted in Narawntapu National Park using varying flight altitudes and image overlap settings, and detection rates were compared across thermal datasets. Manual annotations and basic thresholding method were used to quantify detection success. Results showed that detection rates were highest when surveys were conducted under cooler ambient temperatures and at moderate altitudes (e.g. 60 metres AGL) with 50% image overlap. These findings provide practical guidance for designing nocturnal drone surveys and offers baseline recommendations for using drone-mounted thermal sensors to monitor large-bodied, crepuscular mammals, with broader implications for scalable wildlife monitoring programs.

O_LIThe exodus of swallows from communal nighttime roosts is often visible as an expanding ring-shaped pattern in weather radar data. The WSR-88D network operated by the National Weather Service archives more than 25 years of data across 143 stations in the contiguous US. However, access to information about the roosting behavior of swallows is limited by the cost of manual annotation of these scans. C_LIO_LIWe develop an AI system to detect and track swallow roosts in weather radar data. Our model is based on the Faster R-CNN architecture and is customized to incorporate multiple spatial and temporal channels in volumetric radar scans using novel adaptor layers. We systematically study the impact of network architecture and input representation for this task. We incorporate our detection outputs into an AI-assisted system with an interface for human screening to collect research-grade data about roosting behavior. We deploy the system to collect information from 12 radar stations in the Great Lakes region of the US spanning 21 years. C_LIO_LIThe addition of temporal information improves roost detection performance from 47.0% mean average precision to 54.7%. Temporal information helps the model recognize the expanding pattern of roosts and filter false positives due to rain and static structures. Our system allowed the annotation of 15,628 roost signatures with 64,620 single-frame detections in 612,786 radar scans with 183.6 total hours of human screening, or 1.08 seconds per radar scan. C_LIO_LIOur AI-assisted system provides research-quality roost data with far less human effort than manual annotation of radar scans. The data contains critical information about the phenology and population trends of swallows and martins, a declining group of aerial insectivores. Our successful deployment to collect historical data for 8% of the radar stations in the contiguous US lays the groundwork for continentscale analysis of swallow roosts, and provides a starting point for analysis of other family-specific phenomena in weather radar, such as bat roosts and mayfly hatches. C_LI

Manual annotation of drone imagery is labour-intensive and prone to observer bias, particularly when applied to large datasets across varied environments. To address this, a deep-learning pipeline was developed and evaluated for identifying macropods in visual (RGB) drone imagery, using a convolutional neural network (CNN) adapted from the DeepForest framework. The model was trained on annotated images of Forester kangaroos (Macropus giganteus tasmaniensis) and Bennetts wallabies (Notamacropus rufogriseus) collected across two Tasmanian study sites. Performance was assessed using independent test sets from each site, representing open and forest-edge habitats, as well as a combined multi-site test set. Detection accuracy was quantified using precision, recall, and F1 scores, with further analyses evaluating the effect of solar altitude angle on model performance. The model achieved high recall across sites, indicating strong potential for minimising missed detections under diverse conditions. These results demonstrate the feasibility of applying transfer learning to drone-based wildlife surveys and highlight the promise of deep learning models for reducing manual effort in macropod monitoring, with applications for broader conservation and management workflows.

Accurate monitoring of populations is essential for conservation management, including for vulnerable seabirds. Yet traditional ground-based surveys are logistically challenging and time-consuming, especially in remote environments such as the sub-Antarctic islands. Advances in aerial imagery and artificial intelligence (AI) offer opportunities to improve the efficiency and repeatability of population surveys. In this study, we evaluate an AI-based approach for counting Salvins albatross from high-resolution aerial imagery collected using a piloted fixed-wing aircraft at the Bounty Islands, New Zealand. Imagery acquired during a single-day survey was processed to create orthomosaic images, which were previously analysed using manual counts by an experienced observer. We applied an automated detection and counting model based on a Faster R-CNN architecture with Slicing-Aided Hyper-Inference, and compared AI-derived counts with original human counts in terms of accuracy, consistency, and processing time. The AI achieved an initial F1 score of 92.8% for albatross detection and produced counts within 3% of the manual results, while reducing processing time from approximately 66 hours to just over four minutes. The model was also capable of simultaneously detecting additional species present within the mixed breeding colony, including erect-crested penguins, fulmar prions, and New Zealand fur seals, adding scalable efficiency gains for future surveys. Our results demonstrate that combining piloted aircraft surveys with AI-based image analysis provides a rapid, scalable, and accurate method for monitoring seabird populations, with substantial benefits for conservation management in remote and logistically constrained regions.

Global environmental challenges require comprehensive data to manage and protect biodiversity. Currently, vision-based biodiversity monitoring efforts are mixed, incomplete, human-dependent, and passive. To tackle these issues, we present a portable, modular, low-power device with embedded vision for biodiversity monitoring. Our camera uses interchangeable lenses to resolve barely visible and remote subjects, as well as customisable algorithms for blob detection, region-of-interest classification, and object detection to identify targets. We showcase our system in six case studies from the ethology, landscape ecology, agronomy, pollination ecology, conservation biology, and phenology disciplines. Using the same devices, we discovered bats feeding on durian tree flowers, monitored flying bats and their insect prey, identified nocturnal insect pests in paddy fields, detected bees visiting rapeseed crop flowers, triggered real-time alerts for waterbirds, and tracked flower phenology over months. We measured classification accuracies between 55% and 96% in our field surveys and used them to standardise observations over highly-resolved time scales. The cameras are amenable to situations where automated vision-based monitoring is required off the grid, in natural and agricultural ecosystems, and in particular for quantifying species interactions. Embedded vision devices such as this will help addressing global biodiversity challenges and facilitate a technology-aided global food systems transformation.

O_LICamera trap arrays can generate thousands to millions of images that require exorbitant time and effort to classify and annotate by trained observers. Computer vision has evolved as an automated alternative to manual classification. The most popular computer vision solution is the supervised Machine Learning technique, which uses labeled images to train automated classification algorithms. C_LIO_LIWe propose a multi-step semi-automated workflow that consists of (1) identifying and separating bad-from good-quality images, (2) parsing good images into animals, humans, vehicles, and empty, and (3) cropping animals from images and classifying them into species for manual inspection. We trained, validated, and evaluated this approach using 548,627 images from 46 cameras in two regions of the Arctic (northeastern Norway, and Yamal Peninsula, Russia). C_LIO_LIWe obtained an accuracy of 0.959 for all three steps combined with the complete year test data set at Varanger and 0.922 at Yamal, reducing the number of images that required manual inspection to 7.9% of the original set from Varanger and 3.2% from Yamal. C_LIO_LIResearchers can modify this multi-step process to meet their specific needs for monitoring and surveying wildlife, providing greater flexibility than current options available for image classification. C_LI

Tree detection is a fundamental task in remote sensing for forestry and ecosystem ecology applications. While many individual tree segmentation algorithms have been proposed, the development and testing of these algorithms is typically site specific, with few methods evaluated against data from multiple forest types simultaneously. This makes it difficult to determine the generalization of proposed approaches, and limits tree detection at broad scales. Using data from the National Ecological Observatory Network we extend a recently developed semi-supervised deep learning algorithm to include data from a range of forest types, determine whether information from one forest can be used for tree detection in other forests, and explore the potential for building a universal tree detection algorithm. We find that the deep learning approach works well for overstory tree detection across forest conditions, outperforming conventional LIDAR-only methods in all forest types. Performance was best in open oak woodlands and worst in alpine forests. When models were fit to one forest type and used to predict another, performance generally decreased, with better performance when forests were more similar in structure. However, when models were pretrained on data from other sites and then fine-tuned using a small amount of hand-labeled data from the evaluation site, they performed similarly to local site models. Most importantly, a universal model fit to data from all sites simultaneously performed as well or better than individual models trained for each local site. This result suggests that RGB tree detection models that can be applied to a wide array of forest types at broad scales should be possible.

Automated monitoring of insect pollinators in natural environments with insect camera traps and trained deep learning algorithms provides novel data for insect ecological studies. However, efficient and accurate image recognition analysis of the recorded images or videos is challenging, particularly for images containing small insects against complex backgrounds with diverse vegetation communities. Even when insects can be detected in images, identifying their taxonomy remains difficult, particularly in footage with low image resolution, light conditions, and distances from the plants, and in cases where insects appear blurry or only partially visible. In this work, we present InsectDCT, an AI-based pipeline for automated detection, hierarchical classification, and tracking of insects in footage of natural vegetation tested in different environments. The InsectDCT pipeline consists of three levels: insect Detection and localization, hierarchical taxonomic Classification, and spatio-temporal Tracking. In the first stage, insects are detected in time-lapse images or video recordings using the You Only Look Once (YOLO11) object detection architecture. Detection performance is improved using motion-enhanced images, which improve robustness in cluttered and 3 dimensional environments. The detector is trained on an extensive dataset that contains more than 60,000 images collected using camera traps deployed across a wide range of plant families and floral habitats. In the second stage, detected insects are classified using a hierarchical taxonomy-aware classification framework that covers 80 taxonomic groups. Classification is performed at multiple taxonomic levels, including order, family, and genus/species, allowing coarse and fine-grained ecological analyzes while accounting for varying levels of visual ambiguity. In the third stage, a multi-object tracking module is applied to high temporal-resolution image sequences and video data to associate detections of the same individual across time. InsectDCT code and all datasets are made publicly available. Author summaryInsects are declining worldwide, creating an urgent need for efficient methods to monitor their abundance, activity, and diversity. Traditional insect surveys often require extensive fieldwork and expert taxonomic identification, which limits the scale and frequency of monitoring. In this study, we developed InsectDCT, an artificial intelligence-based pipeline that automatically detects, classifies, and tracks insects in camera-trap recordings collected from natural and semi-natural environments. Our approach combines deep-learning methods for object detection, hierarchical taxonomic classification, and tracking of individual insect observations through time. Unlike many existing systems that are trained for a single habitat or plant species, we designed our framework using images collected across a wide range of flowering plants, camera systems, and insect groups. This makes the system more transferable to new ecological settings. The classifier can identify insects at multiple taxonomic levels and can return higher-level classifications when species-level identification is uncertain. We demonstrate that the pipeline can process large image datasets efficiently, including on low-power edge-computing devices such as Raspberry Pi systems. By providing both the software and the underlying datasets, we aim to support scalable, non-invasive insect monitoring and facilitate future ecological and conservation research.

Radar aeroecology is dedicated to making ecological inference about aerial wildlife from radar-derived information. While producing unique, large-scale datasets describing biological activity in the sky, radar methodologies are largely incapable of relating these to specific species and are thus taxonomically limited. I describe a computational method to increase taxonomic resolution in vertical looking radar data by dividing detected organisms into morphology and movement-based aerial morphotypes. Using the Birdscan MR1 radar target classifier, wing flapping frequency calculation and target size estimation, I demonstrate a nearly 8 fold increase in classification resolution of bird radar data from the Hula Valley Research station, Israel. Furthermore, by relating each species in the regions species pool to its relevant morphotype, I show that most of these newly separated classes are related to small numbers of species (1-10), providing realistic opurtunities to bridge the taxonomy gap in radar data. By using the morphotype approach, radar aeroecologists can start observing and discussing the concept of "Aerodiversity", analogues to widely used biodiversity, a fundamental measure in ecology and conservation sciences. By analitically adressing taxonomy in radar-aeroecology, practitioners will increase the impact and dissemintation of their work and contribute to a better, more complete understanding of the aerial habitat.

1. Camera traps have become a cornerstone of wildlife ecological research, yet the manual analysis of the millions of images they generate requires substantial time and resources. Deep learning-based automation has emerged as a promising solution, existing global general-purpose models exhibit limitations in precisely recognizing local endemic species and adapting to unique local ecosystems. 2. This study developed a high-performance classification model optimized for native species. A large-scale "Korean Wildlife Dataset" was constructed from data collected across diverse domestic habitats, and a novel architecture was proposed to overcome limitations of conventional CNNs. The proposed Graph Attention Contrastive Learning (GACL) model is structured as a two-stage pipeline. Stage one employs YOLOv5 and MegaDetector to detect animals, humans, and vehicles, filtering valid images. Stage two performs fine-grained species classification. GACL captures structural relationships among object parts using a Graph Attention Transformer (GAT) and aligns semantic correspondence between images and textual descriptions via Parallel Contrastive Learning, enabling deeper understanding beyond simple visual features. 3. Evaluation on an independent test set demonstrated that the proposed model robust classification performance with an overall accuracy of 96.83% across four classes (Wildboar, Goral, Deers, and Other). Notably, in a comparative analysis against a global general-purpose model, our model showed distinct advantages in the precise recognition of endemic species. Furthermore, it exhibited a lower false positive rate in identifying animals in empty images, confirming its potential to enhance the efficiency of the data cleaning process. 4. Beyond technical accuracy, this study highlights that region-specific AI models that reflect local ecological characteristics can provide substantial practical value for wildlife monitoring and biodiversity conservation. Future work will require continuous efforts in data diversification and model lightweighting to further improve model robustness and practicality.

O_LIA time-consuming challenge faced by camera trap practitioners all over the world is the extraction of meaningful data from images to inform ecological management. The primary methods of image processing used by practitioners includes manual analysis and citizen science. An increasingly popular alternative is automated image classification software. However, most automated solutions are not sufficiently robust to be deployed on a large scale. Key challenges include limited access to images for each species and lack of location invariance when transferring models between sites. This prevents optimal use of ecological data and results in significant expenditure of time and resources to annotate and retrain deep learning models. C_LIO_LIIn this study, we aimed to (a) assess the value of publicly available non-iconic FlickR images in the training of deep learning models for camera trap object detection, (b) develop an out-of-the-box location invariant automated camera trap image processing solution for ecologist using deep transfer learning and (c) explore the use of small subsets of camera trap images in optimisation of a FlickR trained deep learning model for high precision ecological object detection. C_LIO_LIWe collected and annotated a dataset of images of "pigs" (Sus scrofa and Phacochoerus africanus) from the consumer image sharing website FlickR. These images were used to achieve transfer learning using a RetinaNet model in the task of object detection. We compared the performance of this model to the performance of models trained on combinations of camera trap images obtained from five different projects, each characterised by 5 different geographical regions. Furthermore, we explored optimisation of the FlickR model via infusion of small subsets of camera trap images to increase robustness in difficult images. C_LIO_LIIn most cases, the mean Average Precision (mAP) of the FlickR trained model when tested on out of sample camera trap sites (67.21-91.92%) was significantly higher than the mAP achieved by models trained on only one geographical location (4.42-90.8%) and rivalled the mAP of models trained on mixed camera trap datasets (68.96-92.75%). The infusion of camera trap images into the FlickR training further improved AP by 5.10-22.32% to 83.60-97.02%. C_LIO_LIEcology researchers can use FlickR images in the training of automated deep learning solutions for camera trap image processing to significantly reduce time and resource expenditure by allowing the development of location invariant, highly robust out-of-the-box solutions. This would allow AI technologies to be deployed on a large scale in ecological applications. C_LI

O_LIThe ability of drones to provide detailed information on animals and their surroundings makes them ideal for studying animal behaviour at fine scales. While drones can provide high-resolution images of what animals are doing, they should also, in theory, be able to provide data on where they are. However, reconstructing geo-referenced tracks from drone videos that follow animals is challenging, particularly because current methods require specific drone flight patterns and large computational power. C_LIO_LIHere, we combine deep learning and object tracking methods with a novel geo-referencing algorithm which allows us to track individuals across video frames and reconstruct their geo-referenced trajectories. We used a Region-based Convoluted Neural Network to detect animals and a Hungarian tracking algorithm to link detections across video frames, and then geo-referenced each detection in every frame to reconstruct individual trajectories. C_LIO_LIWe tested our geo-refencing algorithm through multiple drone flights with varying flight parameters over known Ground Control Points. The median (95% CI) geo-referencing error was 2.81 (0.74 - 23.23) meters, which reduced by 50% when the drone camera was positioned between -90{degrees} and -40{degrees}. Error increased with drone height and camera angle (-90{degrees} refers to the camera pointing towards the ground) but was not impacted by drone orientation. C_LIO_LIWe then demonstrate the utility of our framework with empirical examples using consumer-level drones. First, we tracked a volunteer carrying a high-resolution GPS unit and overlayed their GPS tracks on our estimated tracks to quantify tracking error. Next, we used drone videos of two delphinid species (Tursiops truncates gephyreus, and Sousa plumbea) representing varying environmental and flight conditions. We were able to successfully infer individual tracks across all conditions except when individuals formed tight clusters, in which case tracks were assigned a group identifier. C_LIO_LIOur framework demonstrates an easy and robust approach to translate drone videos of moving animals into geo-referenced animal tracks which is applicable in many research contexts. A major advance over previous methods is that our algorithm is robust to different camera angles, and provides tracks with accuracy on-par with, or even exceeding, the accuracy from GPS tracking. C_LI HeadlineAutomated drone-based animal tracking

Terrestrial Laser Scanning (TLS) captures fine-scaled three-dimensional measurements of ecosystem structure, supporting monitoring of the Essential Biodiversity Variables (EBVs). Yet employing TLS across landscapes remains challenging in remote and topographically complex areas. Remote sensing provides a potential pathway for upscaling TLS-derived structural metrics, but to what extent is unquantified particularly in heterogenous environments, like oceanic islands. Here, we investigated the ability of remote sensing to estimate TLS-derived habitat structure across three contrasting habitats (lowland rainforest, montane cloud forest, and subalpine summit scrub) on La Reunion island. Sentinel-1, Sentinel-2, and Aerial LiDAR (ALS) data were acquired over plots where TLS was completed. We derived defined indices of backscatter coefficients, vegetation indices, and LiDAR metrics and assessed their alignment with TLS measurements using a Procrustes analysis. Subsequently, we used General Additive Models to estimate TLS habitat structure from remote sensing variables. Sentinel-2 exhibited the highest multivariate alignment with TLS (r = 0.51). TLS measurements of horizontal and vertical structure were estimated with the highest cross-validated predictive accuracy (R2 0.39 - 0.73), whilst structural complexity metrics were estimated with greater difficulty (R2 0.02 - 0.20). Multi-sensor models outperformed all single-sensor models in prediction estimates. Model performance also varied across habitats, with the highest agreement between predicted and observed values in the lowland rainforest (r = 0.38), and the lowest agreement (r = 0.35) in the montane cloud forest. Yet the dominant structural feature of each habitat was most accurately captured with remote sensing. Our results demonstrate the potential of integrating multi-sensor remote sensing data to upscale key dimensions of TLS-derived ecosystem structure but remains challenging for fine-scale structural complexity. These findings highlight both the potential and constraints of remote sensing for developing scalable, long-term monitoring frameworks for EBVs, especially in structurally complex and underrepresented island ecosystems.

Efficiently finding rare species is a perennial challenge in conservation science. The orange-bellied parrot Neophema chrysogaster is a rare mobile bird that is difficult to locate using traditional field survey techniques with human observers. We harnessed recent advances in bioacoustic technology to create a survey framework that integrates passive acoustic surveys and semi-automated detection to increase monitoring capacity for the orange-bellied parrot. We developed a custom BirdNET classifier for the orange-bellied parrot and compared efficacy of acoustic and field surveys using an occupancy framework. We deployed autonomous recording units across the orange-bellied parrots breeding range in southwest Tasmania and concurrently undertook between three and six repeated point-count surveys at the same 48 sites using human observers. Our custom BirdNET classifier had high accuracy and discrimination abilities. Validation of model scores across a week (5,712 hours of audio) required 60 hours reviewing time and yielded a 95% confidence of a correct BirdNET prediction at scores over 0.998. Occupancy analysis showed that the detection probability of acoustic surveys (p = 0.80) was more than eleven times greater than field surveys by skilled ecologists familiar with the species (p = 0.07). We provide a template for how to implement monitoring of the orange-bellied parrot and recommendations for how our methods can be improved to optimise the classifier to account for other species and locations.

Statewide tracking of urban tree canopy change is essential for evaluating progress toward policy targets, but detecting real change requires both high-resolution mapping and rigorous uncertainty estimation. We produced a four-year canopy cover time series for all California census-designated places using 60-cm NAIP aerial imagery and a U-Net deep learning model trained with semi-automated LiDAR-derived labels and manually annotated tiles. Canopy cover and change were estimated using stratified, error-adjusted area estimation, enabling comparisons across years. Statewide canopy cover showed a modest negative trend from 2016 to 2022 (Sens slope: -0.60% per year), but confidence intervals included zero across all groups and climate zones, indicating that trends were not statistically distinguishable from no change. Urban canopy cover was consistently lower than non-urban canopy by approximately six percentage points, and canopy cover was highest in the Northern California Coast and lowest in the Southwest Desert. Residential parcels accounted for 55-56% of canopy within incorporated urban areas across all years, indicating that statewide canopy increase goals will require engagement with private landowners. Error adjustment substantially altered canopy estimates relative to raw pixel-count totals, with direct implications for AB 2251 canopy tracking where baselines and targets drawn from unadjusted maps may not reflect true canopy extent. This open-source workflow is transferable to future NAIP acquisition years and other U.S. states, providing a scalable framework for long-term urban forest monitoring.

Developing actionable early detection and warning systems for agricultural stakeholders is crucial to reduce the annual $200B USD losses and environmental impacts associated with crop diseases. Agricultural stakeholders primarily rely on labor-intensive, expensive scouting and molecular testing to detect disease. Spectroscopic imagery (SI) can improve plant disease management by offering decision-makers accurate risk maps derived from Machine Learning (ML) models. However, training and deploying ML requires significant computation and storage capabilities. This challenge will become even greater as global scale data from the forthcoming Surface Biology & Geology (SBG) satellite becomes available. This work presents a cloud-hosted architecture to streamline plant disease detection with SI from NASAs AVIRIS-NG platform, using grapevine leafroll associated virus complex 3 (GLRaV-3) as a model system. Here, we showcase a pipeline for processing SI to produce plant disease detection models and demonstrate that the underlying principles of a cloud-based disease detection system easily accommodate model improvements and shifting data modalities. Our goal is to make the insights derived from SI available to agricultural stakeholders via a platform designed with their needs and values in mind. The key outcome of this work is an innovative, responsive system foundation that can empower agricultural stakeholders to make data-driven plant disease management decisions, while serving as a framework for others pursuing use-inspired application development for agriculture to follow that ensures social impact and reproducibility while preserving stakeholder privacy. Key PointsO_LICloud-based plant disease detection system, easily accommodates newly developed and/or improved models, as well as diverse data modalities. C_LIO_LIEmpower agricultural stakeholders to use hyperspectral data for decision support while preserving stakeholder data privacy. C_LIO_LIOutline framework for researchers interested in designing geospatial/remote sensing applications for agricultural stakeholders to follow. C_LI