Although traditional forest inventory methods are comprehensive, they continue to be labor-intensive and time-consuming. Light detection and ranging (lidar) data collected from various platforms can address these shortcomings. This paper presents a case study of multimodal laser scanning for forest inventory at the Millhopper VARIETIES II Forest experimental site, a pine plantation owned by the University of Florida. The study explores the integration of mobile laser scanning (MLS), terrestrial laser scanning (TLS), and uncrewed aerial laser scanning (ULS) for enhanced forest metric extraction. It employed varying scanner mounting angles and laser pulse repetition rates (PRR) in MLS data collection to optimize point cloud density and reduce occlusions. Additionally, it combined MLS and ULS to improve the accuracy of individual tree detection (ITD) and tree height estimation compared to single-platform approaches. A least squares technique was utilized to fit spherical targets geometrically, ensuring precise alignment and fusion of datasets across all modalities. Results indicated that the integrated MLS+TLS+ULS dataset achieved superior ITD accuracy with a precision of approximately 90%, recall of about 99%, F1-score of roughly 94%, and a height root mean square error (RMSE) of 0.36 m. Notably, the MLS+ULS dataset reached a recall of approximately 95%, a precision of around 90%, an F1-score of about 93%, and an RMSE of 0.31 m. The integrated approach provided more accurate and comprehensive forest data than individual laser scanner modality datasets. These findings emphasize the importance of precise lidar data fusion and tuning lidar sensor parameters for scalable and accurate forest inventory, enabling improved tree count estimation and enhanced forest health assessment for sustainable forest management.

Orchards and perennial tree crops are economically and nutritionally vital components of global agriculture, yet their heterogeneous canopy architectures, long production cycles, and complex management requirements pose persistent challenges for reliable, scalable, and repeatable monitoring in support of precision management. Light Detection and Ranging (LiDAR) provides three-dimensional information on canopy architecture, tree structure, terrain, and orchard infrastructure, making it a key technology for perennial crop sensing. This review synthesizes LiDAR research in orchards and perennial tree crops from three perspectives: bibliometric development, sensor and platform evolution, and data-processing workflows. A curated corpus of 288 publications covering 24 crop types was analyzed to characterize temporal trends, crop representation, platform adoption, retrieved parameters, and publication patterns. The results show rapid growth after 2015, driven by UAV-based mapping, mobile laser scanning, compact multi-beam sensors, and increasing demand for structure-aware crop monitoring, while many tropical and subtropical perennial crops remain underrepresented. LiDAR systems are reviewed according to dimensionality, ranging principle, backscatter sampling, structural configuration, deployment platform, measurement attributes, field-condition robustness, and task-specific suitability. Processing workflows are then examined, including preprocessing, registration and SLAM, noise filtering, ground classification, segmentation, machine learning, deep learning, data fusion, and open-source software ecosystems. As the technical component of a two-part review series, this review emphasizes bibliometric trends, sensor technologies, and algorithmic workflows, complementing a companion review focused on LiDAR applications, implementation challenges, and management-oriented opportunities in orchard systems. Particular attention is given to orchard-specific bottlenecks, including canopy occlusion and beam attenuation, non-random missing data, repetitive-row registration drift, wind-induced canopy motion, radiometric inconsistency, limited model transferability, and computational constraints for real-time deployment. By integrating bibliometric evidence, sensor-system analysis, and algorithmic evaluation, this review identifies key priorities for future LiDAR in orchard research: standardized metadata and quality-control reporting, task-specific validation, occlusion-aware modeling, radiometric normalization, multi-platform fusion, transferable learning, and uncertainty-aware digital orchard modeling. Overall, LiDAR is positioned as a methodological foundation for reproducible phenotyping, precision orchard management, robotic perception, and data-driven perennial crop production.

Accurate tree height estimation is essential for orchard phenotyping and management, yet the precision, accuracy, and reference reliability of manual and emerging remote sensing methods remain insufficiently evaluated under repeated field measurements. This study systematically compared five tree height estimation approaches in a citrus orchard to determine their repeatability, identify the most suitable practical reference method, and evaluate their relative accuracy. Five tree height estimation approaches were evaluated: manual pole measurement (MP), airborne LiDAR (AL), airborne SLAM LiDAR (ASL), ground mobile SLAM LiDAR (GMSL), and an AI-driven photogrammetry platform (Agroview). Repeated measurements across four dates were used to assess precision and consistency, and cross-validation between MP and GMSL was performed to identify the most suitable reference method before accuracy evaluation of all approaches. GMSL showed the highest repeatability, with the lowest coefficient of variation (0.44

The use of drones to survey and monitor wildlife populations has increased exponentially. A common protocol used for data collection is planning flights with substantial overlap between successive photographs and lateral lines and then creating orthomosaics by merging the collected images. Because available methods for orthomosaic building assume that landscapes are static, unintended errors arise when counting moving animals. Here, we describe these sources of error and discuss potential solutions and future developments needed. Individuals can appear multiple times, be omitted or appear as faint ghosts or cut in half in the final mosaic. These errors can significantly impact abundance estimates but are rarely acknowledged. Researchers should carefully consider if using orthomosaics is really needed for surveying wildlife. Currently, there is a lack of methods to prevent these errors from arising and to explicitly accommodate them in modelling approaches. Future developments should focus on (a) creating methods to build orthomosaics that minimize these errors in the context of counting moving animals; (b) developing modelling approaches to estimate abundance while accounting for these errors; and (c) exploring alternative flight settings (e.g. amount of lateral overlap, sensor type, flight height and speed). Using an example on Giant Amazon Turtles, we illustrate potential solutions with a method for orthomosaic building that prioritizes moving animals and a modelling approach to estimate the detection errors and correct abundance estimates. The developed prototype approach for creating orthomosaics revealed many more turtle individuals than the conventional approach, although it presented more double counts as well. In the modelling approach, we found that a turtle available for detection during the survey can have a probability of 31% of being omitted or ghosted during the conventional orthomosaic building process. We also found that 12% of the turtles appearing in a conventional orthomosaic correspond to double counts.

Eastern oysters (Crassostrea virginica) generate structurally complex reef systems that offer diverse ecosystem services. However, there is limited understanding of how reef structure translates into reef condition. This knowledge gap might be better addressed if oyster reef structure could be more rapidly assessed. Conventional in situ monitoring techniques are often time-intensive, invasive, and do not provide spatially continuous information on the reef structure. Unoccupied Aircraft Systems (UAS), commonly referred to as drones, equipped with optical sensors can rapidly and non-invasively map intertidal oyster reef surfaces. We demonstrate how a digital surface model from UAS-based light detection and ranging (lidar) can enable very high-resolution characterization and monitoring of intertidal oyster reef surface morphology. Generalized linear models (GLMs) identified relationships between in situ live oyster counts and surface complexity metrics derived from digital surface models produced from lidar point clouds. Statistically significant relationships between surface complexity metrics (e.g., gray level co-occurrence features, volume to area ratio, skewness of elevation) and live oyster counts suggest that surface complexity provides useful proxies for reef condition. Advancing the application of remote sensing to intertidal oyster reefs can help identify reefs that are prone to degradation and inform conservation and restoration strategies.

Coastal dune environments play a critical role in protecting coastal areas from damage associated with flooding and excessive erosion. Therefore, monitoring the morphology of dunes is an important coastal management operation. Traditional ground-based survey methods are time-consuming, and data must be interpolated over large areas, thus limiting the ability to assess small-scale details. High-resolution uncrewed aerial vehicle (UAV) photogrammetry allows one to rapidly monitor coastal dune elevations at a fine scale and assess the vulnerability of coastal zones. However, photogrammetric methods are unable to map ground elevations beneath vegetation and only provide elevations for bare sand areas. This drawback is significant as vegetated areas play a key role in the development of dune morphology. To provide a complete digital terrain model for a coastal dune environment at Topsail Hill Preserve in Florida’s panhandle, we employed a UAV, equipped with a laser scanner and a high-resolution camera. Along with the UAV survey, we conducted a RTK–GNSS ground survey of 526 checkpoints within the survey area to serve as training/testing data for various machine-learning regression models to predict the ground elevation. Our results indicate that a UAV–LIDAR point cloud, coupled with a genetic algorithm provided the most accurate estimate for ground elevation (mean absolute error ± root mean square error, MAE ± RMSE = 7.64 ± 9.86 cm).

Object detection in remote sensing images is one of the most critical computer vision tasks for various earth observation applications. Previous studies applied object detection models to orthomosaic images generated from the SfM (Structure-from-Motion) analysis to perform object detection and counting. However, some small objects that are occluded from the vertical view but observable in raw images from the oblique views cannot be detected in the orthomosaic image, leading to an occlusion issue that cannot be resolved with the traditional orthophoto-based approach. Taking strawberry detection as a case study, the objective of this study is to detect small objects directly from multi-view raw images. Firstly, an object-detection model (Faster R-CNN in this study) was applied to each raw image to identify strawberry fruit and flower objects. Each unique strawberry object on the ground can be detected multiple times in the raw images because images have forward- and side overlap. To find the unique objects from the step one detection results, an improved FaceNet model was proposed to combine the image and position information to calculate the feature distance between those objects, and a clustering algorithm was used to associate the cluster with each unique strawberry using the object distance output from the FaceNet model, from which the final position and number of strawberry fruits and flowers were obtained. Compared with the orthomosaic image alone, this approach using multi-view images effectively solved the occlusion problem and improved overall recognition accuracy of strawberry flowers, unripe fruits, and ripe fruits from 76.28% to 96.98%, 71.64% to 99.09%, and 69.81% to 97.17%, respectively, highlighting the potential of multi-view stereovision (MVS) in small object detection.

Equipping unmanned aerial systems (UASs) with light detection and ranging (lidar) has been made possible with recent advancements, which has made these sensors compact and gradually more cost-effective. Despite the increased proliferation of UAS-lidar in several fields, the geometric accuracy of lidar-generated point clouds, together with their visual qualities, needs to be explored for building surveying applications. Considering that red-blue-green (RGB) cameras are the most prevalent UAS sensors in building surveying, a lidar- and an RGB camera-equipped UAS was deployed on a mixed infrastructure to simultaneously collect data and generate corresponding point clouds. Different geometric features from both RGB and lidar point clouds were measured and compared quantitatively against benchmark field observations. A qualitative analysis on the point clouds' visual qualities was also performed and a sensor recommendation matrix was proposed based on desired application accuracy. Lidar has proven to be a viable alternative, providing better geometric accuracy, data quality, and clarity in all three dimensions.

The Atlantic ribbed mussel (Geukensia demissa) is common in southeastern US salt marshes, where they form dense aggregations (mounds), that occur in the highest densities and sizes on the marsh platform close to the tidal creeks' heads. Within these marshes, mussels help build marsh elevation via their biodeposition of organic and inorganic material, stimulate the growth of the dominant foundation species cordgrass (Spartina alterniflora), and create hotspots of invertebrate biodiversity, nutrient cycling, and drought resilience. Given their powerful role, there is rising interest in assessing natural variation in the distribution of mussel mounds and using such information to guide marsh conservation and restoration strategies. However, gathering such information is challenging, because the small dimension (∼1 m) of the mounds and the presence of overlying vegetation make it difficult to quantify mound distribution on the marsh. Therefore, this study presents a new procedure to compute the distribution, height, radius, volume, and distance of mounds in marsh environments using remote sensing. A high-resolution UAV-Lidar point cloud has been collected over a highly vegetated salt marsh in Georgia, USA, using a custom-built laser scanner system. An original detection algorithm, based on a Random Forest classifier, has been implemented to identify the mounds from the point cloud. The algorithm has been trained and tested on surveyed mounds and provides their location and geometric properties. Results indicate that the classifier can distinguish mussel mounds from non-mussel mound locations with an accuracy of 95 %. The classifier identified ∼8000 mounds, which occupy 10 % of the study domain, and a volume (shells+feces/pseudofeces) of 680 m3. The method is highly useful in efforts to monitor mussel mounds over time and scale up to assess mounds across sites, providing invaluable data for future studies related to the geomorphic evolution of marshes to sea level rise and siting marsh conservation and enhancement projects.

This chapter explores the use of photogrammetry by federal agencies such as the United States Geological Survey, state agencies, and private surveying and engineering companies to develop maps at various scales and for a variety of purposes. The development of two more essential photogrammetric products, digital elevation models, and orthophotos is reviewed. Most digital cameras have nonsquare formats, and thus, the angular field of view is specified for both the along-track and across-track dimensions. Analytical photogrammetry can be considered a suite of methods to calculate three-dimensional (3D) coordinates of points from photography using rigorous mathematical models, and in general, the solution of large redundant systems of equations using least squares. Aerial laser scanning, commonly referred to as airborne light detection and ranging, is introduced as a method to rapidly collect topographic data over large areas at submeter 3D spatial resolution. Finally, applications of unmanned aircraft systems in aerial photogrammetry and remote sensing are assessed.

Florida depends on the oceans, yet its waters have not been extensively mapped to the highest standards. While there is a need for marine spatial data for a wide range of applications and issues, there is also a need to develop data acquisition, processing, and analytical workflows and to integrate different surveying instruments that can capture the complex and extensive coastal environment – both above and below the waterline. This note provides an overview of the research performed by scientists at the School of Forest, Fisheries, and Geomatics Sciences, University of Florida, in the field of hydrography and marine geomatics.

Given the global decline in oyster reef coverage, conservation and restoration efforts are increasingly needed to maintain the ecosystem services these biogenic features offer. However, monitoring and restoration are constrained by a lack of continuous quantitative metrics to effectively assess reef health. Traditional sampling methods typically provide a limited perspective of reef status, as sampling areas are just a fraction of the total reef area. In this study, an unoccupied aircraft system collected LiDAR data over oyster reefs in Cedar Key, FL, USA to develop digital surface models (DSMs) of their 3D structure. Ground sampling was also conducted in randomly placed quadrats to enumerate the live and dead oysters within each plot. Over 20 topographic complexity metrics were derived from the DSM, allowing relationships between various geomorphometric measures and reef health to be quantified. These data informed generalized additive models that explained up to 80% of the deviation of live to dead oyster ratios in the quadrats. While topographic complexity has been associated with reef health in the past, this process quantifies the relationships and indicates what metrics can be relied on to efficiently monitor intertidal oyster reefs using DSMs. The models can also inform restoration efforts on which surface characteristics are best to replicate when building restored reefs.

<p>Bathymetry inversion using remote sensing techniques is a topic of increasing interest in coastal management and monitoring. Freely accessible Sentinel-2 imagery offers high-resolution multispectral data that enables bathymetry inversion in optically shallow waters. This study presents a framework leading to a generalized Satellite-Derived Bathymetry (SDB) model applicable to vast and diversified coastal regions utilizing multi-date images. A multivariate regression random forest model was used to derive bathymetry from optimal Sentinel-2 images over an extensive 210 km coastal stretch along southwestern Florida (United States). Model calibration and validation were done using airborne lidar bathymetry (ALB) data. As ALB surveys are costly, the proposed model was trained with a limited and practically feasible ALB data sample to expand the model’s practicality. Using multi-image bands as individual features in the random forest model yielded high accuracy with root-mean-square error values of 0.42 m and lower for depths up to 13 m.</p>

Identifying and mitigating sources of measurement error is a critical task in geomatics research and the geospatial industry as a whole. In pursuit of such error, accuracy assessments of lidar data have revealed a range bias in low-cost scanners. This phenomenon is a temporally correlated instability in the lidar scanner where the measured distance between target and sensor changes over time while both are held stationary. This research presents an assessment of two low-cost lidar scanners, the Velodyne® HDL–32E and Livox® Mid–40, in which their temporal stability is analyzed and methods to mitigate systematic error are implemented. By immobilizing each scanner as it observes a stationary target surface over the course of multiple hours, trends in scanner precision are identified. Scanner accuracy is then determined using a terrestrial lidar scanner, the Riegl® VZ-400, to observe both subject scanner and target, and extracting the distances between scanner origin and observed surface. Patterns identified in each scanner’s distance measurements indicate temporal autocorrelation, and, by exploiting the high linear correlation between scanner internal temperature and measured distance in the HDL–32E, it is possible to mitigate the resulting error. Application of the proposed solution lowers the Velodyne® scanner’s measurement RMSE by over 60%, providing levels of measurement accuracy comparable to more expensive lidar systems.

The high temporal and spatial resolution of ecosystem data captured by tower-mounted PhenoCams have established these instruments as fundamental tools in phenological studies and positioned them as a critical midstep between airborne or spaceborne and in-situ data in ecological research. However, adding spatial precision can further expand PhenoCam network applications and attract more users, such as drawing more phenological scientists to the near-surface remote sensing research field. In this study, a georeferencing approach was established to enhance research infrastructure for PhenoCams. Advanced photogrammetric techniques were applied to the camera field of view to geo-enable all pixels, tying them to their location on Earth and adding more usable information to datasets in addition to the current "region of interest" (ROI) level data. The georeferencing method is presented along with the photogrammetric equations that enable going from object space coordinates (3D) to image space coordinates (2D). This method was tested and demonstrated on PhenoCam data at Ordway Swisher Biological Station (OSBS), located in Melrose, Florida, USA. Statistical and sensitivity analyses show that projected pixel-location can be as accurate as 1.5 pixels RMSE for the presented case study, corresponding to object space accuracy of 10 cm, 20 cm, and 30 cm at distances of 100 m, 200 m, and 300 m, respectively. In addition, geo-located PhenoCam data at OSBS was co-located with Moderate Imaging Spectrometer (MODIS) data and characterized. These results demonstrate that the techniques presented reliably provide additional data from PhenoCams that are useful for ecosystem-level studies. By providing each pixel's absolute location corresponding to its place in the real world efficiently, this research introduces a higher degree of spatial precision to every phenological observation from the PhenoCam at OSBS. This presentation of reproducible steps and analysis facilitates implementation for other PhenoCam data as well as other obliquely mounted cameras.

The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.

A unique drone-based system for underwater mapping (bathymetry) was developed at the University of Florida. The system, called the “Bathy-drone”, comprises a drone that drags, via a tether, a small vessel on the water surface in a raster pattern. The vessel is equipped with a recreational commercial off-the-shelf (COTS) sonar unit that has down-scan, side-scan, and chirp capabilities and logs GPS-referenced sonar data onboard or transmitted in real time with a telemetry link. Data can then be retrieved post mission and plotted in various ways. The system provides both isobaths and contours of bottom hardness. Extensive testing of the system was conducted on a 5 acre pond located at the University of Florida Plant Science and Education Unit in Citra, FL. Prior to performing scans of the pond, ground-truth data were acquired with an RTK GNSS unit on a pole to precisely measure the location of the bottom at over 300 locations. An assessment of the accuracy and resolution of the system was performed by comparison to the ground-truth data. The pond ground truth had an average depth of 2.30 m while the Bathy-drone measured an average 21.6 cm deeper than the ground truth, repeatable to within 2.6 cm. The results justify integration of RTK and IMU corrections. During testing, it was found that there are numerous advantages of the Bathy-drone system compared to conventional methods including ease of implementation and the ability to initiate surveys from the land by flying the system to the water or placing the platform in the water. The system is also inexpensive, lightweight, and low-volume, thus making transport convenient. The Bathy-drone can collect data at speeds of 0–24 km/h (0–15 mph) and, thus, can be used in waters with swift currents. Additionally, there are no propellers or control surfaces underwater; hence, the vessel does not tend to snag on floating vegetation and can be dragged over sandbars. An area of more than 10 acres was surveyed using the Bathy-drone in one battery charge and in less than 25 min.

This study examines the use of the Multi-Spectral Instrument (MSI) in Sentinel-2 satellite in combination with regression-based random forest models to estimate bathymetry along the extended southwestern Florida nearshore region. In this study, we focused on the development of a framework leading to a generalized Satellite-Derived Bathymetry (SDB) model applicable to an extensive and diversified coastal region (>200 km of coastline) utilizing multi-date images. The model calibration and validation were done using airborne lidar bathymetry (ALB). As ALB surveys are very expensive to conduct, the proposed model was trained with a limited and practically feasible ALB data sample to expand the model’s practicality. Out of the three different sub-models introduced using varying combinations of historical satellite imagery, the combined-band model with the largest feature pool yielded the highest accuracy. The results showed root mean square error (RMSE) values of 8% and lower for the 0–13.5 m depth range (limit of the lidar surveys used) for all areas of interest, indicating the model efficiency and adaptability to varying coastal characteristics. The influence of training sample locations on model performance was evaluated using three distinct model configurations. The difference between these configurations was less than 5 cm, which highlights the robustness of the proposed SDB model. The quality of the satellite imagery is a significant factor that influences the accuracy of the bathymetry estimation. A preliminary methodology incorporating spectral data embedded in Sentinel-2 imagery to effectively select the most optimal satellite imagery was also proposed in this study.

The image-based modeling and simulation of plant growth have numerous and diverse applications. In this study, we used image-based and manual field measurements to develop and validate a methodology to simulate strawberry (Fragaria × ananassa Duch.) plant canopies throughout the Florida strawberry growing season. The simulated plants were used to create a synthetic image using radiative transfer modeling. Observed canopy properties were incorporated into an L-system simulator, and a series of strawberry canopies corresponding to specific weekly observation dates were created. The simulated canopies were compared visually with actual plant images and quantitatively with in-situ leaf area throughout the strawberry season. A simple regression model with L-system-derived and in-situ total leaf areas had an Adj R2 value of 0.78. The L-system simulated canopies were used to derive information needed for image simulation, such as leaf area and leaf angle distribution. Spectral and plant canopy information were used to create synthetic high spatial resolution multispectral images using the Discrete Anisotropic Radiative Transfer (DART) software. Vegetation spectral indices were extracted from the simulated image and used to develop multiple regression models of in-situ biophysical parameters (leaf area and dry biomass), achieving Adj R2 values of 0.63 and 0.50, respectively. The Normalized Difference Vegetation Index (NDVI) and the Red Edge Simple Ratio (SRre) vegetation indices, which utilize the red, red edge, and near infrared bands of the spectrum, were identified as statistically significant variables (p < 0.10). This study showed that both geometric (canopy seize metrics) and spectral variables were successful in modeling in-situ biomass and leaf area. Combining the geometric and spectral variables, however, only slightly improved the prediction model. These results show the feasibility of simulating strawberry canopies and images with inherent geometrical, topological, and spectral properties of real strawberry plants. The simulated canopies and images can be used in applications beyond creating realistic computer graphics for quantitative applications requiring the depiction of vegetation biological processes, such as stress modeling and remote sensing mission planning.

Surveying an area with small, unoccupied aerial systems (UAS) equipped with a lidar mapping payload—absent permanent, stable, geometrical reference surfaces—demands accurate, repeatable data collection procedures. While relative error within a single UAS lidar dataset may reveal itself in strip misalignment, absolute error (particularly horizontal error) can prove more difficult to detect, casting doubt upon the quality of both individual surveys and time change analyses of multiple surveys of the area. To gain insight on the UAS lidar error budget, this study presents an analysis of multiple UAS lidar surveys over a set of accurately surveyed geometric checkpoints. Each flight’s trajectory was processed multiple times using multiple static GNSS base observations, both autonomous and set over surveyed monuments, at varying distances from the study site. Custom algorithms were used to mensurate the geometric targets detected in each UAS lidar survey's point cloud, allowing for precise comparison of both absolute horizontal and vertical accuracy of each survey against the rigorous ground survey. The results of the analysis suggest that high horizontal accuracy can be achieved under a variety of conditions, whereas vertical accuracy is sensitive to the quality of ground control. and a discussion of the results explores the ultimate goal of isolating and understanding the sources and magnitudes of error in the UAS lidar error budget.