Elevation Map Research Articles

Using a Non-Contact Sensor to Delineate Management Zones in Vineyards and Validation with the Rasch Model.

The production of high-quality wines is one of the primary goals of modern oenology. In this regard, it is known that the potential quality of a wine begins to be determined in the vineyard, where the quality of the grape, initially, and later that of the wine, will be influenced by the soil properties. Given the spatial variability of the fundamental soil properties related to the potential grape production, such as texture, soil organic matter content, or cation exchange capacity, it seems that a uniform management of a vineyard is not the most optimal way to achieve higher grape quality. In this sense, the delineation of zones with similar soil characteristics to implement site-specific management is essential, reinforcing the interest in incorporating technologies and methods to determine these homogeneous zones. A case study was conducted in a 3.3 ha vineyard located near Évora, south of Portugal. A non-contact sensor (DUALEM 1S) was used to measure soil apparent electrical conductivity (ECa) in the vineyard, and later, a kriged ECa map was generated. ECa and elevation maps were utilised to delineate homogeneous zones (management zones, MZs) in the field through a clustering process. MZs were validated using some soil properties (texture; pH; organic matter-OM; phosphorous-P2O5; potassium-K2O; the sum of the exchange bases-SEB; and cation exchange capacity-CEC), which were determined from 20 soil samples taken in the different MZs. Validation was also performed using Rasch measures, which were defined based on the formulation of the objective and probabilistic Rasch model, integrating the information from the aforementioned soil properties at each sampling location. The comparison of the MZs was more evident with the use of the Rasch model, as only one value was to be employed in each MZ. Finally, an additional validation was conducted using a vegetation index to consider the plant response, which was different in each MZ. The use of a non-contact sensor to measure ECa constitutes an efficient technological tool for implementing site-specific management in viticulture, which allows for the improvement of decision-making processes by considering the inherent spatial variability of the soil.

Fracture Network Analysis of Karstified Subis Limestone Build-Up in Niah, Sarawak

Understanding complex carbonate fracture networks and karstification at various geological scales is challenging, especially with limited multi-scale datasets. This paper aims to reduce uncertainty in the fracture architecture of Central Luconia karstified reservoirs by narrowing observational gaps between seismic and well data by using the discrete fracture models of exposed limestone outcrops as analogues for the subsurface carbonate reservoir. An outcrop-based fracture network characterisation of a near-surface paleo-karst at Subis Limestone combined with lineament analysis was conducted to extract fracture parameters. The karst structure was first delineated using a digital elevation map and outcrop examination. Then, topology analysis was performed, following the creation of two-dimensional discrete fracture models. Two main fracture sets oriented northeast–southwest and northwest–southeast and 79 potential dolines were identified. Fracture intersections, northeast–southwest major orientations, and drainage systems highly influenced the karst features. The Subis Limestone fracture model revealed that the highest number of fractures and total length of fractures were concentrated in the northern part of the Subis Limestone build-up (X: 250–350, Y: 150–250) and became denser towards the northwest direction of the outcrop (X: 600–800). The fractures in the Subis paleo-karsts appear isolated, with I-nodes ranging from 0.74 to 0.94. Hence, it is crucial to incorporate matrix porosity into multiple scales of fracture network modelling to improve upscaling and the modelling of fracture–vug networks, as well as to minimise the underestimation of discrete fracture networks in fractured and karstified limestone.

Flood Inundation mapping and mitigation options in data-scarce region of Beledwayne town in the Wabi Shebele River Basin of Somalia

Somalia has experienced extreme flash floods in recent years across the arid regions causing tremendous loss of lives and properties. However, the flood magnitude, depth, and frequency of occurrence are not yet quantified. This is mainly due to scarce datasets in the area. . In this study, integration of observed and Climate Hazards Infrared Precipitation (CHIRP) satellite rainfall products and remote sensing raster data were used to improve hydrological model simulation outputs. The Hydrologic Engineering Center namely HEC-HMS and HEC-RAS models were used to simulate the rainfall-runoff processes and flood inundation, respectively. The land use, soil, slope and Digital Elevation map (DEM) were used to set-up the models and generate outputs. The HEC-HMS model calibration results depict that the model is able to reproduce the observed streamflow The simulated flows generated by the model predicted good agreement with the observed flow with values of 0.79, 0.74, 0.78, and 0.78 evaluated through the Nash and Sutcliffe Efficiency (NSE), Runoff Volume Error (RVE), coefficient of determination (R2), and percentage error of peak flow (PEPF), respectively. The HEC-RAS model result indicates that the maximum flood depth and velocity were obtained at the floodplain area. The peak flood at 50, 100, and 200-year return period using General Extreme Value (GEV) distribution revealed 384m3s-1, 409m3s-1, and 434m3s-1, respectively. The 100-year peak flood discharge in a specific part of the river revealed a flood depth of 7.53m. The provision of Levees as mitigation measures revealed reduction of the flood extent by 35% and suggested as possible flood protection measures for the study area.

TTA-GEP: Terrain Traversability Analysis with Geometry and Environmental Perception for the Path Planning of Planetary Rovers

Terrain traversability analysis (TTA), the key to the navigation of planetary rovers, is significant to the safety of the rover. Therefore, owing to its complexity, the Martian terrain is worth analysing comprehensively based on the terrain variability and hazard level. In this work, we propose a novel method for terrain traversability analysis for the path planning of planetary rovers by integrating Martian terrain geometry features with terrain semantic information, which includes geometry and environmental perception (GEP). Specifically, we deploy semantic segmentation to classify common terrain types, such as rocks, bedrocks, and sand, obtaining semantic information as one part of terrain traversability analysis at the same time. Simultaneously, the point cloud is generated by using binocular images from the planetary rover navigation camera (Navcam) to construct a 2.5D elevation map of the environment to analyse the geometric characteristics of the terrain. Besides, we implement path planning based on the results of TTA-GEP. Overall, our proposed method improves the performance of the terrain traversability analysis and reduces the risk of planetary rovers while detecting in an unstructured environment.

The moon before mare

The crust of the Moon experienced a unique geodynamic evolution, beginning with its crystallization from a magma ocean, continuing through a period of heavy impact bombardment, and followed by extensive basaltic mare volcanism. All these events have left crucial records imprinted in the form of topographic features and gravity anomalies. Here, we invert gravity and topography data using a two-layer thin-shell loading model under the premise of pre-mare isostasy to investigate the global structure of the crust and solve for feldspathic crust and mare thickness, together with mare-induced flexure. The tectonic record and partially buried crater population are used to constrain the bulk of mare volcanism to have been emplaced on a 40 km elastic lithosphere, although mare within large impact basins may have formed on a thinner elastic lithosphere. The mare thickness and associated flexure are removed to calculate a map of the surface and crust of the Moon before mare volcanism. The pre-mare surface in the Oceanus Procellarum region is found to be ∼2 km lower than the surrounding nearside, and several possible explanations, including a giant impact, pore space annealing, isostatic adjustment, and crustal erosion induced by a mantle plume or thermal anomaly, are discussed. The pre-mare elevation map further sheds light on the ring structure of Imbrium, which is seen to resemble that of Orientale. Imbrium's outermost ring is observed to be at a larger radial distance to the northeast relative to the south, indicating that some level of lithospheric variability affected ring formation at the time of impact. The western part of Imbrium's ring within Oceanus Procellarum is not found in the pre-mare topography, implying that it either never formed or that some processes erased its signature from gravity and topography. The feldspathic, pre-mare, crust is found to be ∼7 km thinner within large nearside basins than in models not accounting for the high-density mare. The pre-fill floor of these basins was ∼6 km deeper than currently observed, and together with their updated crustal structure, these new insights have implications for impact simulations that try to reproduce the crustal structure of nearside mare basins.

Digital elevation models of the sea-ice surface from airborne laser scanning during MOSAiC

Airborne laser scanners (ALS) are used to map the sea-ice surface at sub-meter resolution. We conducted 64 flights over the Arctic sea ice between September 2019 and September 2020 during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition to measure sea-ice surface elevation. The flights ranged from repeated, local-scale 5 × 5 km2 floe grid surveys to regional-scale transects more than 100 km long. We provide data at different processing levels: geolocated elevation point clouds and gridded segments of elevation and freeboard with a spatial resolution of 0.5 m. The latter product is corrected for atmospheric backscatter, sea-ice drift, and offset in elevation due to degraded INS/GNSS solutions > 85° N. For floe grid surveys, all data are combined to merged two-dimensional elevation maps. Other provided parameters include laser reflectance and echo width. The presented data offer a unique possibility to study the temporal evolution, spatial distribution, and variability of the snow and sea-ice surface and their properties in addition to validating satellite products.

Aerial photogrammetry to characterise and numerically model an ice jam in Southern Quebec

Abstract Collecting data on the dynamic breakup of a river's ice cover is a notoriously difficult task. However, such data are necessary to reconstruct the events leading to the formation of ice jams and calibrate numerical ice jam models. Photogrammetry using images from remotely piloted aircraft (RPA) is a cost-effective and rapid technique to produce large-scale orthomosaics and digital elevation maps (DEMs) of an ice jam. Herein, we apply RPA photogrammetry to document an ice jam that formed on a river in southern Quebec in the winter of 2022. Composite orthomosaics of the 2-km ice jam provided evidence of overbanking flow, hinge cracks near the banks and lengthy longitudinal stress cracks in the ice jam caused by sagging as the flow abated. DEMs helped identify zones where the ice rubble was grounded to the bed, thus allowing ice jam thickness estimates to be made in these locations. The datasets were then used to calibrate a one-dimensional numerical model of the ice jam. The model will be used in subsequent work to assess the risk of ice interacting with the superstructure of a low-level bridge in the reach and assess the likelihood of ice jam flooding of nearby residences.

Multichannel InSAR elevation reconstruction method based on dual-stream network

This paper presents a multichannel InSAR elevation reconstruction method based on deep learning, where a dual-stream network consisting of an elevation reconstruction stream and a boundary detection stream, named as ERSBDS, is built to reconstruct elevation maps for observed terrains, from multiple interferograms. First, the elevation reconstruction stream adopts a modified DeepLabV3+ architecture, in which the Xception network is replaced by the lightweight network called MobileNetV3 in the encoder for not only reducing the network parameters but also maintaining the performance of the network, and then a spatial attention module is added to the encoding and decoding path to enhance the network's attention to the spatial information of feature maps. Second, the boundary detection stream is mainly composed of residual blocks, which can detect the boundary information of observed terrains and merge it into the elevation reconstruction stream to improve the accuracy of elevation reconstruction for observed scenes. Finally, a suitable data set is constructed to enable the trained network to accurately reconstruct elevation maps for observed scenes. The experiments for multichannel InSAR elevation reconstruction for observed scenes demonstrate the effectiveness of the proposed method, and show the advantages of this method in the accuracy and efficiency of elevation reconstruction, compared with some of the most commonly used methods.

Authoring Terrains with Spatialised Style

AbstractVarious terrain modelling methods have been proposed for the past decades, providing efficient and often interactive authoring tools. However, they seldom include any notion of style, which is critical for designers in the entertainment industry. We introduce a new generative network method that bridges the gap between automatic terrain synthesis and authoring, providing a versatile set of authoring tools allowing spatialised style. We build upon the StyleGAN2 architecture and extend it with authoring tools. Given an input sketch or existing elevation map, our method generates a terrain with features that can be authored, enhanced, and augmented using interactive brushes and style manipulation tools. The strength of our approach lies in the versatility and interoperability of the different tools. We validate our method quantitatively with drainage calculation against other previous techniques and qualitatively by asking users to follow a prompt or freely create a terrain.

Sea surface reconstruction from marine radar images using deep convolutional neural networks

The sea surface reconstructed from radar images provides valuable information for marine operations and maritime transport. The standard reconstruction method relies on the three-dimensional fast Fourier transform (3D-FFT), which introduces empirical parameters and modulation transfer function (MTF) to correct the modulation effects that may cause errors. In light of the convolutional neural networks’ (CNN) success in computer vision tasks, this paper proposes a novel sea surface reconstruction method from marine radar images based on an end-to-end CNN model with the U-Net architecture. Synthetic radar images and sea surface elevation maps were used for training and testing. Compared to the standard reconstruction method, the CNN-based model achieved higher accuracy on the same data set, with an improved correlation coefficient between reconstructed and actual wave fields of up to 0.96-0.97, and a decreased non-dimensional root mean square error (NDRMSE) of around 0.06. The influence of training data on the deep learning model was also studied. Additionally, the impact of the significant wave height and peak period on the CNN model’s accuracy was investigated. It has been demonstrated that the accuracy will fluctuate as the wave steepness increases, but the correlation coefficient remains above 0.90, and the NDRMSE remains less than 0.11.

Perceptive Locomotion Through Nonlinear Model-Predictive Control

Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance, and planning of the underactuated dynamics of the system. Reliably optimizing for such motions and interactions in the presence of imperfect and often incomplete perceptive information is challenging. We present a complete perception, planning, and control pipeline, which can optimize motions for all degrees of freedom of the robot in real time. To mitigate the numerical challenges posed by the terrain, a sequence of convex inequality constraints is extracted as local approximations of foothold feasibility and embedded into an online model-predictive controller. Steppability classification, plane segmentation, and a signed distance field are precomputed per elevation map to minimize the computational effort during the optimization. A combination of multiple-shooting, real-time iteration, and a filter-based line search is used to solve the formulated problem reliably and at high rate. We validate the proposed method in scenarios with gaps, slopes, and stepping stones in simulation and experimentally on the ANYmal quadruped platform, resulting in state-of-the-art dynamic climbing.

Investigation of Groundwater Potential Zone Using Geophysical and Geospatial Technology in Akuku-Toru Local Government Area, Rivers State, Nigeria

Background of the Study: The study area is located within four communities in Akuku-Toru Local Government Area, which is a coastal region within the Niger Delta. The study area is heavily reliant on groundwater for domestic, industrial, and agricultural purposes. The hydrogeological dynamics of the area are complex, with diverse geological formations and intricate subsurface structures. As a result, an innovative and integrated approach is necessary for effective groundwater management. The study investigated the potential of groundwater resources in the study area and identification of fresh water zones using electrical resistivity, remote sensing, and GIS which employs geophysical surveys, remote sensing techniques, and geospatial analysis to explore the interplay between aquifer characteristics, geological formations, and topographical attributes. The fresh water zones are regions with low saline content.
 Aim: This study aims to assess groundwater potential in some parts of Akuku-Toru Local Government Area by integrating the geophysical data from Vertical Electrical Sounding (VES) surveys with geospatial analysis from GIS and Remote Sensing Technology. The research seeks to provide a comprehensive understanding of groundwater availability and its correlation with geophysical and geospatial parameters.
 Study Design: A thorough methodology was employed to investigate the possibility of freshwater resources in the study area. The approach involved gathering Vertical Electrical Sounding (VES) data from 8 locations, as well as incorporating geospatial data such as elevation, drainage density, geology, apparent resistivity, and slope maps. The collected data underwent rigorous processing, correlation analysis, and reclassification to explore the potential of freshwater resources in the study area.
 Place and Duration of the Study: The research was conducted in four communities (Abonnema, Ekulama, Jacobkiri and Belema) within the Akuku-Toru Local Government Area over a span of 18months. The area's hydrogeological context and topographical features are investigated to determine groundwater potential zones.
 Methods: The research utilized the Vertical Electrical Sounding (VES) method to obtain aquifer resistivity data, reflecting subsurface Lithological variations. Geospatial analysis involved accessing elevation and drainage density patterns. Correlation analysis was also performed to link the geophysical and geospatial data with qualitative interpretations, facilitating the assignment of numerical values representing groundwater potential zones.
 Results: The Correlation analysis revealed insightful patterns. Aquifer resistivity, elevation and slope were identified as influential parameter affecting groundwater potential. The geology of the study area, categorized into dominant formations, exhibited varying degrees of potential for freshwater resources. The Correlation of geophysical and geospatial data provided a comprehensive understanding of groundwater availability across the study region.
 Conclusion: The integration of geophysical and geospatial analysis offers a robust approach to groundwater potential assessment. The research findings contributed to valuable insights into the spatial distribution of potential freshwater resources in the study area. The correlation between aquifer resistivity, elevation, slope, and geology enhanced our understanding of hydrological conditions and provides a foundation for future groundwater studies and management strategies.

Radar (sentinel 1 data)-based flood mapping in Rivers State, Nigeria

The existence of floods in Nigeria, a notable environmental issue, is primarily linked to human-induced causes. These are largely linked to global warming causing shifts in climate patterns. Flooding is not always a direct result of preceding factors like heavy rain or dam overflow. Instead, it is frequently triggered by human actions such as overloading main rivers or inappropriate land use. The purpose of this study was to pinpoint areas prone to flooding in Rivers State. The main goals included the development of a spatial map depicting areas susceptible to floods. The research leverages multiple data sources, Shuttle Radar Topography Mission Digital Elevation and Rivers State Administrative Map, as well as Sentinel-1 SAR data. Assessment of potential surface runoff and identifying low probability flood-prone areas was conducted through Change Detection method on Google Earth Engine platform. ArcGIS and QGIS played a significant role in assessing the vulnerability to flood risks. The primary analyses conducted involved overlaps analysis. The study shows a vulnerability to flooding in Rivers State across a total area of 29,660 hectares, equating to 2.75% of the state's total size. This research provides important information that can aid decision-making processes in disaster preparedness, land use planning, and the implementation of effective risk reduction measures in Rivers State, and Nigeria at large. We strongly endorse the creation of Geospatial Information Systems to record the documentation of flood zones.

Aeolian Landforms in the White Sea Coast: a Review of Classification and Spatial Distribution

This research provides classification and analysis of the spatial distribution of the Holocene aeolian landforms within the White Sea Coast. We identified 5 types of secondary dunes based on the interpretation of detailed satellite images, topographic charts and analysis of literary sources: foredune plains and beach ridges plains, cliff-top dunes, blowouts and parabolic dunes, transgressive dunefields, sand sheets. The dunes were formed mainly from the Middle-Late Holocene. They gravitate towards sandy shores, where post-glacial uplift rates didn’t exceed ~3 mm/year during the Middle-Late Holocene. The coastal dunes were formed in connection with sediments, which entered the coastal zone with solid river flow and from selective erosion of glacial deposits. The rhythms of aeolian processes activation in different parts of the White Sea Coast were non-synchronous.

Review of interferometric synthetic aperture sonar interferometric phase filtering methods

Interferometric synthetic aperture sonar (InSAS) is a novel three dimensional underwater mapping system, which is widely used in various fields. Interferometric phase filtering is a crucial process in InSAS signal processing. The study of interferogram filtering method has great significance to improve the signal-to-noise ratio (SNR) of interferogram, reduce the difficulty of phase unwrapping, and improve the accuracy of digital elevation maps. Firstly, this article introduces the sources of interferometric phase noise. Secondly, it illustrates the difference between real domain filter and complex domain filter by simulation experiment. Thirdly, the various methods of InSAS interferogram filtering are systematically studied, which can be classified into spatial domain filtering and transform domain filtering. The transform domain filtering can be further divided into frequency domain and wavelet transform filtering. Moreover, the advantages and disadvantages of various methods are analyzed theoretically. Finally, numerical simulation and real-data processing experiments are performed, and the advantages and shortcomings of several methods are compared using residue numbers, root mean square error (RMSE), and CPU time as evaluation criteria.

Monkeypox Detection Through Watershed Segmentation and Appending 2D CNN Based Auto Encoder

Monkeypox, a viral zoonosis, may spread from animals to people. Fever, rashes, and swollen lymph nodes might create medical complications. Its symptoms resemble smallpox. To prevent monkey pox sickness, you must be prepared and treat it immediately. Public health systems should be aware of effective monkeypox mitigation methods because to its global health impacts. Watershed segmentation using CNN-based auto encoder detected monkeypox. Monkeypox may be distinguished from other skin infections. Watershed segmentation, elevation map utilisingsobel, and region-based feature extraction function well on impacted skin photos. Segmenting Monekypox images is tough due to similarities and variations across classes and the difficulties of focusing on skin lesions. Unsupervised learning models like the convolutional autoencoder duplicate the input image in the output layer. Encoders, ConvNets that produce low-dimensional images, process images passed via them.

InDeandCoE: A framework based on multi-scale feature fusion and residual learning for interferometric SAR remote sensing image denoising and coherence estimation

Synthetic aperture radar (SAR) interferometry is a high-resolution microwave remote sensing imaging method. Over the past two decades, many researchers working on remote sensing have applied this technology in various disciplines, including environmental monitoring, disaster monitoring, and elevation mapping. However, due to the existence of many influencing factors in the acquisition stage, such as atmospheric humidity and temperature, the reflected wave signals from the ground will be disturbed when received by remote sensing satellites. The presence of noise in interferograms is inevitable. Therefore, the accuracy of interferometric SAR phase denoising and coherence estimation has a decisive impact on the validity of subsequent processing results. In this paper, we pioneer the use of a nested U-net as a feature extractor for interferometric SAR phase and coherence. In addition, we build a phase filter and a coherence estimator by using the residual learning module. With the aim of determining the unique non-local similarity of InSAR images, we use non-local convolution and channel attention mechanisms to extract features in different dimensions of the interferogram. Through quantitative and qualitative experiments, the proposed method performs better in phase denoising and coherence estimation than state-of-the-art methods.

Paradeep Port Cartography

Abstract: Paradip Port is a natural, deep-water port on the East coast of India in Paradip, just 53 km from Jagatsinghpur city in Jagatsinghpur district of Odisha, India. It is situated at confluence of the Mahanadi river and the Bay of Bengal. ‘ The project detects the origin of paradeep port and the land pattern mouza selection in various revenue areas and growth of port from exports/area increment/climate effect/population growth & socio-economic impact due to expansion.’ hereby in this project , the intial setup zone of paradeep port is to be plot in revenue map by hand drafting and sketching over mouza map and topo map using the scale factor to denote the initial geographical settlement for the site selection, as the area is marked out then as per yearly growth basis the incremental chart and plans are to be prepared depicting addition of more areas,networks & affect on near by areas to see the bad/good impact on livelihood and development of market areas. The tracing and rubber sheeting of the industrial area is to be done using pins and hand sketching on paper board manually. As on the process initial and final map are plotted hence from study the Hereby in this project , the intial setup zone of paradeep port is to be plot in revenue map by hand drafting and sketching over mouza map and topo map using the scale factor to denote the initial geographical settlement for the site selection, as the area is marked out then as per yearly growth basis the incremental chart and plans are to be prepared depicting addition of more areas,networks & affect on near by areas to see the bad/good impact on livelihood and development of market areas. The tracing and rubber sheeting of the industrial area is to be done using pins and hand sketching on paper board manually. As on the process initial and final map are plotted hence from study the growth chart is to be prepared for growth of industry, climate change, social stability and future growth prediction.

Adaptive Robotic Information Gathering via non-stationary Gaussian processes

Robotic Information Gathering (RIG) is a foundational research topic that answers how a robot (team) collects informative data to efficiently build an accurate model of an unknown target function under robot embodiment constraints. RIG has many applications, including but not limited to autonomous exploration and mapping, 3D reconstruction or inspection, search and rescue, and environmental monitoring. A RIG system relies on a probabilistic model’s prediction uncertainty to identify critical areas for informative data collection. Gaussian processes (GPs) with stationary kernels have been widely adopted for spatial modeling. However, real-world spatial data is typically non-stationary—different locations do not have the same degree of variability. As a result, the prediction uncertainty does not accurately reveal prediction error, limiting the success of RIG algorithms. We propose a family of non-stationary kernels named Attentive Kernel (AK), which is simple and robust and can extend any existing kernel to a non-stationary one. We evaluate the new kernel in elevation mapping tasks, where AK provides better accuracy and uncertainty quantification over the commonly used stationary kernels and the leading non-stationary kernels. The improved uncertainty quantification guides the downstream informative planner to collect more valuable data around the high-error area, further increasing prediction accuracy. A field experiment demonstrates that the proposed method can guide an Autonomous Surface Vehicle (ASV) to prioritize data collection in locations with significant spatial variations, enabling the model to characterize salient environmental features.

Using LiDAR Data to Estimate Biomass in Afforested Bottomland Oak Sites in the Southern United States

Abstract The extent of bottomland hardwood forests in the Lower Mississippi Alluvial Valley (LMAV) has diminished, and federal programs like the Conservation Reserve Program provide incentives to afforest marginal agricultural areas with oaks to provide ecosystem services. Remote sensing technologies, like light detection and ranging (LiDAR), can be used to estimate biomass of these stands to potentially allow landowners to take advantage of carbon markets, but data are expensive to collect. Therefore, we determined whether freely available low-density LiDAR data could capture variability in tree- and stand-level characteristics in the LMAV, including aboveground biomass. We found that multiple regression LiDAR models captured more variability in tree-level than stand-level parameters and including soil type generally improved models. Model r2 values predicting tree and stand parameters including tree height, height to the live crown, quadratic mean diameter, crown area, trees per hectare, stand basal area, and stand biomass ranged from 0.34 to 0.82 and root mean square percent error (RMSPE) ranged from 7% to 36%. Specifically, models for stand biomass had an RMSE of about 19 Mg/ha or about 19% of mean values across sites. Therefore, freely available LiDAR data was useful in evaluating afforested bottomland oak sites for tree- and stand-level structural components in the LMAV.