Terrain Model Research Articles

Spines and Inclines: Bioinspired Spines on an Insect-Scale Robot Facilitate Locomotion on Rough and Inclined Terrain.

To navigate complex terrains, insects use diverse tarsal structures (adhesive pads, claws, spines) to reliably attach to and locomote across substrates. This includes surfaces of variable roughness and inclination, which often require reliable transitions from ambulatory to scansorial locomotion. Using bioinspired physical models as a means for comparative research, our study specifically focused on the diversity of tarsal spines, which facilitate locomotion via frictional engagement and shear force generation. For spine designs, we took inspiration from ground beetles (Family Carabidae), which is a largely terrestrial group known for their quick locomotion. Evaluating four different species, we found that the hind legs host linear rows of rigid spines along the entire tarsus. By taking morphometric measurements of the spines, we highlighted parameters of interest (e.g., spine angle and aspect ratio) in order to test their relationship to shear forces sustained during terrain interactions. We systematically evaluated these parameters using spines cut from stainless steel shim attached to a small acrylic sled loaded with various weights. The sled was placed on 3D-printed models of rough terrain, randomly generated using fractal Brownian motion, while a motorized pulley system applied force to the spines. A force sensor measured the reaction force on the terrain, recording shear force before failure occurred. Initial shear tests highlighted the importance of spine angle, with bioinspired anisotropic designs producing higher shear forces. Using this data, we placed the best (50○ angle) and worst (90○ angle) performing spines on the legs of our insect-scale ambulatory robot physical model. We then tested the robot on various surfaces at 0, 10 and 20○ inclines, seeing similar success with the more bioinspired spines.

Measuring ornamental tree canopy attributes for precision spraying using drone technology and self-supervised segmentation

Tree canopy attributes or characteristics, such as canopy volume and density, are important parameters for calculating the precise quantity of agrochemicals required in each tree. Assessing the agrochemical needs in a nursery can help growers effectively estimate expenses, mitigate the risks of overuse or underuse, enhance resource utilization, and promote sustainable agricultural practices. This study aimed to develop an unmanned aerial vehicle (UAV)-based system to measure tree canopy attributes like tree height, tree count, canopy area and canopy volume using an image processing and self-supervised zero-shot segmentation approach. A high-resolution red-green-blue (RGB) sensor mounted on a drone captured three aerial image datasets, D1 and D2 from the first experimental plot and D3 from the second experimental plot. The acquired aerial images were stitched together and processed to generate a digital surface model (DSM) and a digital terrain model (DTM). A self-supervised zero-shot segmentation model, Segment Anything Model (SAM), was applied for tree segmentation and counting. The tree canopy area was calculated by segmenting individual trees and applying a conversion factor to convert pixelwise areas into square meters. Height calculation was done using elevation data of each pixel from DSM-DTM imagery within SAM-derived bounding box coordinates, and the highest pixel was considered as the height of the tree. The drone-calculated heights of 24 randomly selected trees were compared with manually measured heights in all datasets. The results showed an average absolute error of 2.43% in D1, 7.09% in D2 and 12.58% in D3. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were noted to be 0.09 m and 0.23 m in D1, 0.13 m and 0.25 m in D2, and 0.16 m and 0.21 m in D3, respectively. In D3, manual area and volume calculations were performed for validation. The RMSE and MAE for area calculation were calculated as 0.18 m2 and 0.15 m2, respectively, and the RMSE and MAE for volume measurements were calculated as 0.33 m3 and 0.26 m3 respectively, which indicated the level of agreement between manual and drone measurements. Using canopy volume information, calculated by summing pixel heights within each tree’s bounding box and multiplying by the ground sample distance (GSD) of 0.025 m per pixel, the average quantity of agrochemicals needed for a Maple tree was estimated at 0.20 L. These results underscore the high potential of this method for accurate canopy characteristic calculations and precision spray applications in the ornamental industry.

Enhanced deep learning network for accurate digital elevation model generation from LiDAR data

This paper presents a unique hierarchical deep network to tackle the task of identifying and filtering non-ground objects from point cloud data. This task is essential in the building of digital terrain models (DTMs). The proposed network is based on a deep encoder-decoder architecture and includes efficient convolutional connections to improve the identification of items that are not on the ground. In this architectural framework, a block for extracting features is intentionally created to capture a wide range of characteristics at many levels. Additionally, a technique for fusing global and local data is included to further enhance the accuracy of detection. The effectiveness of the proposed deep network in accurately detecting objects is validated by a comparative study with current approaches, utilizing ISPRS data. This analysis demonstrates the superiority of the proposed network in terms of object detection accuracy.

An Enhanced Collaborative Localization Method Based on Belief Propagation Aided by 3D Terrain Modelling

Navigation system performance degrades significantly in complex environments. It is important to analyze satellite visibility through 3D terrain modelling and separate the satellite signals propagated by NLOS to suppress the NLOS error. However, the traditional 3D terrain modelling visibility analysis method based on the pure terrain cover angle is only suitable for determining the visibility of GNSS satellites and may incorrectly separate LOS propagate measurement signals from members with low relative ranges and elevation angles under air–ground swarm conditions. To this end, this paper proposes a belief-propagating cooperative navigation method based on air–ground visibility analysis, which avoids mistakenly separating close-range LOS cooperative navigation signals by simultaneously considering the distances, elevation angles, and azimuths of the signal sources relative to the air–ground swarm members. The simulation shows that the cooperative navigation NLOS identification method based on air–ground visibility analysis proposed in this paper can more accurately realize the separation of NLOS signals under cooperative conditions than the traditional pure angular 3D terrain modelling visibility analysis method can, and the localization error of the members to be assisted is significantly reduced.

Application of UAV Technology for Vegetation Community Identification in Coastal BRIS Wetland

Unmanned aerial vehicles (UAV) based methods for reconnaissance activities aim to update wetlands’ health status and are safer and cost-effective considering that wetlands normally have saturated soils, dense vegetation and wildlife. Vegetation survey work in wetland areas needs many staff to ensure the safety of researchers and equipment. This paper describes the application of UAV technology to identify and demarcate vegetation communities in a dense BRIS (beach ridges interspersed with swales) coastal wetland. The methodology employed in this research has two steps. The first step involves the utilisation of UAV imagery and Geographic Information System (GIS) technology. The second step entails ground truthing, which involves validating tree species using 15 quadrants. The utilisation of UAVs in conjunction with ArcGIS 10.3 demonstrated that the unique characteristics of tree canopy morphology and tree heights could be assessed and analysed. The UAV-GIS results are compared to ground truthing results to validate tree communities’ demarcation. The findings indicate that integrating two datasets, particularly tree canopy morphology and tree height, produced an acceptably accurate vegetation community demarcation. The tree canopy form of Hibiscus tiliaceus (Bebaru) was differentiated from the canopies of Acacia mangium and Nypa fruticans by analysing the image. The tree height analysis involved using ArcGIS 10.3 software to generate the digital surface model (DSM) and digital terrain model (DTM). Validation results indicate an 87% accuracy in the demarcation work. Hence, identification and demarcation of the vegetation communities could be achieved by utilising both tree canopy morphology and tree height data obtained from UAV.

Multi‐decadal changes in vegetation and land use inferred from different data sources: Two case studies at the regional scale in the Slovenian karst

AbstractFew studies have used aerial imagery or various statistical records to analyse temporal land use and vegetation dynamics over large areas. This study presents an innovative approach that merges national‐scale data to provide temporal and spatial digital information on land use and vegetation cover change at high resolution. The work includes 326 historical aerial photographs from 1957 and 1980, derived digital terrain models, digital orthophotos (from 1994 to 1997), current land use (available from 2006), and forestry databases. Using two case studies in Slovenia, a detailed procedure for data preparation, processing (including orthorectification, on‐screen digitisation, visual photointerpretation), and result generation is presented. The used multiple sources showed great potential for the acquisition, mapping and quantifying selected land use categories. The study provided very important material for land use change assessment and implementation of the national documentary heritage. Although the processing methods were time‐consuming, they proved to be very suitable for delineating polygons of change with a spatial resolution of at least 1 m. The results show that the forest in the Rižana catchment has increased by 30 percentage points in the last 60 years and that in the Unica catchment 2/3 of the forest was damaged by large‐scale disturbances in 2014–2018.

Integrating geodiversity in animal spatial ecology: microhabitat selection of Eurasian lynx (Lynx lynx) and European wildcat (Felis silvestris) in a karst landscape

Geodiversity, encompassing various geophysical elements, can have an important impact on species distribution and affect animal behaviour patterns. Although many wild felids are attracted to rugged terrain and conspicuous relief features, most previous research was limited to general topographical characteristics (e.g., slope or terrain ruggedness) and rarely considered the effects of specific microhabitat characteristics. This gap is primarily due to the limited availability of high-resolution digital terrain models (DTMs) and relief features data at larger scales. However, LiDAR DTMs can be used in combination with various automatic methods to detect relief features, enabling non-contact and accurate mapping of large, remote and densely-forested areas. Here, we investigated the selection patterns of various karstic relief features, as well as topographic, anthropogenic and vegetation characteristics, by two sympatric felids, the Eurasian lynx (Lynx lynx) and the European wildcat (Felis silvestris), in the Dinaric Mountains, Slovenia. We used LiDAR DTM to calculate topographic characteristics and detect karst relief features based on automatic methods. We compared the selection of these features between the GPS-collared lynx and wildcats under a use-availability approach. We also investigated the differences in the selection of these features by lynx based on their origin and experience (remnant vs. translocated and naive vs. experienced, respectively). We observed significant impact of relief features on space use by both felids and detected distinct selection patterns between the two species. Lynx selected rugged terrain and proximity of caves, cliffs, karst depressions, ridges, small rocky outcrops, and roads, but avoided human settlements and forest edges. Wildcats selected areas with lower surface slope, closer to main roads, forest edges, caves and ridges, but avoided cliffs, forest roads and human settlements. We observed stronger selection/avoidance patterns among the translocated compared to the remnant lynx, while the differences in experience levels were less important. Our study demonstrates the potential of integrating remote sensing techniques and information on geodiversity into the study of animal spatial ecology. Furthermore, our results indicate that specific relief features provide important abiotic microhabitats for felids and may influence habitat segregation between sympatric species. Our findings provide further evidence for the importance of geodiversity conservation and the need to incorporate abiotic microhabitat features in wildlife habitat selection studies.

Identifying Potential Areas for New Railway Lines across Different Relief Roughness of Austrian Landscapes

This work presents an operational model to identify potential areas for new railway lines in Austria, utilizing a topographic and passenger potential and considering the different inclination requirements for regional and main railway lines. By applying a GIS digital terrain model based on a 500 m squared reference system, we identified 61,497 cells suitable for new regional lines and 11,347 cells suitable for main railway lines. These cells show a variety of spatial potential classes. Out of Austria’s 83,883 km² total surface, all these identified cells sum up to an area of 15,374 km² for new regional lines and 2,837 km² for new main lines. We validate the identified cells with existing and abandoned railway alignments. The model shows a fit of 80%. Furthermore, we put the results into perspective by comparing individual cell potentials with their public transport quality levels of existing supplied services, and we apply a five-scenario sensitivity analysis to identify the impact of the model’s foundations. After debating the model’s particularities and their impact on planning procedures, we conclude that our model is more comprehensive than today’s strategic rail transport planning procedures, primarily based on political desires or selected expert suggestions.

Applied Machine Learning to Study the Movement of Air Masses in the Wind Farm Area

Modeling the atmospheric boundary layer (ABL) in the area of a wind farm using computational fluid dynamics (CFD) methods allows us to study the characteristics of air movement, the shading effect, the influence of relief, etc., and can be actively used in studies of local territories where powerful wind farms are planned to be located. The operating modes of a wind farm largely depend on meteorological phenomena, the intensity and duration of which cause suboptimal operating modes of wind farms, which require the use of modern tools for forecasting and classifying precipitation. The methods and approaches used to predict meteorological phenomena are well known. However, for designed and operated wind farms, the influence of meteorological phenomena on the operating modes, such as freezing rain and hail, remains an urgent problem. This study presents a multi-layered neural network for the classification of precipitation zones, designed to identify adverse meteorological phenomena for wind farms according to weather stations. The neural network receives ten inputs and has direct signal propagation between six hidden layers. During the training of the neural network, an overall accuracy of 81.78%, macro-average memorization of 81.07%, and macro-average memorization of 75.05% were achieved. The neural network is part of an analytical module for making decisions on the application of control actions (control of the boundary layer of the atmosphere by injection of silver iodide, ionization, etc.) and the formation of the initial conditions for CFD modeling. Using the example of the Ulyanovsk wind farm, a study on the movement of air masses in the area of the wind farm was conducted using the initial conditions of the neural network. Digital models of wind turbines and terrain were created in the Simcenter STAR-CCM+ software package, version 2022.1; an approach based on a LES model using an actuating drive disk model (ADM) was implemented for modeling, allowing calculation with an error not exceeding 5%. According to the results of the modeling of the current layout of the wind turbines of the Ulyanovsk wind farm, a significant overlap of the turbulent wake of the wind turbines and an increase in the speed deficit in the area of the wind farm were noted, which significantly reduced its efficiency. A shortage of speed in the near and far tracks was determined for special cases of group placement of wind turbines.

A national-scale high-resolution runoff risk and channel network mapping workflow for diffuse pollution management

Managing diffuse pollution from agricultural land requires a spatially explicit risk assessment that can be applied over large areas. Major components of such assessments are the precise definition of both channel networks that often originate as small channels and streams, and Hydrologically Sensitive Areas (HSAs) of storm runoff that occur on land surfaces. Challenges relate to regions of complex topography and land use patterns, particularly those which have been heavily modified by arterial drainage. In this study, a national scale, transferrable workflow and analysis were developed using a specifically commissioned LiDAR survey. Research on the first half of Northern Ireland (6927 km2) is reported where field-edge drain to major river channels were mapped from 1 m (16 points per metre) digital terrain models, and in-field HSAs were defined across over 400,000 fields with a median field size of 0.86 ha. Manual drainage mapping supplemented with a novel automated drainage channel correction process resulted in an unparalleled high-resolution national drainage network with 37,320 km of channels, increasing mapped channel density from 0.9 km km−2 to 5.5 km km−2. The HSAs were based on a Soil Topographic Index (STI) system using hillslope and contributing area models combined with soil hydraulic characteristics. In all, 249 km2 of runoff risk HSAs were identified by extracting the top 95th percentile of the modelled STI as the areas with the highest propensity to generate in-field runoff. At field and individual farm scale these targeted risk maps of diffuse pollution were delivered to over 13,000 farmers and form part of the nationwide Soil Nutrient Health Scheme programme.

Detailed height mapping of trees and buildings (HiTAB) in Chicago and its implications to urban climate studies

Abstract The vertical dimensions of urban morphology, specifically the heights of trees and buildings, exert significant influence on wind flow fields in urban street canyons and the thermal environment of the urban fabric, subsequently affecting the microclimate, noise levels, and air quality. Despite their importance, these critical attributes are less commonly available and rarely utilized in urban climate models compared to planar land use and land cover data. In this study, we explicitly mapped the height of trees and buildings (HiTAB) across the city of Chicago at 1 m spatial resolution using a data fusion approach. This approach integrates high-precision light detection and ranging (LiDAR) cloud point data, building footprint inventory, and multi-band satellite images. Specifically, the digital terrain and surface models were first created from the LiDAR dataset to calculate the height of surface objects, while the rest of the datasets were used to delineate trees and buildings. We validated the derived height information against the existing building database in downtown Chicago and the Meter-scale Urban Land Cover map from the Environmental Protection Agency, respectively. The co-investigation on trees and building heights offers a valuable initiative in the effort to inform urban land surface parameterizations using real-world data. Given their high spatial resolution, the height maps can be adopted in physical-based and data-driven urban models to achieve higher resolution and accuracy while lowering uncertainties. Moreover, our method can be extended to other urban regions, benefiting from the growing availability of high-resolution urban informatics globally. Collectively, these datasets can substantially contribute to future studies on hyper-local weather dynamics, urban heterogeneity, morphology, and planning, providing a more comprehensive understanding of urban environments.

Long-term evaluation of surface topographic and topsoil grain composition changes in an agricultural landscape

Understanding long-term changes in topography and topsoil grain composition is crucial for the management of agricultural landscapes, especially in areas prone to wind erosion. This study investigates long-term changes in topography and topsoil grain composition within an agricultural landscape in south-western Slovakia. To analyse topographic changes over time, we used high-precision positioning measurements and airborne laser scanning to create digital terrain models (DTM) for the years 2011, 2017 and 2020. To assess changes in soil grain composition, we performed grain size analyses on soil samples collected during three different periods: M1 (1961–1970), M2 (2009–2015) and M3 (2015–2016). Changes in soil texture were evaluated to understand the impact of wind erosion on soil composition. The influence of windbreaks was also analysed by comparing the accumulation and deflation processes. The results showed significant changes in both topography and soil texture over the study period. The DTMs showed marked differences in the accumulation and deflation processes, highlighting areas affected by wind erosion. Comparisons of soil samples showed a shift in dominant soil types from loam and clay loam to silty loam, highlighting the effects of wind erosion. Analysis revealed a decrease in clay and silt content and an increase in sand content, indicating wind-induced soil degradation. The presence of windbreaks played a crucial role in reducing soil erosion by reducing wind speed, promoting soil accumulation and stabilising the landscape up to 80 m windward and 20 m leeward. The study highlights the complex interplay of climate and wind factors in shaping topography and soil properties and emphasises the protective role of windbreaks in agricultural landscapes over time. Our results show that wind erosion significantly alters soil texture, which can affect agricultural productivity. However, windbreaks have proven to be an effective measure in reducing soil erosion and maintaining soil quality.

Climate Change, Anthropogenic Pressure, and Sustainable Development of Karst Geosystems (A Case Study of the Brestnitsa Karst Geosystem in Northern Bulgaria)

This study examines climate change, anthropogenic impacts, and their relationship with the sustainable development of the Brestnitsa karst geosystem. It is representative of the karst in Bulgaria, which is developed on a quarter of its territory. The geosystem approach was used to reveal the interrelationships in a typical karst territory. The data were collected from more than 10 years of integrated monitoring of the geosystem, including continuous instrumental monitoring in the show cave Saeva Dupka, which is part of the geosystem. Various data from other sources were also used, such as satellite observations (a digital terrain model, land cover, and satellite images and products), ground data, and climate reanalyses. A spatial analysis of the karst geosystem in the context of climate change and sustainable development was conducted using a complex of remote sensing methods, geographic information systems, and statistical methods. The main results include the state and trends in the climate of the area, changes and trends in the speleoclimate and gas composition of the air in the Saeva Dupka cave, and changes in land use in the territory of the geosystem. Conclusions about the connections between climate change, current karstogenesis, and the carbon cycle in the geosystem, as well as the response of the geosystem to the consequences of the combined impact of climate change and human activities, have been made. All these impacts on the karst geosystem have been assessed in order to make adequate management decisions to guarantee its sustainable development in the future.

Impact structures on Mercury from MESSENGER data: Implications on their formation processes and crustal structure

In this study high-resolution stereo images and Digital Terrain Models (DTMs) from MErcury Surface, Space ENvironment, Geochemistry and Ranging (MESSENGER) mission were utilized to detect impact structures in regions not covered by Mercury’s Laser Altimeter (MLA), while gravitational data was utilized as a supported data set. We have established an inventory of 314 impact structures ≥150 km, classified on their morphological and gravitational characteristics. 24 basins ≥300 km have been newly discovered. Additionally, we have identified significant surface modifications in impact structures of smooth material infill, which can be either impact-induced or volcanic in origin. The Bouguer anomaly and crustal thinning in the center are displaying an interplay of predominant change of crustal structure in impact basins. Further, this study reveals a common impact history of Mercury and the Moon. Nevertheless, cumulative density distributions suggest the possibility of either a divergence in impactor populations responsible for forming large basins on both celestial bodies or a significant shift in impactor rates. This work holds important implications not only for understanding impact structure formation and evolution processes but also for interpreting the crustal structure. It presents an updated and expanded catalog of impact structures on Mercury, encompassing buried basins, and identifies new areas of interest, potentially serving as target sites for the forthcoming BepiColombo mission.

Efficient adversarial attacks detection for deep reinforcement learning-based autonomous planetary landing GNC

Given the constraints of remote communication and the unpredictability of the environment, autonomous planetary landing mechanisms are expected to achieve the high criteria of autonomy and provide optimal trajectory in future space exploration missions. As the results, applying Deep Reinforcement Learning (DRL) techniques into autonomous landing has produced encouraging findings. Due to the black-box nature of deep learning algorithms, one of the main concerns regarding the robustness of DRL is its vulnerability to adversarial attacks. This constraint prevents the transfer of DRL-based autonomous landing schemes from simulation to real-world applications. In this article, we explore how the DRL-based autonomous landing will be impacted by adversarial attacks and how to protect the system effectively and efficiently. To achieve this, a Long Short Term Memory (LSTM) based adversarial attack detector is been proposed. The proposed method adopts the explainability measurement of the target DRL scheme and flag the detection of adversarial attacks when acting. The proposed method is built and tested on 3D digital terrain model of Candidate Landing Site for 2020 Mission in Jezero Crater to simulate the landing scenario on the Mars. The experimental results demonstrate the proposed methodology can effectively detect adversarial attacks when acting on DRL agent with a high confidence in detection accuracy.

Webbing 3D map from UAV images in Da Lat City, Lam Dong Province

Abstract Worldwide, big cities are accelerating to fulfill the establishment of Spatial Data Infrastructure (SDI), which is a key task towards smart city. The mainstay of SDI is to create an important foundation of data capacity to provide necessary and essential information related to the requirements of urban and smart city management. With the advantages of high accuracy, low cost, and flexibility in 3D data creation, UAV (Unmanned Aerial Vehicle) images have been widely applied for the purpose of digital transformation purposes. This article aims to firstly utilize the new opportunities of using UAVs in 3D mapping and sharing 3D map data through the WebGIS platform. Secondly, processing techniques will be described after collecting the UAV images to create orthomosaics, point clouds, and Digital Surface Model (DSM). A classification of ground points was conducted to create a Digital Terrain Model (DTM) in combining with DSM to form a calibrated surface numerical model (Canopy Height Model, CHM). The images map was then digitalized coupled with the object height extracted from the CHM model and assigning attributable data to each object. The results suggested that ArcGIS Online allows users to share information widely, targeting multiple users, and to reduce the cost of GIS software investment in building thematic classes for urban infrastructure management.

Crater Morphometry on Callisto

Impact crater formation is affected by the physical properties of planetary crusts, allowing comparison of crater dimensions to serve as a proxy for comparing the crustal properties of different planetary bodies. New topographic profiles of Callisto craters, derived from Galileo-based digital terrain models, are presented, and the crater dimensions recorded. These data were used to derive crater morphometry scaling trends, which were then compared to the established trends of Ganymede and the Moon. Our comparative study suggests that the upper brittle portion of Callisto’s ice crust allows for the retention of steep-sided and elevated rim scarps, while subsurface warmer ice leads to an enhanced uplift and shallowing of the crater bowl. Crater dimensions are similar between Callisto and Ganymede, suggesting that the bulk properties of their near-surface crusts are comparable. The most notable difference between craters on these two Galilean moons were the smaller central pit diameters on Callisto. This difference can be explained if the pit formation on these bodies is controlled by the presence and movement (drainage and/or volatile loss) of impact melt water: the lower impact velocity and/or lower expected crustal heat flow on Callisto will result in less impact melt generation, and thus smaller central pits.

Adaptive Color Transfer From Images to Terrain Visualizations.

Terrain mapping is not only dedicated to communicating how high or steep a landscape is but can also help to indicate how we feel about a place. However, crafting effective and expressive elevation colors is challenging for both nonexperts and experts. In this article, we present a two-step image-to-terrain color transfer method that can transfer color from arbitrary images to diverse terrain models. First, we present a new image color organization method that organizes discrete, irregular image colors into a continuous, regular color grid that facilitates a series of color operations, such as local and global searching, categorical color selection and sequential color interpolation. Second, we quantify a series of cartographic concerns about elevation color crafting, such as the "lower, higher" principle, color conventions, and aerial perspectives. We also define color similarity between images and terrain visualizations with aesthetic quality. We then mathematically formulate image-to-terrain color transfer as a dual-objective optimization problem and offer a heuristic searching method to solve the problem. Finally, we compare elevation colors from our method with a standard color scheme and a representative color scale generation tool based on four test terrains. The evaluations show that the elevation colors from the proposed method are most effective and that our results are visually favorable. We also showcase that our method can transfer emotion from images to terrain visualizations.

Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds

ABSTRACT Studies have shown that digital surface models and point clouds generated by the United States Department of Agriculture’s National Agriculture Imagery Program (NAIP) can measure basic forest parameters such as canopy height. However, all measured forest parameters from these studies are evaluated using the differences between NAIP digital surface models (DSMs) and available lidar digital terrain models (DTMs). A survey of NAIP point cloud classification and related ground point-generated DTMs has not yet been undertaken. This study applies a Support Vector Machine (SVM) to classifying ground and nonground points from NAIP point clouds for test sites in Wyoming and Arizona, USA. Light detection and ranging (lidar) data from the U.S. Geological Survey 3D Elevation Program (3DEP) are used to validate the classified NAIP ground points and their corresponding DTMs. Comparing height differences between filtered NAIP ground points and 3DEP ground points, the SVM classifier’s results show that the vertical root mean square error value is 1.87 m and 1.69 m for the Wyoming and Arizona sites, respectively. If NAIP point clouds were continuously measured, the resulting availability of medium-resolution DTMs would benefit the application of multitemporal forest health monitoring and DTM generation.

Group-Motivated and Periodic Fire Setting for Terrorism in Urban Landscapes: An Analysis in Time and Space

One common form of arson is fire raising as an act of terrorism or for extremist cause. The purpose of this study is to contribute to the literature on this ubiquitous problem around the world by analyzing PKK′s systematic use of arsons. Drawing on time and locations of terrorist arson incidents, the research describes temporal patterns and then uses the Risk Terrain Modeling framework to analyze the behavior setting of the outcome event. Results suggest that PKK′s arsons were largely committed around February, over the weekends, and in the dark. In addition, the co-existence of four out of 20 spatial correlates within two street blocks increased the spatial risk of terrorist arson across the study area. The implications of the findings are also discussed for making policies toward these risky environments of terrorist fires settings around symbolic times to control the problem.