Parallelizing visibility computations on triangulated terrains

Abstract

In this paper we address the problem of computing visibility information on digital terrain models in parallel. We propose a parallel algorithm for computing the visible region of an observation point located on the terrain. The algorithm is based on a sequential triangle-sorting visibility approach proposed by De Floriani et al. (1989). Static and dynamic parallelization strategies, both in terms of partitioning criteria and scheduling policies, are discussed. The different parallelization strategies are implemented on an MIMD multicomputer and evaluated through experimental results.     

A partition‐based serial algorithm for generating viewshed on massive DEMs

As increasingly large‐scale and higher‐resolution terrain data have become available, for example air‐form and space‐borne sensors, the volume of these datasets reveals scalability problems with existing GIS algorithms. To address this problem, a kind of serial algorithm was developed to generate viewshed on large grid‐based digital elevation models (DEMs). We first divided the whole DEM into rectangular blocks in row and column directions (called block partitioning), then processed these blocks with four axes followed by four sectors sequentially. When processing the particular block, we adopted the ‘reference plane’ algorithm to calculate the visibility of the target point on the block, and adjusted the calculation sequence according to the different spatial relationships between the block and the viewpoint since the viewpoint is not always inside the DEM. By adopting the ‘Reference Plane’ algorithm and using a block partitioning method to segment and load the DEM dynamically, it is possible to generate viewshed efficiently in PC‐based environments. Experiments showed that the divided block should be dynamically loaded whole into computer main memory when partitioning, and the suggested approach retains the accuracy of the reference plane algorithm and has near linear compute complexity.     

Soil-landscape modelling and spatial prediction of soil attributes

Abstract

Explicit and quantitative models for the spatial prediction of soil and landscape attributes are required for environmental modelling and management. In this study, advances in the spatial representation of hydrological and geomorphological processes using terrain analysis techniques are integrated with the development of a field sampling and soil-landscape model building strategy. Statistical models are developed using relationships between terrain attributes (plan curvature, compound topographic index, upslope mean plan curvature) and soil attributes (A horizon depth, Solum depth, E horizon presence/absence) in an area with uniform geology and geomorphic history. These techniques seem to provide appropriate methodologies for spatial prediction and understanding soil landscape processes.     

Multi‐visibility maps of triangulated terrains

Visibility computation on terrain models is an important research topic with many applications in Geographical Information Systems. A multi‐visibility map is the subdivision of the domain of a terrain into regions that, according to different criteria, encodes the visibility with respect to a set of view elements. We present an approach for visualising approximated multi‐visibility maps of a triangulated terrain corresponding to a set of view elements by using graphics hardware. Our method supports heterogeneous sets of view elements containing points, segments, polygonal chains and polygons and works for weak and strong visibility. Moreover, we are also able to efficiently solve approximated point and polygonal region multi‐visibility queries. To illustrate the usefulness of our approach we present results obtained with an implementation of the proposed algorithms.     

A fast candidate viewpoints filtering algorithm for multiple viewshed site planning

ABSTRACT

The aim of site planning based on multiple viewshed analysis is to select the minimum number of viewpoints that maximize visual coverage over a given terrain. However, increasingly high-resolution terrain data means that the number of terrain points will increase rapidly, which will lead to rapid increases in computational requirements for multiple viewshed site planning. In this article, we propose a fast Candidate Viewpoints Filtering (CVF) algorithm for multiple viewshed site planning to lay a foundation for viewpoint optimization selection. Firstly, terrain feature points are selected as candidate viewpoints. Then, these candidate viewpoints are clustered and those belonging to each cluster are sorted according to the index of viewshed contribution (IVC). Finally, the candidate viewpoints with relatively low viewshed contribution rate are removed gradually using the CVF algorithm, through which, the viewpoints with high viewshed contribution are preserved and the number of viewpoints to be preserved can be controlled by the number of clusters. To evaluate the effectiveness of our CVF algorithm, we compare it with the Region Partitioning for Filtering (RPF) and Simulated Annealing (SA) algorithms. Experimental results show that our CVF algorithm is a substantial improvement in both computational efficiency and total viewshed coverage rate.     

Stability Behavior and Thermodynamic States of Iron and Manganese in Sandy Soil Aquifer,Manukan Island,Malaysia

A total of 20 soil samples were collected from 10 boreholes constructed in the low lying area, which included ancillary samples taken from the high elevation area. Redox processes were investigated in the soil as well as groundwater in the shallow groundwater aquifer of Manukan Island, Sabah, Malaysia. Groundwater samples (n = 10) from each boreholes were also collected in the low lying area to understand the concentrations and behaviors of Fe and Mn in the dissolved state. This study strives to obtain a general understanding of the stability behaviors on Fe and Mn at the upper unsaturated and the lower-saturated soil horizons in the low lying area of Manukan Island as these elements usually play a major role in the redox chemistry of the shallow groundwater. Thermodynamic calculations using PHREEQC showed that the groundwater samples in the study area are oversaturated with respect to goethite, hematite, Fe(OH)₃ and undersaturated with respect to manganite and pyrochroite. Low concentrations of Fe and Mn in the groundwater might be probably due to the lack of minerals of iron and manganese oxides, which exist in the sandy aquifer. In fact, high organic matters that present in the unsaturated horizon are believed to be responsible for the high Mn content in the soil. It was observed that the soil samples collected from high elevation area (BK) comprises considerable amount of Fe in both unsaturated (6675.87 mg/kg) and saturated horizons (31440.49 mg/kg) compared to the low Fe content in the low lying area. Based on the stability diagram, the groundwater composition lies within the stability field for Mn²⁺ and Fe²⁺ under suboxic condition and very close to the FeS/Fe²⁺ stability boundary. This study also shows that both pH and Eh values comprise a strong negative value thus suggesting that the redox potential is inversely dependent on the changes of pH.     

CLIMATIC CONTROL OF pH BUFFERING IN THE A AND B HORIZONS OF UNITED STATES SOILS

In previous studies, models of the climatic controls on pH in the A (pHA) and B horizons (pHB) of U.S. soils were developed which differ in the functional forms and variables incorporated into each model. This study suggests that the differences in the models are explained by climate's control over the soil properties that determine pH buffering. Climatic control of pHA and pHB buffering in most acid soils is through its control over clay mineralogy. However in very acid soils, organic matter content (OM) is a stronger buffer of pHA than is clay mineralogy because in these highly leached soils, OM contents are very high and most OM is concentrated in the A horizon. In basic soils, climate dominates buffering through its control over the chemical reactions involving calcium carbonate (CaCO₃) formation. The pHA model suggests that in basic soils as moisture decreases CaCO₃ content increases causing pHA buffering to increase. In contrast, the pHB model suggests that as moisture decreases pHB buffering decreases. Although leaching of basic soils is limited, when it occurs a highly-concentrated pulse of calcium (Ca²⁺) is illuviated to the B horizon, causing pHB to increase. As leaching decreases, the magnitude of this pulse of Ca²⁺ increases because more Ca ²⁺ is available for leaching and depth of leaching of Ca²⁺ decreases. Studies of basic soils support only the results of the pHA model, but they are based mainly on laboratory studies which do not consider the influence of climate on B horizon chemistry. [Key Words: Soil acidity, pH buffering, climate, modeling, U.S. soils.]     

Uncertainties in the SNOWPACK multilayer snow model for a Canadian avalanche context: sensitivity to climatic forcing data

Abstract

As interest in outdoor activities in remote areas is increasing, there is a strong need for improved avalanche forecasting at the regional scale. Due to important logistical and safety matters, avalanche terrain measurements (avalanche observations, snowpack profiles, and stability tests) are not always possible for practitioners/forecasters. An interesting alternative would be to analyze the snowpack without these challenges by using snow model outputs. The SNOWPACK model is currently used operationally for avalanche forecasting and research in the Swiss Alps. Thus, this paper presents a summary of analyses that have been conducted to assess the potential of using the SNOWPACK model driven with both in-situ and forecasted meteorological data in three different Canadian climate and geomorphological contexts. A comparison of meteorological data from in-situ and predicted datasets for two winters shows that the GEMLAM weather model is the most accurate for the three climatic contexts of this project, but also showed a bias proportional to precipitation intensity/rate. Snow simulations forced with GEMLAM are the closest to field measurements. Finally, predictions of persistent weak layers have been validated using the InfoEx platform from Avalanche Canada. Crust and surface hoar formation dates agree with the information reported in InfoEx.     

Automated terrain feature identification from remote sensing imagery: a deep learning approach

ABSTRACT

Terrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment, ecosystem, and human population. Traditional induction-based approaches are challenged by their inefficiency for generalizing diverse and complex terrain features as well as their performance for scalable processing of the massive geospatial data available. This paper presents a new deep learning (DL) approach to support automatic detection of terrain features from remotely sensed images. The novelty of this work lies in: (1) a terrain feature database containing 12,000 remotely sensed images (1,000 original images and 11,000 derived images from data augmentation) that supports data-driven model training and new discovery; (2) a DL-based object detection network empowered by ensemble learning and deep and deeper convolutional neural networks to achieve high-accuracy object detection; and (3) fine-tuning the model’s characteristics and behaviors to identify the best combination of hyperparameters and other network factors. The introduction of DL into geospatial applications is expected to contribute significantly to intelligent terrain analysis, landscape scene interpretation, and the maturation of spatial data science.     

CostMAP: an open-source software package for developing cost surfaces using a multi-scale search kernel

ABSTRACT

Cost surfaces are a crucial aspect of route optimization and least cost path (LCP) calculations and are used in awide range of disciplines including computer science, landscape ecology, and energy-infrastructure modeling. Linear features present akey weakness to traditional routing calculations along cost surfaces because they cannot identify whether moving from acell to its adjacent neighbors constitutes crossing alinear barrier (increased cost) or following acorridor (reduced cost). Following and avoiding linear features can drastically change predicted routes. We introduce an approach to address this adjacency issue using asearch kernel that identifies these critical barriers and corridors. We have built this approach into anew Java-based open-source software package– CostMAP (cost surface multi-layer aggregation program)– which calculates cost surfaces and cost networks using the search kernel. CostMAP allows users to input multiple GIS data layers and to set weights and rules for developing aweighted-cost network. We compare CostMAP performance with traditional cost surface approaches and show significant performance gains– both following corridors and avoiding barriers– by modeling the movement of alarge terrestrial animal– the Baird’s Tapir (Tapirus bairdii)– in amovement ecology framework and by modeling pipeline routing for carbon capture and storage (CCS).     

Creating and coupling a high-resolution DTM with a 1-D hydraulic model in a GIS for scenario-based assessment of avulsion hazard in a gravel-bed river

In this paper we explore the development and assimilation of a high resolution topographic surface with a one-dimensional hydraulic model for investigation of avulsion hazard potential on a gravel-bed river. A detailed channel and floodplain digital terrain model (DTM) is created to define the geometry parameter required by the 1D hydraulic model HEC-RAS. The ability to extract dense and optimally located cross-sections is presented as a means to optimize HEC-RAS performance. A number of flood scenarios are then run in HEC-RAS to determine the inundation potential of modeled events, the post-processed output of which facilitates calculation of spatially explicit shear stress (τ) and level of geomorphic work (specific stream power per unit bed area, ω) for each of these. Further enhancing this scenario-based approach, the DTM is modified to simulate a large woody debris (LWD) jam and active-channel sediment aggradation to assess impact on innundation, τ, and ω, under previously modeled flow conditions. The high resolution DTM facilitates overlay and evaluation of modeled scenario results in a spatially explicit context containing considerable detail of hydrogeomorphic and other features influencing hydraulics (bars, secondary and scour channels, levees). This offers advantages for: (i) assessing the avulsion hazard potential and spatial distribution of other hydrologic and fluvial geomorphic processes; and (ii) exploration of the potential impacts of specific management strategies on the channel, including river restoration activities.     

Integrated Teaching of Geographic Information Science and Physical Geography Through Digital Terrain Analysis

Abstract

We present an outline of a course in digital terrain analysis that provides students with integrated instruction in geographic information science (GISci) and topics in physical geography and earth science. Integrated teaching of GISci and other sub-fields of geography has value because it presents GISci technologies in the context of their application. Nevertheless, the design of college and university geography courses is often fragmented rather than integrated. Our course provides one potential model for such integration. Assessment of student reaction, using focus groups of both enrolled and non-enrolled students, revealed a generally positive attitude toward the course and pointed out the barriers that prevent some students from taking it.     

Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing,China

ABSTRACT

One of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM_2.5) concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth (AOD) has been increasingly used as a proxy of ground PM_2.5 observations, although it suffers from non-trivial missing data problems. To address these issues, we developed a multi-stage statistical model in which daily PM_2.5 concentrations can be obtained with complete spatial coverage. The model consists of three stages – an inverse probability weighting scheme to correct non-random missing patterns of AOD values, a spatio-temporal linear mixed effect model to account for the spatially and temporally varying PM_2.5-AOD relationships, and a gap-filling model based on the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE). Good model performance was achieved from out-of-sample validation as shown in R² of 0.93 and root mean square error of 9.64 μg/m³. The results indicated that the multi-stage PM_2.5 prediction model proposed in the present study yielded highly accurate predictions, while gaining computational efficiency from the INLA-SPDE.     

Dynamic hybrid terrain representation based on convexity limits identification

Hybrid digital terrain models combine terrain data with different topologies and resolutions. Cartographic digital terrain models are typically composed of regular grid data that can be locally refined by adding a Triangulated Irregular Network (TIN) that represents morphologically complex terrain parts. Direct rendering of both datasets to visualize the digital terrain model generates discontinuities, as the meshes are disconnected. The utilization of complete/partial precomputed tessellation solutions solves the problem of quality, but limits the applicability of the representation to models with a fixed relative position between datasets. In this paper, we present a new scheme for hybrid terrain representation that permits the dynamic generation of the adaptive tessellation required to join the grid and TIN models. Our proposal permits the dynamic modification of the relative position between datasets. This increases the representation capabilities for those applications where this property is interesting as, for example, urban and landscape planning applications. The algorithm we propose is based on the identification of convex areas on the TIN and the efficient generation of triangles to join the models based on this convex structure. As a result, high quality models without discontinuities are obtained, increasing the flexibility of previous solutions based on fixed precomputations.     

Soil heterogeneity and the distribution of desert and steppe plant species across a desert-grassland ecotone

Bouteloua gracilis (Kunth) Lag. ex Griffiths (blue grama), Bouteloua eriopoda (Torr.) Torr. (black grama), and Larrea tridentata Coville (creosotebush) are dominant plants on the McKenzie Flats portion of the Llano de Manzano landform within Sevilleta National Wildlife Refuge in central New Mexico, part of the biome ecotone from the Colorado Shortgrass Steppe to the Chihuahuan Desert. In this study, we examine the hypothesis that soil heterogeneity, determined by variation in surface soil depth, carbonate accumulation, and fine-textured fraction, controls relative dominance of the three species. The area is flat, generally <1% slope; however, abrupt soil differences exist even within the flattest parts of the landscape that correspond to the pattern of buried channels incised in a petrocalcic horizon (caliche) formed in a 0.5–1.2 million year-old paleosol beneath the current surface soil. Multivariate analyses of soil-moisture-related variables suggest that B. gracilis, a Colorado Shortgrass Steppe indicator, dominates the buried paleochannels where Holocene surface deposits are deepest and the argillic (clay-rich) B-horizon is thickest. B. eriopoda, dominant in Chihuahuan Desert grasslands, is most abundant where the buried petrocalcic horizon lies within 40–60 cm of the surface and the argillic horizon is thinner and weakly developed. L. tridentata, an indicator of desertified Chihuahuan Desert shrubland, is dominant where the petrocalcic horizon is exposed or near the surface. This study illustrates the strong relationship between geomorphology, soil development and vegetation patterns in arid and semi-arid environments.     

A context-sensitive correlated random walk: a new simulation model for movement

Computational Movement Analysis focuses on the characterization of the trajectory of individuals across space and time. Various analytic techniques, including but not limited to random walks, Brownian motion models, and step selection functions have been used for modeling movement. These fall under the rubric of signal models which are divided into deterministic and stochastic models. The difficulty of applying these models to the movement of dynamic objects (e.g. animals, humans, vehicles) is that the spatiotemporal signal produced by their trajectories a complex composite that is influenced by the Geography through which they move (i.e. the network or the physiography of the terrain), their behavioral state (i.e. hungry, going to work, shopping, tourism, etc.), and their interactions with other individuals. This signal reflects multiple scales of behavior from the local choices to the global objectives that drive movement. In this research, we propose a stochastic simulation model that incorporates contextual factors (i.e. environmental conditions) that affect local choices along its movement trajectory. We show how actual global positioning systems observations can be used to parameterize movement and validate movement models and argue that incorporating context is essential in modeling movement.     

Planetary urbanisation: une affaire de perception

ABSTRACT

This paper suggests that planetary urbanisation offers an antidote to the narrow-mindedness of our toxic times. It conceives planetary urbanisation as “an affair of perception,” as a vision that begins vast, at the horizon, and sees particular parts (including your own particular part) comprising an interdependent totality. To envision the world through the lens of planetary urbanisation has certain distinct advantages. After all, it’s a viewpoint expressive of commonality rather than difference, of a mutually shared planet in which people who look different, who talk different from one another, who don’t know one another, who may even hate one another, have more in common than they might think.     

Measurements and numerical simulations of the degree of activity and vegetation cover on parabolic dunes in north-eastern Brazil

In this work we present measurements of vegetation cover over parabolic dunes with different degrees of activation along the north-eastern Brazilian coast. We extend the local values of the vegetation cover density to the whole dune by correlating measurements with the relative brightness index C of high resolution QuickBird panchromatic satellite images of the dune field. We then introduce the vegetation data into a continuous model for vegetated dunes, coupling sand erosion and vegetation growth, and perform simulations of the evolution of the morphology and vegetation cover of parabolic dunes. Finally, from the comparison of both, the measurements and the simulation results, we show that the model is able to predict the dune shape and the vegetation distribution of real parabolic dunes as result of the evolution of a blow-out.     

Obfuscating spatial point tracks with simulated crowding

ABSTRACT

Spatial point tracks are of concern for an increasing number of analysts studying spatial behaviour patterns and environmental effects. Take an epidemiologist studying the behaviour of cyclists and how their health is affected by the city’s air quality. The accuracy of such analyses critically depends on the positional accuracy of the tracked points. This poses a serious privacy risk. Tracks easily reveal a person’s identity since the places visited function as fingerprints. Current obfuscation-based privacy protection methods, however, mostly rely on point quality reduction, such as spatial cloaking, grid masking or random noise, and thus render an obfuscated track less useful for exposure assessment. We introduce simulated crowding as a point quality preserving obfuscation principle that is based on adding fake points. We suggest two crowding strategies based on extending and masking a track to defend against inference attacks. We test them across various attack strategies and compare them to state-of-the-art obfuscation techniques both in terms of information loss and attack resilience. Results indicate that simulated crowding provides high resilience against home attacks under constantly low information loss.