Polygon Features Research Articles

Historic and contemporary dune inventories to assess dune vulnerability to climate change impacts

The overall status of dune health in Ireland is assessed as ‘inadequate’ and declining due to the on-going losses and pressures which signify the urgent need for integrated, problem- and community-focussed coastal management plans. Historically, Ireland has only being monitoring the health of our coastal dunes very intermittently. This research reviews published coastal dune inventories in Ireland (Kinahan and McHenry, 1882; Young, 1977; Curtis, 1991; Quigley, 1991; Ryle et al., 2009; Delaney et al., 2013) and uses one of the most current inventories (Coastal Monitoring Project 2004-2006 in Ryle et al., 2009) to map the current distribution of dunes in thirteen coastal counties in the Republic of Ireland and assess the vulnerability of these dune ecosystems to projected changes in storminess. The CMP Report data was downloaded into a GIS and a filter query was applied to identify the dune area extent in each county and isolate the three major dune categories (Fixed, Embryonic and Mobile) which are critical for beach-dune sediment budgets via sediment exchange processes. The polygon features for each dune category (a total of 179) were designated a shoreline orientation based on the cardinal and ordinal wind directions. Counties Donegal (36%), Mayo (24%) and Kerry (14%) contain 74% of the total dune area in Ireland (c.76sq. km). Fixed dunes comprise 93% of the total dune area of interest in this study; mobile (5%) and embryonic (2%) dunes have a much smaller areal extent. The severity of the impacts from storms depends not only on the character (size, duration, direction) of the extremes but also on the exposure and vulnerability of coastal ecosystems and communities to these extremes.

Historic and contemporary dune inventories to assess dune vulnerability to climate change impacts

The overall status of dune health in Ireland is assessed as ‘inadequate’ and declining due to the on-going losses and pressures which signify the urgent need for integrated, problem- and community-focussed coastal management plans. Historically, Ireland has only being monitoring the health of our coastal dunes very intermittently. This research reviews published coastal dune inventories in Ireland (Kinahan and McHenry, 1882; Young, 1977; Curtis, 1991; Quigley, 1991; Ryle et al., 2009; Delaney et al., 2013) and uses one of the most current inventories (Coastal Monitoring Project 2004-2006 in Ryle et al., 2009) to map the current distribution of dunes in thirteen coastal counties in the Republic of Ireland and assess the vulnerability of these dune ecosystems to projected changes in storminess. The CMP Report data was downloaded into a GIS and a filter query was applied to identify the dune area extent in each county and isolate the three major dune categories (Fixed, Embryonic and Mobile) which are critical for beach-dune sediment budgets via sediment exchange processes. The polygon features for each dune category (a total of 179) were designated a shoreline orientation based on the cardinal and ordinal wind directions. Counties Donegal (36%), Mayo (24%) and Kerry (14%) contain 74% of the total dune area in Ireland (c.76sq. km). Fixed dunes comprise 93% of the total dune area of interest in this study; mobile (5%) and embryonic (2%) dunes have a much smaller areal extent. The severity of the impacts from storms depends not only on the character (size, duration, direction) of the extremes but also on the exposure and vulnerability of coastal ecosystems and communities to these extremes.

Geo-feature modeling uncertainties in discrete global grids: a case study of downtown Calgary, Canada

The Open Geospatial Consortium has officially adopted discrete global grid systems (DGGS) as a new option for Earth reference standards. Many state-of-the-art DGGS implementations have been developed, revealing the potential for DGGS applications. Before the wide application of DGGS in solving real-world problems, however, the potential uncertainties of modeling on DGGS should be investigated and documented. This study focused on the uncertainties of geo-feature modeling on DGGS, quantitatively measured the point position displacement and line and polygon features’ geometry distortion, and evaluated the validity of topological relationships. Specifically, traffic cameras (points), main streets (lines), and land-cover classes (polygons) of downtown Calgary (AB, Canada) were modeled in various DGGS configurations at multiple resolutions. Results showed that the point displacement and polygon distortion generally reduced when being modeled at a higher resolution. The tessellations with the monotonical convergence characteristic are recommended if cell indices are expected to represent levels of model precision. Line features’ fidelity was affected by grid tessellations, resolution levels, grid orientation relative to the Earth, and the rotated line directions. The degree of the line distortion was not straightforward to forecast. Maintaining the topological validity between spatial objects with various granularities was challenging and needed further algorithm development for DGGS implementations. The study outcomes can serve as useful guidelines in the selection among grid types, refinement ratios, and resolution levels when applying DGGS implementations to urban environments. This paper also pinpoints several research directions that can benefit the quantization and analysis of vector features on DGGS.

Remarkable enhancement in hybrid improper ferroelectricity of Ca3Ti2O7 ceramics by a simple sol-gel process

Ca3Ti2O7 with Ruddlesden-Popper structure is a typical hybrid improper ferroelectric with large polarization. But the room-temperature ferroelectricity of Ca3Ti2O7 ceramics prepared by a solid-state method is not good enough. In this work, Ca3Ti2O7 ceramics with remarkably enhanced hybrid improper ferroelectricity have been successfully fabricated by a simple tartaric acid sol-gel method. The ceramic sample exhibits the highest remnant polarization (4.32 μC/cm2) to our knowledge and lower coercive electric field (108.7 kV/cm) at room temperature. The enhanced ferroelectricity of Ca3Ti2O7 ceramics results from the larger antiferroelectric distortion displacements and the larger grain with irregular polygon features. These findings are beneficial for developing room-temperature single phase multiferroic materials with strong magnetoelectric coupling and excellent ferroelectricity.

How much boreal lake shoreline is burned by wildfire? Implications for emulating natural disturbance in riparian forest management

Understanding of post-fire residual vegetation patterns is important when shoreline vegetation management aims to emulate natural disturbance (END) patterns. To assess the impacts of fire, lake and lakeshed sizes on the burning pattern of shorelines and lakesheds, we quantified the burned shorelines and post-fire residual vegetation patterns in the lakesheds of 123 lakes of Ontario affected by 26 wildfires between 2005 and 2007. We used ArcGIS and Ontario’s Enhanced Forest Resources Inventory (eFRI) GIS data to digitize burn patterns. The lake catchments for all lakes were delineated using ArcGIS via lake, river, and elevation data from the Integrated Hydrology geodatabase (MNRF, 2016). The shorelines of fire impacted lakes were generated from the eFRI polygon feature classes, and the polylines were then split according to the digitized burn pattern polygons by running a geometric intersection with these data. The results of this study show that the percentages of burned shorelines and lakesheds are positively correlated with the size of fire and negatively correlated with the sizes of lake and lakeshed. However, irrespective of the size of fire, lake or lakeshed, shorelines are not left completely unburned, which is contrary to existing practice of retaining fixed-width shoreline buffers. It may imply that under END based management, forest harvesting can be possible up to the shorelines in some areas of the landscapes that are left unharvested under a fixed-width riparian buffer management system. However, areas of strong hydrological connectivity between land and water serve as biogeochemical control points and require protection from disturbance during forest management planning and operations. We suggest that GIS-based models developed based on the hydrological and topographical features associated with the unburned shorelines and lakesheds might be useful to predict shoreline residual forest pattern and facilitate END based shoreline forest management.

Recognition of group patterns in geological maps by building similarity networks

The recognition of structures is fundamental to map generalization, furnishing structural information that assists in choosing and parameterizing generalization operators. We specifically focus on the process of recognizing groups of small polygons in geological maps as a prerequisite to subsequent aggregation or typification operators. Proximity between polygons represents an essential criterion in identifying neighboring map objects. Here, network-based analysis is used for effective definition and refinement of candidate group members, applying criteria such as the distance between polygons, polygon size, shape, orientation, and feature attributes such as rock type. Starting off from the Delaunay triangulation of the polygon centroids, the global and local long edges, which initially define the network, are removed. The modified network is loaded with additional criteria, and edges are kept or removed based on the local similarities of the polygons they connect. This approach leads to more homogeneous, meaningful groups of polygon features in geological maps.

Using artificial neural network for labelling polygon features in topographic maps

Abstract The purpose of this article was to present the methodology which enables automatic map labelling. This topic is particularly important in the context of the ongoing research into the full automation of visualization process of spatial data stored in the currently used topographic databases (e.g. OpenStreetMap, Vector Map Level 2, etc.). To carry out this task, the artificial neural network (multilayer perceptron) was used. The Vector Map Level 2 was used as a test database. The data for neural network learning (the reference label localization) was obtained from the military topographic map at scale 1 : 50 000. In the article, the method of applying artificial neural networks to the map labelling is presented. Detailed research was carried out on the basis of labels from the feature class “built-up area”. The results of the analyses revealed that it is possible to use the artificial intelligence computational methods to automate the process of placing labels on maps. The results showed that 65% of the labels were put on the topographic map in the same place as in the case of the labelling which was done manually by a cartographer. The obtained results can contribute both to the enhancement of the quality of cartographic visualization (e.g. in geoportals) and the partial elimination of the human factor in this process. Highlights for public administration, management and planning: • Map label placement is among key variables ensuring the usability of topographic maps across disciplines. • We present the neural network approach for automating the process of labelling topographic maps with locality names. • The presented case study applies to the military map in scale 1:50 000, but can be applied on other maps and geoportals.

An approach for establishing correspondence between OpenStreetMap and reference datasets for land use and land cover mapping

AbstractOpenStreetMap (OSM) provides free source data for land use and land cover (LULC) mapping of many regions globally. Earlier work has used just manual and subjective approaches to establish correspondence between paired OSM and reference datasets, an essential step for LULC mapping. This study proposes an approach to establish correspondence via three steps: (1) convert line feature(s) into polygon feature(s); (2) merge multiple polygon feature(s) into a single layer; and (3) establish correspondence and reclassify OSM and/or reference datasets. Study areas in Sheffield, London, Rome, and Paris were used for testing, and two measures (overall accuracy, OA and kappa index) were used for evaluation. Experiments were designed to verify this approach, with each pair of OSM and reference datasets initially compared after reclassification. Correspondence from one study area was then applied to another for further validation. Results show that OA was between 70 and 90% and the kappa index varied between 0.6 and 0.8. Evaluation also indicates that the correspondence obtained from one study area is applicable to another, and we illustrate the effectiveness of this approach.

A polygon aggregation method with global feature preservation using superpixel segmentation

As the map scale decreases, conflicts can appear among polygonal features such as water areas and buildings. Aggregation is usually employed to clearly represent polygonal features on small-scale maps. Over the past several decades, a number of polygon aggregation algorithms based on vector data have been proposed by various scholars. In contrast, few existing aggregation methods are based on raster data, and it is difficult to simultaneously consider polygonal features with different shape characteristics such as water areas and buildings. However, with the continuous development and progress of computer vision technology, advanced theories and methods, such as superpixel segmentation, have provided brand new opportunities and challenges for polygon aggregation. Both superpixel segmentation and area object aggregation employ spatial clustering to increase the representation level at a coarser resolution. Therefore, this paper proposes a new algorithm called superpixel polygon aggregation (SUPA) for the aggregation of general polygons and buildings based on raster data. In this method, general polygons are first segmented using superpixel algorithms. Then, general polygons are globally aggregated by superpixel selection. In this process, the different semantic characteristics of an object, such as a building or natural water area, control the aggregation decisions, such as the handling of boundaries. Finally, the aggregate boundaries of general polygons (buildings) are locally adjusted by Fourier descriptors (superpixel filling and removal). To test the proposed SUPA method, both water areas and buildings are used to perform aggregation. Compared with the existing traditional method in ArcGIS software, the results show that the proposed SUPA method can preserve the global features of general polygons and the orthogonal features of buildings while maintaining reliable aggregation results.

A new approach to prioritising groundwater dependent vegetation communities to inform groundwater management in New South Wales, Australia

Groundwater dependent ecosystems (GDEs) require access to groundwater to meet all or some of their water requirements to maintain community structure and function. The increasing demand of surface and groundwater resources has seen the NSW Government put in place management mechanisms to enable the sharing of water between irrigators, the environment, industry, towns and communities via water sharing plans. The groundwater sharing plans aim to provide adaptive management of GDEs by prioritising for protection those that are considered the most ecologically valuable within each plan area. The High Ecological Value Aquatic Ecosystems (HEVAE) framework has already been adopted to prioritise riverine ecosystems for management in surface water sharing plans. Here, we provide a method developed using the HEVAE framework to prioritise vegetation GDEs for management. The GDE HEVAE methods provide a derived ecological value dataset for identified groundwater dependent vegetation that is used to inform the planning and policy decisions in NSW. These decisions are required to manage and mitigate current and future risks caused by groundwater extraction. This is achieved via the identification of ecologically valuable assets to then use as the consequence component in a risk assessment for the groundwater sources, to provide vegetation GDE locations for setback distances for new groundwater production bores, and for the assessment of impacts due to current and potential future groundwater extraction. The GDE HEVAE method uses recorded and predicted spatial data to provide weighted scores for each attribute associated with the four HEVAE criteria (distinctiveness, diversity, vital habitat and naturalness). The combined scores categorise the ecological value of each groundwater dependent vegetation community (depicted as geographic information system (GIS) polygon features) from very high to very low. We apply the GDE HEVAE method to three catchments in order to demonstrate the method’s applicability across the Murray–Darling Basin with varying elevation and climate characteristics. The ecological value outcomes derived from the methods have been used to inform planning and policy decisions by NSW Government processes to allow for protection in not only areas that are currently at risk but to also manage for potential future risks from groundwater extraction.

Inferring Spatial Distribution Patterns in Web Maps for Land Cover Mapping

Web maps represent an effective source for land cover mapping in capturing human activities. However, due to spatial heterogeneity, previous research has mainly focused on generating land cover maps in partial areas. Inferring spatial distribution patterns in Web maps may provide an alternative perspective on improving map production on a larger scale. This paper represents a novel approach to investigating the spatial distribution in Web maps for land cover mapping. First, linear features from Web maps are utilised to delineate parcels with insufficient Web map data for classification. Then, spatial factors are constructed from point and polygon features to identify the spatial variety of Web maps, with an artificial neural network classifier being adopted to classify land cover automatically. Land cover mapping is finally proposed by combining classified parcels and existing polygon features. The proposed method is applied in Guangzhou, Guangdong Province, using a Web map from AutoNavi. The results show an approximately 88% classification accuracy and an overall mapping accuracy of 85.06%. The results indicate that the proposed approach has the potential to be utilised in land cover mapping, and the constructed spatial factors are effective at characterising land cover information.

Emulsion Droplets Stabilized by Close-Packed Janus Regular Polygonal Particles.

In Pickering-Ramsden emulsions, the packing structure of the colloidal particles at the liquid-liquid (or liquid-gas) interface significantly affects the structure and behavior of the emulsion. Here, using a series of platelike particles with regular polygonal shapes and Janus amphiphilicity, we created emulsion droplets stabilized by close-packed polygonal particles at the interface. The systematic variation of the particle morphology shows that the geometrical features of the regular polygons in (curved) planar packing dominate over the self-assembled structures. The structures are tessellations of triangular, square, and hexagonal particles at the surface for large droplets and regular tetrahedral, cubic, and dodecahedral particle shells of triangular, square, and pentagonal particles for small droplets, respectively. This work creates the possibility of geometrically designing the structure and functionality of emulsions.

Integrating Drone Technology with GPS Data Collection to Enhance Forestry Students Interactive Hands-On Field Experiences

Undergraduate students pursuing a Bachelor of Science in Forestry (BSF) at Stephen F. Austin State University (SFA) within the Arthur Temple College of Forestry and Agriculture (ATCOFA) attend an intensive 6-week hands-on instruction in applied field methods. The second week of field station is focused on land measurement activities to introduce students to practical, hands-on, and technology based ways to survey forest boundaries. On Monday of the second week students are introduced to the concepts of how to use a handheld compass to navigate from point to point, use a consumer-grade handheld Global Positioning System (GPS) unit for collecting the geographic coordinates of given locations, use a GPS unit to calculate the area of a forest opening, use a GPS unit to walk and record a forest hiking trail, and evaluate the accuracy of their GPS derived locations via a Root Mean Square Error (RMSE) analysis. RMSE analysis between a students collected geographic coordinates and the instructors collected geographic coordinates indicated that the students were sufficient in correctly recording the geographic coordinates of point, line, and polygon features identified in the field. Grades on the student submitted reports summarizing Monday’s activities resulted in 33 of 56 students (59.0%) receiving a high A, 14 of 56 students (25.0%) receiving a low A, and 9 of 56 students (16.0%) receiving a high B indicating that the interactive hands-on nature of ATCOFA’s field station is effective at providing students with real-world applications whereby they will be ready to make a difference the day after graduation. Interactive drone imagery and video integrated into the daily activities in the field to enhance a student’s understanding of their specific objectives provided the students in the field with a bird’s eye perspective of the landscape to aid their understanding and planning of the field tasks assigned. In conclusion, employers can have confidence that when hiring recent BSF graduates from ATCOFA that the students have been introduced to geospatial technologies within a proven one-on-one instruction methodology designed to increase cognitive retention and can traverse from location to location accurately and record the geographic coordinates of earth surface features correctly.

Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece

The main purpose of this study is to explore the impact of analysis scale on the performance of a quantitative model for landslide susceptibility assessment through empirical analyses in the northern Peloponnese, Greece. A multivariate statistical model like logistic regression (LR) was applied at two different scales (a regional and a more detailed scale). Due to this scale difference, the implementation of the model was based on two landslide inventories representing in a different way the landslide occurrence (as point and polygon features), and two datasets of similar geo-environmental factors characterized by a different size of grid cells (90 m and 20 m). Model performance was tested by a standard validation method like receiver operating characteristics (ROC) analysis. The validation results in terms of accuracy (about 76%) and prediction ability (Area under the Curve (AUC) = 0.84) of the model revealed that the more detailed scale analysis is more appropriate for landslide susceptibility assessment and mapping in the catchment under investigation than the regional scale analysis.

Application of Indian space-based augmentation service on geospatial data collections in Sri Lanka

GPS Aided Geo Augmented Navigation (GAGAN) is the Indian Satellite-based Augmentation System, fully operational since 2013 with the objective of providing the accuracy and integrity required for civilian and aviation applications. This study investigates the usability of GAGAN service as an accurate, reliable and a cost-effective source of positioning and navigation for geospatial applications in Sri Lanka. Point, line and polygon features were collected under different challenging conditions to test the usability and potential accuracy of GAGAN. The average 3D positional accuracy enhancement over SPS is about 70 and 78% for GAGAN and DGPS observations, respectively. Similarly, the average GAGAN enhancement over SPS observation was 79% and 83% for linear and area measurements, respectively, and the improvement for DGPS was 86% for both feature types. This confirms that GAGAN can improve geospatial data collection accurately, almost similar to DGPS without a use of local reference station, or national CORS networks.

Classifying the Built-Up Structure of Urban Blocks with Probabilistic Graphical Models and TerraSAR-X Spotlight Imagery

Up-to-date maps of a city’s urban structure types (USTs) are very important for effective planning, as well as for different studies and applications. We present an approach for the classification of USTs at the level of urban blocks based on high-resolution spaceborne radar imagery. Images obtained at the satellite’s ascending and descending orbits were used for extracting line and polygon features from each block. Most of the attributes considered in the classification concern the geometry of these features, as well as their spatial disposition inside the blocks. Furthermore, assuming the UST classes of neighboring blocks are probabilistically dependent, we explored the framework of probabilistic graphical models and propose different context-based classification models. These models differ with respect to (i) their type, i.e., Markov or conditional random fields, (ii) their parameterization and (iii) the criterion applied for establishing pairwise neighboring relationships between blocks. In our experiments, 1,695 blocks from the city of Munich (Germany) and five representative UST classes were considered. A standard classification performed with the Random Forest algorithm achieved an overall accuracy of nearly 70%. All context-based classifications achieved overall accuracies up to 7% higher than that. The results indicate that denser pairwise block-neighborhood structures lead to better results and that the accuracy improvement is higher when the strength of the contextual influences is proportional to the similarity of the neighboring blocks attributes.

New spatial data on ethnicity

Research on ethnic politics and political violence has benefited substantially from the growing availability of cross-national, geo-coded data on ethnic settlement patterns. However, because existing datasets represent ethnic homelands using aggregate polygon features, they lack information on ethnic compositions at the local level. Addressing this gap, this article introduces the Spatially Interpolated Data on Ethnicity (SIDE) dataset, a collection of 253 near-continuous maps of local ethno-linguistic, religious and ethno-religious settlement patterns in 47 low- and middle-income countries. We create these data using spatial interpolation and machine learning methods to generalize the ethnicity-related information in the geo-coded Demographic and Health Surveys (DHS). For each DHS survey we provide the ethnic, religious and ethno-religious compositions of cells on a raster that covers the respective countries at a resolution of 30 arc-seconds. The resulting data are optimized for use with geographic information systems (GIS) software. Comparisons of SIDE with existing categorical datasets and district-level census data from Uganda and Senegal are used to assess the data’s accuracy. Finally, we use the new data to study the effects of local polarization between politically relevant ethnic groups, finding a positive effect on the risk of local violence such as riots and protests. However, local ethno-political polarization is not statistically associated with violent events pertaining to larger-scale processes such as civil wars.

Synergistic object-based multi-class feature extraction in urban landscape using airborne LiDAR data

This study addresses the potential of geospatial information extraction by using light detection and ranging (LiDAR) data and aerial optical images in an urban landscape. We have adopted an advanced geographic object-based image analysis (GEOBIA) technique consisting of rule-based procedures relying upon the integration of spectral, textural, and spatial characteristics of aerial imagery and roughness of point cloud of LiDAR to fuse aerial imagery and airborne LiDAR for effective urban geospatial information extraction. This study is focused on the extraction of four tangible geospatial features, e.g., buildings, trees, marine vessels, and cars. LiDAR-derived normalized digital surface model (nDSM) was insufficient in delineating the polygon features because of the sparse point cloud density at the edges of features, which greatly affected the accuracy of extracting the polygon features. Therefore aerial imagery was supplemented in order to enhance the quality of extraction. The final feature extraction accuracy was assessed against manual digitization by visual interpretation, statistical analysis, and confusion matrix. The accuracy of feature extraction was found to be ranging from 90 to 95%. The accuracy of buildings class was improved using intensity image generated from LiDAR data and Hough image along with morphological operations. In a nutshell, this study highlights robust improvements in the geospatial extraction of urban features by merging more than one dataset synergistically.

Topology optimization of self-supporting structures with polygon features for additive manufacturing

This work aims at developing a topology optimization method for the design of self-supporting structures that can be directly realized by additive manufacturing (AM). In the proposed method, polygon-featured holes are introduced as basic design primitives whose movements, deformations and intersections allow to control the structural topology. In addition, overhang constraints are introduced directly into the geometry description, so that the inclined angles along the overhang boundaries are not less than the critical overhang angle (COA). Meanwhile, unprintable V-shaped areas caused by polygon intersections are gradually eliminated through polygon modifications and re-optimization. Numerical examples are tested to demonstrate the effectiveness of the proposed method.

Simulation and Feasibility Studies of Rooftop PV System for University Campus Buildings in Surabaya, Indonesia

Present work simulates and analyzes the rooftop photovoltaic (PV) system on buildings roofs of the University of Surabaya, Indonesia for electricity power generation. The work also to calculate greenhouse gas (GHG) emission reduction that can be obtained by PV system mounted on the building roofs. The surface area of the roofs was determined using Polygon feature of Google Earth TM . The energy output of the system was simulated with SolarGIS pvPlanner software program. The grid-connected PV system type was chosen in the simulation. Greenhouse gas (GHG) emission reduction analysis was carried out using RETScreen program simulation. It was found that about 10,353 m 2 of the rooftop of the university buildings could be used for panel installation. The total capacity of the panels is found about 2,070 kWp with total electricity production is about 3,180 MWh per year and could supply up to 80% of the campus energy demand. The system would serve as a means of reducing 3,367.6; 2,477.2, or 1,195.7 tons of CO 2 to the atmosphere in comparison to the same amount of electricity produced by burning coal, oil, or natural gas respectively. The unit cost of PV electricity was found ranging from 0.10 – 0.20 USD/kWh. From economic aspects, the rooftops PV system has the potential to provide power at a competitive cost in comparison to other alternative options of power generation.