Raster-based Method Research Articles

A raster-based method for the hierarchical selection of river networks based on stream characteristics

Computer screens often constrain the level of detail and clarity of displays. High-density data require a predefined strategy to select significant features hierarchically to allow interactive data zooming. Although many methods are available for hierarchically selecting rivers from vector data, some approaches for raster data are better than others for maintaining accuracy when the original river data are in a raster format during generalization. In this study, a raster-based approach is proposed to allow hierarchical superpixel selection in river networks. Linear spectral clustering segmentation was applied to divide the original raster river networks into superpixels at multiple levels. A graph was constructed to organize the generated river network superpixels based on the distances between adjacent superpixels by considering the weights determined by the four types of rules. Finally, the total weight values were ranked, the river-network superpixels were selected according to their weights, and the redundant pixels at the river-network intersections were removed. Compared with the traditional vector selection method, the proposed superpixel river network selection method can effectively consider the characteristics of river width without artificial river grading and preserve the main structure and connectivity features during hierarchical mapping. Notably, the average geometry and density changes decreased by 15.8% and 5.1%, respectively.

Raster-based method for building selection in the multi-scale representation of two-dimensional maps

In the multi-scale representation of maps, a selection operation is usually applied to reduce the number of map elements and improve legibility while maintaining the original distribution characteristics. During the past few decades, many methods for vector building selection have been developed; however, pixel-based methods are relatively lacking. In this paper, a multiple-strategy method for raster building selection is proposed. In this method, to preserve the distribution range, a new homogeneous linear spectral clustering (HLSC) superpixel segmentation method is developed for the relatively homogeneous spatial division of building groups. Then, to preserve the relative distribution density, multi-level spatial division is performed according to the local number of buildings. Finally, to preserve the local geometric, attributive and geographical characteristics, four selection strategies, namely, the minimum centroid distance, minimum boundary distance, maximum area and considering geographical element strategies, are designed to generate selection results. To evaluate the proposed method, dispersed buildings in a suburban area are utilized to perform selection tasks. The experimental results indicate that the proposed method can effectively select dispersed irregular buildings at different levels of detail while maintaining the original distribution range and relative distribution density. In addition, the use of multiple selection strategies considering various geometric, attributive and geographical characteristics provides multiple options for cartography.

Towards generating network of bikeways from Mapillary data

Nowadays, biking is flourishing in many Western cities. While many roads are used for both cars and bicycles, buffered bike lanes are marked for the safety of cyclists. In many cities, segregated paths are built up to have physical separation from motor vehicles. These types of biking ways are regarded as attributes in geographic information system (GIS) data. This information is required and important in the service of route planning, as cyclists may prefer certain types of bikeways. This paper presents a framework for generating networks of bikeways with attribute information from the data collected on the collaborative street view data platform Mapillary. The framework consists of two layers: The first layer focuses on constructing a bikeway road network using Global Positioning System (GPS) information of Mapillary images. Mapillary sequences are classified into walking, cycling, driving (ordinary road), and driving (motorway) trajectories based on the transportation mode with a trained XGBoost classifier. The bikeway road network is then extracted from cycling and driving (ordinary road) trajectories using a raster-based method. The second layer focuses on extracting attribute information from Mapillary images. Cycling-specific information (i.e., bicycle signs/markings) is extracted using a two-stage detection and classification model. A series of quantitative evaluations based on a case study demonstrated the ability and potential of the framework for extracting bikeway road information to enrich the existing OSM cycling road data.

New Method for Automated Quantification of Vertical Spatio-Temporal Changes within Gully Cross-Sections Based on Very-High-Resolution Models

Gully erosion is one of the most prominent natural denudation processes of the Mediterranean. It causes significant soil degradation and sediment yield. Most traditional field methods for measurement of erosion-induced spatio-temporal changes are time and labor consuming, while their accuracy and precision are highly influenced by various factors. The main research question of this study was how the measurement approach of traditional field sampling methods can be automated and upgraded, while satisfying the required measurement accuracy. The VERTICAL method was developed as a fully automated raster-based method for detection and quantification of vertical spatio-temporal changes within a large number of gully cross-sections (GCs). The developed method was tested on the example of gully Santiš, located at Pag Island, Croatia. Repeat unmanned aerial vehicle (UAV) photogrammetry was used, as a cost-effective and practical method for the creation of very-high-resolution (VHR) digital surface models (DSMs) of the chosen gully site. A repeat aerophotogrammetric system (RAPS) was successfully assembled and integrated into one functional operating system. RAPS was successfully applied for derivation of interval (the two-year research period) DSMs (1.9 cm/pix) of gully Santiš with the accuracy of ±5 cm. VERTICAL generated and measured 2379 GCs, along the 110 m long thalweg of gully Santiš, within which 749 052 height points were sampled in total. VERTICAL proved to be a fast and reliable method for automated detection and calculation of spatio-temporal changes in a large number of GCs, which solved some significant shortcomings of traditional field methods. The versatility and adaptability of VERTICAL allow its application for other, similar scientific purposes, where multitemporal accurate measurement of spatio-temporal changes in GCs is required (e.g., river material dynamics, ice mass dynamics, tufa sedimentation and erosion).

Point Label Placement on Hexagonal Map Grids

Abstract. The past few decades have seen the development of automatically feature labelling when manual label placement was thought to be time and labour consuming. Emerging techniques like volunteered geographic information (VGI) collection are making label placement more complexed with many features in a limited space, especially for points of interest (POI). In order to improve the quality and the efficiency of point feature labelling, there have been massive researches focusing on issues like position models, assessment criteria and optimization methods. Most of the researches were using vector-based methods while raster-based methods were less used, because vector-based methods have the advantage of easy definition of features and labels but are usually followed by computation complexity problems for features with high density. In contrast raster-based methods are faster and more flexible, though being harder to represent features and labels precisely on the map grids. Considering that hexagon partitioning was rarely used in raster-based methods, compared with the most commonly used square portioning, and hexagon was potentially useful for its oblique sides and isotropic orientations, hexagonal grids were used in this research to investigate better point feature labelling approaches.A new raster-based method was promoted to figure out high quality label placement of POI in dense area. Labels were placed on a hexagonal map grids based on the principles that one Chinese character is set to one hexagon unit with the mathematical relationship of h = ((√3+1)/2)a, while h is the side length of a hexagon unit and a is the size of a Chinese character. Considering that hexagon grids are divided into flat topped type and pointy topped type, which leads to different orientations, split hexagons were promoted to extend orientations from 6 to 8 based on pointy topped grids. A hexagon is partitioned into two parts labelled ‘left’ and ‘right’ and a split hexagon is the combination of a ‘left’ part and a ‘right’ part separately from two neighboring hexagons, as shown in figure 1. Then every hexagon on the grid will have four status: not-occupied {(0,0)}, half-occupied {(0,1) and (1,0)} and both-occupied {(1,1)}. Based on the fundamental concepts above, specific definitions were made on how labels were supposed to be represented on hexagonal map grids, including the length, orientation, writing direction, character orientation and position of the labels.The approach first initially arranges labels of POI with different combinations of label orientations while pursuing coherence as much as possible, including procedures of rasterization of vector data, POI grouping and initial scheme computation. Every POI in a same group would have same label orientation and every POI group may have several accessible orientations thus making initial schemes diverse. Then a second positioning algorithm was conducted to handle overlapping (labels with POI, labels with labels) problems and improve the overall quality of labelling. The algorithm used the methods of position changing and label turning, which allow label to change its position around POI and sometimes change the orientation when it is necessary to avoid collisions. Quality of labels in a closed block was assessed from three aspects: preferential orientation, occlusion and spaciousness. POI data was chosen from restaurant, hotel and shop facilities and figure 2 showed one of the examples of label placement results using this method. The results have shown good orientation consistency of labels and occlusions were reduced to the lowest, though several label-label occlusions remained due to the limited space. After being compared with vector-based method, the approach has shown better performance on maintaining map legibility, aesthetics and harmony.

A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests

Estimation of biophysical variables based on airborne laser scanning (ALS) data using tree detection methods concentrates mainly on delineation of single trees and extraction of their attributes. This study provides new insight regarding the potential and limits of two detection methods and underlines some key aspects regarding the choice of the more appropriate alternative. First, we applied the multisource-based method implemented in reFLex software (National Forest Centre, Slovakia), which uses the information contained in the point cloud and a priori information. Second, we applied the raster-based method implemented in OPALS software (Vienna University of Technology, Austria), which extracts information from several ALS-derived height models. A comparative study was conducted for a part of the university forest in Zvolen (Slovakia, Central Europe). ALS-estimated variables of both methods were compared (1) to the ground reference data within four heterogonous stands with an area size of 7.5 ha as well as (2) to each other within a comprehensive forest unit with an area size of 62 ha. We concluded that both methods can be used to evaluate forest stand and ecological variables. The overall performance of both methods achieved a matching rate within the interval of 52%–64%. The raster-based method provided faster and slightly more accurate estimate of most variables, while the total volume was more precisely estimated using the multisource-based method. Specifically, the relative root mean square errors did not exceed 7.2% for mean height, 8.6% for mean diameter, 21.4% for total volume, 29.0% for stand density index, and 7.2% for Shannon’s diversity index. Both methods provided estimations with differences that were statistically significant, relative to the ground data as well as to each other (p < 0.05).

Raster-Based Background Filtering for Roadside LiDAR Data

The roadside deployed light detecting and ranging (LiDAR) has been a solution to fill the data gap for the transition period from the unconnected-vehicles environment to the connected-vehicles system. For the roadside LiDAR system, background filtering is an initial but important step. This paper presented a raster-based method for background filtering with roadside LiDAR data. The proposed method contains four major parts: region of interest (ROI) selection, rasterization, background area detection, and background array generation. The location of the background points was stored in a 3D array. The performance of the raster-based method was tested with the data collected at different scenarios. The comparison to the state-of-the-art also confirmed the robustness of the proposed method.

Derivation of Three-Dimensional Displacement Vectors from Multi-Temporal Long-Range Terrestrial Laser Scanning at the Reissenschuh Landslide (Tyrol, Austria)

Deep-seated gravitational slope deformations (DSGSDs) endanger settlements and infrastructure in mountain areas all over the world. To prevent disastrous events, their activity needs to be continuously monitored. In this paper, the movement of the Reissenschuh DSGSD in the Schmirn valley (Tyrol, Austria) is quantified based on point clouds acquired with a Riegl VZ®-6000 long-range laser scanner in 2016 and 2017. Geomorphological features (e.g., block edges, terrain ridges, scarps) travelling on top of the landslide are extracted from the acquired point clouds using morphometric attributes based on locally computed eigenvectors and -values. The corresponding representations of the extracted features in the multi-temporal data are exploited to derive 3D displacement vectors based on a workflow exploiting the iterative closest point (ICP) algorithm. The subsequent analysis reveals spatial patterns of landslide movement with mean displacements in the order of 0.62 ma − 1 , corresponding well with measurements at characteristic points using a differential global navigation satellite system (DGNSS). The results are also compared to those derived from a modified version of the well-known image correlation (IMCORR) method using shaded reliefs of the derived digital terrain models. The applied extended ICP algorithm outperforms the raster-based method particularly in areas with predominantly vertical movement.

Hexagon-Based Adaptive Crystal Growth Voronoi Diagrams Based on Weighted Planes for Service Area Delimitation

Delimiting the service area of public facilities is an essential topic in spatial analysis studies. The adaptive crystal growth Voronoi diagrams based on weighted planes are one of the recently proposed methods to address service area delimitation, and consider the geographic distribution of the clients the facilities in question serve and the characteristic of the socioeconomic context, while at the same time mitigate the modifiable areal unit problem when dealing with the socioeconomic context, since the method is based on continuous weighted planes of socioeconomic characteristics rather than arbitrary areal units. However, in the method, the environmental and socioeconomic contexts are rasterized and represented by regular square grids (raster grid). Compared with a raster grid, the hexagon grid tiles the space with regularly sized hexagonal cells, which are closer in shape to circles than to rectangular cells; hexagonal cells also suffer less from orientation bias and sampling bias from edge effects since the distances to the centroids of all six neighbor cells are the same. The purpose of this study is to compare the raster grid and hexagon grid for implementing adaptive crystal growth Voronoi diagrams. With the case study of delimitating middle school service areas, the results are compared based on the raster grid and hexagon grid weighted planes. The findings indicate that the hexagon-based adaptive crystal growth Voronoi diagrams generate better delineation results compared with the raster-based method considering how commensurate the population in each service area is with the enrollment capacity of the middle school in the service area and how accessible the middle schools are within their service areas. The application of hexagon-based adaptive crystal growth Voronoi diagrams may help city managers to serve their citizens better and allocate public service resources more efficiently.

An Efficient Vector-Raster Overlay Algorithm for High-Accuracy and High-Efficiency Surface Area Calculations of Irregularly Shaped Land Use Patches

The Earth’s surface is uneven, and conventional area calculation methods are based on the assumption that the projection plane area can be obtained without considering the actual undulation of the Earth’s surface and by simplifying the Earth’s shape to be a standard ellipsoid. However, the true surface area is important for investigating and evaluating land resources. In this study, the authors propose a new method based on an efficient vector-raster overlay algorithm (VROA-based method) to calculate the surface areas of irregularly shaped land use patches. In this method, a surface area raster file is first generated based on the raster-based digital elevation model (raster-based DEM). Then, a vector-raster overlay algorithm (VROA) is used that considers the precise clipping of raster cells using the vector polygon boundary. Xiantao City, Luotian County, and the Shennongjia Forestry District, which are representative of a plain landform, a hilly topography, and a mountain landscape, respectively, are selected to calculate the surface area. Compared with a traditional method based on triangulated irregular networks (TIN-based method), our method significantly reduces the processing time. In addition, our method effectively improves the accuracy compared with another traditional method based on raster-based DEM (raster-based method). Therefore, the method satisfies the requirements of large-scale engineering applications.

Automated tree detection and crown delineation using airborne laser scanner data in heterogeneous East-Central Europe forest with different species mix

Identifying tree locations is a basic step in the derivation of other tree parameters using remote sensing techniques, particularly when using airborne laser scanning. There are several techniques for identifying tree positions. In this paper, we present a raster-based method for determining tree position and delineating crown coverage. We collected data from nine research plots that supported different mixes of species. We applied a raster-based method to raster layers with six different spatial resolutions and used terrestrial measurement data as reference data. Tree identification at a spatial resolution of 1.5 m was demonstrated to be the most accurate, with an average identification ratio (IR) of 95% and average detection ratio of 68% being observed. At a higher spatial resolution of 0.5 m, IR was overestimated by more than 600%. At a lower spatial resolution of 3 m, IR was underestimated at less than 44% of terrestrial measurements. The inventory process was timed to enable evaluation of the time efficiency of automatic methods.

Raster-based outranking method: a new approach for municipal solid waste landfill (MSW) siting.

MSW landfill siting is a complicated process because it requires integration of several factors. In this paper, geographic information system (GIS) and multiple criteria decision analysis (MCDA) were combined to handle the municipal solid waste (MSW) landfill siting. For this purpose, first, 16 input data layers were prepared in GIS environment. Then, the exclusionary lands were eliminated and potentially suitable areas for the MSW disposal were identified. These potentially suitable areas, in an innovative approach, were further examined by deploying Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) II and analytic network process (ANP), which are two of the most recent MCDA methods, in order to determine land suitability for landfilling. PROMETHEE II was used to determine a complete ranking of the alternatives, while ANP was employed to quantify the subjective judgments of evaluators as criteria weights. The resulting land suitability was reported on a grading scale of 1-5 from 1 to 5, which is the least to the most suitable area, respectively. Finally, three optimal sites were selected by taking into consideration the local conditions of 15 sites, which were candidates for MSW landfilling. Research findings show that the raster-based method yields effective results.

Multi-scale assessment of overflow-driven lateral connectivity in floodplain and backwater channels using LiDAR imagery

Overflow-driven lateral connectivity significantly influences the spatial distribution and diversity of floodplain habitats and biota. Proper understanding of lateral connectivity in floodplain and backwater channels is therefore critical for assessment of river quality and for targeting management or restoration actions. In this study, we present a methodological framework for spatial and temporal assessments of overflow-driven lateral connectivity at two spatial scales: bypass reach and backwater channel. Firstly, we compute the relative elevations, as well as overflow discharge, duration, and frequency using a simple, raster-based method that uses a LiDAR digital elevation model (DEM), rating curves, and streamflow time series. Subsequently, we analyse the accuracy of this approach with respect to the accuracy of a DEM and evaluate its further applications. Altogether, four 10-km-long bypass reaches and 11 backwater channels are analysed, located along the Rhône River corridor in France. The results proved the precision of the method to be affected by the LiDAR DEM accuracy, which was on average more precise in a typically homogeneous floodplain setting rather than for backwater channel plugs with pronounced topographic complexity and usually riparian forest canopy. Amongst the four studied reaches, Brégnier Cordon proved to have the greatest flooding dynamics, followed by Belley and Chautagne. The hydrological connectivity pattern of Pierre Bénite differed significantly. Three longitudinal patterns of hydrological connectivity of backwater channels displayed stepwise advancement of the water. The presented results can be used to assess ecological potential of floodplain habitats and their historic and prospective evolution through time. Copyright © 2014 John Wiley & Sons, Ltd.

Advanced view factor analysis method for radiation exchange

A raster-based method for determining complex view factor patterns is presented (HURES model). The model uses Johnson and Watson's view factor analysis method for fisheye lens photographs. The entire sphere is divided into 13 different view factors: open sky; sunny and shaded building walls, vegetation (trees) and ground surfaces above and below 1.2m from the ground surface. The HURES model gave reasonable view factor results in tests at two urban study sites on summer days: downtown Nanaimo, B.C., Canada and Changwon, Republic of Korea. HURES gave better estimates of open sky view factors determined from fisheye lens photographs than did ENVI-met 3.1 and RayMan Pro. However, all three models underestimated sky view factor. For view factor analysis in outdoor urban areas, the 10° interval of rotation angle at 100m distance of annuli will be suitable settings for three-dimensional computer simulations. The HURES model can be used for the rapid determination of complex view factor patterns which facilitates the analysis of their effects. Examples of how differing view factor patterns can affect human thermal sensation indices are given. The greater proportion of sunny view factors increased the computed predicted mean vote (PMV) by 1.3 on the sunny side of the street compared with the shady side during mid-morning in downtown Nanaimo. In another example, effects of differing amounts of open sky, sunny ground, sunny buildings and vegetation combined to produce only slight differences in PMV and two other human thermal sensation indices, PET and UTCI.

Measuring geographic access to health care: raster and network-based methods

BackgroundInequalities in geographic access to health care result from the configuration of facilities, population distribution, and the transportation infrastructure. In recent accessibility studies, the traditional distance measure (Euclidean) has been replaced with more plausible measures such as travel distance or time. Both network and raster-based methods are often utilized for estimating travel time in a Geographic Information System. Therefore, exploring the differences in the underlying data models and associated methods and their impact on geographic accessibility estimates is warranted.MethodsWe examine the assumptions present in population-based travel time models. Conceptual and practical differences between raster and network data models are reviewed, along with methodological implications for service area estimates. Our case study investigates Limited Access Areas defined by Michigan’s Certificate of Need (CON) Program. Geographic accessibility is calculated by identifying the number of people residing more than 30 minutes from an acute care hospital. Both network and raster-based methods are implemented and their results are compared. We also examine sensitivity to changes in travel speed settings and population assignment.ResultsIn both methods, the areas identified as having limited accessibility were similar in their location, configuration, and shape. However, the number of people identified as having limited accessibility varied substantially between methods. Over all permutations, the raster-based method identified more area and people with limited accessibility. The raster-based method was more sensitive to travel speed settings, while the network-based method was more sensitive to the specific population assignment method employed in Michigan.ConclusionsDifferences between the underlying data models help to explain the variation in results between raster and network-based methods. Considering that the choice of data model/method may substantially alter the outcomes of a geographic accessibility analysis, we advise researchers to use caution in model selection. For policy, we recommend that Michigan adopt the network-based method or reevaluate the travel speed assignment rule in the raster-based method. Additionally, we recommend that the state revisit the population assignment method.

Surface area processing in GIS for different mountain regions

Surface area is a fundamental parameter derived from terrain analysis in geographical information systems (GIS) for modeling the real world. Therefore, an evaluation of the techniques implemented by GIS models is important in order to obtain reliable area measurements. The study compares two raster-based methods with a TIN-based (triangulated irregular network) method for surface area approximation using a digital elevation model (DEM) with 25 m × 25 m resolution in six mountain regions of Shandong Province, China. Raster-based method I calculates surface-area grids from DEMs directly. Eight 3-dimensional triangles connecting the center point of each cell with the center points of the eight surrounding cells are generated and then the areas of the portions of each triangle that lay within the cell boundary are calculated and summed. Raster-based method II produces the surface area of each cell directly from the pixel size and the slope value for the pixel. The results demonstrate that the surface areas calculated by raster-based method I are equal to those of the TIN-based method. The results of raster-based method II are the lowest but the difference in areas between the two raster-based methods decreases with the decrease in terrain complexity. Compared to the method with TINs, raster-based method II can do better because it has some advantages, such as neighborhood analysis, more consistent output and faster processing speed. The results also demonstrate that the difference between surface and horizontal areas should be considered if more than 30% of the area of a region has slopes steeper than 18.2 degrees.

A raster-based method for computing Voronoi diagrams of spatial objects using dynamic distance transformation

The Voronoi diagram has been suggested as an appropriate model for the description of relations between spatial objects (Gold 1992) and it is the only possible solution (which is currently available) to dynamic (measurement-based) GIS (Wright and Goodchild 1997). A Voronoi diagram can be computed either in vector mode or in raster mode. Most existing methods are vector-based. However, vector-based methods are complex for line sets and area sets. To overcome this serious deficiency, attempts have also been made to use rasterbased methods. This paper describes a raster-based method for computing Voronoi diagrams of spatial objects (including points, line and areas) using dynamic distance transformation achieved by the dilation operator in mathematical morphology. Furthermore, an extension is presented to accommodate complex spatial objects.