This study is an approach to better estimate the groundwater recharge potential zones using geographical information system (GIS), influence factor and RS techniques. This concept has been applied in ...Ottapidaram taluk in Tuticorin district to determine the overall groundwater recharge potentiality. Survey of India toposheets and Indian Remote Sensing-1C satellite imageries are used to prepare various thematic layers such as: lithology, slope, land use, lineament, drainage, soil, and rainfall. These layers were then transformed into raster data using the feature to raster converter tool in ArcGIS 9.3 software. Subjective weights are assigned to the respective thematic layers and overlaid in GIS platform for the identification of potential groundwater zones within the study area. These potential zones were categorized as ‘high’, ‘moderate’, and ‘low’ zones with respect to the assigned weightage of different thematic layers. The results reveal that the areas of good groundwater potential are estimated to cover 260.25 km²(35 % of the study area), moderate potential 297.43 km²(40 %), and low potential 185 km²(25 %). Moreover, only 34 % of the total average annual precipitated water (680 mm) percolates into subsurface and ultimately contributes to recharge the groundwater. These results suggest that the high potential zones will have a key role in future expansion of drinking water and irrigation development in the study area.
► We developed a rural typology which divides areas into homogeneous recognisable units. ► It is a response to new policy needs to recognise the diversity in regional rurality. ► Based on geography, ...population density and accessibility at a high spatial resolution. ► Effective at several spatial scales by using the geographical differences in the EU.
The role that the agricultural sector plays in rural areas has considerably changed in the last five decades, and is reflected in a major shift towards multi-dimensional, multi-sectoral land use. Existing European rural typologies are mostly one-dimensional, based on a rather coarse administrative scale data and are unable to define adequately the diversity of the regions involved. The rural typology presented in this paper has been produced as a response to a new policy need for typologies addressing the diversity in regional rurality. This paper describes the method developed and explores the relevance of the results for future rural policies. This new rural typology incorporates two dimensions identified by statistical screening of a range of geographical and socioeconomic data related to the territorial variation of European rural land. The use of high-resolution raster data at 1
km
2 resolution provides large flexibility for the construction of individual classifications, with a variable number of classes for a variety of objectives. In the example presented, nine divisions were produced, which were subsequently summarised into three rural classes termed Peri-urban, Rural and Deep Rural. The rural typology enables the consistent identification of comparable rural areas and intergradations with urban land in the European territory, and describes the degree of generalisation that is possible. In addition, it provides a spatially explicit framework for scientific analysis and communication to both European policy makers and local stakeholders.
In climate science, teleconnection analysis has a long standing history as a means for describing regions that exhibit above average capability of explaining variance over time within a certain ...spatial domain (e.g., global). The most prominent example of a global coupled ocean-atmosphere teleconnection is the El Nin ?o Southern Oscillation. There are numerous signal decomposition methods for identifying such regions, the most widely used of which are (rotated) empirical orthogonal functions. First introduced by van den Dool, Saha, and Johansson (2000), empirical orthogonal teleconnections (EOT) denote a regression based approach that allows for straight-forward interpretation of the extracted modes. In this paper we present the R implementation of the original algorithm in the remote package. To highlight its usefulness, we provide three examples of potential use- case scenarios for the method including the replication of one of the original examples from van den Dool et al. (2000). Furthermore, we highlight the algorithms use for cross- correlations between two different geographic fields (identifying sea surface temperature drivers for precipitation), as well as statistical downscaling from coarse to fine grids (using Normalized Difference Vegetation Index fields).
Given a grid of cells each having an associated cost value, a raster version of the least-cost path problem seeks a sequence of cells connecting two specified cells such that its total accumulated ...cost is minimized. Identifying least-cost paths is one of the most basic functions of raster-based geographic information systems. Existing algorithms are useful if the path width is assumed to be zero or negligible compared to the cell size. This assumption, however, may not be valid in many real-world applications ranging from wildlife corridor planning to highway alignment. This paper presents a method to solve a raster-based least-cost path problem whose solution is a path having a specified width in terms of Euclidean distance (rather than by number of cells). Assuming that all cell values are positive, it does so by transforming the given grid into a graph such that each node represents a neighborhood of a certain form determined by the specified path width, and each arc represents a possible transition from one neighborhood to another. An existing shortest path algorithm is then applied to the graph. This method is highly efficient, as the number of nodes in the transformed graph is not more than the number of cells in the given grid and decreases with the specified path width. However, a shortcoming of this method is the possibility of generating a self-intersecting path which occurs only when the given grid has an extremely skewed distribution of cost values.
Dimensionality reduction of hyperspectral images is essential for reduction of computational complexity and faster analysis. A novel method for band reduction has been proposed here, which has been ...adapted from the genetic algorithm (GA) along with spatial clustering. Spatial clustering generates overall signature variation present in a particular scene and in turn removes huge redundancy present in the raster data set. GA is applied on the clustered signatures to extract the reduced set of bands that is computed to be the “fittest” i.e., those bands that provide the most discriminating information in a hyperspectral image. This has been computed by taking the sum of Kullback–Leibler divergences (KLD) between consecutive selected bands. A higher KLD value amongst adjacent selected band implies higher divergence in value. The selected band-set image has been classified and the accuracy indices are evaluated respectively. The proposed method shows high performance on the basis of classification accuracy and efficient execution while comparing with two other state-of-the-art methods.
The integration of the raster data cube alongside another form of geospatial data (e.g., vector data) raises considerable challenges when it comes to managing and representing it using knowledge ...graphs. Such integration can play an invaluable role in handling the heterogeneity of geospatial data and linking the raster data cube to semantic technology standards. Many recent approaches have been attempted to address this issue, but they often lack robust formal elaboration or solely concentrate on integrating raster data cubes without considering the inclusion of semantic spatial entities along with their spatial relationships. This may constitute a major shortcoming when it comes to performing advanced geospatial queries and semantically enriching geospatial models. In this paper, we propose a framework that can enable such semantic integration and advanced querying of raster data cubes based on the virtual knowledge graph (VKG) paradigm. This framework defines a semantic representation model for raster data cubes that extends the GeoSPARQL ontology. With such a model, we can combine the semantics of raster data cubes with features-based models that involve geometries as well as spatial and topological relationships. This could allow us to formulate spatiotemporal queries using SPARQL in a natural way by using ontological concepts at an appropriate level of abstraction. We propose an implementation of the proposed framework based on a VKG system architecture. In addition, we perform an experimental evaluation to compare our framework with other existing systems in terms of performance and scalability. Finally, we show the potential and the limitations of our implementation and we discuss several possible future works.
The extraction of skeleton lines of buildings is a key step in building spatial analysis, which is widely performed for building matching and updating. Several methods for vector data skeleton line ...extraction have been established, including the improved constrained Delaunay triangulation (CDT) and raster data skeleton line extraction methods, which are based on image processing technologies. However, none of the existing studies have attempted to combine these methods to extract the skeleton lines of buildings. This study aimed to develop a building skeleton line extraction method based on vector–raster data integration. The research object was buildings extracted from remote sensing images. First, vector–raster data mapping relationships were identified. Second, the buildings were triangulated using CDT. The extraction results of the Rosenfeld thin algorithm for raster data were then used to remove redundant triangles. Finally, the Shi–Tomasi corner detection algorithm was used to detect corners. The building skeleton lines were extracted by adjusting the connection method of the type three triangles in CDT. The experimental results demonstrate that the proposed method can effectively extract the skeleton lines of complex vector buildings. Moreover, the skeleton line extraction results included a few burrs and were robust against noise.
High-resolution satellite data are an excellent way to monitor the growth of an urban area in terms of vertical and horizontal growth. Time-series data over two different zones from the same ...satellite sensor or contemporary sensors act as a good test bed for change detection. In most of the cases, 2D images of different time frames are spatially registered, and pixel difference is calculated which enables the detection of change in horizontal growths. Three-dimensional change detection to mark a change in the vertical direction can also be computed by comparing high-resolution digital surface models (DSM) of two different times and detect changes in topography. Using accurate DSM and derived digital terrain models (DTM) information from DSM, exact and accurate heights of the building footprints can be extracted. Using 3D city models, information about horizontal growth and vertical growth of the city can be assessed using change detection over the temporal data. Three-dimensional change detection can also enable district and state administration to discern the planned growth of the city, illegal constructions and future planning of the city, especially in the projects like smart cities. In this study, we are comparing 2D raster images of different time frames to assess change in horizontal direction, very high-resolution DSMs and DTMs datasets of two different time zones to assess change in vertical directions and visualizing 3D change detection of Ahmedabad city, in terms of its horizontal and vertical changes in urban growth area. We are also making the assessment of the growth of the city (5% change in building structures) and population in the studied area. It is inferred that the city population in the year 2018 is more than 35% as compared to the population in the year 2011. Further, we are calculating geophysical parameters of land surface temperature (LST) and normalized difference vegetation index (NDVI) over a time using satellite datasets, which provides a proxy observation for the changes in the urban growth. Using satellite data, it is concluded that NDVI is reduced over the study area whereas there is an increase in LST temperature at night time during the winter season. We concluded that increased urbanization and population (> 35%) are also contributing for rise in the LST temperature at nights in the city apart from the other big environmental parameters such as global warming, etc.
Context
Lacunarity as a scale-dependent measure of spatial heterogeneity has received great attention in landscape ecology. Most lacunarity measures have been obtained from greyscale or binary (0 and ...1) data for an entire study area or fixed rectangular windows, and a zonal lacunarity tool for discrete raster data is still lacking in current geographic information systems.
Objectives
This short communication presents the development of a free zonal lacunarity analysis tool for ArcGIS to support applications involving scale-dependent analysis of spatial heterogeneity, including landscape ecology. The application of the tool is also demonstrated using 2001 and 2011 land cover data from the National Land Cover Database (NLCD).
Methods
Based on the gliding-box algorithm for lacunarity estimation, a tool for zonal lacunarity analysis of discrete raster data is developed using ArcPy and the Python programming language. The tool uses discrete raster data as input, an optional zone feature class as zone data to partition the input raster data into different zones, and a spreadsheet with zonal lacunarity values as output.
Results
As a demonstration, lacunarity measurements of grasslands in Corinth and Lake Dallas, Texas were calculated from the 2001 and 2011 NLCD data using box sizes (scales) of 2, 3, 4, 5, 6, 7, 8, 9, and 10. The results show that measures of grassland lacunarity in Lake Dallas were higher than Corinth at all scales, and the measures of grassland lacunarity in 2011 were higher than 2001 for both cities because of the increasing gap sizes in grasslands. The increasing gap sizes in grasslands were caused by converting the grasslands into developed areas.
Conclusions
The results suggest that the zonal lacunarity analysis tool can provide important information on the spatial distribution of gaps in the input discrete raster data at different scales. It is hoped that the zonal lacunarity analysis tool can be further evaluated using different datasets in landscape ecology.
Due to rapid development in the Internet and other communication technologies, it becomes quite easy to copy and distribute multiple illegal copies of high value and sensitive data. Raster data is ...one of the high voluminous data and it requires huge efforts to sense and generate this data. Therefore, ownership protection as well as its integrity become one of the key problems in spatial information service. There are lot many schemes are available for watermarking and encryption individually, but if both are combined gives manifold advantages. This paper presents a cryptowatermarking scheme by combining watermarking and encryption to protect the copyright of raster data as well as to provide security dissemination level. We have proposed a scheme by employing double transposition, LSB substitution watermarking and Merkle Hash Tree for encryption and watermarking. It has been observed that the proposed scheme is not only robust against encryption attacks, but also has transparency, strongness, large data hiding capacity and correct extraction of watermark.
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