Raster Data Model Research Articles

Using a discrete global grid system for a scalable, interoperable, and reproducible system of land-use mapping

ABSTRACT An accurate and timely land-use map of an area is a valuable geospatial dataset. Making one requires assembling heterogeneous geospatial input data. This becomes computationally expensive as the number of inputs grow or the combinational classification logic becomes more convoluted. We present as a solution an analytical framework based on a DGGS. This provides a spatial data structure that: facilitates horizontal scaling; improves geospatial data interoperability; and makes classified geospatial data easier to reproduce. We demonstrate a benchmark of land-use assignment with a DGGS against the vector and raster geospatial data models. Significant performance benefits are apparent over the vector case, while performance is equivalent to raster. Yet a DGGS has other compelling benefits that makes it more convenient to use in the domain of land-use assignment than the raster data model.

Integrating remote sensing, GIS-based, and AHP techniques to delineate groundwater potential zones in the Moulouya Basin, North-East Morocco

Groundwater is a valuable shared resource in the Moulouya Basin, but it has been in decline due to recent years of low rainfall and rapid population growth. To support socio-economic development, there is increased demand for this precious resource. This paper presents a standard methodology for delimiting potential groundwater zones using geographic information systems (GIS), an integrated analytical hierarchy process (AHP), and remote sensing techniques. Seven parameters that monitor the presence and mobility of groundwater, including drainage density, lithology, slope, precipitation, land use/land cover, distance to river, and lineament density, were incorporated into a raster data model using ArcGIS software. AHP-based expert knowledge was used to prepare a groundwater potential index and assign weights to the thematic layers. The study classified the area into five zones of varying groundwater potential: very high (26%), high (51%), moderate (13%), poor (9%), and very poor (1%). The accuracy of the model was validated by comparing the Groundwater Potential Zones map with data from 96 wells and boreholes across the basin. The validity of the results was confirmed by comparing them with the specific yield of the aquifer in the study area, yielding a high correlation coefficient (R2) of 0.79. The analysis revealed that 89.5% of the boreholes were situated in the high and very high potential zones, demonstrating the reliability and robustness of the employed approach. These findings can aid decision-making and planning for sustainable groundwater use in the water-stressed region.

The Dynamic Nature of Wrack: An Investigation into Wrack Movement and Impacts on Coastal Marshes Using sUAS

This study investigates the use of small unoccupied aerial systems (sUAS) as a new remote sensing tool to identify and track the spatial distribution of wrack on coastal tidal marsh systems. We used sUAS to map the wrack movement in a Spartina alterniflora-dominated salt marsh monthly for one year including before and after Hurricane Isaias that brought strong winds, rain, and storm surge to the area of interest in August 2020. Flight parameters for each data collection mission were held constant including collection only during low tide. Wrack was visually identified and digitized in a GIS using every mission orthomosaic created from the mission images. The digitized polygons were visualized using a raster data model and a combination of all of the digitized wrack polygons. Results indicate that wrack mats deposited before and as a result of a hurricane event remained for approximately three months. Furthermore, 55% of all wrack detritus was closer than 10 m to river or stream water bodies, 64% were within 15 m, and 71% were within 20 m, indicating the spatial dependence of wrack location in a marsh system on water and water movement. However, following the passing of Isaias, the percentage of wrack closer than 10 m to a river or creek decreased to a low of 44%, which was not seen again during the year-long study. This study highlights the on-demand image collection of a sUAS for providing new insights into how quickly wrack distribution and vegetation can change over a short time.

Ganos Aero: A Cloud-Native System for Big Raster Data Management and Processing

The development of Earth Observation technology contributes to the production of massive raster data. It is vital to manage and conduct analytical tasks on the raster data. Existing solutions employ dedicated systems for the raster data management and processing, respectively, incurring problems such as data redundancy, difficulty in updating, expensive data transferring and transformation, etc. To cope with these limitations, this demonstration presents Ganos Aero, a cloud-native system for big raster data management and processing. Ganos Aero proposes a unified raster data model for both the data management and processing, which stores a single copy of the raster data and without performing an expensive tiling procedure, and thus achieves significant improvement in the storage and updating efficiency. To enable efficient query and batch task processing, Ganos Aero implements an on-the-fly tile production mechanism, and optimizes its performance using the cloud features including decoupling compute from storage and pushing costly operations closer to the storage layer. Since deployed in Alibaba Cloud in 2022, Ganos Aero has been playing a critical role in many real applications including the modern agriculture, environment monitoring and protection, et al.

A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion

Engineering geological conditions include the nature of rock and soil, geological structure, landform, hydrogeological conditions, and adverse geological processes. Among them, faults, fissures, folds, karst, and lithology changes seriously affect the safety and construction cost of mountain tunnels, hydraulic tunnels, and other projects. For this reason, a new method based on feature fusion is proposed to detect the geological anomalies in London and Sheffield. It established a 3D raster data model oriented to attribute information modeling and visualization of urban underground space to obtain geological data. Based on this acquired data, authors adopted the feature-level fusion extraction method based on the multi-attribute geological abnormal body to extract, fuse, fill and surface the multi-attribute data of underground space geological data. Smooth processing can realize the detection of abnormal geological bodies in underground space. It has been proved that this method can be used in geological data display, feature extraction, feature fusion, and abnormal physical examination.

Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China

This study evaluated causative factors in landslide susceptibility assessments and compared the performance of five landslide susceptibility models based on the certainty factor (CF), logistic regression (LR), analytic hierarchy process (AHP), coupled CF–analytic hierarchy process (CF-AHP), and CF–logistic regression (CF-LR). Kaiyang County, China, has complex geological conditions and frequent landslide disasters. Based on field observations, nine influencing factors, namely, altitude, slope, topographic relief, aspect, engineering geological rock group, slope structure, distance to faults, distance to rivers, and normalized difference vegetation index, were extracted using the raster data model. The precision of the five models was tested using the distribution of disaster points for each grade and receiver operating characteristic curve. The results showed that the landslide frequency ratios accounted for more than 75% within the high and very high susceptibility zones according to the model prediction, and the AUC evaluating precision was 0.853, 0.712, 0.871, 0.873, and 0.895, respectively. The accuracy sequencing of the five models was CF-LR > CF-AHP > LR > CF > AHP, indicating that the CF-AHP and CF-LR models are better than the others. This study provides a reliable method for landslide susceptibility mapping at the county-level resolution.

The Directly-Georeferenced Hyperspectral Point Cloud: Preserving the Integrity of Hyperspectral Imaging Data

The raster data model has been the standard format for hyperspectral imaging (HSI) over the last four decades. Unfortunately, it misrepresents HSI data because pixels are not natively square nor uniformly distributed across imaged scenes. To generate end products as rasters with square pixels while preserving spectral data integrity, the nearest neighbor resampling methodology is typically applied. This process compromises spatial data integrity as the pixels from the original HSI data are shifted, duplicated and eliminated so that HSI data can conform to the raster data model structure. Our study presents a novel hyperspectral point cloud data representation that preserves the spatial-spectral integrity of HSI data more effectively than conventional square pixel rasters. This Directly-Georeferenced Hyperspectral Point Cloud (DHPC) is generated through a data fusion workflow that can be readily implemented into existing processing workflows used by HSI data providers. The effectiveness of the DHPC over conventional square pixel rasters is shown with four HSI datasets. These datasets were collected at three different sites with two different sensors that captured the spectral information from each site at various spatial resolutions (ranging from ∼1.5 cm to 2.6 m). The DHPC was assessed based on three data quality metrics (i.e., pixel loss, pixel duplication and pixel shifting), data storage requirements and various HSI applications. All of the studied raster data products were characterized by either substantial pixel loss (∼50–75%) or pixel duplication (∼35–75%), depending on the resolution of the resampling grid used in the nearest neighbor methodology. Pixel shifting in the raster end products ranged from 0.33 to 1.95 pixels. The DHPC was characterized by zero pixel loss, pixel duplication and pixel shifting. Despite containing additional surface elevation data, the DHPC was up to 13 times smaller in file size than the corresponding rasters. Furthermore, the DHPC consistently outperformed the rasters in all of the tested applications which included classification, spectra geo-location and target detection. Based on the findings from this work, the developed DHPC data representation has the potential to push the limits of HSI data distribution, analysis and application.

Implementation of the directly-georeferenced hyperspectral point cloud

Before pushbroom hyperspectral imaging (HSI) data can be applied in remote sensing applications, it must typically be preprocessed through radiometric correction, atmospheric compensation, geometric correction and spatial resampling procedures. After these preprocessing procedures, HSI data are conventionally given as georeferenced raster images. The raster data model compromises the spatial-spectral integrity of HSI data, leading to suboptimal results in various applications. Inamdar et al. (2021) developed a point cloud data format, the Directly-Georeferenced Hyperspectral Point Cloud (DHPC), that preserves the spatial-spectral integrity of HSI data more effectively than rasters. The DHPC is generated through a data fusion workflow that uses conventional preprocessing protocols with a modification to the digital surface model used in the geometric correction. Even with the additional elevation information, the DHPC is still stored with file sizes up to 13 times smaller than conventional rasters, making it ideal for data distribution. Our article aims to describe the DHPC data fusion workflow from Inamdar et al. (2021), providing all the required tools for its integration in pre-existing processing workflows. This includes a MATLAB script that can be readily applied to carry out the modification that must be made to the digital surface model used in the geometric correction. The MATLAB script first derives the point spread function of the HSI data and then convolves it with the digital surface model input in the geometric correction. By breaking down the MATLAB script and describing its functions, data providers can readily develop their own implementation if necessary. The derived point spread function is also useful for characterizing HSI data, quantifying the contribution of materials to the spectrum from any given pixel as a function of distance from the pixel center. Overall, our work makes the implementation of the DHPC data fusion workflow transparent and approachable for end users and data providers.•Our article describes the Directly-Georeferenced Hyperspectral Point Cloud (DHPC) data fusion workflow, which can be readily implemented with existing processing protocols by modifying the input digital surface model used in the geometric correction.•We provide a MATLAB function that performs the modification to the digital surface model required for the DHPC workflow. This MATLAB script derives the point spread function of the hyperspectral imager and convolves it with the digital surface model so that the elevation data are more spatially consistent with the hyperspectral imaging data as collected.•We highlight the increased effectiveness of the DHPC over conventional raster end products in terms of spatial-spectral data integrity, data storage requirements, hyperspectral imaging application results and site exploration via virtual and augmented reality.

Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.

Fast summarizing algorithm for polygonal statistics over a regular grid

We describe a data structure and associated algorithm called Fast Zonal Statistics (FZS) for the retrieval of the summary characteristics of an arbitrary polygon derived from a regular grid. The FZS algorithm can return numerical (e.g., mean, sum, and count) attributes for a polygonal object over a regular grid (e.g., raster data model). The computational complexity of the FZS algorithm is constant in relation to the length of the polygon perimeter. This contrasts with existing approaches which scale in relation to the polygon area, therefore we expect and measure geometric decreases in execution time using the proposed approach for simple polygon surfaces. We demonstrate applications of the algorithm and data structure on example datasets extracting the sum of impervious surface for watershed boundaries in the Chesapeake Bay watershed, a common use case.

Predicting current and future suitable habitat and productivity for Atlantic populations of maritime pine (Pinus pinaster Aiton) in Spain

By combining inventory data and spatially-continuous environmental information, we were able to develop models for Atlantic populations of maritime pine ( Pinus pinaster Aiton) in Spain in order to predict suitable habitat and site index at a spatial resolution of 250 × 250 m. Currently available, spatially continuous environmental information was used to make reliable predictions about suitable habitat and forest productivity. To develop raster-based distribution and productivity models for Atlantic populations of maritime pine in Spain to predict current and future suitable habitat and productivity. Occurrence data and site index values were obtained from the Third Spanish National Forest Inventory and research plots, respectively. After testing different algorithms, random forest were selected for modelling the relationships between maritime pine occurrence, site index and spatially continuous environmental variables. The overall accuracy of the suitable habitat model was 73%, and climate (mainly thermal properties) and soil physical properties were the most important variables. The site index model explained 60% of the observed variability, and lithological properties were the most important variables. A slight increase in site index (0.46–0.51%) and a large increase in suitable habitat (50–66%) are expected for 2070 under the most pessimistic climate change scenario. The currently available spatial continuous information enables the development of accurate raster data models for predicting suitable habitat and site productivity without the need for fieldwork. Climate change is expected to increase the potentially suitable habitat of Atlantic maritime pine populations in Spain in the coming decades.

Calculate Accurate 3D Cost Distance Efficiently

Abstract. Cost distance is one of the fundamental functions in geographic information systems (GIS), which has been used in various applications such as route planning, construction of Thiessen polygons and distance weighted interpolation. Conventional 2D cost distance function, due to its limited movement directions (either 4 or 8 neighbourhood cells) in the raster data model, overestimates the least cost and the problem is especially severe with a homogeneous friction surface. 3D cost distance function removes the limitation that movement must occur on a planar surface. It can therefore take into account tunnels and bridges when calculating least cost paths. In addition, it can also be used in many other application domains which deal with 3D geospatial data such as in atmospheric science, geology, and oceanography. Based on the method in Tomlin (2010), which can completely eliminate the overestimation when traveling on a homogeneous friction surface, this research proposes an algorithm that calculates accurate least cost with both homogeneous and heterogeneous friction in 3D space. When extending the cost distance function from 2D to 3D, the number of voxels in the propagation front increases significantly and efficiency is an imperative issue. This research also improves the computational efficiency by developing a data structure that combines a binary heap and a hash table. Our results show that the proposed algorithm can calculate accurate 3D cost distance in a homogeneous friction space, and the proposed data structure (i.e., heap plus hash table) not only significantly reduces the algorithm’s runtime but also benefits more in 3D than in 2D. In addition, we have applied the method in a 3D drone delivery routing application in a city environment (Figure 1). Additional applications, such as calculating groundwater flow paths of least hydraulic resistance in a heterogeneous 3D hydraulic conductivity field, are currently under development.

Point-to-Grid Conversion in Flux Footprints: Implications of Method Choice and Spatial Resolution for Regional-Scale Studies

Flux-footprint modelling is commonly used for determining source areas of land–atmosphere gas-exchange observations from a single meteorological tower to determine the relative flux contribution of different areas within the station fetch. In doing so, a conversion from a continuous function to a two-dimensional spatial representation takes place prior to the interpretation of results. This interpreted result is commonly used to validate satellite regional-scale, remote-sensing-based, gas-exchange models. However, little attention has been paid to exactly how the conversion is performed. The conversion to a raster data model can result in the loss of information. This loss can be cell size dependent. Therefore, it is important that the spatial-conversion method used is appropriate for the specific application. We present here an assessment of the influence of spatial resolution (i.e., cell size) and conversion method on the accuracy of modelled flux footprints using tower-based flux instrumentation. Five methods for aggregating flux footprints to a two-dimensional grid are presented and a sensitivity analysis is performed. Two well-validated flux-footprint models are used at spatial resolutions ranging from 10 m to 1000 m. Results indicate significant data loss, change of apparent maximum location, and change in the representative fetch of the point measurement. Furthermore, these results are measurement site dependent. This often-overlooked step in the conversion of a flux-footprint model to a grid is important. These results provide guidelines for those utilizing a flux-footprint model to validate spatial models for assessing gas exchange.

Evaluating Land Use and Land Cover Changes in the Kingdom of Bahrain Using GIS (Sitra Island Case Study)

This study aims to detect and analyze land-use, and land cover (LULC) changes in Sitra Island during the past 50 years, from 1967 to 2018, and to assess changes in class categories of LULC for each year of the study. The data were collected from various sources such as aerial photographs, satellite images, maps, previous studies and reports of government projects in addition to official statistical data of the study period. GIS was used to prepare the required maps to detect changes LULC during the study period and construct two Vector and Raster Data Models. Six class categories of land (built-up, agricultural, barren, wet, reclaimed, and industrial) were identified. The results indicated that the total area of Sitra Island land increased from 8.08 km 2 in 1967 to 15.1 km 2 in 1998, and increased from 16.4 km 2 in 2009 to more than 22.5 km 2 in 2018. As a result, the area of agricultural land, which constituted more than 30.4% of the total island area, decreased from 2.5 km 2 in 1967 to less than 1% (0.2 km 2) in 2018. The area of reclaimed areas for industrial and residential purposes increased to constitute more than 30% of the island area (6.82 km 2) in 2018. The industrial areas surrounding the island expanded to more than 30.6% of its area to reach 6.9 km 2 in 2018.

A hierarchical indexing strategy for optimizing Apache Spark with HDFS to efficiently query big geospatial raster data

ABSTRACTEarth observations and model simulations are generating big multidimensional array-based raster data. However, it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model, distributed physical data storage model, and the data pipeline in distributed computing frameworks. To efficiently process big geospatial data, this paper proposes a three-layer hierarchical indexing strategy to optimize Apache Spark with Hadoop Distributed File System (HDFS) from the following aspects: (1) improve I/O efficiency by adopting the chunking data structure; (2) keep the workload balance and high data locality by building the global index (k-d tree); (3) enable Spark and HDFS to natively support geospatial raster data formats (e.g., HDF4, NetCDF4, GeoTiff) by building the local index (hash table); (4) index the in-memory data to further improve geospatial data queries; (5) develop a data repartition strategy to tune the query parallelism while keeping high data locality. The above strategies are implemented by developing the customized RDDs, and evaluated by comparing the performance with that of Spark SQL and SciSpark. The proposed indexing strategy can be applied to other distributed frameworks or cloud-based computing systems to natively support big geospatial data query with high efficiency.

Geographic Representation in GIS and Art: Common Threads as Exemplified in Paintings by Seurat, Signac, Mondrian, and Diebenkorn

This article explores the relationship between geographic representation in geographic information systems (GIS) and in art, as illustrated by the vector and raster spatial data models used in GIS and their affinity with representational painting techniques employed by the painters Georges Seurat (1859–1891), Paul Signac (1863–1935), Piet Mondrian (1872–1944), and Richard Diebenkorn (1922–1993). The location-based raster data model is discussed in the context of the pointillism painting technique espoused by the French neo-Impressionist painters Seurat and Signac, whereas the object-based vector data model is related to the geometric abstraction techniques employed by the Dutch abstract painter Mondrian and the American abstract painter Diebenkorn. Commonalities between the spatial data models used in GIS and the representational techniques employed by these painters are explored within the context of both the cognitive basis of geographic representation and the representational objectives embodied by the artistic and scientific movements within which such representational approaches were developed.

THE DESIGN OF A HIGH PERFORMANCE EARTH IMAGERY AND RASTER DATA MANAGEMENT AND PROCESSING PLATFORM

Abstract. This paper summarizes the general requirements and specific characteristics of both geospatial raster database management system and raster data processing platform from a domain-specific perspective as well as from a computing point of view. It also discusses the need of tight integration between the database system and the processing system. These requirements resulted in Oracle Spatial GeoRaster, a global scale and high performance earth imagery and raster data management and processing platform. The rationale, design, implementation, and benefits of Oracle Spatial GeoRaster are described. Basically, as a database management system, GeoRaster defines an integrated raster data model, supports image compression, data manipulation, general and spatial indices, content and context based queries and updates, versioning, concurrency, security, replication, standby, backup and recovery, multitenancy, and ETL. It provides high scalability using computer and storage clustering. As a raster data processing platform, GeoRaster provides basic operations, image processing, raster analytics, and data distribution featuring high performance computing (HPC). Specifically, HPC features include locality computing, concurrent processing, parallel processing, and in-memory computing. In addition, the APIs and the plug-in architecture are discussed.

THE DESIGN OF A HIGH PERFORMANCE EARTH IMAGERY AND RASTER DATA MANAGEMENT AND PROCESSING PLATFORM

This paper summarizes the general requirements and specific characteristics of both geospatial raster database management system and raster data processing platform from a domain-specific perspective as well as from a computing point of view. It also discusses the need of tight integration between the database system and the processing system. These requirements resulted in Oracle Spatial GeoRaster, a global scale and high performance earth imagery and raster data management and processing platform. The rationale, design, implementation, and benefits of Oracle Spatial GeoRaster are described. Basically, as a database management system, GeoRaster defines an integrated raster data model, supports image compression, data manipulation, general and spatial indices, content and context based queries and updates, versioning, concurrency, security, replication, standby, backup and recovery, multitenancy, and ETL. It provides high scalability using computer and storage clustering. As a raster data processing platform, GeoRaster provides basic operations, image processing, raster analytics, and data distribution featuring high performance computing (HPC). Specifically, HPC features include locality computing, concurrent processing, parallel processing, and in-memory computing. In addition, the APIs and the plug-in architecture are discussed.

Map algebra based analysis for directed flow networks

AbstractBased on the concept of map algebra, this research developed a network neighborhood analysis framework for directed flow networks. The analysis framework has two components: the first component defines how neighborhoods are delineated on networks and the second component calculates various statistics within the neighborhoods. The power and value of the analysis framework lie in its capability to delineate versatile network neighborhoods and its flexibility in calculating various statistics within the neighborhoods. It extends the raster map algebra to networks and provides a consistent analysis framework for the raster, vector, and network data models. Using a small section of the National Hydrography Dataset (NHD) networks, a prototype Web application is implemented to experiment and demonstrate the concept and uses of the analysis framework.

A development of spatiotemporal queries to analyze the simulation outcomes from a voxel automata model

The raster and vector spatial data models are the most commonly used in geographic information systems (GIS) practice but are insufficient for the representation of dynamic spatiotemporal phenomena that operates in multiple dimensions. Although numerous improvements to the spatial data models have been proposed and various prototype implementations have been developed in order to address this limitation, the problem has persisted. One of the proposals for spatial data representation is the geoatom data model which is a theoretical concept used for conceptualizing geographic information as it pertains to the four-dimensional (4D) space-time continuum. The objective of this study is to develop and apply the spatiotemporal queries in order to analyze and explore the evolution of the 4D geospatial process. The geo-atom data model and voxel automata have been used for the simulation of a dynamic 4D process of snow cover retreat in order to test the developed theoretical concepts. The developed queries for spatio-temporal analysis are volume, surface area, rate of change and temporal ordering. The data used have spatial, temporal and attribute components and represent the voxels units generated from the simulation of the process. Obtained results are the outcomes of various spatio-temporal queries that permit the analysis of the snow retreat process in 4D. This study contributes to conceptual and applied advancements in the field of 4D GIS and multidimensional analysis, and is relevant to geography, earth and environmental sciences, the disciplines where the phenomena need to be studied in 4D.