Raster Datasets Research Articles

New implementation of OGC Web Processing Service in Python programming language. PyWPS-4 and issues we are facing with processing of large raster data using OGC WPS

The OGC® Web Processing Service (WPS) Interface Standard provides rules for standardizing inputs and outputs (requests and responses) for geospatial processing services, such as polygon overlay. The standard also defines how a client can request the execution of a process, and how the output from the process is handled. It defines an interface that facilitates publishing of geospatial processes and client discovery of processes and and binding to those processes into workflows. Data required by a WPS can be delivered across a network or they can be available at a server. <br><br> PyWPS was one of the first implementations of OGC WPS on the server side. It is written in the Python programming language and it tries to connect to all existing tools for geospatial data analysis, available on the Python platform. During the last two years, the PyWPS development team has written a new version (called PyWPS-4) completely from scratch. <br><br> The analysis of large raster datasets poses several technical issues in implementing the WPS standard. The data format has to be defined and validated on the server side and binary data have to be encoded using some numeric representation. Pulling raster data from remote servers introduces security risks, in addition, running several processes in parallel has to be possible, so that system resources are used efficiently while preserving security. Here we discuss these topics and illustrate some of the solutions adopted within the PyWPS implementation.

A cost path and network analysis methodology to calculate distances along a complex river network in the Peruvian Amazon

Distance is a key variable in explicating environmental, social, and economic conditions and in defining spatial and temporal patterns. Prior research has primarily focused on using simple to complex algorithms for calculating distances along road networks. In contrast, few algorithms are available for distance calculations along fluvial networks which are often more erratic, divergent and transient than road networks. Fluvial transportation is relied upon worldwide, particularly in developing regions, where communities use river networks for transportation, access to natural resources and for trade and commerce. This paper presents a methodology developed for mapping complex fluvial networks for travel distance analysis. The methodology was applied in four major river basins in western Amazonia over some 35,000 km of river length and incorporating 919 communities as origins/destinations. A cost path and network analysis methodology was created using vector and raster datasets in a Geographic Information System (GIS) to assess interactions among communities and the distances traveled by river to reach district capitals, major urban centres and marketplaces. An accuracy assessment using distance values calculated from a previous study using a different methodological approach in the region as well as Google Earth Pro, found a high degree of concordance for distance calculations. Our methodology creates a very flexible approach for complex river systems that can be use to calculate river distances in an adaptive and efficient manner and that can be use in other regions of the world where rural communities must rely on rivers for transportation.

An intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs)

AbstractSpatial modeling methods usually use pixels and image objects as fundamental processing units to address real‐world objects, geo‐objects, in image space. To do this, both pixel‐based and object‐based approaches typically employ a linear two‐staged workflow of segmentation and classification. Pixel‐based methods segment a classified image to address geo‐objects in image space. In contrast, object‐based approaches classify a segmented image to identify geo‐objects from raster datasets. These methods lack the ability to simultaneously integrate the geometry and theme of geo‐objects in image space. This article explores Geographical Vector Agents (GVAs) as an automated and intelligent processing unit to directly address real‐world objects in the process of remote sensing image classification. The GVA is a distinct type of geographic automata characterized by elastic geometry, dynamic internal structure, neighborhoods and their respective rules. We test this concept by modeling a set of objects on a subset IKONOS image and LiDAR DSM datasets without the setting parameters (e.g. scale, shape information), usually applied in conventional Geographic Object‐Based Image Analysis (GEOBIA) approaches. The results show that the GVA approach achieves more than 3.5% improvement for correctness, 2% improvement for quality, although no significant improvement for completeness to GEOBIA, thus demonstrating the competitive performance of GVAs classification.

Spatially simplified scatterplots for large raster datasets

Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain focus on visual manipulations, but do not take into account the inherent nature of information redundancy in most geographic data. These methods may also result in alterations of statistical properties of data. Recent developments in spatial statistics, particularly the formulation of effective sample size and the fast approximation of the eigenvalues of a spatial weights matrix, make it possible to assess the information content of a georeferenced data-set, which can serve as the basis for resampling such data. Experiments with both simulated data and actual remotely sensed data show that an equivalent scatterplot consisting of point clouds and fitted lines can be produced from a small subset extracted from a parent georeferenced data-set through spatial resampling. The spatially simplified data subset also maintains key statistical properties as well as the geographic coverage of the original data.

The Role of Highly-Resolved Gust Speed in Simulations of Storm Damage in Forests at the Landscape Scale: A Case Study from Southwest Germany

Routinely collected booking records of salvaged timber from the period 1979–2008 were used to empirically model the (1) storm damage probability; (2) proportions of storm-damaged timber and (3) endemic storm damage risk in the forest area of the German federal state of Baden-Wuerttemberg by applying random forests. Results from cross-validated predictor importance evaluation demonstrate that the relative impact of modeled gust speed fields on the predictive accuracy of the random forests models was greatest compared to the impact of forest and soil features. Forest areas prone to storm damage occurring within a period of five years were mainly located in mountainous upland regions where maximum gust speed exceeds 31 m/s in a five-year return period and conifers dominate the tree species composition. While mean storm damage probability continuously increased with increasing statistical gust speed proportions of storm-damaged timber peaked at a statistical maximum gust speed value of 29 m/s occurring in a five-year return period. Combining the statistical gust speed field with daily gust speed fields of two exceptional winter storms improved model accuracy and considerably increased the explained variance. Endemic storm damage risk was calculated from endemic storm damage probability and proportions of endemically storm-damaged timber. In combination with knowledge of local experts the storm damage risk modeled in a 50 m × 50 m resolution raster dataset can easily be used to identify areas prone to storm damage and to adapt silvicultural management regimes to make forests more windfirm.

Multi-resolution, pattern-based segmentation of very large raster datasets

GIScience 2016 Short Paper Proceedings Multi-resolution, pattern-based segmentation of very large raster datasets J. Jasiewicz 1,2 , J. Niesterowicz 1 , T. F. Stepinski 1 Space Infromatics Lab, University of Cincinnati, 401 Braunstein Hall, Cincinnati, OH 45221-0131, US Email: {niestejk; stepintz}@mail.uc.edu Instititute of Geoecology and Geoinformation, Adam Mickiewicz University, Dziegielowa 27, Poznan, Poland Email: jarekj@amu.edu.pl Abstract We present an algorithm which efficiently segments very large categorical rasters based on patterns of their categories. It operates on a grid of motifels – square blocks of raster cells representing a local pattern. Our algorithm is based on the seeded region growing principle but it uses a novel grid topology and seeds stack with individual thresholds. It has a single free parameter – the spatial scale of a pattern. Algorithm was proven to be robust on land cover data, topographic landforms data, and high resolution color-quantized RGB images. We present a multi-scaled segmentation of NLCD2011 as an example. Potential applications of the new algorithm include ecology, geomorphology, pedology, forestry, agriculture, and urban studies. 1. Introduction Segmentation is the process of partitioning a raster dataset into multiple homogeneous segments. The goal of segmentation is to spatially generalize a raster so it provides more insight and is easier to analyze. The bulk of the existing work (Zhang et al. 2008) has focused on segmentation of images of relatively small scenes. However, segmentation of datasets that originated from remote sensing and cover large, continental or even global-scale areas, are also important, but existing segmentation algorithms are ineffective for such large datasets and the custom algorithms are lacking. Examples of such datasets are the National Land Cover Dataset (NLCD), which covers the conterminous US (CONUS) with the resolution of 30 m, or the SRTM-based DEM, which has a world-wide extent at 90 m resolution. Segmentation of NLCD could yield landscape types – precursors to ecoregions, and segmentation of world-wide DEM could delineate physiographic regions. In this paper we describe a segmentation algorithm especially designed for very large rasters. Specificities of such datastes are as follows. (1) They are the mosaics of multiple datasets, thus it is better to segment a secondary product of uniform quality (for example, a land cover) rather than a montage of primary data of variable quality (for example, a montage of Landsat scenes). (2) They are large; for example, the NLCD consists of ~8 billion cells and has the size of ~16 GB. (3) The goal of the segmentation is to identify regions characterized by patterns which are homogeneous on the scale that is large in comparison to the resolution of the raster, since the need for pattern-based segmentation. To deal with a large size of the input the proposed algorithm is based on the concept of Complex Object-Based Image Analysis (COBIA) (Vatsavai 2013, Stepinski et al. 2015). In COBIA the

Creating a Coastal National Elevation Database (CoNED) for Science and Conservation Applications

ABSTRACT Thatcher, C.A.; Brock, J.C.; Danielson, J.J.; Poppenga, S.K.; Gesch, D.B.; Palaseanu-Lovejoy, M.E.; Barras, J.A.; Evans, G.A., and Gibbs, A.E., 2016. Creating a Coastal National Elevation Database (CoNED) for science and conservation applications. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 64–74. Coconut Creek (Florida), ISSN 0749-0208. The U.S. Geological Survey is creating the Coastal National Elevation Database, an expanding set of topobathymetric elevation models that extend seamlessly across coastal regions of high societal or ecological significance in the United States that are undergoing rapid change or are threatened by inundation hazards. Topobathymetric elevation models are raster datasets useful for inundation prediction and other earth science applications, such as the development of sediment-transport and storm surge models. ...

Land Surface Temperature Anomalies in Central Asia: A Regional Analysis - 2001-2014.

This study analyzes day and night times mean monthly anomalies of Land Surface Temperatures in Central Asia Region from 2001 to 2014 based on the raster datasets derived from TERRA-MODIS observations. The results shows that larger proportion of the total area experienced fluctuations between -10 degrees and 5 degrees Celsius in both day and night times temperatures during the winter months. Most of the summer months show frequent annual swaps of these anomalies but the number of years recording higher temperatures in larger proportion of the region are more than those recording lower temperatures. The analysis of overall maximum, minimum and mean day and night time temperature anomalies shows that most of the areas experienced extreme anomalies up to minus 12 degrees and plus 12 degrees at one or the other time. The composite results show that only a few areas having continuously high anomalies up to 2.5 degrees whereas most of the areas experienced temperature decrease up to -2.5 degree Celsius..

A simple depression-filling method for raster and irregular elevation datasets

A variety of methods have been developed to process digital elevation models (DEMs) in order to extract morphological properties of land surfaces. Almost all rely on depression filling to facilitate drainage analysis. This study proposes an intuitive and relatively simple depression-filling algorithm, which is readily applicable to raster and irregular datasets. In this study, topographic data of two watershed areas were provided to test the capability of this new algorithm. The results show that the proposed algorithm can accurately identify the locations of depressions and efficiently assign pseudo flow directions across the filled depressions.

Automated measurement of sand dune migration using multi-temporal lidar data and GIS

Remote sensing has been used for coastal and desert sand dune studies over the past four decades, yet few methods have been developed for automated detection and measurement of dune migration directions and migration rates in large dune fields. Using high-resolution, high-accuracy, and multi-temporal light detection and ranging (lidar) data acquired in the White Sands Dune Field in New Mexico (USA) on 24 January 2009 and 6 June 2010, an automated method named Pairs of Source and Target Points (PSTP) was developed in a geographical information system (GIS) environment for automated detection and measurement of dune migration directions and migration rates. As markers for dune movement, dune slip faces were automatically extracted from lidar-derived digital elevation models (DEM), based on the range of the angle of repose, and converted into source lines and target lines through vectorization. Random target points were then generated on target lines and used to search for source points on source lines to form PSTP, thereby obtaining source direction, migration distance, and migration rate for each target point. Continuous raster data sets for dune migration rates were also created through spatial interpolation and point statistics to show dune field scale spatial patterns of dune migration rates. A total of 5936 PSTP were identified, producing dune migration directions and migration rates at 5936 locations in the study area of 9 km × 2.4 km. Histogram analysis revealed that a majority of the 3025 target points with source direction in the range 225°–285° (direction of prevailing winds) have a migration rate of 4–7 m year−1 and an average migration rate of 5.56 m year−1. The applicability of the PSTP method in special cases, and its potential as a generic method for change detection and measurement, are also discussed. The study obtained important results both in methodology development and in the study area.

Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

Unmanned Aerial Systems (UAS) are now capable of gathering high-resolution data, therefore, landslides can be explored in detail at larger scales. In this research, 132 aerial photographs were captured, and 85,456 features were detected and matched automatically using UAS photogrammetry. The root mean square (RMS) values of the image coordinates of the Ground Control Points (GPCs) varied from 0.521 to 2.293 pixels, whereas maximum RMS values of automatically matched features was calculated as 2.921 pixels. Using the 3D point cloud, which was acquired by aerial photogrammetry, the raster datasets of the aspect, slope, and maximally stable extremal regions (MSER) detecting visual uniformity, were defined as three variables, in order to reason fissure structures on the landslide surface. In this research, an Adaptive Neuro Fuzzy Inference System (ANFIS) and a Logistic Regression (LR) were implemented using training datasets to infer fissure data appropriately. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic (ROC) curves and by calculating the area under the ROC curve (AUC). The experiments exposed that high-resolution imagery is an indispensable data source to model and validate landslide fissures appropriately.

INFLUENCE OF THE PRECISION OF LIDAR DATA IN SURFACE WATER RUNOFF ESTIMATION FOR ROAD MAINTENANCE

Abstract. Roads affect the natural surface and subsurface drainage pattern of a hill or a watershed. Road drainage systems are designed with the objective of reducing the energy generated by the flowing water and the presence of excess water or moisture within the road. A poorly designed drainage may affect to road maintenance causing cut or fill failures, road surface erosion and degrading the engineering properties of the materials with which it was constructed. Surface drainage pattern can be evaluated from Digital Elevation Models typically calculated from point clouds acquired with aerial LiDAR platforms. However, these systems provide low resolution point clouds especially in cases where slopes with steep grades exist. In this work, Mobile LiDAR systems (aerial and terrestrial) are combined for surveying roads and their surroundings in order to provide complete point cloud. As the precision of the point clouds obtained from these mobile systems is influenced by GNSS outages, Gaussian noise with different standard deviation values is introduced in the point cloud in order to determine its influence in the evaluation of water runoff direction. Results depict an increase in the differences of flow direction with the decrease of cell size of the raster dataset and with the increase of Gaussian noise. The last relation fits to a second-order polynomial Differences in flow direction up to 42º are achieved for a cell size of 0.5 m with a standard deviation of 0.15 m.

Band selection in hyperspectral imagery using spatial cluster mean and genetic algorithms

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.

Predicting the extent of lakeshore development using GIS datasets

Land use along lakeshores impacts littoral habitat. We used raster-based land use and land cover datasets as well as a statewide dock polygon dataset to assess the development status of lake shoreland on 150 Minnesota lakes. A dataset containing dock polygons identified using a semiautomated process performed best statewide. An older raster dataset that classified rural development based on the presence of buildings performed better than a recent dataset based on dominant land cover. We classified points along the shore as developed or undeveloped using proximity to a development indicator: either a raster cell with a developed land use or a dock point. We compared classifications derived from GIS data to actual development, which was defined by the presence of a manually identified dock on aerial photos, and used total operating characteristic (TOC) analysis to evaluate the performance of each dataset. All 3 datasets classified development better than random chance. The dock dataset performed best, and its results were consistent statewide, while the raster datasets' performance varied among ecoregions. Researchers should be aware that the prevalence of the condition being classified has a large impact on some commonly used metrics, such as accuracy and positive predictive value. The costs and tradeoffs of different types of error (false alarms or missed detections) will vary in different situations and should be explicitly considered when deciding how and when to use classification systems like these.

Complexities of designing terrain maps illustrated with horizontal hachures

ABSTRACTDesigning maps involves numerous decisions on the part of the cartographer, while designing terrain maps adds unique complexities. Terrain is often conceptualized as varying in a smooth and continuous manner, leading to data being stored with field-based models, often using raster datasets. The focus of many map design books and other literature, however, is on symbolizing object-based models. Vector layers such as contours and hachures can be derived from grids, but are conceptually abstract and require special consideration for three-dimensional display. We propose a classification scheme for terrain maps based on four principal elements of design: layer tinting, shading, texturing and form lines. We also propose a new method for automating horizontal hachures that illustrates how the latter three elements meet at a critical design juncture where location, orientation, derived attributes and the use of visual variables to symbolize the terrain intersect. Horizontal hachures combine multiple design elements into an innovative methodology, and can also be combined with terrain mapping elements on other design levels.

Focal-Test-Based Spatial Decision Tree Learning

Given learning samples from a raster data set, spatial decision tree learning aims to find a decision tree classifier that minimizes classification errors as well as salt-and-pepper noise. The problem has important societal applications such as land cover classification for natural resource management. However, the problem is challenging due to the fact that learning samples show spatial autocorrelation in class labels, instead of being independently identically distributed. Related work relies on local tests (i.e., testing feature information of a location) and cannot adequately model the spatial autocorrelation effect, resulting in salt-and-pepper noise. In contrast, we recently proposed a focal-test-based spatial decision tree (FTSDT), in which the tree traversal direction of a sample is based on both local and focal (neighborhood) information. Preliminary results showed that FTSDT reduces classification errors and salt-and-pepper noise. This paper extends our recent work by introducing a new focal test approach with adaptive neighborhoods that avoids over-smoothing in wedge-shaped areas. We also conduct computational refinement on the FTSDT training algorithm by reusing focal values across candidate thresholds. Theoretical analysis shows that the refined training algorithm is correct and more scalable. Experiment results on real world data sets show that new FTSDT with adaptive neighborhoods improves classification accuracy, and that our computational refinement significantly reduces training time.

Identifying Urban Watershed Boundaries and Area, Fairfax County, Virginia

AbstractUrban hydrology differs from that of natural environments, and thus urban watersheds require innovative evaluation techniques. Typical geospatial evaluation of urban hydrology begins with identification of water flow and watershed boundaries. This study identifies steps to delineate a highly urbanized watershed in Fairfax County, Virginia. Using standard techniques for natural watersheds and one-meter2 resolution lidar, watershed and flow accumulation raster datasets were derived. Then, modifications encountered within urban landscapes i.e., impervious surfaces, stormwater inlets, pipes, and retention ponds along with high-resolution aerial photos and lidarderived contour lines were integrated into the analysis. Regions redirecting water flow from stream channels and areas redirecting water flow into the stream channels were identified. These areas were removed or added, reducing the area by almost 17 percent, and the watershed boundary was significantly altered. This analysis illustrates the significance of the distinctive characteristics of the urban landscape in accurate delineations of urban watersheds.

Reconstructing the Past Through Utah Sanborn Fire Insurance Maps: A Geospatial Approach to Library Resources

Cartographic maps have the ability to convey information and ideas in ways text cannot. The Utah Sanborn Fire Insurance Maps are one such resource, depicting detailed information on buildings, layouts, compositions and boundaries of cities and towns. As time has progressed, the interest in these resources has continued to grow, opening the door for the creation of an updated method for viewing and examining this valuable collection. Through the incorporation of GIS and geospatial technology, the printed and scanned materials have been converted into georeferenced raster datasets, allowing viewers the ability to geospatially interact with the information and apply the information to their research in new and exciting ways. This article describes the Digital Scholarship Lab's endeavor to convert these valuable resources into research driven geospatial datasets, providing a new format for how the library information is presented as well as a new method for interacting and examining the information in detail.

Thermal comfort design zone delineation for India using GIS

This paper describes a procedure devised using Geographical Information System (GIS) to delineate boundaries of zones, where any change in thermal comfort requirement indicates corresponding change in responsive building design strategies. Very high resolution interpolated climatic data subset for India has been used in this analysis so as to distinctly represent the variety of climates experienced in India. The raster datasets of climatic parameters have been analyzed in GIS to get derived and classified parameters required in the Mahoney Tables method for computation of six climatic indicators. These six indicators have been used in the selection of suitable climate-responsive building strategies from 8 strategy groups for the whole dataset. The selected strategies in 8 groups are superimposed together to identify 62 Thermal Comfort Design Zones with unique combination of building design strategies. The proposed zones can be useful in the legislative tools for implementation of energy conservation measures and sustainable building practices.

Design Scheme for Spatial Database of Climatic and Environmental Variables in Mexico, Integrating Big Data Technology

Abstract Big Data technology offers new and potentially more effective solutions to the continuing challenge of how large raster data sets may be effectively stored and retrieved. Big Data technology not only enables the integration of diverse information sources, but also supports the management and storage of large data sets. As such, it offers an alternative approach to existing spatial database technology. This paper proposes a design scheme that uses Big Data technology as a means to store for retrieval large volumes of raster data forming a spatial database of climatic and other environmental data for Mexico.