Pawel Netzel

Temporal and spatial variations of fog in the Western Sudety Mts., Poland

The ridges of the Western Sudety are well exposed to the humid maritime air masses that are mainly associated with westerly atmospheric circulation. Fog is the most frequently observed atmospheric ...

LandEx—A GeoWeb Tool for Query and Retrieval of Spatial Patterns in Land Cover Datasets

The vast amount of data collected by satellites via remote sensing is a valuable resource, however, it lacks machine search capabilities. In particular, large land cover datasets, such as the 30-m/cell NLCD 2006 covering the entire conterminous United States, are rarely analyzed as a whole due to the lack of tools beyond the basic statistics and SQL queries. Consequently, the NLCD is underutilized relative to its potential. We address this issue by introducing LandEx-a GeoWeb application for real time, content-based exploration and mining of land cover patterns in large datasets. By combining the functionality of online computerized maps with the power of the pattern recognition algorithm, LandEx provides an easy to use visual search engine for the entire extent of the NLCD at its full resolution. The user selects a pattern of interest (a query) and the tool produces a similarity map indicating the spatial distribution of locations having patterns of land cover similar to that in the query. Pattern-based query and retrieval addresses the issue of structural similarity between landscapes. The core of the method is the similarity function between two patterns which is based on 2D land cover class/clump size histograms and the Jensen-Shannon divergence. The search relies on exhaustive evaluation using an overlapping sliding window approach. LandEx is implemented using Free Open Source Software (FOSS) software and adheres to the Open Geospatial Consortium (OGC) standards. The wait time for an answer to a query is only several seconds due to the high level of system optimization. The methodology and implementation of LandEx are described in detail and illustrative examples of its application to different domains, including agriculture, forestry, and urbanization are given.

On Using a Clustering Approach for Global Climate Classification

AbstractClassifying the land surface into climate types provides means of diagnosing relations between Earth’s physical and biological systems and the climate. Global climate classifications are also used to visualize climate change. Clustering climate datasets provides a natural approach to climate classification, but the rule-based Koppen–Geiger classification (KGC) is the one most widely used. Here, a comprehensive approach to the clustering-based classification of climates is presented. Local climate is defined as a multivariate time series of mean monthly climatic variables and the authors propose to use dynamic time warping (DTW) as a measure of dissimilarity between local climates. Also discussed are the choice of climatic variables, the importance of their proper normalization, and the advantage of using distance-based clustering algorithms. Using the WorldClim global climate dataset and different combinations of clustering parameters, 32 different clustering-based classifications are calculated. ...

GeoPAT: A toolbox for pattern-based information retrieval from large geospatial databases

Abstract Geospatial Pattern Analysis Toolbox (GeoPAT) is a collection of GRASS GIS modules for carrying out pattern-based geospatial analysis of images and other spatial datasets. The need for pattern-based analysis arises when images/rasters contain rich spatial information either because of their very high resolution or their very large spatial extent. Elementary units of pattern-based analysis are scenes – patches of surface consisting of a complex arrangement of individual pixels (patterns). GeoPAT modules implement popular GIS algorithms, such as query, overlay, and segmentation, to operate on the grid of scenes. To achieve these capabilities GeoPAT includes a library of scene signatures – compact numerical descriptors of patterns, and a library of distance functions – providing numerical means of assessing dissimilarity between scenes. Ancillary GeoPAT modules use these functions to construct a grid of scenes or to assign signatures to individual scenes having regular or irregular geometries. Thus GeoPAT combines knowledge retrieval from patterns with mapping tasks within a single integrated GIS environment. GeoPAT is designed to identify and analyze complex, highly generalized classes in spatial datasets. Examples include distinguishing between different styles of urban settlements using VHR images, delineating different landscape types in land cover maps, and mapping physiographic units from DEM. The concept of pattern-based spatial analysis is explained and the roles of all modules and functions are described. A case study example pertaining to delineation of landscape types in a subregion of NLCD is given. Performance evaluation is included to highlight GeoPATs applicability to very large datasets. The GeoPAT toolbox is available for download from http://sil.uc.edu/ .

Connected components labeling for giga-cell multi-categorical rasters

Labeling of connected components in an image or a raster of non-imagery data is a fundamental operation in fields of pattern recognition and machine intelligence. The bulk of effort devoted to designing efficient connected components labeling (CCL) algorithms concentrated on the domain of binary images where labeling is required for a computer to recognize objects. In contrast, in the Geographical Information Science (GIS) a CCL algorithm is mostly applied to multi-categorical rasters in order to either convert a raster to a shapefile, or for statistical characterization of individual clumps. Recently, it has become necessary to label connected components in very large, giga-cell size, multi-categorical rasters but performance of existing CCL algorithms lacks sufficient speed to accomplish such task. In this paper we present a modification to the popular two-scan CCL algorithm that enables labeling of giga-cell size, multi-categorical rasters. Our approach is to apply a divide-and-conquer technique coupled with parallel processing to a standard two-scan algorithm. For specificity, we have developed a variant of a standard CCL algorithm implemented as r.clump in GRASS GIS. We have established optimal values of data blocks (stemming from the divide-and-conquer technique) and optimal number of computational threads (stemming from parallel processing) for a new algorithm called r.clump3p. The performance of the new algorithm was tested on a series of rasters up to 160Mcells in size; for largest size test raster a speed up over the original algorithm is 74 times. Finally, we have applied the new algorithm to the National Land Cover Dataset 2006 raster with 1.6x10^1^0 cells. Labeling this raster took 39h using two-processors, 16 cores computer and resulted in 221,718,501 clumps. Estimated speed up over the original algorithm is 450 times. The r.clump3p works within the GRASS environment and is available in the public domain.

Pattern-Based Assessment of Land Cover Change on Continental Scale With Application to NLCD 2001–2006

We present a method for assessing land cover change on a continental scale and with high spatial resolution. This is a postclassification method, but instead of tracking transitions of land cover classes on a cell-by-cell basis, the method measures the change at a tile level by quantifying a difference between local patterns of land cover at two different time steps. Pattern-based change assessment is well suited for the large-scale survey as it addresses landscape dynamics rather than just simple land class transitions. A tile is defined as a local area consisting of a large enough number of land cover cells to sample a distribution of landscape but small enough to detect change with high spatial resolution; 4.5 km × 4.5 km square tiles are used. The level of change is measured as the dissimilarity between motifs of tile patterns at two time steps and is calculated using information-theoretic metric called the JSS. The method is able to discriminate between different types of change, including change in geometric pattern, change in class composition, and numerous class transitions without significant changes in either pattern or composition. The methodology is applied to the National Land Cover Dataset to obtain a 2001-2006 change map of the conterminous U.S. The resultant map shows (in a high resolution of 3 km/cell) a spatial distribution of the degree to which the landscape has changed in this time period. Both large regions (southeastern and Gulf regions, Pacific Northwest region, and the state of Maine) of heightened landscape dynamics and small regions of sudden change due to fires, urban growth, etc., are clearly identifiable from the map. A fully featured online application for fast and convenient exploration of the change map together with original land cover maps in their full resolutions is available at http://sil.uc.edu/dataeye/.

Modelling meteorological conditions for the episode (December 2009) of measured high PM 10 air concentrations in SW Poland – application of the WRF model

The weather research and forecasting model has been applied to derive information on meteorological variables for the period with high concentrations of PM 10 (1–30 December 2009) in SW Poland. Three one-way nested domains have been used and the results for the innermost domain have been compared with surface and radiosonde meteorological measurements for pressure (PRES), air temperature (TMP), specific humidity (SPFH), wind speed (WIND) and direction (WDIR). The model results are in good agreement with the surface measurements for TMP, PRES and SPFH, with the index of agreement (IOA) above 0.9. The model underestimate the observed PRES, TMP and SPFH except for the mountainous site Śniezka. The WIND is biased high, the overall IOA is 0.62, and range from 0.41 to 0.73 for all stations. The IOA is above 0.73 for TMP and SPFH for radiosonde measurements and the errors decrease with height.

Comprehensive framework for visualizing and analyzing spatio-temporal dynamics of racial diversity in the entire United States

The United States is increasingly becoming a multi-racial society. To understand multiple consequences of this overall trend to our neighborhoods we need a methodology capable of spatio-temporal analysis of racial diversity at the local level but also across the entire U.S. Furthermore, such methodology should be accessible to stakeholders ranging from analysts to decision makers. In this paper we present a comprehensive framework for visualizing and analyzing diversity data that fulfills such requirements. The first component of our framework is a U.S.-wide, multi-year database of race sub-population grids which is freely available for download. These 30 m resolution grids have being developed using dasymetric modeling and are available for 1990-2000-2010. We summarize numerous advantages of gridded population data over commonly used Census tract-aggregated data. Using these grids frees analysts from constructing their own and allows them to focus on diversity analysis. The second component of our framework is a set of U.S.-wide, multi-year diversity maps at 30 m resolution. A diversity map is our product that classifies the gridded population into 39 communities based on their degrees of diversity, dominant race, and population density. It provides spatial information on diversity in a single, easy-to-understand map that can be utilized by analysts and end users alike. Maps based on subsequent Censuses provide information about spatio-temporal dynamics of diversity. Diversity maps are accessible through the GeoWeb application SocScape (http://sil.uc.edu/webapps/socscape_usa/) for an immediate online exploration. The third component of our framework is a proposal to quantitatively analyze diversity maps using a set of landscape metrics. Because of its form, a grid-based diversity map could be thought of as a diversity “landscape” and analyzed quantitatively using landscape metrics. We give a brief summary of most pertinent metrics and demonstrate how they can be applied to diversity maps.

World Climate Search and Classification Using a Dynamic Time Warping Similarity Function

We present a data-mining approach to climate classification and analysis. Local climates are represented as time series of climatic variables. A similarity between two local climates is calculated using the dynamic time warping (DTW) function that allows for scaling and shifting of the time axis to model the similarity more appropriately than a Euclidean function. A global grid of climatic data is clustered into 5 and 13 climatic classes, and the resultant world-wide map of climate types is compared to the empirical Koppen–Geiger classification. We also present a concept of climate search—an interactive, Internet-based application that allows retrieval and mapping of world-wide locations having climates similar to a user-selected location query.

Retrieval of pattern-based information from giga-cells categorical rasters — Concept and new software

Rapid development of computer technology together with the growing availability of giga-scale data sources brings new possibilities to geo-spatial analysis [1, 2]. We define giga-scale datasets as those having size exceeding 109 cells, regardless of their physical scale. They may represent local regions at ultra-high resolution (of the order of centimeters) offered by LiDAR technology or global mosaics of satellite imagery or digital elevation models (DEMs) at medium resolution (of the order of 10-100 meters). Frequently, these giga-scale datasets are categorical rasters - products derived from processing of original data. Examples include land cover/land use (LCLU), landforms, vegetation, and urban maps. In such rasters important information is stored not only at the level of individual cells, but also, and maybe predominantly, at the level of patterns of the categories [3, 4]. Urban structures, plant habitats, geomorphological surfaces, and landscapes are examples of such patterns; they have collective functions and meaning and thus contain valuable information that cannot be inferred at the level of cell-based analysis.