Chuanrong Zhang

The roles of geography markup language (GML), scalable vector graphics (SVG), and Web feature service (WFS) specifications in the development of Internet geographic information systems (GIS)

Abstract.The objective of this paper is to address two issues of current Internet Geographic Information Systems (GIS) programs – interoperability and graphic image output issues – using standard-based technologies, specifically, the Geography Markup Language (GML), Scalable Vector Graphics (SVG) and the OpenGIS Web Feature Service (WFS) Implementation Specifications developed by the OpenGIS Consortium (OGC). A strategy is proposed to use GML as a coding and data transporting mechanism to achieve data interoperability, SVG to display GML data on the Web, and WFS as a data query mechanism to access and retrieve data at the feature level in real time on the Web. Two case studies are reported to implement this strategy. Our case studies show that the combination of GML, SVG, and WFS has an immense potential to achieve interoperability while not requiring considerable changes to existing legacy data. Data can be in their original formats and still be retrieved using WFS and transformed into GML in real time. SVG can produce superior quality vector maps on a Web browser. More research is needed to explore the full potential of these new standards and to test them in real-world situations.

Gaps-fill of SLC-off Landsat ETM+ satellite image using a geostatistical approach

Using appropriate techniques to fill the data gaps in SLC‐off ETM+ imagery may enable more scientific use of the data. The local linear histogram‐matching technique chosen by USGS has limitations if the scenes being combined exhibit high temporal variability and radical differences in target radiance due, for example, to the presence of clouds. This study proposes using an alternative interpolation method, the kriging geostatistical technique, for filling the data gaps. The case study shows that the ordinary kriging techniques may provide a powerful tool for interpolating the missing pixels in the SLC‐off ETM+ imagery. While the standardized ordinary cokriging has been shown to be particularly useful when samples of the variable to be predicted are sparse and samples of a second, related variable are plentiful, the case study demonstrates that it provides little improvement in interpolating the data gap in the SLC‐off imagery.

The Roles of Web Feature and Web Map Services in Real-time Geospatial Data Sharing for Time-critical Applications

Many time-critical applications such as emergency response, location-based services, and real time traffic management need instant access to diverse data to make quick decisions and take instantaneous actions. However, two issues block time-critical applications to quickly acquire and integrate spatial data over the web: (1) the heterogeneity of existing GIS systems, and (2) the file-level data sharing systems over the web. This research examines current open standards, protocols, and technologies capable of solving the two issues for real-time spatial data sharing over the web. Focusing on investigating the role of Web Feature Services (WFS) and Web Map Services (WMS), this research has developed a solution for real-time geospatial data sharing at the feature level over the web. A prototype has been implemented to query, extract, create, delete, update, and map geographic features stored in web-accessible OGC (Open Geospatial Consortium) simple feature datastores for transportation emergency applications. The prototype results show that the OGC WFS and WMS play important roles in real-time geospatial data sharing and exchange from heterogeneous sources at the feature level for time-critical applications. The WFS and WMS eliminate time-consuming data translation and facilitate reuse of existing geospatial data over the web. Several issues related to the solution are also discussed in the paper.

Restoration of clouded pixels in multispectral remotely sensed imagery with cokriging

The presence of clouds and their shadows in remotely sensed images limits their potential uses for extracting information. The commonly used methods for replacing clouded pixels by land cover reflection estimates usually yield poor results if the images being combined exhibit radical differences in target radiance due, for example, to large date separation and high temporal variability. This study focuses on introducing geostatistical techniques for interpolating the DN values of clouded pixels in multispectral remotely sensed images using traditional ordinary cokriging and standardized ordinary cokriging. Two case studies were conducted in this study. The first case study shows that the methods work well for the small clouds in a heterogeneous landscape even when the images being combined show high temporal variability. Although the basic spatial structure in large size clouds can be captured, image interpolation‐related artefacts such as smoothing effects are visually apparent in a heterogeneous landscape. The second case study indicates that the cokriging methods work better in homogenous regions such as the dominantly agricultural areas in United States Midwest. Various statistics including both global statistics and local statistics are employed to confirm the reliability of the methods.

Towards logic-based geospatial feature discovery and integration using web feature service and geospatial semantic web

Open geospatial consortium (OGC) web feature services (WFSs) facilitate feature-level spatial data sharing over the web. However, OGC WFSs only emphasize technical data interoperability via standard interfaces and cannot resolve semantic heterogeneity problems in spatial data sharing. The lack of explicit semantics in the OGC WFS description proves to be a major limitation to automatic geospatial feature discovery and WFS composition. To overcome these limitations, this study proposed a solution for searching, discovering, and composing semantically heterogeneous transportation spatial data at feature level from different sources over the web through providing semantic specifications of WFSs. Geospatial semantic web technologies such as description logic, description logic-based reasoner, inference rules, and web ontology language ontologies were used to support geospatial feature data interoperability at the semantic level. Algorithms for automatic geospatial feature discovery and WFS composition were developed in this article.

Using geographically weighted regression to solve the areal interpolation problem

Areal interpolation is used to transfer attribute information from the initial set of source units with known values to the target units with unknown values before subsequent spatial analysis can occur. The areal units with unknown attribute information can be either at a finer scale or misaligned with respect to the source data layer. This article presents and describes a geographically weighted regression (GWR) method for solving areal interpolation problems for nested areal units and misaligned areal units. Population data, selected as the attribute information, are interpolated from census tracts to block groups (a finer scale) and pseudo-tracts (misaligned from tracts but at the same approximate scale). Root mean square error, adjusted root mean square error, and mean absolute error are calculated to evaluate the performance of the interpolation methods. The land cover data derived from Landsat Thematic Mapper Satellite Imagery with a 30×30 m spatial resolution are applied to as the ancillary data to describe the underlying distribution of population. To evaluate the utility of GWR as an areal interpolation method, the simple areal weighting method, a dasymetric method, and different ordinary least squares regression methods are used in this article as comparison methods. Results suggest that GWR is a better interpolator for the misaligned data problem than for the finer scale data problem. The latter is a result of issues associated with the scaling step to ensure the pycnophylatic property required in areal interpolation.

A Markov chain-based probability vector approach for Modeling spatial uncertainties of soil classes

Integrating livestock with cotton (Gossypium hirsutum L.) offers profitable alternatives for producers in the southeastern USA, but could result in soil water depletion and soil compaction. We conducted a 3-yr field study on a Dothan loamy sand (fine-loamy, kaolinitic, thermic Plinthic Kandiudult) in southern Alabama to develop a conservation tillage system for integrating cotton with winter-annual grazing of stocker cattle under rainfed conditions. Winter annual forages and tillage systems were evaluated in a strip-plot design where winter forages were oat (Avena sativa L.) and annual ryegrass (Lolium mutiflorum L.). Tillage systems included moldboard and chisel plowing and combinations of noninversion deep tillage (none, in-row subsoil, or paratill) with or without disking. We evaluated forage dry matter, N concentration, average daily gain, net returns from grazing, soil water content, and cotton leaf stomatal conductance, plant populations, and yield. Net returns from winter-annual grazing were between US

Geospatial data sharing based on geospatial semantic web technologies

185 to US

The framework of a geospatial semantic web-based spatial decision support system for Digital Earth

200 ha - yr - . Soil water content was reduced by 15% with conventional tillage or deep tillage, suggesting that cotton rooting was increased by these systems. Oat increased cotton stands by 25% and seed-cotton yields by 7% compared with ryegrass. Strict no-till resulted in the lowest yields-30% less than the overall mean (3.69 Mg ha -1 ). Noninversion deep tillage in no-till (especially paratill) following oat was the best tillage system combination (3.97 Mg ha -1 ) but deep tillage did not increase cotton yields with conventional tillage. Integrating winter-annual grazing can be achieved using noninversion deep tillage following oat in a conservation tillage system, providing producers extra income while protecting the soil resource.

Source Apportionment of Heavy Metals in Soils Using Multivariate Statistics and Geostatistics

Geospatial data sharing is a concern in geospatial science because of the heterogeneity of existing geographical information systems. This study aims to examine the use of Geospatial Semantic Web technologies such as ontology web services and the service‐oriented architecture for enabling disparate heterogeneous legacy GIS to share and integrate information in a cost effective way to reduce spatial data duplication. A framework based on the Geospatial Semantic Web technologies is proposed in this study. Experimental results from an implemented prototype show that the proposed framework allows searching and accessing geospatial data and services at the semantic level based on their content instead of keywords in the metadata.