Geoinformatics and digital earth initiatives: a German perspective

Abstract

This paper discusses the role of Geoinformatics as a new scientific discipline designed for handling of geospatial information. Depending on the scientific background of the people involved in shaping the emerging discipline, emphasis may be placed on different aspects of Geoinformatics. Applications and developments may address geoscientific, spatial planning, or computer science related matters. The scientific field of Geoinformatics encompasses the acquisition and storing of geospatial data, the modelling and presentation of spatial information, geoscientific analyses and spatial planning, and the development of algorithms and geospatial database systems. It is the position of the author that these tools from Geoinformatics are necessary to bridge the gap between Digital Earth models and the real world with its real-world problems (‘connecting through location’). It is, however, crucial that Geoinformatics represents a coherent integrated approach to the acquisition, storage, analysis, modeling, presentation, and dissemination of geo-processes and not a patchwork solution of unconnected fields of activity. Geoinformatics is as such not a part of Geography, Surveying, or Computer Science, but a new self-contained scientific discipline. The current paper highlights international and national trends of the discipline and presents a number of Geoinformatics initiatives. The research and teaching activities of the newly formed Institute for Geoinformatics and Remote Sensing (IGF) at the University of Osnabrueck serve as an example for these initiatives. All these developments have lead to the long overdue formation of a scientific ‘Society for Geoinformatics’ (German: Gesellschaft für Geoinformatik – GfGI) in Germany.     

Development of GIS-based environmental information system: an Indian scenario

Abstract

Ideally, scientists should be able to format, explore, analyse, and visualise data in a simple, powerful and fast application that would seamlessly integrate georeferenced data from a variety of data sources into an intuitive visualisation. The focus of an Environmental Information System is providing environmental information to decision makers, policy planners, scientists and engineers, research workers, etc. which ensures integration of data collection, collation, storage, retrieval and dissemination to all concerned. All such queries should be responded to supplying substantive information in the form of reports. The paper presents an innovative way to utilise the geographic information associated with the environmental data. The stand-alone application is the integration of using ArcObjects Environmental System Research Institute ArcGIS Engine 9.1 and VB.Net. The geographic information system (GIS)-based application, a framework of digital earth in terms of environmental information system provides a user-friendly query interface, which gives information about various environmental parameters in tabular as well as on map display. It also provides the visual interpretation to make further analysis and future decisions at multiple scales, locations and extents. The facility for modifying the map attributes and corresponding databases is integrated to update the information system. Output spatial data are produced in the form of reports using selected fields with display on map.     

Modelling of urban growth boundary using geoinformatics

Abstract

Urban growth boundary (UGB) is a regulatory measure of local government for delineating limits of urban growth over a period of time. Land within the UGB allows urban development, while the land outside of this boundary remains primarily non-urban. The increasing popularity of UGB demands an easy and effective method to design this boundary. This article introduces a new concept, Ideal Urban Radial Proximity (IURP), to designate a spatial UGB using geoinformatics in the digital environment. The Kolkata urban agglomeration was considered to demonstrate this model. Remotely sensed imageries of three temporal instants (years 1975, 1990 and 2005) were considered to determine the information on urban extent and growth of the city. These data were then used as inputs to model the UGB for the years 2020 and 2035. The proposed model discourages scattered development and increase in urban growth rate. It preserves urban vegetation, water bodies and any other important non-urban areas within the inner city space. The IURP concept will also be useful to make the cities circular and polycentric urban blobs into a monocentric tract. Apart from the proposed model and derived results, this research also proves the potential of geoinformatics in modelling a UGB.     

A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America

Abstract

It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations. To better manage the natural resources, and to better understand the closely linked Earth systems, the concept of Digital Earth has been strongly promoted since US Vice President Al Gore's speech in 1998. One core element of Digital Earth is the use and integration of remote sensing data. Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use, but also to detect more subtle changes related for instance to climate change. To uncover global change effects on vegetation activity and phenology, it is important to establish high quality time series characterizing the past situation against which the current state can be compared. With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution. Particular emphasis was put on noise removal. Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale. Without temporal smoothing, subtle spatio-temporal patterns in vegetation composition, density and phenology would be hidden by atmospheric noise and undetected clouds. Such noise is immanent in data that have undergone solely a maximum value compositing. Within the present study, the Whittaker smoother (WS) was applied to a SPOT VGT time series. The WS balances the fidelity to the observations with the roughness of the smoothed curve. The algorithm is extremely fast, gives continuous control over smoothness with only one parameter, and interpolates automatically. The filtering efficiently removed the negatively biased noise present in the original data, while preserving the overall shape of the curves showing vegetation growth and development. Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data. Analysis of the data also revealed spatially consistent key phenological markers. Extracted seasonality parameters followed a clear meridional trend. Compared to the unfiltered data, the filtered time series increased the separability of various land cover classes. It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.     

Analysis of the factors affecting LiDAR DTM accuracy in a steep shrub area

The creation of a quality Digital Terrain Model (DTM) is essential for representing and analyzing the Earth in a digital form. The continuous improvements in the acquisition and the potential of airborne Light Detection and Ranging (LiDAR) data are increasing the range of applications of this technique to the study of the Earth surface. The aim of this study was to determine the optimal parameters for calculating a DTM by using an iterative algorithm to select minimum elevations from LiDAR data in a steep mountain area with shrub vegetation. The parameters were: input data type, analysis window size, and height thresholds. The effects of slope, point density, and vegetation on DTM accuracy were also analyzed. The results showed that the lowest root mean square error (RMSE) was obtained with an analysis window size of 10 m, 5 m, and 2.5 m, rasterized data as input data, and height thresholds equal to or greater than 1.5 m. These parameters showed a RMSE of 0.19 m. When terrain slope varied from 0–10% to 50–60%, the RMSE increased by 0.11 m. The RMSE decreased by 0.06 m when point density was increased from 4 to 8 points/m², and increased by 0.05 m in dense vegetation areas.     

Mapping alteration minerals using sub-pixel unmixing of ASTER data in the Sarduiyeh area,SE Kerman,Iran

This paper is an attempt to introduce the role of earth observation technology and a type of digital earth processing in mineral resources exploration and assessment. The sub-pixel distribution and quantity of alteration minerals were mapped using linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) algorithms in the Sarduiyeh area, SE Kerman, Iran, using the visible-near infrared (VNIR) and short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument and the results were compared to evaluate the efficiency of methods. Three groups of alteration minerals were identified: (1) pyrophylite-alunite (2) sericite-kaolinite, and (3) chlorite-calcite-epidote. Results showed that high abundances within pixels were successfully corresponded to the alteration zones. In addition, a number of unreported altered areas were identified. Field observations and X-ray diffraction (XRD) analysis of field samples confirmed the dominant mineral phases identified remotely. Results of LSU and MTMF were generally similar with overall accuracy of 82.9 and 90.24%, respectively. It is concluded that LSU and MTMF are suitable for sub-pixel mapping of alteration minerals and when the purpose is identification of particular targets, rather than all the elements in the scene, the MTMF algorithm could be proposed.     

Mapping salt diapirs and salt diapir-affected areas using MLP neural network model and ASTER data

Abstract

This study employs visible-near infrared and short wave infrared datasets of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to map salt diapirs and salt diapir-affected areas using Multi-Layer Perceptron (MLP) in the Zagros Folded Belt, Iran, and introduces the role of earth observation technology and a type of digital earth processing in lithological mapping and geo-environmental impact assessment. MLP neural network model with several learning rates between 0.01 and 0.1 was carried out on ASTER L1B data, and the results were compared using confusion matrices. The most appropriate classification image for L1B input to MLP was produced by learning rate of 0.01 with Kappa coefficient of 0.90 and overall accuracy of 92.54%. The MLP result of input data set mapped lithological units of salt diapirs and demonstrated affected areas at the southern and western parts of the Konarsiah and Jahani diapirs, respectively. Field observations and X-ray diffraction analyses of field samples confirmed the dominant mineral phases identified remotely. It is concluded that MLP is an efficient approach for mapping salt diapirs and salt-affected areas.     

Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?

Abstract

The geospatial sciences face grand information technology (IT) challenges in the twenty-first century: data intensity, computing intensity, concurrent access intensity and spatiotemporal intensity. These challenges require the readiness of a computing infrastructure that can: (1) better support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries; (2) provide real-time IT resources to enable real-time applications, such as emergency response; (3) deal with access spikes; and (4) provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge. The emergence of cloud computing provides a potential solution with an elastic, on-demand computing platform to integrate – observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback – the essential elements of the geospatial sciences. We discuss the utilization of cloud computing to support the intensities of geospatial sciences by reporting from our investigations on how cloud computing could enable the geospatial sciences and how spatiotemporal principles, the kernel of the geospatial sciences, could be utilized to ensure the benefits of cloud computing. Four research examples are presented to analyze how to: (1) search, access and utilize geospatial data; (2) configure computing infrastructure to enable the computability of intensive simulation models; (3) disseminate and utilize research results for massive numbers of concurrent users; and (4) adopt spatiotemporal principles to support spatiotemporal intensive applications. The paper concludes with a discussion of opportunities and challenges for spatial cloud computing (SCC).     

Tracking desertification on the Mongolian steppe through NDVI and field-survey data

Abstract

Changing environmental and socio-economic conditions make land degradation, a major concern in Central and East Asia. Globally satellite imagery, particularly Normalized Difference Vegetation Index (NDVI) data, has proved an effective tool for monitoring land cover change. This study examines 33 grassland water points using vegetation field studies and remote sensing techniques to track desertification on the Mongolian plateau. Findings established a significant correlation between same-year field observation (line transects) and NDVI data, enabling an historical land cover perspective to be developed from 1998 to 2006. Results show variable land cover patterns in Mongolia with a 16% decrease in plant density over the time period. Decline in cover identified by NDVI suggests degradation; however, continued annual fluctuation indicates desertification – irreversible land cover change – has not occurred. Further, in situ data documenting greater cover near water points implies livestock overgrazing is not causing degradation at water sources. In combination of the two research methods – remote sensing and field surveys – strengthen findings and provide an effective way to track desertification in dryland regions.