A Tutorial on Geographic Information Systems: A Ten-year Update

This tutorial provides a foundation on geographic information systems (GIS) as they relate to and are part of the IS body of knowledge. The tutorial serves as a ten-year update on an earlier CAIS tutorial (Pick, 2004). During the decade, GIS has expanded with wider and deeper range of applications in government and industry, widespread consumer use, and an emerging importance in business schools and for IS. In this paper, we provide background information on the key ideas and concepts of GIS, spatial analysis, and latest trends and on the status and opportunities for incorporating GIS, spatial analysis, and locational decision making into IS research and in teaching in business and IS curricula

Integrated Approach for the Assessment and Development of Groundwater Resources in Arid Lands: Applications in the Quetta Valley, Pakistan

The lack of adequate field measurements (e.g., precipitation and stream flow) and difficulty in obtaining them often hampers the construction and calibration of rainfallrunoff models over many of the world’s watersheds, leaving key elements of the hydrologic cycle unconstrained. We adopted methodologies that rely heavily on readily available remote sensing datasets as viable alternatives and useful tools for assessing, managing, and modeling the water resources of such remote and inadequately gauged regions. The Soil and Water Assessment Tool was selected for continuous (1998–2005) rainfall-runoff modeling of the northeast part of the Pishin Lora basin (NEPL), a politically unstable area that lacks adequate rain gauge and stream flow data. To account for the paucity of rain gauge and stream flow gauge data, input to the model included satellite-based Tropical Rainfall Measuring Mission TRMM precipitation data. Modeled runoff was calibrated against satellite-based observations including: (1) monthly estimates of the water volumes impounded by the Khushdil Khan (latitude 30° 40'N, longitude 67° 40'E) and the Kara Lora (latitude 30° 34'N, longitude 66° 52'E) reservoirs, and (2) inferred wet versus dry conditions in streams across the NEPL throughout this period. Calibrations were also conducted against observed flow reported from the Burj Aziz Khan station at the NEPL outlet (latitude 30°20'N; longitude 66°35'E). Model simulations indicate that (1) average annual precipitation (1998–2005), surface runoff, and net recharge are 1,300 × 106 m3, 148 × 106 m3, and 361 × 106 m3, respectively; (2) within the NEPL watershed, precipitation and runoff are high for the northeast (precipitation: 194 mm/year; runoff: 38 × 106 m3/year) and northwest (134 mm/year; 26 × 106 m3/y) basins compared to the southern basin (124 mm/year; 8 × 106 m3/year); and (3) construction of delay action dams in the northeast and northwest basins of the NEPL could increase recharge from 361 × 106 m3/year up to 432 × 106 m3/year and achieve sustainable extraction. The adopted methodologies are not a substitute for traditional approaches that require extensive field datasets, but they could provide first-order estimates for rainfall, runoff, and recharge in the arid and semi-arid parts of the world that are inaccessible and/or lack adequate coverage with stream flow and precipitation data

Interpreting Vegetation and Soil Anomalies in the Guarumen Area of Northwestern Venezuela Using Remote Sensing Applications

The Guarumen area of Venezuela is a tectonically active region that is approximately 1,640 mi2 across the northern portions of the Barinas Basin and the foothills of the Mérida Andes. It is structurally influenced by the Caribbean plate to the north, the Nazca plate to the west, and the Maracaibo block against the Guyana Shield of the South American Plate. These result in an oblique boundary that gives rise to the fold-and-thrust belt of the Mérida Andes to the west, and the Caribbean Mountain system to the north, in concordance to the right-lateral shearing that is evidenced by the Boconó fault system. The goal of this research was to investigate the geological setting of northwestern Venezuela and further understand the geologic controls of the region, as it has become a region of interest for mineral, oil, and gas exploration. To achieve the goal, hyperspectral and multispectral data analysis were used to address land cover types by reducing hyperspectral and multispectral spectra to unique endmembers for use in classification. Then, provide an accurate land cover analysis using derived endmembers to characterize the outcomes concerning the influence of geological phenomena, and determine if microclimate analysis using satellite-based land surface temperature data can be effectively used to infer geologic structure or geomorphology, particularly soils and vegetation. Based on the hyperspectral data, an in-depth endmember analysis was conducted with image-derived spectra. These spectra were plotted in comparison with spectral libraries to identify the anomaly classification. It was determined that the natural vegetation make up of a specific region helped identify soil type. The Guarumen area was influenced by the sediment transport of the alluvial stream geomorphology of both the Merida Andes and the Caribbean Mountain System and both its respective geologies. The microclimate analysis shoa land surface temperature comparison of two separate Landscenes. Both shoa similar mean temperature range due to Venezuela’s tropical climate, but differed in other classifications. Results from this research show that remote sensing applications with limited field data can provide accurate land cover analysis concerning geological phenomena, but further field analysis is needed for more detailed classification

Soil erosion in the Alps : causes and risk assessment

The issue of soil erosion in the Alps has long been neglected due to the low economic value of the agricultural land. However, soil stability is a key parameter which affects ecosystem services like slope stability, water budgets (drinking water reservoirs as well as flood prevention), vegetation productivity, ecosystem biodiversity and nutrient production. In alpine regions, spatial estimates on soil erosion are difficult to derive because the highly heterogeneous biogeophysical structure impedes measurement of soil erosion and the applicability of soil erosion models. However, remote sensing and geographic information system (GIS) methods allow for spatial estimation of soil erosion by direct detection of erosion features and supply of input data for soil erosion models. Thus, the main objective of this work is to address the problem of soil erosion risk assessment in the Alps on catchment scale with remote sensing and GIS tools. Regarding soil erosion processes the focus is on soil erosion by water (here sheet erosion) and gravity (here landslides). For these two processes we address i) the monitoring and mapping of the erosion features and related causal factors ii) soil erosion risk assessment with special emphasis on iii) the validation of existing models for alpine areas. All investigations were accomplished in the Urseren Valley (Central Swiss Alps) where the valley slopes are dramatically affected by sheet erosion and landslides. For landslides, a natural susceptibility of the catchment has been indicated by bivariate and multivariate statistical analysis. Geology, slope and stream density are the most significant static landslide causal factors. Static factors are here defined as factors that do not change their attributes during the considered time span of the study (45 years), e.g. geology, stream network. The occurrence of landslides might be significantly increased by the combined effects of global climate and land use change. Thus, our hypothesis is that more recent changes in land use and climate affected the spatial and temporal occurrence of landslides. The increase of the landslide area of 92% within 45 years in the study site confirmed our hypothesis. In order to identify the cause for the trend in landslide occurrence time-series of landslide causal factors were analysed. The analysis revealed increasing trends in the frequency and intensity of extreme rainfall events and stocking of pasture animals. These developments presumably enhanced landslide hazard. Moreover, changes in land-cover and land use were shown to have affected landslide occurrence. For instance, abandoned areas and areas with recently emerging shrub vegetation show very low landslide densities. Detailed spatial analysis of the land use with GIS and interviews with farmers confirmed the strong influence of the land use management practises on slope stability. The definite identification and quantification of the impact of these non-stationary landslide causal factors (dynamic factors) on the landslide trend was not possible due to the simultaneous change of several factors. The consideration of dynamic factors in statistical landslide susceptibility assessments is still unsolved. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. Thus, we evaluated the effect of dynamic landslide causal factors on the validity of landslide susceptibility maps for spatial and temporal predictions. For this purpose, a logistic regression model based on data of the year 2000 was set up. The resulting landslide susceptibility map was valid for spatial predictions. However, the model failed to predict the landslides that occurred in a subsequent event. In order to handle this weakness of statistic landslide modelling a multitemporal approach was developed. It is based on establishing logistic regression models for two points in time (here 1959 and 2000). Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of dynamic causal factors on the landslide probability. The deviation map explained 85% of new independent landslides occurring after 2000. Thus, we believe it to be a suitable tool to add a time element to a susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions or human interactions. In contrast to landslides that are a direct threat to buildings and infrastructure, sheet erosion attracts less attention because it is often an unseen process. Nonetheless, sheet erosion may account for a major proportion of soil loss. Soil loss by sheet erosion is related to high spatial variability, however, in contrast to arable fields for alpine grasslands erosion damages are long lasting and visible over longer time periods. A crucial erosion triggering parameter that can be derived from satellite imagery is fractional vegetation cover (FVC). Measurements of the radiogenic isotope Cs-137, which is a common tracer for soil erosion, confirm the importance of FVC for soil erosion yield in alpine areas. Linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and the spectral index NDVI are applied for estimating fractional abundance of vegetation and bare soil. To account for the small scale heterogeneity of the alpine landscape very high resolved multispectral QuickBird imagery is used. The performance of LSU and MTMF for estimating percent vegetation cover is good (r²=0.85, r²=0.71 respectively). A poorer performance is achieved for bare soil (r²=0.28, r²=0.39 respectively) because compared to vegetation, bare soil has a less characteristic spectral signature in the wavelength domain detected by the QuickBird sensor. Apart from monitoring erosion controlling factors, quantification of soil erosion by applying soil erosion risk models is done. The performance of the two established models Universal Soil Loss Equation (USLE) and Pan-European Soil Erosion Risk Assessment (PESERA) for their suitability to model erosion for mountain environments is tested. Cs-137 is used to verify the resulting erosion rates from USLE and PESERA. PESERA yields no correlation to measured Cs-137 long term erosion rates and shows lower sensitivity to FVC. Thus, USLE is used to model the entire study site. The LSU-derived FVC map is used to adapt the C factor of the USLE. Compared to the low erosion rates computed with the former available low resolution dataset (1:25000) the satellite supported USLE map shows “hotspots” of soil erosion of up to 16 t ha-1 a-1. In general, Cs-137 in combination with the USLE is a very suitable method to assess soil erosion for larger areas, as both give estimates on long-term soil erosion. Especially for inaccessible alpine areas, GIS and remote sensing proved to be powerful tools that can be used for repetitive measurements of erosion features and causal factors. In times of global change it is of crucial importance to account for temporal developments. However, the evaluation of the applied soil erosion risk models revealed that the implementation of temporal aspects, such as varying climate, land use and vegetation cover is still insufficient. Thus, the proposed validation strategies (spatial, temporal and via Cs-137) are essential. Further case studies in alpine regions are needed to test the methods elaborated for the Urseren Valley. However, the presented approaches are promising with respect to improve the monitoring and identification of soil erosion risk areas in alpine regions

An attempt of the Variscan (Hercynian) basement top reconstruction in some sectors of Italy (on land and offshore in the Adriatic Sea)

The last decade's growth in technology has brought a major development in computational tools, hardware and software, in the field of earth sciences. The work is the result of synergistic activity between the classical regional geology and the analytical processing methods typical of the geostatistics and geological. The main objective was to reconstruct, on the basis of available data, the Variscan top surface, preceding the pre-Alpine sedimentary cycle (Upper Carboniferous - Permian) and the Alpine properly Mesozoic and Tertiary, over an area covering the italian peninsula and the Adriatic Sea. The integration of surface geology with the underground information and the interpreted seismic reflection data has been the base of the computation, first on small scale using geostatistical tools and then on large scale with the development of a detailed 3D geologic volume model detail in a geothermal area of ​​Tuscany. The geostatistical processing has produced a series of maps on a national scale relating to the depth of the investigated surface. Available crustal seismic profile were used to this aim, and the first reconstruction was done in the time domain. Then, the velocity was calibrated using subsurface information from deep weel stratigraphy, and the obtained model were used to obtain the surface depth values. The 3D geological model constructed on the basis of subsurface data, widespread in the Tuscan geothermal provinces, has instead highlighted the volumes and geometric relationships between the various rocks taken into account, and defined in detail the shape of the Variscan surface. The resulting degree of confidence in the results is uneven, depending on the data distribution of subsurface information from weels. Howerver, this method has been readily replicable, and produce easy and fast updated result when new data were introduced. The obtained tools are powerful in supporting activities to study and identify natural deep georesources

The Analysis of Open Source Software and Data for Establishment of GIS Services Throughout the Network in a Mapping Organization at National or International Level

Federal agencies and their partners collect and manage large amounts of geospatial data but it is often not easily found when needed, and sometimes data is collected or purchased multiple times. In short, the best government data is not always organized and managed efficiently to support decision making in a timely and cost effective manner. National mapping agencies, various Departments responsible for collection of different types of Geospatial data and their authorities cannot, for very long, continue to operate, as they did a few years ago like people living in an island. Leaders need to look at what is now possible that was not possible before, considering capabilities such as cloud computing, crowd sourced data collection, available Open source remotely sensed data and multi source information vital in decision-making as well as new Web-accessible services that provide, sometimes at no cost. Many of these services previously could be obtained only from local GIS experts. These authorities need to consider the available solution and gather information about new capabilities, reconsider agency missions and goals, review and revise policies, make budget and human resource for decisions, and evaluate new products, cloud services, and cloud service providers. To do so, we need, choosing the right tools to rich the above-mentioned goals. As we know, Data collection is the most cost effective part of the mapping and establishment of a Geographic Information system. However, it is not only because of the cost for the data collection task but also because of the damages caused by the delay and the time that takes to provide the user with proper information necessary for making decision from the field up to the user’s hand. In fact, the time consumption of a project for data collection, processing, and presentation of geospatial information has more effect on the cost of a bigger project such as disaster management, construction, city planning, environment, etc. Of course, with such a pre-assumption that we provide all the necessary information from the existing sources directed to user’s computer. The best description for a good GIS project optimization or improvement is finding a methodology to reduce the time and cost, and increase data and service quality (meaning; Accuracy, updateness, completeness, consistency, suitability, information content, integrity, integration capability, and fitness for use as well as user’s specific needs and conditions that must be addressed with a special attention). Every one of the above-mentioned issues must be addressed individually and at the same time, the whole solution must be provided in a global manner considering all the criteria. In this thesis at first, we will discuss about the problem we are facing and what is needed to be done as establishment of National Spatial Data Infra-Structure (NSDI), the definition and related components. Then after, we will be looking for available Open Source Software solutions to cover the whole process to manage; Data collection, Data base management system, data processing and finally data services and presentation. The first distinction among Software is whether they are, Open source and free or commercial and proprietary. It is important to note that in order to make distinction among softwares it is necessary to define a clear specification for this categorization. It is somehow very difficult to distinguish what software belongs to which class from legal point of view and therefore, makes it necessary to clarify what is meant by various terms. With reference to this concept there are 2 global distinctions then, inside each group, we distinguish another classification regarding their functionalities and applications they are made for in GIScience. According to the outcome of the second chapter, which is the technical process for selection of suitable and reliable software according to the characteristics of the users need and required components, we will come to next chapter. In chapter 3, we elaborate in to the details of the GeoNode software as our best candidate tools to take responsibilities of those issues stated before. In Chapter 4, we will discuss the existing Open Source Data globally available with the predefined data quality criteria (Such as theme, data content, scale, licensing, and coverage) according to the metadata statement inside the datasets by mean of bibliographic review, technical documentation and web search engines. We will discuss in chapter 5 further data quality concepts and consequently define sets of protocol for evaluation of all datasets according to the tasks that a mapping organization in general, needed to be responsible to the probable users in different disciplines such as; Reconnaissance, City Planning, Topographic mapping, Transportation, Environment control, disaster management and etc… In Chapter 6, all the data quality assessment and protocols will be implemented into the pre-filtered, proposed datasets. In the final scores and ranking result, each datasets will have a value corresponding to their quality according to the sets of rules that are defined in previous chapter. In last steps, there will be a vector of weight that is derived from the questions that has to be answered by user with reference to the project in hand in order to finalize the most appropriate selection of Free and Open Source Data. This Data quality preference has to be defined by identifying a set of weight vector, and then they have to be applied to the quality matrix in order to get a final quality scores and ranking. At the end of this chapter there will be a section presenting data sets utilization in various projects such as “ Early Impact Analysis” as well as “Extreme Rainfall Detection System (ERDS)- version 2” performed by ITHACA. Finally, in conclusion, the important criteria, as well as future trend in GIS software are discussed and at the end recommendations will be presented

The application of data mining techniques to interrogate Western Australian water catchment data sets

Current environmental challenges such as increasing dry land salinity, waterlogging, eutrophication and high nutrient runoff in south western regions of Western Australia may have both cultural and environmental implications in the near future. Advances in computer science disciplines, more specifically, data mining techniques and geographic information services provide the means to be able to conduct longitudinal climate studies to predict changes in the Water catchment areas of Western Australia. The research proposes to utilise existing spatial data mining techniques in conjunction of modern open-source geospatial tools to interpret trends in Western Australian water catchment land use. This will be achieved through the development of a innovative data mining interrogation tool that measures and validates the effectiveness of data mining methods on a sample water catchment data set from the Peel Harvey region of WA. In doing so, the current and future statistical evaluation on potential dry land salinity trends can be eluded. The interrogation tool will incorporate different modern geospatial data mining techniques to discover meaningful and useful patterns specific to current agricultural problem domain of dry land salinity. Large GIS data sets of the water catchments on Peel-Harvey region have been collected by the state government Shared Land Information Platform in conjunction with the LandGate agency. The proposed tool will provide an interface for data analysis of water catchment data sets by benchmarking measures using the chosen data mining techniques, such as: classical statistical methods, cluster analysis and principal component analysis.The outcome of research will be to establish an innovative data mining instrument tool for interrogating salinity issues in water catchment in Western Australia, which provides a user friendly interface for use by government agencies, such as Department of Agriculture and Food of Western Australia researchers and other agricultural industry stakeholders