Geographic Information System Layers Research Articles

An improved topographic database for King George Island: compilation, application and outlook

A new topographic database for King George Island, one of the most visited areas in Antarctica, is presented. Data from differential GPS surveys, gained during the summers 1997/98 and 1999/2000, were combined with up to date coastlines from a SPOT satellite image mosaic, and topographic information from maps as well as from the Antarctic Digital Database. A digital terrain model (DTM) was generated using ARC/INFO GIS. From contour lines derived from the DTM and the satellite image mosaic a satellite image map was assembled. Extensive information on data accuracy, the database as well as on the criteria applied to select place names is given in the multilingual map. A lack of accurate topographic information in the eastern part of the island was identified. It was concluded that additional topographic surveying or radar interferometry should be conducted to improve the data quality in this area. In three case studies, the potential applications of the improved topographic database are demonstrated. The first two examples comprise the verification of glacier velocities and the study of glacier retreat from the various input data-sets as well as the use of the DTM for climatological modelling. The last case study focuses on the use of the new digital database as a basic GIS (Geographic Information System) layer for environmental monitoring and management on King George Island.

Using a Geographic Information System for Automated Decision Making in Highway Cost Analysis

Highway planning involves selection of a cost-effective corridor in an environmentally responsible manner. With the recent advances in geographic information systems (GISs), most environmental, real estate, and design databases and maps are available in the form of GIS layers. Several mathematical optimization algorithms have been developed to optimize highway alignments, among which genetic algorithms appear most promising because of their ability to avoid getting trapped in local optima. In recent studies, a GIS was integrated with genetic algorithms, enabling full exploitation of GIS features in highway alignment optimization. Those optimization models still lack some dynamic decision-making capabilities during optimal search, such as consideration of bridge cost when rivers are encountered and intersection and interchange costs when roads are encountered. A study that used a GIS as a dynamic decision-making tool in performing highway optimization is presented. Algorithms are developed to estimate costs associated with bridges, tunnels, intersections, and overpasses. A fully automated model is developed that allows dynamic data exchange between the GIS and the optimization algorithm. An example that uses real geographic information from Maryland is presented. The results indicate that consideration of bridges and intersections is significant in highway alignment optimization.

Predicting the Likelihood of Eurasian Watermilfoil Presence in Lakes, a Macrophyte Monitoring Tool

In regions with abundant and diverse freshwater resources, it is difficult and costly to survey all lakes at the level required to detect invasive plants. Effective allocation of monitoring resources requires tools that identify waterbodies where exotic species are most likely to invade. We developed and tested models that predict conditions in which Eurasian watermilfoil, Myriophyllum spicatum, is most likely to survive and successfully colonize. We used logistic regression to model the likelihood of M. spicatum presence or absence using a suite of biological, chemical, and physical lake characteristics which are easily obtainable from public databases. We evaluated model fit by the Aikake criterion and model performance by the percentage of misclassification errors as well as the costs associated with acquiring data for variables modeled. Several models fit our data well, misclassifying only 1.3–11.0% of the lakes where M. spicatum was observed, and used relatively inexpensive landscape variables (percent forest cover in a drainage basin, presence and type of public boat launch, and bedrock type) that typically exist as information layers in geographic information systems (GISs) or recreational atlases. We found that the most important factors affecting the presence or absence of M. spicatum were those that influence water quality factors known to impact M. spicatum growth, rather than factors associated with human activity and dispersal potential. In particular, the amount of forest cover in the lake watershed was consistently important and could control the level of dissolved inorganic carbon in lakes, one of the factors known to affect M. spicatum growth rates. Factors such as the number of game fish species and number and types of boat ramps or proximity to roads were generally less important lake characteristics. Our models can be useful tools for developing management strategies to prevent or slow the spread of M. spicatum and aquatic invaders, such as the zebra mussel, that can attach to it and thus be dispersed. Our models also exemplify a general approach for slowing or stopping the spread of other invading species.

PREDICTING THE LIKELIHOOD OF EURASIAN WATERMILFOIL PRESENCE IN LAKES, A MACROPHYTE MONITORING TOOL

In regions with abundant and diverse freshwater resources, it is difficult and costly to survey all lakes at the level required to detect invasive plants. Effective allocation of monitoring resources requires tools that identify waterbodies where exotic species are most likely to invade. We developed and tested models that predict conditions in which Eurasian watermilfoil, Myriophyllum spicatum, is most likely to survive and successfully colonize. We used logistic regression to model the likelihood of M. spicatum presence or absence using a suite of biological, chemical, and physical lake characteristics which are easily obtainable from public databases. We evaluated model fit by the Aikake criterion and model performance by the percentage of misclassification errors as well as the costs associated with acquiring data for variables modeled. Several models fit our data well, misclassifying only 1.3–11.0% of the lakes where M. spicatum was observed, and used relatively inexpensive landscape variables (percent forest cover in a drainage basin, presence and type of public boat launch, and bedrock type) that typically exist as information layers in geographic information systems (GISs) or recreational atlases. We found that the most important factors affecting the presence or absence of M. spicatum were those that influence water quality factors known to impact M. spicatum growth, rather than factors associated with human activity and dispersal potential. In particular, the amount of forest cover in the lake watershed was consistently important and could control the level of dissolved inorganic carbon in lakes, one of the factors known to affect M. spicatum growth rates. Factors such as the number of game fish species and number and types of boat ramps or proximity to roads were generally less important lake characteristics. Our models can be useful tools for developing management strategies to prevent or slow the spread of M. spicatum and aquatic invaders, such as the zebra mussel, that can attach to it and thus be dispersed. Our models also exemplify a general approach for slowing or stopping the spread of other invading species.

Computer simulation of shallow landsliding in New Zealand hill country

Much of the North Island hill country in New Zealand was converted from forest to pasture in the late 19th century, leaving slopes susceptible to landsliding during heavy rainstorms. Excessive soil loss has called into question the sustainability of pastoral farming, and increased sedimentation has caused problems downstream, such as increased flood risk and degraded fish habitats. A catchment-wide model of landslide erosion and sediment delivery to streams is required to help plan soil conservation and mitigate offsite effects. We describe a computer simulation model of shallow landslides and associated sediment delivery to streams, based on a high-resolution digital terrain model (DTM). The probability of landsliding during a storm is determined for each DTM pixel from GIS (geographic information system) layers of land systems, rainfall, vegetation/land cover, and slope. Where landslides occur, they are routed down the hillside, leaving a trail of sediment about 20 cm thick, on average, until they either reach a stream or become exhausted. Sediment delivery ratios can be calculated for any area. We demonstrate the model by assessing the effects of two afforestation scenarios on soil loss and sediment delivery for a simulated heavy storm in the Waipaoa catchment (220 x 103 ha), using a DTM of 3200 x 3600 25m pixels.

Integrated risk assessment and management: overview and state of the art

Risk assessment and management includes both spatially distributed as well as dynamic problems. While geographic information systems (GIS) provide powerful tools for spatial analysis, their capabilities for complex, and dynamic analysis are limited. Traditional simulation models, on the other hand, are powerful tools for complex and dynamic situations, but they often lack the intuitive visualization and spatial-analysis functions that GIS offers. Obviously, the integration of GIS and simulation models, together with the necessary databases and expert systems, within a common and interactive graphical user interface should make for more powerful, easy-to-use and easy-to-understand risk information systems. More than ten years ago, starting in 1986, these ideas were first implemented in a series of projects involving IIASA, Delft Hydraulics, the JRC, VROM, and the RIVM. The still-ongoing XENVIS project (developing a risk information system for the Netherlands) provides a unique opportunity to review some basic and emerging concepts of integrated risk assessment. Based on a dedicated GIS as the central tool and user interface, databases of hazardous installations and hazardous chemicals are linked in a hypertext structure. They include tools for spatial risk assessment based on externally generated risk contours, and links to models describing accidental and continuous atmospheric releases, spills into surface water systems, and transportation risk analysis. All the models used are fully georeferenced and integrated with the underlying GIS layer, and include an embedded rule-based expert system to help with model input specification and the interpretation of model results. Model results take the form of interactive graphics and animated topical maps for an intuitive understanding and a more efficient interactive analysis.

Predicting Stream Pathogen Loading from Livestock using a Geographical Information System‐Based Delivery Model

AbstractRecent cases of drinking water contamination by pathogens have underscored the importance of preventing livestock waste from entering surface waters. To this end, analytical tools are needed that can identify subwatersheds or livestock operations that contribute disproportionately to contamination. This paper presents a geographical information system (GIS)‐based transport model (SEDMOD) that provides an index of pathogen loading potential to streams by characterizing five key transport parameters: flow‐path hydraulic roughness, gradient, and slope shape, stream proximity, and a normalized soil moisture index. We applied SEDMOD to 12 subwatersheds (10 agricultural, 2 forested controls) of the Saw Kill, a tributary of the Hudson River, New York, and compared model predictions with measured fecal coliform (FC) levels. The transport model, combined with a livestock density GIS layer, could explain 50% of the variation in average FC discharge among the subwatersheds (r = 0.71, P = 0.01, n = 12). By contrast, neither total livestock FC output nor predicted FC transport were correlated with geometric mean FC concentration (P > 0.05). In a multiple regression, predicted FC transport, mean water temperature, and mean turbidity could account for 80% of the observed variation in FC discharge (r = 0.90, P = 0.001, n = 12). We conclude that, although more field work and algorithm development is needed to yield more accurate quantitative predictions, the model is useful for predicting the relative contribution of diverse livestock operations within a varied landscape. This provides watershed managers and regulators with a rating method to prioritize sites for nonpoint source pollution control.

Processing techniques for mapping urban growth: Kathmandu, Nepal

Urban expansion in the Kathmandu Valley, Nepal was accurately mapped using both visual and digital analysis of SPOT multispectral data. This study clearly demonstrates the importance of the appropriate date for data acquisition. Of three SPOT images obtained, only one was in a temporal window where useful results could be extracted. Masking out features of spectral confusion using Geographic Information System (GIS) layers and spatial filtering were effective processing strategies. Field experience, as in most remote sensing studies, was a necessary component to maximize the information extracted and to provide a method of accuracy assessment. The considerations and strategies in this study may be useful in other areas for urban mapping using satellite data, however, appropriate techniques are often data and site dependent.

From Geographic Holism to Geographic Information System

Substantial changes in a core idea of geography, integration, have occurred since Alexander von Humboldt published Kosmos (1845-1862). These changes are part of a larger shift in Western civilization to mechanistic reasoning. This shift led to the strengthening of system-based analysis, central to the development of geographic information systems (GIS). The duality of holism and the systems approach has led to an apparent contradiction in geography. R. Hartshorne in The Nature of Geography described this contradiction, but as did Alfred Hettner and Emil Wisotzki before, moved to partial systems as the core concept of geographic integration. Hartshorne's concept of vertical integration is the antecedent for the ubiquitous GIS layer model. The reduction of geographic relationships and processes to mechanistic components (layers) aids the systematic approach, but may lessen geographic understanding of a place's interrelationships. Although the partiality of the system approach was already acknowledged by Finch and Hartshorne in the 1930s, the tension between holistic and system approaches in geography remains. Holism and system-based approaches are indeed complementary methods for developing geographic understanding. Using holistic approaches to understand geographic phenomena, before we teleologically (following a purpose) analyze phenomena as a system, extends GIS to include broader interrelationships of geography in specific locations.

96/05230 The effect of inertinite content on coal combustion reactivity

Forest information over a landscape is often represented as a spatial mosaic of polygons, separated by differences in species composition, height, age, crown closure, productivity, and other variables. These polygons are commonly delineated on medium-scale photography (e.g., 1:15,000) by a photo-interpreter familiar with the inventory area, and displayed and stored in a Geographic Information System (GIS) layer as a forest cover map. Forest cover maps are used for multiple purposes including timber and habitat supply analyses, and carbon inventories, at a regional or management unit level, and for parks planning, operational planning, and selection of stands for many purposes at a local level. Attribute data for each polygon commonly include the variables used to delineate the polygon, and other variables that can be measured or estimated using these medium-scale photographs. Additional measures that can only be obtained via expensive ground measures or possibly on high resolution photographs (e.g., volume per unit area, biomass components per unit area, tree-list of species and diameters) are available only for a sample of polygons, or may have been gathered independently using a sample survey over the land area. Improved linkages over a variety of data sources may help to support landscape level analyses. This study presents an approach to combine information from a systematic (grid) ground survey, forest cover (polygon) data, and Landsat Thematic Mapper (TM) imagery using variable-space nearest neighbor methods to estimate (i) mean ground-measured attributes for each polygon, in particular, volume per ha (m3/ha), stems per ha, and quadratic mean diameter for each polygon; and (ii) variation of these ground attributes within polygons. The approach was initially evaluated using Monte Carlo simulations with known measures of these attributes. Nearest neighbor methods were then applied to an approximate 5000 ha area (about 1000 polygons) of high productivity, mountainous forests located near the Pacific Coast of British Columbia, Canada. Based on the simulation results, the use of Landsat pixel reflectances to estimate volume per ha, average tree size (i.e., quadratic mean diameter), and stems per ha did not show great promise in improving estimates for each polygon over using forest cover data alone. However, in application, the use of remotely sensed data provided estimates of within-polygon variability. At the same time, the estimated means of these three imputed variables over the entire study area were very similar to the representative sample estimates using the ground data only. Extensions to other variables such as ranges of diameters and numbers of snags may also be possible providing useful data for habitat and forest growth analysis.

96/05236 The effects of properties and pyrolysis conditions on coal and char ignition and oxidation reactivity

Forest information over a landscape is often represented as a spatial mosaic of polygons, separated by differences in species composition, height, age, crown closure, productivity, and other variables. These polygons are commonly delineated on medium-scale photography (e.g., 1:15,000) by a photo-interpreter familiar with the inventory area, and displayed and stored in a Geographic Information System (GIS) layer as a forest cover map. Forest cover maps are used for multiple purposes including timber and habitat supply analyses, and carbon inventories, at a regional or management unit level, and for parks planning, operational planning, and selection of stands for many purposes at a local level. Attribute data for each polygon commonly include the variables used to delineate the polygon, and other variables that can be measured or estimated using these medium-scale photographs. Additional measures that can only be obtained via expensive ground measures or possibly on high resolution photographs (e.g., volume per unit area, biomass components per unit area, tree-list of species and diameters) are available only for a sample of polygons, or may have been gathered independently using a sample survey over the land area. Improved linkages over a variety of data sources may help to support landscape level analyses. This study presents an approach to combine information from a systematic (grid) ground survey, forest cover (polygon) data, and Landsat Thematic Mapper (TM) imagery using variable-space nearest neighbor methods to estimate (i) mean ground-measured attributes for each polygon, in particular, volume per ha (m3/ha), stems per ha, and quadratic mean diameter for each polygon; and (ii) variation of these ground attributes within polygons. The approach was initially evaluated using Monte Carlo simulations with known measures of these attributes. Nearest neighbor methods were then applied to an approximate 5000 ha area (about 1000 polygons) of high productivity, mountainous forests located near the Pacific Coast of British Columbia, Canada. Based on the simulation results, the use of Landsat pixel reflectances to estimate volume per ha, average tree size (i.e., quadratic mean diameter), and stems per ha did not show great promise in improving estimates for each polygon over using forest cover data alone. However, in application, the use of remotely sensed data provided estimates of within-polygon variability. At the same time, the estimated means of these three imputed variables over the entire study area were very similar to the representative sample estimates using the ground data only. Extensions to other variables such as ranges of diameters and numbers of snags may also be possible providing useful data for habitat and forest growth analysis.

Problems in maintaining associativity in LIS with particular reference to the needs of the utility industry

Abstract Associativity is defined as the association between features in different layers in Land and Geographic Information Systems (LIS/GIS). The associativity problem occurs when the changes in one layer cannot be transmitted automatically to the associated features in other layers. In which case, the correct relationships between features in layers cannot be maintained and accurate analysis cannot be done. This paper examines the causes of the associativity problem in the states of Victoria and New South Wales in Australia, and how some of the utility companies in these states are tackling the problem.

VALIDATION OF AGNPS FOR SMALL WATERSHEDS USING AN INTEGRATED AGNPS/GIS SYSTEM1

ABSTRACT: The AGNPS (AGricultural NonPoint Source) model was evaluated for predicting runoff and sediment delivery from small watersheds of mild topography. Fifty sediment yield events were monitored from two watersheds and five nested subwater‐sheds in East Central Illinois throughout the growing season of four years. Half of these events were used to calibrate parameters in the AGNPS model. Average calibrated parameters were used as input for the remaining events to obtain runoff and sediment yield data. These data were used to evaluate the suitability of the AGNPS model for predicting runoff and sediment yield from small, mild‐sloped watersheds.An integrated AGNPS/GIS system was used to efficiently create the large number of data input changes necessary to this study. This system is one where the AGNPS model was integrated with the GRASS (Geographic Resources Analysis Support System) GIS (Geographical Information System) to develop a decision support tool to assist with management of runoff and erosion from agricultural watersheds. The integrated system assists with the development of input GIS layers to AGNPS, running the model, and interpretation of the results.