Raster Geographic Information System Research Articles

기상레이더와 지형정보시스템을 이용한 분포형 강우-유출 유역모형의 개발과 검정

기상레이더와 지형정보 시스템을 이용한 홍수사상에 기초하고, 운동역학적이며, 초과강우가 고려된 분포형 강우-유출 유역모형이 개발되었다. 이 유역모형에서 강우로 인한 지표면 유출 및 지표면 흐름과 관련된 각종 변수의 공간적 변동성과 불확실성을 인식하고 설명한다. 개발된 모형은 래스터 지형정보시스템과 공간적ㆍ시간적으로 변하는 강우자료와 호환된다. 몬테칼로 모의와 우도값이 이 모형의 검정을 위하여 이용되었으며, 검정 모형으로부터 반응되는 시스템의 가능범위가 허용되었다. 레이더-강우 추정에 대한 보정으로 강우계가 이용되고, 복잡한 토지이용 상태인 미국 덴버시 도시배수홍수조절 구역내에 있는 두 개 유역들(Ralston Creek와 Goldsmith Gulch 유역)의 제한된 기왕 홍수사상에 이 모형이 적용되었다. 제한된 수의 몬테칼로 모의들과 고려된 홍수사상들을 근거로 관측수문곡선과 계산수문곡선을 비교하여, Nash와 Sutcliffe 효율점수의 범위를 얻게 되었으며, 그 범위는 Ralston Creek과 Goldsmith Gulch 유역에 대한 검정모형들로부터 각각 -0.19∼0.95와 -0.75∼0.81이다. 또 한, Ralston Creek과 Goldsmith Gulch 유역의 Nash와 Sutcliffe 효율점수는 유출용적에 대해 각각 0.88과 0.1, 첨두유량에 대해 0.14와 0.71, 첨두유량 도달시간에 대해 0.99와 0.95로 평가되었다. An event-based, kinematic, infiltration-excess, and distributed rainfall-runoff model using weather radar and Geographic Information System(GIS) was developed to acknowledge and account lot the spatial variability and uncertainty of several parameters relevant to storm surface runoff and surface flow The developed model is compatible with raster GIS and spatially and temporally varied rainfall data. To calibrate the model, Monte Carlo simulation and a likelihood measure are utilized; allowing for a range of possible system responses from the calibrated model. Using rain gauge adjusted radar-rainfall estimates, the developed model was applied and evaluated to a limited number of historical events for the Ralston Creek and Goldsmith Gulch basins within the Denver Urban Drainage and Flood Control District (UDFCD) that contain mixed land use classifications. While based on a limited number of Monte Carlo simulations and considered flood events, Nash and Sutcliffe efficiency score ranges of -0.19∼0.95 / -0.75∼0.81 were obtained from the calibrated models for the Ralston Creek and Goldsmith Gulch basins, based on a comparison of observed and simulated hydrographs. For the Ralston Creek and Goldsmith Gulch basins, Nash and Sutcliffe efficiency scores of 0.88/0.10, 0.14/0.71, and 0.99/0.95 for runoff volume, peak discharge, and time to peak, respectively, were obtained from the model.

Scale-independent Land-use Allocation Modeling in Raster GIS

: A common application of raster-based geographic information systems (GIS) is as an aid in multi-criteria, multi-objective land-use decision problems. However, as the cell resolution increases by reducing cell size, the number of rows and columns in the raster representation also increases. The size of raster representations of land-use problems is often a determining factor in the type of methodology used in solving such problems. Previous land-use allocation models integrated with a raster GIS have used either decision heuristics or exact methods based on linear programming models. The former is fairly scale independent but produces only approximate answers, whereas the latter produces optimal solutions but remains more scale dependent. This paper presents a specialized dual simplex method adapted to the generalized assignment problem that can be used to solve large-scale land-use allocation problems. The dual approach only requires that information for one pixel be stored at a time thus allowing the solution of problems based on any size raster database.

ESTIMATING NEAR–SURFACE SOIL MOISTURE USING ACTIVE MICROWAVE SATELLITE IMAGERY AND OPTICAL SENSOR INPUTS

Recent advances in radar remote sensing techniques illustrate the potential for monitoring soil moistureconditions at spatial and temporal scales required for regional and local modeling efforts. This research examined thefeasibility of producing accurate and spatially distributed estimates of soil moisture using a time series of ERS2 radar imagesfor a tallgrass prairie ecosystem in northeast Kansas. Methods used included field data collection of soil moisture, digitalimage interpretation of optical (NOAA AVHRR and LANDSAT TM) and radar (ERS2) imagery, and environmental modelingin a raster geographic information system (GIS) and image processing environment. Critical to this study was determiningthe scattering behavior of overlying vegetation, or the contribution of vegetation backscatter (.oveg) to the total backscattercoefficient (.ototal), which was simulated using a modified water cloud model. By removing .oveg from .ototal, the amount ofbackscatter contributed by the soil surface (.osoil) was isolated and the linear relationship between .osoil and volumetric soilmoisture determined. Singledate correlations averaged r = 0.62 and r = 0.67 for a burned and unburned watershed,respectively, within the study area. While previous studies have questioned the sensitivity of Cband radars to nearsurfacesoil moisture conditions, these results show that ERS2 data may be capable of monitoring soil moisture conditions over evenextremely dense natural grassland vegetation.

Identifying environmental risk factors for endemic cholera: a raster GIS approach

The bacteria that cause cholera are known to be normal inhabitants of surface water, however, the environmental risk factors for different biotypes of cholera are not well understood. This study identifies environmental risk factors for cholera in an endemic area of Bangladesh using a geographic information systems (GIS) approach. The study data were collected from a longitudinal health and demographic surveillance system and the data were integrated within a geographic information system database of the research area. Two study periods were chosen because they had different dominant biotypes of the disease. From 1992 to 1996 El Tor cholera was dominant and from 1983 to 1987 classical cholera was dominant. The study found the same three risk factors for the two biotypes of cholera including proximity to surface water, high population density, and poor educational level. The GIS database was used to measure the risk factors and spatial filtering techniques were employed. These robust spatial methods are offered as an example for future epidemiological research efforts that define environmental risk factors for infectious diseases.

Spatial filtering using a raster geographic information system: methods for scaling health and environmental data

Despite the use of geographic information systems (GIS) in academic research, it is still uncommon for public health officials to use such tools for addressing health and environmental issues. Complexities in methodological issues for addressing relationships between health and environment, investigating spatial variation of disease, and addressing spatial demand and supply of health care service, hinder the use of GIS in the health sector. This paper demonstrates simple spatial filtering methods for analyzing health and environmental data using a raster GIS. Computing spatial moving average rates reduces individual affects and creates a continuous surface of phenomena. Another spatial analytical method discussed is computation of exposure status surfaces including neighbors’ influences weighted by distance decay. These methods describe how health and environmental data can be scaled in order to better address health problems. Spatial filtering methods are demonstrated using health and population surveillance data within a GIS that were collected for approximately 210,000 people in Matlab, Bangladesh.

A contribution towards simplifying area-wide tsetse surveys using medium resolution meteorological satellite data.

A raster or grid-based Geographic Information System with data on tsetse, trypanosomiasis, animal production, agriculture and land use has recently been developed in Togo. The area-wide sampling of tsetse fly, aided by satellite imagery, is the subject of two separate papers. This paper follows on a first paper, published in this journal, describing the generation of digital tsetse distribution and abundance maps and how these accord with the local climatic and agro-ecological setting. Such maps when combined with data on the disease, the hosts and their owners, should contribute to the knowledge of the spatial epidemiology of trypanosomiasis and assist planning of integrated control operations. Here we address the problem of generating tsetse distribution and abundance maps from remotely sensed data, using a restricted amount of field data. Different discriminant analysis models have been applied using contemporary tsetse data and remotely sensed, low resolution data acquired from the National Oceanographic and Atmospheric Administration (NOAA) and Meteosat platforms. The results confirm the potential of satellite data application and multivariate analysis for the prediction of the tsetse distribution and abundance. This opens up new avenues because satellite predictions and field data may be combined to strengthen and/or substitute one another. The analysis shows how the strategic incorporation of satellite imagery may minimize field collection of data. Field surveys may be modified and conducted in two stages, first concentrating on the expected fly distribution limits and thereafter on fly abundance. The study also shows that when applying satellite data, care should be taken in selecting the optimal number of predictor variables because this number varies with the amount of training data for predicting abundance and on the homogeneity of the distribution limits for predicting fly presence. Finally, it is suggested that in addition to the use of contemporary training data and predictor variables, training and predicted data sets should refer to the same eco-geographic zone.

A GIS raster technique to optimise contaminated soil removal

A geographic information system (GIS) raster technique has been developed and used interactively with remediation designers to evaluate the optimum extent of excavating soil contaminated by chlorinated solvents. The technique and the results of its application are presented. The site was a former chemical storage plant for acids and solvents. Two distinct solvent plumes were detected within the ground using a photo-ionisation detector. The solvents were found to be dissolved in the groundwater and migrating in the general direction of groundwater flow. A remediation strategy was proposed involving the localised excavation of contamination ‘hot spots’ followed by the implementation of a groundwater remediation system. A number of excavation options were discussed and the GIS raster technique was developed to evaluate these options in terms of contaminant removed and excavation cost. The plumes were initially mapped using a triangular irregular network (TIN). These TIN models were rasterised to produce a regular grid of rectangular cells, each cell having a value relating to the concentration of contaminant at that spatial point. The proposed excavation zones were then overlaid on to the raster models as masks. The relationship between the value of contaminant concentration of cells within the mask (or excavation zone) and the total value of contaminant concentration of cells within the solvent plume was used to determine the efficiency of the excavation. The excavation options were compared taking into account the percentage of the contaminant plume removed, the excavation area (soil volumes) and related costs. Once the GIS raster technique had been developed, it proved very quick to rerun the analysis for the other excavation zones. The optimum excavation zone, based upon cost and contaminant recovery, was found for the site. The technique helped by targeting the worst area of contamination and provided the client with a cost-benefit analysis of the different remediation options.

Habitat evaluation using GIS: A case study applied to the San Joaquin Kit Fox

Concern over the fate of plant and animal species throughout the world has accelerated over recent decades. Habitat loss is considered the main culprit in reducing many species’ abundance and range, leading to numerous efforts to plan and manage habitat preservation. Our work uses Geographic Information Systems (GIS) data and modeling to define a spatially explicit analysis of habitat value, using the San Joaquin Kit Fox ( Vulpes macrotis mutica) of California (USA) as an example. Over the last 30 years, many field studies and surveys have enhanced our knowledge of the life history, behavior, and needs of the kit fox, which has been proposed as an umbrella or indicator species for grassland habitat in the San Joaquin Valley of California. There has yet been no attempt to convert much of this field knowledge into a model of spatial habitat value useful for planning purposes. This is a significant omission given the importance and visibility of the imperiled kit fox and increasing trends toward spatially explicit modeling and planning. In this paper we apply data from northern California to derive a small-cell GIS raster of habitat value for the kit fox that incorporates both intrinsic habitat quality and neighborhood context, as well the effects of barriers such as roads. Such a product is a useful basis for assessing the presence and amounts of good (and poor) quality habitat and for eventually constructing GIS representations of viable animal territories that could be included in future reserves.

Soil–landscape resource assessment for plantations — a conceptual framework towards an explicit multi-scale approach

Soil survey is a major component of forest land resource assessment. Conceptual and operational problems arise from employing the conventional methods of survey in forest lands, namely: implicit methods of landscape interpretation (lack of explicit procedures), transfer of data by analogy to unsampled landscapes by inferences which are scale-, and interpreter-dependent, variability of intuitive surveyor judgement, and poor expression of soil variation within map units. These issues are being addressed through the forestland resource assessment and modeling study (FRAMS). This study redefines the conceptual process of resource assessment, and applies soil–landscape modeling (developed here as regolith–terrain modeling) by developing explicit relationships between soil–landscape attributes within a digital, spatial geographic information system (GIS) framework. Soil survey (advanced here as regolith–terrain modeling) is the science and art of predicting soil attribute patterns in the 3D landscape. The FRAMS attempts to overcome some scale and procedural issues related to soil mapping in forest site assessment by adopting a multi-scale and explicit landscape modeling approach. The conceptual aspects of the method presented here aim to predict the ranges in variation of soil–geomorphic attributes that are relevant to forest plantation management. Soil–landscape analysis is adapted in this study to encompass regolith–terrain analysis (i.e. the complete regolith within an understanding of geomorphic systems) employed at three environmental scales: ‘hillslope’, ‘catenary’, and ‘landscape’. There is no linear relationship of data resolution and expression of regolith–terrain attributes between these scales. Each scale is a scale-dependent system linked by an explicit multi-scale method. When combined with geological and climatic data analysis the resultant model provides an advanced, stratified sampling scheme for subsequent field survey procedures in forestland resource assessment. The field analysis, remote-sensing and digital terrain model (DTM) analyses are managed in a raster GIS and can then be effectively classified, a posteriori, according to ‘fuzzy logic’ rules. In the FRAMS, we investigate the scale effect on both the regolith–terrain parameters and their notional relationships to forestland management by investigation at finer scales: hillslope and catenary scales (in southeast Queensland for planted native hoop pine ( Araucaria cunninghamii)), and at a broader scale: the landscape scale (in north Queensland for native species reforestation). The study is still in the preliminary stages so the model is not yet fully functional nor have the components been validated so far.

THE TWO‐DIMENSIONAL UPLAND EROSION MODEL CASC2D‐SED1

ABSTRACT: The two‐dimensional soil erosion model CASC2D‐SED simulates the dynamics of upland erosion during single rainstorms. The model is based on the raster‐based surface runoff calculations from CASC2D. Rainfall precipitation is distributed in time and space. Infiltration is calculated from the Green‐Ampt equations. Surface runoff is calculated from the diffusive wave approximation to the Saint‐Venant equations in two‐dimensions. Watershed data bases in raster Geographical Information System (GIS) provide information on the soil type, size fractions, soil erodibility, cropping management, and conservation practice factors for soil erosion calculations. Upland sediment transport is calculated for the size fractions (sand, silt, and clay), and the model displays the sediment flux, the amount of suspended sediment, and the net erosion and deposition using color graphics. The model has been tested on Goodwin Creek, Mississippi. The peak discharge and time to peak are within ± 20 percent and sediment transport rates within −50 percent to 200 percent.

The spatial pattern of trypanosomosis prevalence predicted with the aid of satellite imagery.

Information on the spatial pattern of African animal trypanosomosis forms a prerequisite for rational disease management, but few data exist for any country in the continent. The present study describes a raster or grid-based Geographic Information System for Togo, a country representative of subhumid West Africa, with data layers on tsetse, trypanosomosis, animal production, agriculture and land use. The paper shows how trypanosomosis prevalence and packed cell volume (PCV) map displays may be predicted from correlations between representative field data and environmental and satellite data acquired from the National Oceanographic and Atmospheric Administration (NOAA) and Meteosat platforms. Discriminant analytical methods were used to assess the relationship between the amount of field data used and the accuracy of the predictions obtained. The accuracy of satellite derived predictions decreases from tsetse abundance to trypanosomosis prevalence to PCV value. The predictions improve when eco-climatic and epidemiological predictors are combined. In Togo, and probably elsewhere, the patterns of trypanosomosis prevalence and PCV are much influenced by animal husbandry and other anthropogenic factors. Additional predictor variables, incorporating these influences might therefore further improve the models.

Coupling land use allocation models with raster GIS

As geographic information systems (GIS) have moved from information storage and retrieval operations towards more decision support functions, there is a need for more integration of spatial analytical modules that can assist in locational decisions. This paper presents a methodology for coupling land use allocation models with a raster GIS. For raster systems, the integration of any decision module has been limited by the size of raster datasets that may contain hundreds of thousands of pixels. Therefore, decision heuristics have been used rather than exact methods such as mathematical programming models. For the problem of land use allocation, the special structure of the generalized assignment problem is used here to handle large scale datasets. The advantage of the mathematical programming approach is the additional information associated with the dual variables and opportunity costs that can be used in subsequent sensitivity analyses.

A systematic approach to area-wide tsetse distribution and abundance maps

AbstractA raster or grid-based Geographic Information System with data on tsetse, trypanosomosis, animal production, agriculture and land use has recently been developed in Togo. This paper describes the generation of area-wide digital tsetse distribution and abundance maps and how these accord with the local climatic and agro-ecological setting. Results include: (i) a spatial demarcation of ecologically distinct areas, producing a seasonal cluster map based on seasonal weather data and temporal series of satellite derived National Oceanic and Atmospheric Administration (NOAA) AVHRR and METEOSAT variables: (ii) tsetse distribution maps of Glossina tachinoides Westwood, G. palpalis palpalis (Robineau-Desvoidy), G. morsitans submorsitansNewstead, G. longipalpis Wiedemann, G. medicorum Austen and G. fusca fusca (Walker); and (iii) tsetse abundance or ‘risk’ maps, corrected for within database seasonal fluctuations, for G. tachinoides and G. p. palpalis. It is concluded that grid-based sampling is the ideal method for rapid assessment of the current vector and disease situation within any country or region, and that remote sensing has an important role to play in planning such a sampling system.

Una familia de modelos de localización óptima para actividades indeseables : propuesta y aplicación con sistemas de información geográfica

The growing social interest on environmental issues has fostered the attention paid by various disciplines to problems of locating noxious facilities. During some years, the location theory, mainly developed to answer questions such as the best place for economic or social facilities, has also been considering this subject and has formulated several models to solve well-structured problems. Grounded on this trend, this paper, first of all, summarizes some limitations of existing models, and next proposes a unified optimization model that enlarges the scope of the former ones. Additionally, and incorporating different transformations of the basic model, it is shown how it can be adapted to solve a wider set of problems. Finally, and as an example, a simple application is carried out using a raster geographical information system.

Teaching Raster GIS Operations with Spreadsheets

Many universities are introducing courses to teach students the principles of geographic information systems (GIS). In addition to lectures, exercises with commercial GIS software are offered to show basic operations. Although students learn to execute such operations, the software may hide their internal structure and logic. We propose using a spreadsheet program as a teaching tool for raster operations such as filter and overlay. Spreadsheets offer a practical way to demonstrate and experiment with raster operations, because the raster structure is captured in the form of rows and columns. With this tool, students are able to perform and visualize operations as well as to see how the data are processed by the algorithms. Our approach is new in that we concentrate on the algorithms of operations. We make explicit which raster functions are actually evaluated when performing a particular operation. We conclude that there are good reasons for using spreadsheets in comparison to traditional GIS software when teaching raster operations. These are demonstration in class, simple user interface, familiarity to students, low cost, flexibility of changing cell values, ease of changing parameters, easy programming environment, and the possibility to look behind the scenes of operations by viewing the code.

Application of LandSat TM data for quality of life assessment in an urban environment

Landsat TM data were used in conjunction with census data for urban quality of life assessment, using the Athens-Clarke County, Georgia in the United States as an example. The satellite image data were used to extract environmental variables of land cover, vegetation index, and surface temperature, while socioeconomic variables were obtained from the census data. Using the social space conceptualization put forth by Chombart de Lauwe, quality of life as a collective attribute that adheres to groups of people was assessed with reference to the morphological and sociocultural environment. A principal components analysis and a raster geographic information system overlay of variables were found to be useful approaches that effectively integrated biophysical and socioeconomic data together to give an accurate assessment of quality of life within the study area. It is concluded that high resolution satellite image data are a useful complement to census data by giving the environmental perspective in urban analysis.

Remodeling census population with spatial information from LandSat TM imagery

In geographic information systems (GIS) studies there has been some difficulty integrating socioeconomic and physiogeographic data. One important type of socioeconomic data, census data, offers a wide range of socioeconomic information, but is aggregated within arbitrary enumeration districts (EDs). Values reflect either raw counts or, when standardized, the mean densities in the EDs. On the other hand, remote sensing imagery, an important type of physiogeographic data, provides large quantities of information with more spatial details than census data. Based on the dasymetric mapping principle, this study applies multivariable regression to examine the correlation between population counts from census and land cover types. The land cover map is classified from LandSat TM imagery. The correlation is high. Census population counts are remodeled to a GIS raster layer based on the discovered correlations coupled with scaling techniques, which offset influences from other than land cover types. The GIS raster layer depicts the population distribution with much more spatial detail than census data offer. The resulting GIS raster layer is ready to be analyzed or integrated with other GIS data.

The pixel: A snare and a delusion

The pixel is an explicit feature of remotely-sensed imagery, and a primary concept of the raster GIS (Geographical Information System) which is the usual vehicle for integration. This Letter addresses the underlying spatial conceptualization of the pixel, which is the parallel of the grid cell in spatial analysis, and the regular grid in sampling. It is argued that integration of remote sensing and GIS can only possibly advance if we develop methods to address the conceptual short-comings of the pixel as a spatial entity, and stop pretending that it is a true geographical object. Three major strands of research which address this issue are highlighted, including mixture modelling, geostatistics and fuzzy classification.

Cellular automata and geographic information systems

Contemporary geographic information systems (GIS) suffer from a variety of problems, These include poor performance for many operators, poor ability to handle dynamic spatial models, and poor handling of the temporal dimension. Cellular automata (CA) have much in common with raster GIS and also excel in many of the areas in which GIS are deficient. Specifically, CA provide explicit handling of dynamic spatial models and time. In addition, if special hardware—cellular automata machines—are used, the potential for considerable performance benefits exists. Many spatial analytical operators behave, in effect, as CA, with the specific GIS functions representing the CA transition rules. Examples of such operations include filtering and diffusion. If the spatial operators are considered to be CA, an improved ability to characterize the operators mathematically is achieved, resulting in an improved dynamic spatial modeling ability. In this research the similarities between the two models (GIS and CA) are examined and the ability to implement each in the other is demonstrated. In addition, the advantages of integration of the two systems, by means of a cellular automata machine as the analytical engine for GIS, are discussed.

LAND‐USE IMPACT ON WATERSHED RESPONSE: THE INTEGRATION OF TWO‐DIMENSIONAL HYDROLOGICAL MODELLING AND GEOGRAPHICAL INFORMATION SYSTEMS

The integration of a two-dimensional, raster-based rainfall-runoff model, CASC2D, with a raster geographical information system (GIS), GRASS, offers enhanced capabilities for analysing the hydrological impact under a variety of land management scenarios. The spatially varied components of the watershed, such as slope, soil texture, surface roughness and land-use disturbance, were characterized in GRASS at a user-specified grid cell resolution for input into the CASC2D model. CASC2D is a raster-based, single-event rainfall-runoff model that divides the watershed into grid cell elements and simulates the hydrological processes of infiltration, overland flow and channel flow in response to distributed rainfall precipitation. The five-step integration of CASC2D and GRASS demonstrates the potential for analysing spatially and temporally varied hydrological processes within a 50 square mile semi-arid watershed. By defining possible land-use disturbance scenarios for the watershed, a variety of rainfall-runoff events were simulated to determine the changes in watershed response under varying disturbance and rainfall conditions. Additionally, spatially distributed infiltration outputs derived from the simulations were analysed in GRASS to determine the variability of hydrological change within the watershed. Grid cell computational capabilities in GRASS allow the user to combine the scenario simulation outputs with other distributed watershed parameters to develop complex maps depicting potential areas of hydrological sensitivity. This GIS-hydrological model integration provides valuable spatial information to researchers and managers concerned with the study and effects of land-use on hydrological response.