Catchment infiltration II: Contributing areas

This study considers two issues of interest to the hydrologic and geographical information systems community. One deals with identifying the spatial distribution of infiltration and runoff contributing areas. The other addresses process modelling within a GIS framework. The study operates on the premise that partitioning of precipitation into runoff or infiltration depends on rainfall intensity and on soil properties. The problem is that neither local rainfall intensity, nor soil properties such as infiltration capacity and macroporosity are known well enough for all points of a catchment and need to be estimated. We infer local intensity from the interpolated distribution of cumulated rain depths over the catchment and record duration at the official met site. Measured values of sorptivity and hydraulic conductivity define infiltration. Negative head infiltration describes macroporosity. To scale-up measured point values to larger areas and to model infiltration and macropore continuity at a catchment scale we use geostatistical kriging and conditional simulation together with standard GIS techniques of overlay manipulation. Results delineate areas contributing to runoff and infiltration and relate them to macroporosity. By intersecting overlays of precipitation with those of infiltration we create alternate GIS masks targeting specific portions of the watershed as either runoff or infiltration contributing zones. Choice of cell size and time interval define the scales of averaging for the application. Kriged surfaces illustrate the distribution of catchment infiltration, while conditional simulation provides a mechanism to define model uncertainty.     

Using GIS in Areawide Pest Management: A Case Study in South Dakota

Infestations of corn rootworms (Coleoptera: Chrysomelidae) create economic and environmental concerns in the Corn Belt region of the United States. To supplement the population control tactics of areawide pest management programs, we believe that a better understanding of the spatial relationships between biotic and abiotic or physical factors at the landscape scale is needed. Our research used several geographical information systems (GIS) and spatial analytical techniques to examine relationships between corn rootworm metapopulation dynamics, soil texture, and elevation. Within GIS, several spatially explicit procedures were used that include an interpolation technique, spatial autocorrelation analysis, and contingency analysis. Corn rootworm metapopulation distributions were found to be aggregated and related to soil texture and elevation. We review techniques and discuss our preferences for using particular spatially explicit procedures. The information derived from the spatial analyses demonstrates how GIS can be used in areawide pest management to provide inputs for spatially explicit models to predict future pest populations and formulate more well‐informed pest management decisions. The techniques described in this paper could easily be extended to study the spatial dynamics between other pest populations in agricultural landscapes.     

Effects of watershed discretization on estimation of hydrologic parameters

This study evaluates how watershed discretization affects estimation of hydrologic parameters using GIS data. Two aggregation methods were evaluated using three GIS data sets for a large watershed in Kansas, which is discretized into five different levels. The two aggregation methods are weighted-average and dominant-value. The three GIS data sets, soils, land use, and temperature, constitute three commonly used hydrologic parameters with distinct spatial patterns. The study evaluated the aggregation effects measured in terms of statistical distribution, spatial distribution, information level, and spatial dependence of the aggregated data. Results indicate that: (1) statistically, the mean and modal values of the source data are well preserved through aggregation but with a reduced standard deviation; (2) changes in spatial patterns are less predictable than those of the statistical distribution, and the changes depend on the geometric similarity and spatial overlap between the source and target polygons; (3) the information level in general decreases with aggregation for the dominant method, and it increases for the average method although the original values are altered; and (4) spatial dependence generally increases with aggregation.     

The spatial prediction of tree species diversity in savanna woodlands of Southern Africa

In this study, we tested the utility of remotely sensed data in predicting tree species diversity in savanna woodlands. Specifically, we developed linear regression functions based on a combination of the coefficient of variation of near infrared (NIR) radiance and the soil-adjusted vegetation index (SAVI), both derived from advanced space-borne thermal emission and reflection radiometer satellite imagery. Using the regression functions in a Geographic Information System (GIS), we predicted the spatial variations in tree species diversity. Our results showed that tree species diversity can be predicted using a combination of the coefficient of variation of NIR radiance and SAVI. We conclude that remotely sensed data can be used to spatially predict tree species diversity in savanna woodlands.     

A GIS for hydrological modelling in the semi-arid, HAPEX-Sahel experiment area of Niger, Africa

During the HAPEX-Sahel experiment (1991–94), water redistribution processes were studied at the meso-scale (10 000 km²) near Niamey, Niger. A project now under way at ORSTOM aims at modelling the regional water balance through a spatial approach combining GIS data organization and distributed hydrological modelling. The main objective is to extend the surface water balance, by now available only on a few, small (around 1 km²) unconnected endoreic catchments, to a more significant part of the HAPEX-Sahel square degree, a 1500 km² region called SSZ that includes most of the environmental and hydrology measurement sites. GIS architecture and model design consistently consider data and processes at the local, catchment scale, and at the regional scale. The GIS includes spatial and temporal hydrological data (rainfall, surface runoff, ground water), thematic maps (topography, soil, geomorphology, vegetation) and multi-temporal remote sensing data (SPOT, aerial pictures). The GIS supports the simulation of the composite effect at the regional scale of highly variable and discontinuous component hydrologic processes operating at the catchment scale, in order to simulate inter-annual aquifer recharge and response to climatic scenarios at the regional scale.     

Morphometric analysis to determine floods in the Upper Krishna basin using Cartosat DEM

The evaluation of basin characteristics from the morphometric parameters helps in understanding the physical behaviour of the catchments with respect to floods. The advanced technologies, such as Remote sensing and Geographic Information System (GIS), were used for extraction of drainage networks using Cartosat Digital Elevation Model (DEM) for the Upper Krishna basin, to evaluate the morphometric analysis. Basin morphometric parameters were applied to assess the major influencing catchments which cause flooding in the main Krishna River. The morphometric analysis for the ten major potential flood prone river catchments of the basin reveals that, the river catchments such as Krishna, Koyna, Yerla having the greater tendency to peak discharge in a short period of time to the main Krishna River because of high relief ratio (Rh), high ruggedness number and less time of concentration (Tc). The Don catchment having the highest drainage density (Dd), stream frequency, mean bifurcation ratio and infiltration number causes greater runoff influence on the main Krishna River. The Dudhganga and Panchaganga catchments having highest form factor, medium Dd, texture ratio, Rh and time of concentration causes moderate runoff influence towards main Krishna River. The study indicates that systematic analysis of morphometric parameters derived from Cartosat DEM using GIS provide useful information about catchment characteristics with respect to floods management.     

矢量图形数据的空间数据库存取方法的研究

用现代数据库统一来存放和管理空间数据与属性数据是GIS的发展趋势：利用Oracle数据库的Spatial模块,可以进行空间几何数据的相关存取,方便地实现对空间几何数据的管理、通过开发基于J2EE构架的Oracle数据库应用程序,实现了矢量图形数据的存取,提出了一种开发GIS矢量图形系统的新思路     

基于GIS的配电网管理信息系统

结合面向对象与数据库技术,本文设计并实现了基于组件式GIS的配电网管理信息系统。根据GIS与配电网管理的特点,介绍了系统的总体结构与主要功能,简要论述了系统实现时的关键技术。利用GIS技术开展配电网管理工作,达到电网基础地理数据、电力设备图形数据以及属性数据的有机结合与集成管理,进而通过空间分析实现高级分析功能。通过系统试运行,达到了简化操作方式、提高工作效率的效果。     

Using GIS to enhance agricultural planning: The example of inter-seasonal rainfall variability in Zimbabwe

Inter-seasonal rainfall variability is evaluated as a potential source of much needed information for agroecological or agroclimatological classifications. For some food crop production areas, inter-seasonal rainfall variability appears to dominate decisions in the crop-production strategy. We constructed 31 years worth of seasonal rainfall surfaces for Zimbabwe using techniques as described by Hutchinson (1995) and his software ANUSPLIN. We evaluated these surfaces in an effort to describe the main rainfall period (October to March) for Zimbabwe in terms of rainfall variability. Our results were then put into the context of an agroecological study which produced a Natural Regions map for Zimbabwe. GIS technology enables the synthesis and integration of many more data than was possible in a pre-computer era and robust rainfall variability surfaces contribute towards improved agroecological and agroclimatological classifications for planning (natural resource management and agricultural) purposes. GIS technology also enables a shift in the design of such agroecological or agroclimatological studies: dynamic characterization can readily produce objective specific classifications, classifications which use the wealth of data readily accessible in a GIS to produce maps and databases reflecting specific boundary conditions.     

Fields as a framework for integrating GIS and environmental process models. Part 1: Representing spatial continuity

Linking a GIS to a spatially distributed, physically-based environmental model offers many advantages. However, the implementation of such linkages is generally problematic. Many problems arise because the relationship between the reality being represented by the mathematical model and the data model used to organize the spatial data in the GIS has not been rigorously defined. In particular, while many environmental models are based on theories that assume continuity and incorporate physical fields as independent variables, current GISs can only represent continuous phenomena in a variety of discrete data models. This paper outlines a strategy in which field variables are used to enable modellers to work directly with the spatial data as spatially continuous phenomena. This allows the manner in which the spatial data has been discretized and the ways in which it can be manipulated to be treated independently from the conceptual modelling of physical processes. Modellers can express their spatial data needs as representations of reality, rather than as elements of a GIS database, and a GIS-independent language for model development may result. By providing a formal linkage between the various models of spatial phenomena, a mechanism is created for the explicit expression of transformation rules between the different spatial data models stored and manipulated by a GIS.     

Modeling and Communicating the Conceptual Intent of Geo‐Analytical Tasks for Human‐GIS Interaction

One of the fundamental issues of geographical information science is to design GIS interfaces and functionalities in a way that is easy to understand, teach, and use. Unfortunately, current geographical information systems (including ArcGIS) remains very difficult to use as spatial analysis tools, because they organize and expose functionalities according to GIS data structures and processing algorithms. As a result, GIS interfaces are conceptually confusing, cognitively complex, and semantically disconnected from the way human reason about spatial analytical activities. In this article, we propose an approach that structures GIS analytical functions based on the notion of “analytical intent”. We describe an experiment that replaces ArcGIS desktop interface with a conversational interface, to enable mixed‐initiative user‐system interactions at the level of analytical intentions. We initially focus on the subset of GIS functions that are relevant to “finding what's inside” as described by Mitchell, but the general principles apply to other types of spatial analysis. This work demonstrates the feasibility of delegating some spatial thinking tasks to computational agents, and also raises future research questions that are key to building a better theory of spatial thinking with GIS.     

空间统计分析与GIS在区域经济分析中的应用

首先概述了度量空间自相关、空间关联的一些空间统计分析方法以及识别区域空间关联的标准,然后探讨了将空间统计分析嵌入到一个GIS系统中的可行性,开发了一个分析空间关联的模块,并举例说明其在区域经济分析中的应用。     

GIS and simple systems for visual,interactive, spatial analysis

Abstract

In much the same way as a spreadsheet is more appropriate to some needs than a fully relational DBMS, many users interested in analysis of spatially referenced data have fairly unsophisticated requirements for extensive data storage or complex retrieval functions, and limited needs for topographic detail, cartographic accuracy or advanced map editing; however, they do require access to a wide range of statistical functions for data transformation, reduction and smoothing, multivariate analysis, and the modelling of spatial relationships. This paper argues that the needs of such users are poorly met by the 'mainstream' model for GIS that is often presented in the literature. A PC-based system, equivalent in GIS terms to the spreadsheet, is described. It integrates simple data structures, limited map creation and editing, a powerful range of standard and spatially orientated statistical functions, and the ability to interactively present results through chloropleth, contour, proportionate symbol, or proximally shaded maps. It is particularly designed to be easily accessible and to encourage visual interactive analysis. For some users such a system has acted as an adequate solution in its own right and for others it has provided a useful educational transition to more extensive and powerful systems.     

GIS在物流配送系统中的应用

主要介绍GIS用于物流配送系统的研究现状,提出将GIS的空间数据分析及可视化功能融入到物流配送过程中。并以ArcGIS为平台,研究GIS与物流配送系统集成的基本框架,阐述系统实现的主要功能,并对功能实现的关键技术进行探讨。     

Landslide susceptibility mapping of a catchment area using frequency ratio,fuzzy logic and multivariate logistic regression approaches

Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images, inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators, in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst accuracy (69%).     

基于GIS的海南省人口空间分布模式统计分析

在GIS平台上利用空间自相关统计分析方法对海南省人口的全局和局部空间差异的特征、分布形态与变化趋势进行初步的探索分析,从根本上改变了传统的统计分析观念,改善了统计分析方法与方式。研究的结果显示,海南省人口分布呈现显著的空间相关性,且表现为从中部山区逐渐向周边沿海区域扩展的环带状聚集模式。根据这一结论提出几点促进海南省未来人口与经济可持续发展的建议。     

A Spatial Analysis System for Integrating Data,Methods and Models on Environmental Risks and Health Outcomes

Integrating data on health outcomes with methods of disease mapping and spatially explicit models of environmental contaminants are important aspects of environmental health surveillance. In this article, we describe a modular, web‐based spatial analysis system that uses GIS, spatial analysis methods and software services delivered over computer networks to achieve this end. The Environmental Health Surveillance System (EHSS) is a prototype system that is designed to serve three purposes: a secure environment for producing maps of disease outcomes from individual‐level data while preserving privacy; an automated process of linking environmental data, environmental models, and GIS tasks like geocoding for the purposes of estimating individual exposures to environmental contaminants; and mechanisms to visualize the spatial patterns of disease outcomes via Web‐based mapping interfaces and interactive tools like Google Earth.     

Knowledge Based Soil Attribute Mapping In GIS: The Expector Method

EXPECTOR is a method of combining data and 'expert' knowledge within a Geographic Information System to provide information on the occurrence of spatially distributed attributes. It was developed to predict soil property values from spatially variable input data. Although initially developed to provide soil surveyors with a quantitative soil mapping method, it also has applications in land evaluation, land capability assessment, geological mapping and in precision agriculture. It operates on the basis that the state of a particular property, which may be difficult to measure directly, can be inferred from other (more measurable) entities and a knowledge of their inter–relationships. The method has been implemented as a stand–alone 'Knowledge Editing' module for the PC that can be linked to raster GIS packages. This paper describes the basis of the method and illustrates its use with an example describing the production of a surface clay content map for a small catchment in south–western Western Australia.     

Integration of remote sensing and GIS in monitoring the dynamics of land cover in Oroba-Kibos catchment area, Kenya

Land cover in Kenya is in a state of fl ux at different spatial and temporal scales. This compromises environmental integrity and socioeconomic stability of the population hence increasing their vulnerability to the externalities of environmental change. The Oroba-Kibos catchment area in western Kenya is one locality where rapid land use changes have taken place over the last 30 years. The shrubs, swamps, natural forests and other critical ecosystems have been converted on the altar of agriculture, human settlement, fuel wood and timber. This paper presents the results of a study that aimed at providing spatially-explicit information for effective remedial response through (a) Mapping the land cover; (b) Identifying the spatial distribution of land cover changes; (c) Determining the nature, rates and magnitude of the land cover changes, and; (d) Establishing the drivers of land use leading to land cover changes in Oroba-Kibos catchment area. Bi-temporal Landsat TM imagery, fi eld observation, household survey and ancillary data were obtained. Per-fi eld classifi cation of the Landsat TM imagery was performed in a GIS and the resultant land cover maps assessed using the fi eld observation data. Post-classifi cation comparison of the maps was then done to detect changes in land cover that had occurred between 1994 and 2008. SPSS was used to analyze the household survey data and attribute the detected land cover changes to their causes. The fi ndings showed that 9 broad classes characterize the catchment area including the natural forests, swamps, natural water bodies, woodlands, shrublands, built-up lands, grasslands, bare lands and croplands. Croplands are dominant and accounted for about 65% (57122 ha) of the total land in 1994, which increased at the rate of 0.89% to 73% (64772 ha) in 2008, while natural water bodies has the least spatial coverage accounting for about 0.6% (561 ha) of the total land in 1994, which diminished at the rate of 3.57% to 0.3% (260 ha) in 2008. Climate, altitude, access and rights to land, demographic changes, poverty, political governance, market availability and economic returns are the interacting mix of proximate and underlying factors that drive the land cover changes in Oroba-Kibos catchment area.