/pdf/handbook-on-methods-for-climate-change-impact-assessment-and-2u8gon5mzn.pdf

Handbook on Methods for Climate Change Impact Assessment and Adaptation Strategies

Although theoretical and empirical studies show that spatial heterogeneity has important effects on the dynamics of populations and the structure of communities, there has been little rigorous quantification of terms like ''patchiness'' or ''spatial heterogeneity'' in studies of lotic systems. In order to compare the spatial heterogeneity of different systems and understand the causes and consequences of that heterogeneity, we must first be able to quantitatively measure it. Spatial heterogeneity has many aspects that change with the scale of our observations, so we need a battery of descriptive measures that explicitly consider the scale-dependence of ecological pattern Response variables exhibiting similar frequency distributions (i.e., similar overall variability) can have very different spatial distributions; consequently, descriptions of spatial heterogeneity require spatial data, i.e., data related to geographic locations (maps). We review statistical techniques for quantitatively describing aspects of heterogeneity in spatial data, emphasizing the decomposition of heterogeneity into different scales of variation (trends, overall variability and spatial dependence or autocorrelation). Gradients in spatial data can be evaluated using trend analyses (e.g., regressions), whereas the spatial structure of variation around trends can be evaluated using geostatistical methods. The central concept of geostatistics is spatial dependence, which is the degree to which values of a response variable differ as a function of the distance (lag) between sampling locations. Semivariograms plot variation among samples separated by a common lag Versus lag, and can be objectively decomposed by piece-wise regression techniques to estimate the strength and scales of spatial dependence. A variety of other methods can be used to quantify spatial heterogeneity from categorical and numerical maps depending on the question of interest and the underlying structure of the spatial data (e.g., methods derived from fractal geometry and information theory, nearest neighbor analysis, spectral analysis, Mantel's test). Spatial heterogeneity in stream organisms is driven by local variation in environmental conditions, by interactions between individuals of the same or different species, and by the effects of organisms on their abiotic environment. By applying geostatistical methods to spatial data collected from field experiments, stream ecologists can evaluate the effects of biotic and abiotic factors on the spatial arrangement of organisms in streams. We present examples of data obtained from experiments examining how consumers affect, and respond to, spatial heterogeneity in their resources. The results indicate that consumer-resource feedbacks should be considered when modeling the causes and consequences of spatial heterogeneity in streams.

Quantifying Spatial Heterogeneity in Streams

The environmental and social context in which a leptospirosis outbreak took place during the summer of 1996 in the Rio de Janeiro Western Region was examined by using spatial analysis of leptospirosis cases merged with population and environmental data in a Geographical Information System (GIS). Important differences were observed between places where residences of leptospirosis cases are concentrated and other places in the region. Water supply coverage, solid waste collection, sewerage system coverage and flood risk area were the main determining variables from an initial list of ten. The influence of these unfavorable social and environmental factors is verified hundreds of meters distant from the leptospirosis case residences, demonstrating a necessity to broaden the area of health surveillance practices. The geocoding indicated that some cases did not report contact with flood water, even though they were geographically adjacent to cases who did report this contact. Cases may only report exposures they believe are related to the disease. Geocoding is a useful tool for evaluating such bias in the exposure recall.

/pdf/socio-environmental-determinants-of-the-leptospirosis-kqxb40dg56.pdf

Socio-environmental determinants of the leptospirosis outbreak of 1996 in western Rio de Janeiro: a geographical approach.

Geographic information systems (GIS) have developed in response to a range of needs, and provide highly structured environments for working with particular classes of information. The environment and structure of a GIS is defined by its data model; the paper reviews the basic data models of GIS. The needs of discrete transportation modeling have led to a number of extensions of the basic data models. Further necessary extensions are identified. The paper ends with a discussion of appropriate strategies and future directions.

Geographic information systems and disaggregate transportation modeling

The satellite imagery has been effectively utilized for classifying land covertypes and detecting land cover conditions. The Advanced Spaceborne Thermal Emissionand Reflection Radiometer (ASTER) sensor imagery has been widely used in classificationprocess of land cover. However, atmospheric corrections have to be made by preprocessingsatellite sensor imagery since the electromagnetic radiation signals received by the satellitesensors can be scattered and absorbed by the atmospheric gases and aerosols. In this study,an ASTER sensor imagery, which was converted into top-of-atmosphere reflectance(TOA), was used to classify the land use/cover types, according to COoRdination ofINformation on the Environment (CORINE) land cover nomenclature, for an arearepresenting the heterogonous characteristics of eastern Mediterranean regions inKahramanmaras, Turkey. The results indicated that using the surface reflectance data ofASTER sensor imagery can provide accurate (i.e. overall accuracy and kappa values of83.2% and 0.79, respectively) and low-cost cover mapping as a part of inventory forCORINE Land Cover Project.

/pdf/using-aster-imagery-in-land-use-cover-classification-of-3gatm4msos.pdf

Using ASTER Imagery in Land Use/cover Classification of Eastern Mediterranean Landscapes According to CORINE Land Cover Project

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E. Lynn Usery

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Geographical Information Systems: Principles and Applications