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Principles of Geographical Information Systems for Land Resources Assessment
21 Aug 1986-
TL;DR: Geographical information systems Data structures for thematic maps Digital elevation models Data input, verification, storage, and output Methods of data analysis and spatial modelling Data quality, errors, and natural variation: sources of error Errors arising through processing.
Abstract: Geographical information systems Data structures for thematic maps Digital elevation models Data input, verification, storage, and output Methods of data analysis and spatial modelling Data quality, errors, and natural variation: sources of error Errors arising through processing The nature of boundaries Classification methods Methods of spatial interpolation Choosing a geographical information system Appendices Index.
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TL;DR: McGarigal et al. as mentioned in this paper developed a spatial pattern analysis program for quantifying landscape structure called FRAGSTATS, which is almost completely automated and thus requires little technical training.
Abstract: McGarigal, Kevin; Marks, Barbara J. 1995. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. Gen. Tech. Rep. PNW-GTR-351. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 122 p. This report describes a program, FRAGSTATS, developed to quantify landscape structure. FRAGSTATS offers a comprehensive choice of landscape metrics and was designed to be as versatile as possible. The program is almost completely automated and thus requires little technical training. Two separate versions of FRAGSTATS exist: one for vector images and one for raster images. The vector version is an Arc/Info AML that accepts Arc/Info polygon coverages. The raster version is a C program that accepts ASCII image files, 8or 16-bit binary image files, Arc/Info SVF files, Erdas image files, and IDRISI image files. Both versions of FRAGSTATS generate the same array of metrics, including a variety of area metrics, patch density, size and variability metrics, edge metrics, shape metrics, core area metrics, diversity metrics, and contagion and interspersion metrics. The raster version also computes several nearest neighbor metrics. In this report, each metric calculated by FRAGSTATS is described in terms of its ecological application and limitations. Example landscapes are included, and a discussion is provided of each metric as it relates to the sample landscapes. Several important concepts and definitions critical to the assessment of landscape structure are discussed. The appendices include a complete list of algorithms, the units and ranges of each metric, examples of the FRAGSTATS output files, and a users guide describing how to install and run FRAGSTATS.
4,315 citations
Cites methods from "Principles of Geographical Informat..."
...For example, fractal dimension is a measure of shape complexity (Burrough 1986, Mandelbrot 1982, Milne 1988) that can be computed for each patch and then averaged for the landscape, or it can be computed from the landscape as a whole (by using the box-count method [Morse and others 1985])....
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...Fractal analysis usually is applied to the entire landscape mosaic by using the perimeter-area relationship A = k P2/D, where k is a constant (Burrough 1986)....
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...If sufficient data are available, the slope of the line obtained by regressing log(P) on log(A) is equal to 2/D (Burrough 1986)....
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TL;DR: In this paper, the authors focus on the characterization of landscape patterns and their effects on ecological processes and demonstrate that a long history of ecological studies provides a basis for the study of spatial patterns and landscape-level processes.
Abstract: Consideration of spatial dynamics in many areas of ecology has received increased attention during the past decade. For example, the role of disturbance in creating and maintaining a spatial mosaic in the rocky intertidal zone was studied. Patch size could be predicted very well by using a model based on past patterns of disturbance and on measured patterns of mussel movement and recruitment. The dynamics of many natural disturbances and their effects on the spatial mosaic have received considerable study in a variety of terrestrial and aquatic systems. This paper demonstrates that a long history of ecological studies provides a basis for the study of spatial patterns and landscape-level processes. However, the emphasis previously was on describing the processes that created the patterns observed in the biota. The explicit effects of spatial patterns on ecological processes have not been well studied; the emphasis on pattern and process is what differentiates landscape ecology from other ecological disciplines. Therefore, this review focuses on the characterization of landscape patterns and their effects on ecological processes.
3,065 citations
TL;DR: In this article, the spatial heterogeneity of populations and communities plays a central role in many ecological theories, such as succession, adaptation, maintenance of species diversity, community stability, competition, predator-prey interactions, parasitism, epidemics and other natural catastrophes, ergoclines, and so on.
Abstract: The spatial heterogeneity of populations and communities plays a central role in many ecological theories, for instance the theories of succession, adaptation, maintenance of species diversity, community stability, competition, predator-prey interactions, parasitism, epidemics and other natural catastrophes, ergoclines, and so on. This paper will review how the spatial structure of biological populations and communities can be studied. We first demonstrate that many of the basic statistical methods used in ecological studies are impaired by autocorrelated data. Most if not all environmental data fall in this category. We will look briefly at ways of performing valid statistical tests in the presence of spatial autocorrelation. Methods now available for analysing the spatial structure of biological populations are described, and illustrated by vegetation data. These include various methods to test for the presence of spatial autocorrelation in the data: univariate methods (all-directional and two-dimensional spatial correlograms, and two-dimensional spectral analysis), and the multivariate Mantel test and Mantel correlogram; other descriptive methods of spatial structure: the univariate variogram, and the multivariate methods of clustering with spatial contiguity constraint; the partial Mantel test, presented here as a way of studying causal models that include space as an explanatory variable; and finally, various methods for mapping ecological variables and producing either univariate maps (interpolation, trend surface analysis, kriging) or maps of truly multivariate data (produced by constrained clustering). A table shows the methods classified in terms of the ecological questions they allow to resolve. Reference is made to available computer programs.
2,166 citations
TL;DR: Kriging is the method of interpolation deriving from regionalized variable theory that depends on expressing spatial variation of the property in terms of the variogram, and it minimizes the prediction errors which are themselves estimated.
Abstract: Geographical information systems could be improved by adding procedures for geostatistical spatial analysis to existing facilities Most traditional methods of interpolation are based on mathematical as distinct from stochastic models of spatial variation Spatially distributed data behave more like random variables, however, and regionalized variable theory provides a set of stochastic methods for analysing them Kriging is the method of interpolation deriving from regionalized variable theory It depends on expressing spatial variation of the property in terms of the variogram, and it minimizes the prediction errors which are themselves estimated We describe the procedures and the way we link them using standard operating systems We illustrate them using examples from case studies, one involving the mapping and control of soil salinity in the Jordan Valley of Israel, the other in semi-arid Botswana where the herbaceous cover was estimated and mapped from aerial photographic survey
1,632 citations
TL;DR: In this article, the authors investigated how similarity changes with distance in biological communities, and explored whether growth form, dispersal type, rarity, or support affected the rate of distance decay in similarity.
Abstract: Aim Our aim was to understand how similarity changes with distance in biological communities, to use the distance decay perspective as quantitative technique to describe biogeographic pattern, and to explore whether growth form, dispersal type, rarity, or support affected the rate of distance decay in similarity. Location North American spruce-fir forests, Appalachian montane spruce-fir forests. Methods We estimated rates of distance decay through regression of log-transformed compositional similarity against distance for pairwise comparisons of thirty-four white spruce plots and twenty-six black spruce plots distributed from eastern Canada to Alaska, six regional floras along the crest of the Appalachians, and six regional floras along the east‐west extent of the boreal forest. Results Similarity decreased significantly with distance, with the most linear models relating the log of similarity to untransformed distance. The rate of similarity decay was 1.5‐1.9 times higher for vascular plants than for bryophytes. The rate of distance decay was highest for berry-fruited and nut-bearing species (1.7 times higher than plumose-seeded species and 1.9 times higher than microseeded/spore species) and 2.1 times higher for herbs than woody plants. There was no distance decay for rare species, while species of intermediate frequency had 2.0 times higher distance decay rates than common species. The rate of distance decay was 2.7 times higher for floras from the fragmented Appalachians than for floras from the contiguous boreal forest. Main conclusions The distance decay of similarity can be caused by either a decrease in environmental similarity with distance (e.g. climatic gradients) or by limits to dispersal and niche width differences among taxa. Regardless of cause, the distance decay of similarity provides a simple descriptor of how biological diversity is distributed and therefore has consequences for conservation strategy.
1,529 citations
Cites background from "Principles of Geographical Informat..."
...…1991), leading to spatially distance or lag (Ripley, 1988), a critical step in the optimal autocorrelated distributional patterns that were described in interpolation technique called kriging (Burrough, 1986; Webster the early biogeographic literature under the age and area and & Oliver, 1990)....
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