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We propose a method that has general relevance to the digital representation of spatial variation of multivariate landscape data. It is based on the average similarity that operational geographic units (OGU) have with the adjacent ones according to characters relevant understanding landscape patterns and dynamics. The method is flexible and easily executable within the technological framework of geographic information systems (GIS) that today is available even free of charge or at very low cost. An example shows how the method, applied to spatial data of a floristic project for the urban area of Trieste (NE-Italy), can identify floristically homogeneous patches and can quantify the heterogeneity of the transition zones between such patches.

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Cereal Research Communications
Authors: Norbert Schlang, Ulrike Steiner, Heinz-Wilhelm Dehne, Jiro Murakami, Etienne Duveiller, and Erich-Christian Oerke

Fusarium Head Blight (FHB) is one the most important diseases in small grain cereals and often is caused by a complex of Fusarium species. Some of these species are able to produce one or several mycotoxins. The spatial distribution of the disease and associated mycotoxins was examined in this study. Results were mapped and analysed with a geographic information system (GIS). Correlations between the incidence of the deoxynivalenol (DON) producing Fusarium species and DON contamination of kernels were rather weak. The level of DON contamination seemed to be less influenced by the frequency of DON producing Fusarium species than by other factors.

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Multi-band remotely sensed image data contain information on landscape pattern and temporal changes that are greatly underutilized in this technological era when monitoring of disturbance and ecological dynamics is increasingly important to address questions regarding sustainability of ecosystem health and climate change. Among the reasons for this loss of analytical opportunity are the inadequacy of methods for systematic extraction of pattern elements, incongruity between information paradigms for remote sensing and geographic information systems (GIS), and the sheer volume of remotely sensed image data when acquired regularly over time. Long-term cooperative landscape ecological investigations concerning habitat and change detection in conjunction with remote sensing and GIS have yielded a pattern-based approach to progressively segmenting images (PSI) that culminates in a doubly segmented image representation by sets of approximating signal vectors that serve as parsimonious proxies for pixel vectors. The coarser level of segmentation is entirely congruent with raster map structures for GIS, and yet mimics the appearance of an image display by colorization using information on typical spectral properties of segments contained in attribute tables. The components of the coarser representation as spatial segments constitute explicit elements of pattern at several levels. The explicit nature of these pattern elements enables spatial pattern matching for change detection that resolves difficulties with phenological variability and continuity of sensor configurations over time. Conversion to segmented representation can be applied to multi-temporal change indices so as to elicit longer-term patterns of change from temporal sequences of images. The finer level of segmentation for spectral detail enables restoration of image bands in the manner of a low-pass filter for analysis according to the usual paradigms of remote sensing. Mapping of the residuals for the finer detail of image approximation provides further information on exceptional features of landscape ecological pattern.

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We suggest a classificatory approach for land cover analysis that integrates fuzzy set theory with permutation techniques. It represents a non parametric alternative and/or a complement of traditional multivariate statistics when data are scarce, missing, burdened with high degree of uncertainty and originated from different sources and/or times. According to this approach, the Operational Geographic Units (OGUs) in which landscape is subdivided and sampled are classified with hierarchical clustering methods. The clusters of a classification which are significantly sharp are used to define fuzzy sets. In this way, the original data scores are transformed by degrees of belonging. We introduce the concepts of endogenous and exogenous fuzzy sets and we suggest to apply the Mantel test between the similarity matrices of these fuzzy sets to test the predictivity of internal variables with respect to external variables. The approach is applied to OGUs corresponding to the smallest administrative units (kebeles) of the Ethiopian Rift Valley, a degrading area with high risk of further degradation. We found that: 1) there is a high correlation between geo-physical features of the landscape (geology, rainfall and elevation) and some indicators of the human pressure such as land use/cover, land management for livestock breeding and human, household and livestock densities, 2) there is a high correlation between land degradation, measured with relative loss of Normalized Difference Vegetation Index (NDVI) and the human pressure. However, the correlation is higher when the human pressure is considered in the geo-physical context of the landscape. The approach can be easily applied to produce maps useful for planning purposes thanks to geographical information system (GIS) technology that is becoming available at low cost even to small administrative units of developing countries.

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Olivieri, S. T. and E. H. Backus. 1992. Geographic information systems (GIS) applications in biological conservation. Biology International 25: 10-16. Geographic information systems (GIS) applications in biological conservation

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Community Ecology 4 163 183 Olivieri, S. T. and E. H. Backus. 1992. Geographic information systems (GIS

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