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Displaying 736 - 740 of 1605Within-territory use of different land cover types by tropical forest birds in a fragmented landscape
Many terrestrial species of birds have been observed to depend on large contiguous areas of forest; in the tropics this appears to be especially true for terrestrial insectivores. Far less is understood about how more-common species, that are less dependent on interior forests, use both forested and non-forested habitat in fragmented landscapes.
Ecological land suitability analysis through spatial indicators: An application of the Analytic Network Process technique and Ordered Weighted Average approach
Multicriteria-Spatial Decision Support Systems (MC-SDSS) are increasingly popular tools in decision-making processes and in policy making, thanks to their significant new capabilities in the use of spatial or geospatial information. Many spatial problems are complex and require the use of integrated analysis and models. The present paper illustrates the development of a MC-SDSS approach for studying the ecological connectivity of the Piedmont Region in Italy.
Degradation of cultivated bench terraces in the Three Gorges Area: Field mapping and data mining
Due to resettlements, construction of new infrastructure, and new land reclamation the rapid agricultural changes in the Three Georges Area (TGA) in Central China are expected to force the degradation of the cultivated terraced landscape. Consequently, increased soil erosion can hamper a sustainable land management in the mountainous TGA. This paper presents the model framework TerraCE (Terrace Condition Erosion) for determining the causes for different terrace conditions and terrace degradation based on field surveys and spatial data mining.
Clustering based on eigenspace transformation – CBEST for efficient classification
Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden of information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transformation to the dataset followed by compressing the data in the eigenspace and storing them in coded matrices and vectors.
Classifying a high resolution image of an urban area using super-object information
In this study, a multi-scale approach was used for classifying land cover in a high resolution image of an urban area. Pixels and image segments were assigned the spectral, texture, size, and shape information of their super-objects (i.e. the segments that they are located within) from coarser segmentations of the same scene, and this set of super-object information was used as additional input data for image classification.