Remote sensing technology has great potential for acquisition of detailed and accurate land-use information for management and planning of urban regions. However, the determination of land-use data with high geometric and thematic accuracy is generally limited by the availability of adequate remote sensing data, in terms of spatial and temporal resolution, and digital image analysis techniques. This study introduces a methodology using information on image spatial form—landscape metrics—to describe urban land-use structures and land-cover changes that result from urban growth. The analysis is based on spatial analysis of land-cover structures mapped from digitally classified aerial photographs of the urban region Santa Barbara, CA. Landscape metrics were calculated for segmented areas of homogeneous urban land use to allow a further characterization of the land use of these areas. The results show a useful separation and characterization of three urban land-use types: commercial development, high-density residential, and low-density residential. Several important structural land-cover features were identified for this study. These were: the dominant general land cover (built up or vegetation), the housing density, the mean structure and plot size, and the spatial aggregation of built-up areas. For two test areas in the Santa Barbara region, changes (urban growth) in the urban spatial land-use structure can be described and quantified with landscape metrics. In order to discriminate more accurately between the three land-cover types of interest, the landscape metrics were further refined into what are termed ‘landscape metric signatures’ for the land-use categories. The analysis shows the importance of the spatial measurements as second-order image information that can contribute to more detailed mapping of urban areas and towards a more accurate characterization of spatial urban growth pattern.

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Observation and habitat data were compiled and analyzed in conjunction with recovery planning for the endangered California Condor (Gymnogyps californianus). A geographic information system (GIS) was used to provide a quantitative inventory of recent historical Condor habitats, to measure the association of Condor activity patterns and mapped habitat variables, and to examine spatio‐temporal changes in the range of the species during its decline. Only five percent of the study area within the historic range is now used for urban or cultivated agricultural purposes. Observations of Condor feeding perching, and nesting were nonrandomly associated with mapped land cover, in agreement with life history information for the species. The precipitous decline in numbers of Condors in this century produced only a small reduction in the limits of the observed species’ range, as individual birds continued to forage over most of the range. Some critical risk factors such as shooting and lead poisoning are difficult to map and have not been included in the database. Besides the applications demonstrated in this case study, GIS can be a valuable tool for recovery planning, in the design of stratified sampling schemes, or for extrapolation of habitat models over unsurveyed regions. We conclude with recommendations from this case study regarding when to consider using GIS and the importance of pilot studies and sensitivity analysis.

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This paper describes a framework for an image processing procedure for operational agricultural crop area estimation. This operational framework has been conceived within the development of an Advanced Agricultural Information System (AAIS) for the "Regione del Veneto " (RdV -Veneto Region) in northeastern Italy. The objective of this program is to develop the ability to generating timely and accurate area estimates and production information for four major agricultural crops: soybeans, sugar beets, corn, and small grains. AAIS uses state of the art methods in remote sensing and geographic information systems (GIS) technology and integrates a variety of data types including satellite imagery. This paper describes the methodology developed for image and ancillary data processing for the production of crop area statistics. Using a combination of standard unsupervised classification and GIS operations that incorporate knowledge about the agricultural system, a "sequential masking" classification procedure was derived. This sequential masking procedure yielded crop classification accuracies that at the study site level range between 76% and 99% depending on the crop under study. We believe that classification accuracies will improve with full system implementation, along with the incorporation of new and/or improved thematic information and operational experience using AAIS-based estimation.

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