Geology topics

Leonard Gaydos

Publications and source records attributed to Leonard Gaydos.

5 recordsLinked to original sources

USGS1998

Loose-coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and Washington/Baltimore

Prior research developed a cellular automaton model, that was calibrated by using historical digital maps of urban areas and can be used to predict the future extent of an urban area. The model has now been applied to two rapidly growing, but remarkably different urban areas: the San Francisco Bay region in California and the Washington/Baltimore corridor in the Eastern United States. This paper presents the calibration and prediction results for both regions, reviews their data requirements, compares the differences in the initial configurations and control parameters for the model in the two settings, and discusses the role of GIS in the applications. The model has generated some long term predictions that appear useful for urban planning and are consistent with results from other models and observations of growth. Although the GIS was only loosely coupled with the model, the model's provision of future urban patterns as data layers for GIS description and analysis is an important outcome of this type of calculation.

California, Maryland↗

USGS1985

Interim program for land cover mapping in Alaska utilizing Landsat digital data

The enactment of the Alaska National Interest Lands Conservation Act (ANILCA) in 1980 imposed mandates on all major land management agencies in Alaska to prepare comprehensive resource and management plans to assess wildlife habitat, oil and gas exploration and development, wild and scenic river, land disposals, timber production, and archaeological and cultural resources, To meet these objective, the U. S. Geological Survey (USGS) has embarked on a plan to classify land cover for the entire State of Alaska using Landsat digital data. the USGS, in cooperation with other agencies, has completely Landsat-derived land use and land cover classification of 115 million acres for the State of Alaska. With this work as a substantial foundation, the USGS has prepared a comprehensive plan for classifying the remaining areas of Alaska. The development of this program will lead to a complete interim land use and land cover classification system for Alaska and provide for the dissemination of map products, statistics, and acreage summaries for all areas of Alaska at 1:250,000 scale. It also allows for the dissemination of Landsat digital data for those areas.

Alaska↗

USGS1984

PROCESSING OF SCANNED IMAGERY FOR CARTOGRAPHIC FEATURE EXTRACTION.

Digital cartographic data are usually captured by manually digitizing a map or an interpreted photograph or by automatically scanning a map. Both techniques first require manual photointerpretation to describe features of interest. A new approach, bypassing the laborious photointerpretation phase, is being explored using direct digital image analysis. Aerial photographs are scanned and color separated to create raster data. These are then enhanced and classified using several techniques to identify roads and buildings. Finally, the raster representation of these features is refined and vectorized. 11 refs.

Conference Paper↗

USGS1978

Low-cost computer classification of land cover in the Portland area, Oregon, by signature extension techniques

Computer-aided techniques for interpreting multispectral data acquired by Landsat offer economies in the mapping of land cover. Even so, the actual establishment of the statistical classes, or "signatures," is one of the relatively more costly operations involved. Analysts have therefore been seeking cost-saving signature extension techniques that would accept training data acquired for one time or place and apply them to another. Opportunities to extend signatures occur in preprocessing steps and in the classification steps that follow. In the present example, land cover classes were derived by the simplest and most direct form of signature extension: Classes statistically derived from a Landsat scene for the Puget Sound area, Wash., were applied to the Portland area, Oreg., using data for the next Landsat scene acquired less than 25 seconds down orbit. Many features can be recognized on the reduced-scale version of the Portland land cover map shown in this report, although no statistical assessment of its accuracy is available. The cost of classifying 5,607 square kilometers (2,165 sq. mi.) in the Portland area was less than 8 cents per square kilometer ($0.0788, or $0.2041 per square mile). Besides saving in costs, this and other signature extension techniques may be useful in completing land use and land cover mapping in other large areas where multispectral and multitemporal Landsat data are available in digital form but other source materials are generally lacking.

Oregon, Washington↗

USGS1978

Inventory of land use and land cover of the Puget Sound region using Landsat digital data

Landsat multispectral scanner digital data from four bands were analyzed using computers to produce land use and land cover information of the Puget Sound region, Wash., for use by agencies in that area. The data were first geographically registered to map coordinates. This registration enabled samples of known land cover types to be digitized from the maps. Samples of the same land cover were grouped together and then subdivided by cluster analysis into spectrally similar classes. Spectral categories were associated with specific land cover classes and used to determine spectral signatures for classification of the entire region. Reclustering and reclassification techniques were developed and then employed to minimize certain classification errors. The classified data were displayed in color using a film recorder. This color image was enlarged photographically to a 1:100000 scale to match new base maps of the region. Although the result resembles a conventional polygonal land use and land cover map, certain image-like qualities remain and yield additional information about the landscape.

Washington↗