Cover ImageCover Image
The cover image shows a pair of false/true color composite images acquired by the Flight Landata Inc.'s (FLI) Computerized Airborne Multicamera Imaging System (CAMIS). CAMIS is a compact, direct-sen-sor- to-computer imaging system. The synchronized multiple-channel streams with GPS data are digitized simultaneously, displayed live, and recorded online to an airborne Windows NT workstation. A typical CAMIS data product is four spectral bands of sequential aerial image frames with GPS data written to a CD-ROM. The false/true color composite pairs and their mosaics can be produced as shown on the cover. For further information regarding CAMIS and its applications, contact Dr. Xiuhong Sun or Dick Hordon at FLI, 1 Parker St., Lawrence, MA 01843; 508-682-7767. The image of the Charles River was taken while flying over Cambridge, Mass. at an altitude of ~1350m with a resolution of ~0.8m and a swath width of ~500m. Alongside the river are Soldiers Field Road (left) and US3/Memorial Drive (right). The false/true color image pairs are composed from four spectral bands at the nominal central pass wavelengths of 800nm (NIR), 650nm (G), and 470nm (B) respectively with nominal bandwidth of 10nm each. The left of the image pair is a false color composite whose RGB components correspond to NIR, R, and G spectral bands respectively. The right is a true RGB color composite. GPS data stamps are tagged along the right edge.
Peer-Reviewed Articles (Click the linked titles to see the full abstract)
1243 Forward: Special Issue on GIS (Adobe PDF 675Kb)
1244 Guest Editors
1245 An Automated Method for Digitizing Color Thematic Maps
Rick L. Lawrence, Joseph E. Means, and William J. Ripple
A rapid, easy-to-use method for digitizing color thematic maps that makes use of a digital camera fol-lowed by supervised spectral classification and post-classification smoothing was developed.
1249 Mapping Ecological Land Systems and Classification Uncertainties
from Digital Elevation and Forest-Cover Data Using Neural Networks
P. Gong, R. Pu, and J. Chen
The best overall classification accuracy was 52.0 percent when the neural network classification result was compared with the existing map.
1261 Spatial and Compositional Pattern of Alpine Treeline, Glacier
National Park, Montana
Thomas R. Allen and Stephen J. Walsh
Satellite image processing, digital terrain modeling, and landscape pattern metrics were integrated in a GIS to empirically analyze the organization of the alpine treeline ecotone.
1269 Predicting Rare Orchid (Small Whorled Pogonia) Habitat Using
GIS
Molly B. Sperduto and Russell G. Congalton
The equal weight model correctly predicted 57 percent of the sites as potential habitat, and the chi-square model correctly predicted 78 percent of the sites.
1281 Application of a Modified Habitat Suitability Index Model for
Moose
Jeffrey A. Hepinstall, LLoyd P. Queen, and Peter
A. Jordan
A modified moose HSI was applied in a GIS environment using a mirror data set for modeling boundary areas and a moving window with 50 percent overlap to estimate moose habitat suitability.
1287 Using Genetic Learning Neural Networks for Spatial Decision Making
in GIS
Jiang Zhou and Daniel L. Civco
Genetic learning neural networks can provide an alternative for and improvement over traditional suit-ability analysis methods in GIS.
1297 Extending the Applicability of Viewsheds in Landscape Planning
Peter F. Fisher
The application of alternative viewshed operations, providing more precise and versatile responses to user queries about the landscape, is described.
1303 A Comprehensive Managed Areas Spatial Database for the Conterminous
United States
R. Gavin McGhie, Joseph Scepan,and John E.
Estes
Procedures, problems, and relevant issues encountered in compiling and integrating multiple data sources are described.