PE&RS January 2002


PE&RS January 2002Cover Image
The National Elevation Dataset (NED) is a seamless raster product produced by the U.S. Geological Survey (USGS). The NED provides elevation data coverage of the continental United States, Alaska, Hawaii, and the island territories in a seamless format with consistent projection, resolution, elevation units, and horizontal and vertical datums. “Application-ready” NED products are available in several formats through a Web-based seamless data distribution system. The NED is the result of the maturation of the USGS elevation production program, in which national coverage of quadrangle-based digital elevation models has been completed. Because it is regularly maintained and updated and is easily accessible, the NED provides basic elevation data for many GIS applications, thus fulfilling many of the concepts of the “best available” framework geospatial data as envisioned for the National Spatial Data Infrastructure. The use of the NED facilitates applications by allowing users to focus on analysis rather than data preparation. This month’s Highlight Article (pg. 5) gives details on the technical specifications of the NED, its production methodology, and ongoing related development. Further information is available on the NED Web site at

Highlight Article

5 The National Elevation Dataset
Dean Gesch, Michael Oimoen, Susan Greenlee, Charles Nelson, Michael Steuck, and Dean Tyler

Peer-Reviewed Articles (Click the linked titles to see the full abstract)

33 A Synergistic Automatic Clustering Technique (SYNERACT) for Multispectral Image Analysis
Kai-Yi Huang

A new effective synergistic automatic clustering technique (SNYERACT) was developed to serve as a substitute for ISODATA when applied to remote sensing image analysis with a large data set.

41 Fuzzy Objects: Their Changes and Uncertainties
Tao Cheng

Uncertainty issues in GIS and remote sensing are addressed.

51 Scale and Texture in Digital Image Classification
Christopher J.S. Ferro and Timothy Warner

Simulated and actual data experiments were used to determine the effects various texture scales had upon a maximum-likelihood classifier and to suggest an approach that might aid in the selection of appropriate window sizes for texture feature extraction.

65 Impacts of Patch Size and Land-Cover Heterogeneity on Thematic Image Classification Accuracy
Jonathan H. Smith, James D. Wickham, Steven V. Stehman, and Limin Yang

The effects of landscape characteristics on image classification accuracy are assessed and compared with those due to land-cover class confusion.

71 Drought Monitoring with NDVI-Based Standardized Vegetation Index
Albert J. Peters, Elizabeth A. Walter-Shea, Lei Ji, Andrés Viña, Michael Hayes, and Mark D. Svoboda

The Standardized Vegetation Index, derived from AVHRR satellite data, describes the relative condition of vegetation status and can assess the extent and severity of drought at a 1-kilometer spatial resolution.

77 Subpixel Classification of Alder Trees Using Multitemporal Landsat Thematic Mapper Imagery
Kazuo Oki, Hiroyuki Oguma, and Mikio Sugita

Estimation of alders from multitemporal TM imagery of the Kushiro mire wetlands was achieved by an unmixing method using ground-truth data.

83 Use of Contour-Based DEMs for Deriving and Mapping Topographic Attributes
Hiroko Mizukoshi and Masamu Aniya

The methods to calculate slope gradient and aspect and to classify slope form using contour-based DEMs are presented.

22 Integrating Remote Sensing at the Global, Regional, and Local Scale — Call for Papers
49 Call for Papers — Challenges in Geospatial Analysis and Visualization
64 ASPRS-ACSM 2002 Conference

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