Date of Award:

5-1997

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Environment and Society

Department name when degree awarded

Geography and Earth Resources

Committee Chair(s)

Allan Falconer

Committee

Allan Falconer

Committee

Dave Roberts

Committee

Doug Ramsey

Abstract

The Army National Guard Bureau has implemented a cooperative project with Utah State University to help with the use, display, and evaluation of environmental data for maintaining land condition. Camp Grayling, Michigan, is comprised of deciduous and evergreen forest types. Use of remote sensing for classification has been limited in this region due to the difficulty of species-level classification using single-date remote-sensing techniques. Also, remote sensing has traditionally focused on mapping homogenous zones rather than vegetation boundaries, while one of the concerns for land managers is the nature of vegetation edges (ecotones).

This study analyzed each season and band from multiseasonal satellite imagery for their contribution to separating vegetation type and density classes. Then spectral reflectance values for each vegetation and density class were used in discriminant models that define vegetation cover types and densities. These models were then tested against points within 200 m of vegetation boundaries to determine the performance of the models at edges of vegetation types. The reflectance values for vegetation types on Landsat Thematic Mapper (TM), Landsat MultiSpectral Sensor (MSS), and Advanced Very High Resolution Radiometer (AVHRR) imagery were used.

Single-band separability decreased with decreasing resolution of the remote sensing data, and the number of spectral bands that could separate means of vegetation and density cover classes was much greater than expected. Winter bands provided more separability than expected for density classes. A VHRR data were shown to provide very little separation and were not included in the discriminant analysis. In the evaluation of the discriminant models, both resubstitution and crossvalidation tests showed that TM and MSS were nearly equal in their ability to discriminate cover types and densities.

At the vegetation boundary zones, classification accuracy increased with increasing distance from the edge. These results are encouraging for future classification and monitoring of ecotones using satellite imagery, as picture elements (pixels) of ecotones generally exhibit the characteristics of a mixing of the boundary vegetation types. Further investigation into fuzzy set classification and ecotone classification and monitoring appears warranted.

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