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Mayer, K. E.

Publications and source records attributed to Mayer, K. E..

Using ecological zones to increase the detail of Landsat classifications

Changes in classification detail of forest species descriptions were made for Landsat data on 2.2 million acres in northwestern California. Because basic forest canopy structures may exhibit very similar E-M energy reflectance patterns in different environmental regions, classification labels based on Landsat spectral signatures alone become very generalized when mapping large heterogeneous ecological regions. By adding a seven ecological zone stratification, a 167% improvement in classification detail was made over the results achieved without it. The seven zone stratification is a less costly alternative to the inclusion of complex collateral information, such as terrain data and soil type, into the Landsat data base when making inventories of areas greater than 500,000 acres.

Fox, L., III↗

The application of LANDSAT remote sensing technology to natural resources management. Section 1: Introduction to VICAR - Image classification module. Section 2: Forest resource assessment of Humboldt County.

A teaching module on image classification procedures using the VICAR computer software package was developed to optimize the training benefits for users of the VICAR programs. The field test of the module is discussed. An intensive forest land inventory strategy was developed for Humboldt County. The results indicate that LANDSAT data can be computer classified to yield site specific forest resource information with high accuracy (82%). The "Douglas-fir 80%" category was found to cover approximately 21% of the county and "Mixed Conifer 80%" covering about 13%. The "Redwood 80%" resource category, which represented dense old growth trees as well as large second growth, comprised 4.0% of the total vegetation mosaic. Furthermore, the "Brush" and "Brush-Regeneration" categories were found to be a significant part of the vegetative community, with area estimates of 9.4 and 10.0%.

Fox, L., III↗

Computer classified Landsat data used as a forest stratifier

Results of two forest stratification projects are presented to show that Landsat data, when combined with guided clustering techniques, can provide detailed stratification of forest environment. It is shown that conifer species groupings, vegetative cover classes, and three size classes can be discriminated with accuracies ranging from 83 to 91%.

Fox, L., III↗