Forest resource information system, phase 3
There are no author-identified significant results in this report.
Mroczynski, R. P.↗
Engineering topics
Goodrick, F. E.
Publications and source records attributed to Goodrick, F. E..
Delineation of the boundaries of a buried pre-glacial valley with LANDSAT-1 data
The continuity of a narrow meandering strip of Udoll (prairie) soils running east and west for approximately 40 miles across north central Indiana in an area predominantly of Udalfs (timber soils) was detected from LANDSAT-1 data taken on June 9, 1973. This data was processed through a clustering procedure and classified with resulting increased definition of the boundaries among soils grouped according to nine categories and vegetation to two categories of reflectance. This dark stretch of prairie soil is believed to have formed in the heavy textured, poorly drained glacial debris which filled a major pre-glacial tributary of the Teays River System. Ready identification and location of the valley has significance to soil survey and land classification people as a guide to soil classification and land use and to geologists as a guide to location of a potentially economically significant aquifer.
Peterson, J. B.↗
An interdisciplinary analysis of ERTS data for Colorado mountain environments using ADP techniques. An early analysis of ERTS-1 data
There are no author-identified significant results in this report. The principal problem encountered has been the lack of good quality, small scale baseline photography for the test areas. Analysis of the ERTS-1 data for the San Juan Site will emphasize development of a preliminary spectral classification defining grass cover categories, and then selection of subframes for intensive investigation of the forestry, geologic, and hydrologic properties of the area. Primary work has been devoted to the selection and digitization of areas for topographic modeling, and compilation of ground based data maps necessary for computer analysis. Study effort has emphasized: geomorphic features; macro-vegetation; micro-vegetation; snow-hydrology; insect/disease damage; and blow-down. Analysis of a frame of the Lake Texoma area indicates a great deal of potential in the analysis and interpretation of ERTS imagery. Preliminary results of investigations of geologic, forest, range, cropland, and water resources of the area are summarized.
Hoffer, R. M.↗
A study of the utilization of ERTS-A data from the Wabash River Basin. An early analysis of ERTS-1 data
The author has identified the following significant results. A classification of a portion of frame E-1016-16050 CCT was completed using the LARSYS software. The categories: row crops (corn or soybeans), forest and woodland areas, diverted acres of pastureland or nonproductive grassland areas, water (rivers), clouds, and cloud shadows, were represented by one or more spectral classes. The results of this classification are significant in that they show potential for accurate identification and delineation of forested and agricultural areas using automatic data handling techniques.
Landgrebe, D. A.↗
A first-look machine analysis of ERTS-1 data
There are no author-identified significant results in this report. The preliminary analysis of a black and white image of channel 5 data together with tapes from the multispectral scanner is discussed. Four sub-projects based on the initial analysis are discussed. The sub-projects are the analysis by multispectral pattern recognition techniques of the full frame and two particular subframes and a study of the data quality. The procedures for conducting the study and the limitations in analyzing the data are examined. Methods for improving the analysis of the frames by first and second iteration are presented.
Landgrebe, D. A.↗
Variables in automatic classification over extended remote sensing test sites.
Four major causes of variation in response of multispectral scanner data were examined utilizing data from two large test sites. In one case, data throughout a 70-mile flightline was automatically classified with a high degree of accuracy (98%), utilizing training samples from a single small segment of the data. In the second case, spectral data for wheat and other cover types were calibrated and utilized to train the computer, resulting in data up to 90 miles away being classified with an acceptable degree of accuracy (91%), although significant changes in solar illumination and ground cover conditions existed.
Hoffer, R. M.↗