Search USGSSearch

Geology topics

R. M. Haralick

Publications and source records attributed to R. M. Haralick.

2 recordsLinked to original sources

Use of thermal-infrared imagery in ground-water investigations, northwestern Montana

Thermal-infrared imagery was used to locate ground-water inflow along a 50-mile (80-kilometre) reach of the Kootenai River and Lake Koocanusa and a 55-mi (88-km) reach of the Clark Fork of the Columbia River in northwestern Montana and northeastern Idaho. The imagery confirmed that measured streamflow gains below Noxon Rapids Dam, ranging from 1,000 to 2,500 cubic feet per second (28 to 71 cubic metres per second), resulted from inflow of ground water, which was about 2.5° Celsius warmer than surface water. The thermal scanner (8.5-11 micrometres) used in May 1972 and March 1973 was mounted in a twin-engined aircraft. On the March 1973 flight, the data were recorded in an analog format on magnetic tape in flight, later were converted to digital format, and then were computer processed using an assignment of patterns to indicate differences in water temperature. Thus, subtle temperature differences are much easier to identify than they are oh conventional film-negative displays. The output data from the image-processing program can be converted to temperature maps having an isotherm spacing of 0.5°C.

Montana

Using radar imagery for crop discrimination: a statistical and conditional probability study

A number of the constraints with which remote sensing must contend in crop studies are outlined. They include sensor, identification accuracy, and congruencing constraints; the nature of the answers demanded of the sensor system; and the complex temporal variances of crops in large areas. Attention is then focused on several methods which may be used in the statistical analysis of multidimensional remote sensing data. Crop discrimination for radar K-band imagery is investigated by three methods. The first one uses a Bayes decision rule, the second a nearest-neighbor spatial conditional probability approach, and the third the standard statistical techniques of cluster analysis and principal axes representation. Results indicate that crop type and percent of cover significantly affect the strength of the radar return signal. Sugar beets, corn, and very bare ground are easily distinguishable, sorghum, alfalfa, and young wheat are harder to distinguish. Distinguishability will be improved if the imagery is examined in time sequence so that changes between times of planning, maturation, and harvest provide additional discriminant tools. A comparison between radar and photography indicates that radar performed surprisingly well in crop discrimination in western Kansas and warrants further study.

Remote Sensing of Environment