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Comparisons of low level L-band brightness temperature (TB) and thermal infrared (TIR) data as well as the following data sets: soil map and land cover data; direct soil moisture measurement; and a computer generated contour map were statistically evaluated using regression analysis and linear discriminant analysis. Regression analysis of footprint data shows that statistical groupings of ground variables (soil features and land cover) hold promise for qualitative assessment of soil moisture and for reducing variance within the sampling space. Dry conditions appear to be more conductive to producing meaningful statistics than wet conditions. Regression analysis using field averaged TB and TIR data did not approach the higher sq R values obtained using within-field variations. The linear discriminant analysis indicates some capacity to distinguish categories with the results being somewhat better on a field basis than a footprint basis.

Cooperative projects between RSI and numerous South Dakota agencies have provided a means of incorporating remote sensing techniques into operational programs. Eight projects discussed in detail are: (1) detection of high moisture zones near interstate 90; (2) thermal infrared census of Canada geese in South Dakota; (3) dutch elm disease detection in urban environment; (4) a feasibility study for monitoring effective precipitation in South Dakota using TIROS-N; (5) open and abandoned dump sites in Spink county; (6) the influence of soil reflectance on LANDSAT signatures of crops; (7) A model implementation program for Lake Herman watershed; and (8) the Six-Mile Creek investigation follow-on.

Landsat data have been investigated extensively to determine crop types and acreage. However, confounding site factors have been found to reduce accuracy. Soils data in a small, contiguous area in southeast South Dakota were used to stratify Landsat data. A June 5 and July 29 CCT were used in a statistical analysis of corn training data. Significant soil parameters causing differences in study area soils were slope and parent material. Implication of the results is that, in this region, stratification of CCT data along parent material boundaries would improve corn classification accuracy. Research expanding on the interaction of soils and crops is both in progress and scheduled for additional studies in east central South Dakota.

There are no author-identified significant results in this report.

The author has identified the following significant results. Change in the vegetative structure was taking place in the Black Hills. Temporal analysis of the areal extent of open meadows was accomplished using black and white and color infrared aerial photography. A reduction of nearly 1100 hectares of open meadows was determined using photointerpretation. Techniques were developed for the management of meandering lakes, including use of LANDSAT imagery for continuous monitoring, classification of hydrophytes on low altitude CIR imagery, and planning and evaluation of improvements and multiple uses on aerial photography and photo mosaics. LANDSAT data were analyzed statistically from small and entire study scene areas to determine the effect of soils stratifications of corn signatures. Band 5 early season and band 7 later season recorded the strongest evidence of the influence of soils on corn signatures. Significant strata were determined by a multiple range test.

There are no author-identified significant results in this report.

The author has identified the following significant results. Digital analysis of LANDSAT CCT's indicated that two discrete spectral background zones occurred among the five soil zone. K-CLASS classification of corn revealed that accuracy increased when two background zones were used, compared to the classification of corn stratified by five soil zones. Selectively varying film type developer and development time produces higher contract in reprocessed imagery. Interpretation of rangeland and cropped land data from 1968 aerial photography and 1976 LANDSAT imagery indicated losses in rangeland habitat. Thermal imagery was useful in locating potential sources of sub-surface water and geothermal energy, estimating evapotranspiration, and inventorying the land.