Mapping tillage practices with landstat thematic mapper based logistic regression models

Tillage information is crucial in environmental modeling as it has a direct impact on sediment delivery, phosphorus loss, and water holding capacity of agricultural soils. Remote sensing techniques can provide information about tillage practices over large areas. In this study, six Thematic Mapper (TM)-based logistic regression models proposed by van Deventer et al. (1997) were used to distinguish conventional and conservation tillage practices in the Lower Minnesota River watershed located in southern Minnesota. Accuracy assessments of tillage maps derived from Landsat TM data were made using field data collected by the Natural Resources Conservation Service (NRCS). Regression models were easy to use, cost and time effective, and produced reasonably accurate tillage maps. The "percentage correct" and kappa (k) values varied from 42-77% and 0.03-0.51, respectively, with best values for logistic regression models that use TM band 5 or the difference between TM bands 3 and 5 images. This approach is promising for the rapid collection of tillage information on individual fields over large areas.