Automatic photointerpretation for land use management in Minnesota

There are no author-identified significant results in this report

Automatic photointerpretation for plant species and stress identification (ERTS-A1)

The author has identified the following significant results. Automatic stratification of forested land from ERTS-1 data provides a valuable tool for resource management. The results are useful for wood product yield estimates, recreation and wildlife management, forest inventory, and forest condition monitoring. Automatic procedures based on both multispectral and spatial features are evaluated. With five classes, training and testing on the same samples, classification accuracy of 74 percent was achieved using the MSS multispectral features. When adding texture computed from 8 x 8 arrays, classification accuracy of 90 percent was obtained

Automatic photointerpretation for land use management in Minnesota

The author has identified the following significant results. Automatic photointerpretation techniques were utilized to evaluate the feasibility of data for land use management. It was shown that ERTS-1 MSS data can produce thematic maps of adequate resolution and accuracy to update land use maps. In particular, five typical land use areas were mapped with classification accuracies ranging from 77% to over 90%

Automatic photointerpretation for land use management in Minnesota

There are no author-identified significant results in this report

Computerized Interpretation of ERTS Data for Forest Management

Multispectral and spatial features are evaluated for automatic delineation of forest and associated land types. The principal components algorithm was used for determining the efficacy of multispectral bands in making class separations. Four spatial algorithms were evaluated for texture measurements. A thematic map was generated using four multispectral bands as features. Clusters were also used for generating a thematic map