Engineering topics

Basu, J. P.

Publications and source records attributed to Basu, J. P..

3 records

NASA NTRS1979

Natural sampling strategy

A natural stratum-based sampling scheme and the aggregation procedures for estimating wheat area, yield, and production and their associated prediction error estimates are described. The methodology utilizes LANDSAT imagery and agrophysical data to permit an improved stratification in foreign areas by ignoring political boundaries and restratifying along boundaries that are more homogeneous with respect to the distribution of agricultural density, soil characteristics, and average climatic conditions. A summary of test results is given including a discussion of the various problems encountered.

Hallum, C. R.↗

NASA NTRS1978

Large Area Crop Inventory Experiment (LACIE). Second-generation sampling strategy evaluation report

The author has identified the following significant results. The stratification procedure in the new sampling strategy for LACIE included: (1) correlation test results indicating that an agrophysical stratum may be homogeneous with respect to agricultural density, but not with respect to wheat density; and (2) agrophysical unit homogeneity test results indicating that with respect to agricultural density many agrophysical units are not homogeneous, but removal of one or more refined strata from any such current agrophysical unit can make the strata homogeneous. The apportioning procedure results indicated that the current procedure is not performing well and that the apportioned estimates of refined strata wheat area are often unreliable.

Basu, J. P.↗

NASA NTRS1975

Data resolution versus forestry classification and modeling

This paper examines the effects on timber stand computer classification accuracies caused by changes in the resolution of remotely sensed multispectral data. This investigation is valuable, especially for determining optimal sensor and platform designs. Theoretical justification and experimental verification support the finding that classification accuracies for low resolution data could be better than the accuracies for data with higher resolution. The increase in accuracy is constructed as due to the reduction of scene inhomogeneity at lower resolution. The computer classification scheme was a maximum likelihood classifier.

Kan, E. P.↗