Engineering topics

Chapman, G. M.

Publications and source records attributed to Chapman, G. M..

3 records

NASA NTRS1982

Technical summary of accomplishments made in preparation for the USSR barley exploratory experiment

The highlights of the work accomplished under each subcomponent of the U.S.S.R. Barley Pilot Experiment, which is scheduled for completion in 1984, are summarized. A significant amount of developmental system implementation activity was in the final stages of preparation prior to the rescoping of project tasks. Unpublished materials which are significant to this exploratory experiment are incorporated into the appendixes.

Chapman, G. M.↗

NASA NTRS1982

Automated pixel screening and selection technique

One approach for estimating crop acreage on the basis of Landsat data involves the sampling of portions of Landsat imagery which represent 5 x 6-n mi areas on the ground, taking into account acreage estimates for each of these areas. Difficulties concerning the estimating procedures are related to labeling. These difficulties arise in particular in connection with mixed pixels. The present investigation is concerned with the employment of a suitable automated technique for overcoming these difficulties. A description of the selected technique is presented, and the test conducted for the evaluation of the technique is discussed. It is found that the employed automated pixel screening and selection procedure provides representative samples of the scene. The amount of time spent on pure pixel selection is significantly reduced by using the automated method.

Dailey, C. L.↗

NASA NTRS1981

Evaluation of the procedure 1A component of the 1980 US/Canada wheat and barley exploratory experiment

Several techniques which use clusters generated by a new clustering algorithm, CLASSY, are proposed as alternatives to random sampling to obtain greater precision in crop proportion estimation: (1) Proportional Allocation/relative count estimator (PA/RCE) uses proportional allocation of dots to clusters on the basis of cluster size and a relative count cluster level estimate; (2) Proportional Allocation/Bayes Estimator (PA/BE) uses proportional allocation of dots to clusters and a Bayesian cluster-level estimate; and (3) Bayes Sequential Allocation/Bayesian Estimator (BSA/BE) uses sequential allocation of dots to clusters and a Bayesian cluster level estimate. Clustering in an effective method in making proportion estimates. It is estimated that, to obtain the same precision with random sampling as obtained by the proportional sampling of 50 dots with an unbiased estimator, samples of 85 or 166 would need to be taken if dot sets with AI labels (integrated procedure) or ground truth labels, respectively were input. Dot reallocation provides dot sets that are unbiased. It is recommended that these proportion estimation techniques are maintained, particularly the PA/BE because it provides the greatest precision.

Chapman, G. M.↗