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Brumfield, J. O.

Publications and source records attributed to Brumfield, J. O..

10 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Comparative techniques used to evaluate Thematic Mapper data for land cover classification in Logan County, West Virginia

Several digital data processing techniques were evaluated in an effort to identify and map active/abandoned, partially reclaimed, and fully revegetated surface mine areas in the central portion of Logan County. The TM data were first subjected to various enhancement procedures, including a linear contrast stretch, principal components and canonical analysis transformations. At the same time, four general procedures were followed to produce six classifications as a means of comparing the techniques involved. Preliminary results show that various feature extraction/data reduction techniques provide classification results equal or superior to the more straightforward unsupervised clustering technique. Analyst interaction time for labelling clusters is reduced using the canonical analysis and principal components procedures, though the canonical technique has clearly produced better results to date.

Brumfield, J. O.↗
NASA NTRS1984

A comparison of the usefulness of canonical analysis, principal components analysis, and band selection for extraction of features from TMS data for landcover analysis

Three feature extraction methods, canonical analysis (CA), principal component analysis (PCA), and band selection, have been applied to Thematic Mapper Simulator (TMS) data in order to evaluate the relative performance of the methods. The results obtained show that CA is capable of providing a transformation of TMS data which leads to better classification results than provided by all seven bands, by PCA, or by band selection. A second conclusion drawn from the study is that TMS bands 2, 3, 4, and 7 (thermal) are most important for landcover classification.

Boyd, R. K.↗
NASA NTRS1982

Optimization of a Non-traditional Unsupervised Classification Approach for Land Cover Analysis

The conditions under which a hybrid of clustering and canonical analysis for image classification produce optimum results were analyzed. The approach involves generation of classes by clustering for input to canonical analysis. The importance of the number of clusters input and the effect of other parameters of the clustering algorithm (ISOCLS) were examined. The approach derives its final result by clustering the canonically transformed data. Therefore the importance of number of clusters requested in this final stage was also examined. The effect of these variables were studied in terms of the average separability (as measured by transformed divergence) of the final clusters, the transformation matrices resulting from different numbers of input classes, and the accuracy of the final classifications. The research was performed with LANDSAT MSS data over the Hazleton/Berwick Pennsylvania area. Final classifications were compared pixel by pixel with an existing geographic information system to provide an indication of their accuracy.

Boyd, R. K.↗
NASA NTRS1982

A Temporal Approach to Monitor Surface Mine Reclamation Progress via LANDSAT

Using LANDSAT satellite imagery, the mine reclamation process can be studied on a temporal and continuing basis. Not only can the progress of reclamation be readily monitored, but also a breakdown in the mining reclamation process can be detected. In viewing reclamation, it is important to monitor the mined site well past initial revegation stages. With present mining law and bonding procedures, fast revegetational growth is encouraged, often leading to poor soil fertilizing and inappropriate stabilizing species. As a result, the initial reclamation may exhibit good qualities for one or two years but then may experience vegetational deterioration after the state has relinquished the mining company from it's responsibility. It is this small-scale breakdown in the reclamation process that was detected using an unsupervised classification technique with eight-year temporal LANDSAT imagery coverage.

Davis, A. L.↗
NASA NTRS1982

Potential Applications of LANDSAT Data in Energy Management Associated with Kenya's Forests

LANDSAT can be effectively used to monitor the extent and magnitude of forest cover change in Kenya in order to evaluate the potential for energy supply. Digital processing of LANDSAT data provides a reliable monitoring technique for forest resource management in Kenya. Data analysis was used to illustrate that Kenya's forests are indeed diminishing. A model used to make projections for the availability of fuelwood as an energy source is presented. The resulting figures imply that Kenya's forest will all but disappear around the end of the 20th century. Analysis of LANDSAT data for Mau East substantiates these alarming findings.

Maghenda, M. M.↗
NASA NTRS1982

Comparing digital data processing techniques for surface mine and reclamation monitoring

The results of three techniques used for processing Landsat digital data are compared for their utility in delineating areas of surface mining and subsequent reclamation. An unsupervised clustering algorithm (ISOCLS), a maximum-likelihood classifier (CLASFY), and a hybrid approach utilizing canonical analysis (ISOCLS/KLTRANS/ISOCLS) were compared by means of a detailed accuracy assessment with aerial photography at NASA's Goddard Space Flight Center. Results show that the hybrid approach was superior to the traditional techniques in distinguishing strip mined and reclaimed areas.

Witt, R. G.↗
NASA NTRS1981

An unsupervised classification approach for analysis of Landsat data to monitor land reclamation in Belmont county, Ohio

Two unsupervised classification procedures for analyzing Landsat data used to monitor land reclamation in a surface mining area in east central Ohio are compared for agreement with data collected from the corresponding locations on the ground. One procedure is based on a traditional unsupervised-clustering/maximum-likelihood algorithm sequence that assumes spectral groupings in the Landsat data in n-dimensional space; the other is based on a nontraditional unsupervised-clustering/canonical-transformation/clustering algorithm sequence that not only assumes spectral groupings in n-dimensional space but also includes an additional feature-extraction technique. It is found that the nontraditional procedure provides an appreciable improvement in spectral groupings and apparently increases the level of accuracy in the classification of land cover categories.

Brumfield, J. O.↗
NASA NTRS1981

Application of LANDSAT data to monitor land reclamation progress in Belmont County, Ohio

Strip and contour mining techniques are reviewed as well as some studies conducted to determine the applicability of LANDSAT and associated digital image processing techniques to the surficial problems associated with mining operations. A nontraditional unsupervised classification approach to multispectral data is considered which renders increased classification separability in land cover analysis of surface mined areas. The approach also reduces the dimensionality of the data and requires only minimal analytical skills in digital data processing.

Bloemer, H. H. L.↗