POTENTIALITY OF USING CLASSIFICATION AND ULTIVARIATE ALTERATION DETECTION METHODS FOR LAND COVER CHANGES
Abstract
Test made for the potential use of maximum likelihood classification and MAD (multivariate alteration detection) techniques as land cover changes in El Rawashda forest, Sudan is measured. The applicability of maximum likelihood classification and the MAD method in multi-temporal satellite imagery change detection studies is demonstrated and an interpretation approach based on change matrix and correlation matrix was given. The study proved that maximum likelihood classification provided an accurate way to quantify, map and analyze changes over time in land cover. MAD transformation found to be good unsupervised change detection method for satellite images. In addition, applied on any spatial and/or spectral subset of the full data set.
Four main land cover classes namely grassland, close forest, open forest and bare land detected. Change matrix performed to map the land cover changes from 2003 to 2006.
The results show a noticeable increase in area on both close forest and open forest areas with decrease in grasslands. More than one third of grassland (36%) was converted to close forest, one-fourth (24%) to open forest areas.
In this span of time, 9079 hectares of open forest, (8% of the investigation area), were transformed to close forest. A linear transformation performed by applying the multivariate alteration detection (MAD), then the MAD components examined to identify the quality of changes.
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References
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