Application of Spectral Change Detection Techniques to Identify Forest Harvesting Using Landsat TM Data

Abstract

The main objective of this study was to determine the spectral change technique best suited to detect complete forest harvests (clearcuts) in the Southern United States. In the pursuit of this objective eight existing change detection techniques were quantitatively evaluated and a hybrid method was also developed. Secondary objectives were to determine the impact of atmospheric corrections applied before the change detection, and the affect post-processing methods to eliminate small groups of misclassified pixels ("salt and pepper" effect) had on accuracy.

Landsat TM imagery of Louisa County, Virginia was acquired on anniversary dates in both 1996 and 1998 (Path 16, Row 34), clipped to the study area boundary, and registered to one another. Previous to the change detection exercise, two levels of atmospheric corrections were applied to the imagery separately to produce three data sets. The three data sets were evaluated to determine what level of pre-processing is necessary for harvest change detection. In addition, eight change detection techniques were evaluated: 1) the 345 TM band differencing, 2) 35 TM band differencing, 3) NDVI differencing, 4) principal component 1 differencing, 5) selection of a change band in a multitemporal PCA, 6) tasseled cap brightness differencing, 7) tasseled cap greenness differencing, and 8) univariate differencing using TM band 7. A hybrid method that used the results from the eight previous techniques was developed. After performing the change detection, majority filters using window sizes of 3x3 pixels, 5x5 pixels, and 7x7 pixels were applied to the change maps to determine how eliminating small groups of misclassified pixels would affect accuracies. Accuracy assessments of the binary (harvested or not harvested) change maps were used to evaluate the accuracies of the various methods described using 256 validation points collected by the Virginia Department of Forestry.

The atmospheric corrections did not seem to significantly benefit the change detection techniques, and in some cases actually degraded accuracies. Of the eight techniques applied to the original dataset, univariate differencing using TM band 7 performed the best with a 90.63% overall accuracy, while Tasseled Cap Greenness returned the worst result with an overall accuracy of 78.91%. Principal component 1 differencing and 35 differencing also performed well. The hybrid approach returned good results, but at its best returned an overall accuracy of 90.63%, matching the TM band 7 method. The majority filters using the 3x3 and 5x5 window sizes increased the accuracy in many cases, while the majority filter using the 7x7 window size degraded overall accuracy.