Detecting Changes in Forest Vegetation using Multitemporal Landsat TM Data : A case study in the Shibata Forest, Niigata Prefecture

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This paper outlines three change detection methods, i.e., (1) direct multidate classification (MDC), (2) 12-dimensional multitemporal principal component (MPG) and (3) 2-dimensional multitemporal principal component, to detect specific changes in forest cover due to new artificial regeneration, natural growth and forest cutting. Of these 3 techniques, the MDC technique which simply classifies the multidate bands directly consistently provided better delineation of forest change. In the selection of band dimensionality, the 12-dimensional MPC is the most effective. Not only this technique can reduce the dimensionality of the original data, but also effectively picks up the change of interest and provides nearly as the first technique. For all date-pairs (3- to 7-year intervals) the changes due to forest cutting and new artificial regeneration and tree height growth of young plantations were detected; however, the height growth of the larger trees, i.e., S1-S2/3, P1-P2 and P2-P3, could not be detected. As indicated in this study, as the time interval increases, the ability of multidate TM to detect forest cover change increases. Within the young forest plantation, differences in the density of under-story vegetation of Japanese cedar seedlings sometimes led to misclassification.
森林計画学会の論文