When applying high-spatial-resolution satellite imagery to classify of earth surface targets,
traditional pixel-based classification methods often produce unsatisfactory results. In contrast,
using object-based image classification with image segmentation approaches over the last decade
achieved further improvements. In this study, 29,400 ha of forestland involving the Lioukuei
Experimental Forest and the surrounding area in Kaohsiung City was used as the experimental
area. Three Formosat-II (FS2) images of the experimental area obtained in the summers of 2006,
2009, and 2011 were respectively selected and preprocessed through radiometric calibration and
pan-sharpening fusion without distorting their spectral characteristics. The normalized difference
vegetation index (NDVI) of each FS2 image was established for follow-up image segmentation
with a mean-shift algorithm. After NDVI segmentation with the mean-shift method, landslide
and bare (LSAB) areas for each date were automatically extracted using a simple rule including a
shape index and average slope of an object to filter out noise objects and mountain village areas. To
improve the accuracy assessment for extracting of LSAB areas for each date, an assessment was
performed by comparing to manually digitized sub-images of each pan-sharpened image through
a system sampling method. Results showed that the overall accuracies of the 3 dates were > 95%,
and their Kappa coefficients were > 85%; thus, the mean shift procedure can successfully be
applied to extract LSAB areas from multi-date FS2 images. The area transition rate of Un-LSAB
to LSAB areas between 2006 and 2009 after typhoon Morakot was 3.5-times that in the following
2 yr, and from 2009 to 2011, the area transition rate of vegetation regeneration was 18.7-times that
between 2006 and 2009. Inspecting the spatial distribution of newly increased LSAB areas from
2009 to 2011 with extended tributaries derived from digital elevation model, transition areas from
vegetation cover to LSAB were mostly located in headward areas.