In order to reveal the dynamic characteristics of the forest in Bashang area of Hebei Province, MODIS reflectivity and NDVI data with a spatial resolution of 250 m were used for forest classification, and a Thematic Mapper (TM) image in 2005 was resorted to aid training sample selection. With Random Forest Algorithm and time series of MODIS images, the forest in Bashang area was monitored from 2000 to 2015 in every two years. Compared with widely used classifiers such as maximum likelihood classifier and BP artificial neural network algorithm, Random Forest classifier showed the best performance with its overall accuracy and Kappa coefficient being 91.89% and 0.88 respectively. Binary coding was applied to the eight phases of forest distribution images, which can easily and rapidly reflect the changing trajectory from phase to phase. It showed that the severe forest changes mainly occurred in counties of Fengning, Weichang, Zhangbei and Guyuan during the years of 2000, 2010, and 2013.
