Abstract: Forest classification plays an important role in understanding the structure and function of forest ecosystem. Shennongjia Forest district belongs to the northern subtropical monsoon climate zone with vegetations distributed in different vertical belts. Because of its high altitude, complicated climate, rich forest types and fragmented landscape, it is difficult to classify the vegetation types in forest region of Shennongjia district. This paper constructed a time series analyses for vegetation classification. The time series of factors collection consisted of the vegetation indices, the first principal component (PC1) and topographic factors, which were calculated from 24 HJ-1A/B CCD images of 2013 and DEM. The vegetation indices collection included NDVI (Normalized Difference Vegetation Index), DVI (Difference Vegetation Index) and RVI (Ratio Vegetation Index). The topographic factors collection included altitude, slope and aspect. By means of the C5.0 Intelligent decision tree algorithm, seven categories were classified in the Shennongjia forest district. They were:cold-temperate coniferous forest, temperate coniferous and broad-leaved mixed forest, temperate deciduous broad-leaved forest, broad-leaved evergreen and deciduous mixed forest, north-subtropical evergreen broad-leaved forest, shrub and meadow. The results showed that:(1) Using the intelligent decision tree algorithm, the overall accuracy of the classification reached 72.7%, with a Kappa coefficient of 0.67; (2) the Vegetation Index values of north-subtropical evergreen broad-leaved forest, the broad-leafed evergreen and deciduous mixed forest, temperate deciduous broad-leafed forest and the temperate coniferous and broad-leafed mixed forest were higher than the cold temperate coniferous forest, scrub and meadow in June to August; the window period of forest vegetation classification was identified as June to August. Using the time series of factors collection can show up "the window" and provide a theoretical basis for the vegetation classification; (3) the time series of PC1, NDVI and altitude as well had a significant contribution to the forest classification; the time series of DVI can help to distinguish between north-subtropical evergreen broad-leaved forest, broad-leafed evergreen and deciduous mixed forest, temperate coniferous and broad-leaved mixed forest, temperate deciduous broad-leaved forest; while slope, aspect and the time series of RVI had much less help in the automatic generation of classification rules, thus, they were of little value to vegetation classification. Overall, the time series of data derived from 30m-resolution HJ-1A/B CCD imagery can accurately categorize the Shennongjia forest vegetation into seven classes based on the C5.0 decision tree method. As an intelligent decision tree classification algorithm, the method has the advantages of identifying more categories and is of high application value. There is still space to improve the classification accuracy, i.e., a more considerate classification system of Shennongjia Forest, the increasing of field samples at mixed vegetation belt, and the use of higher spatial and temporal resolution images.

Key words: C5.0 Intelligent decision tree, HJ-1A/B CCD data, Shennongjia, time-series, forest sub-categories