Abstract: [Objective] This paper presents a new forest land type precise classification method based on hierachical classification strategy using high spatial resolution remote sensing image and multi-source auxiliary data in complex mountainous area, which locates in Baihua Forest Farm, Xiaolongshan Forestry Experimental Bureau, Gansu Province.[Method] In the test area, SPOT5 HRG2 and two scenes of GF-1 PMS2 images, ancillary information such as forest resources inventory results, forest form map and forest distribution map, and field measurement sample point data of land cover types and fine forest types were applied in classification process. The multivariate characteristics based on high resolution remote sensing images, including image spectral features, vegetation indices, textural features and image time-phase changed features, and topography characteristics, were used as the significant indicators to develop multi-level information extraction methods and forest type fine identification methods, which were particularly suitable for the complex mountainous terrain area of typical natural secondary forest region in warm temperate zone. Four vegetation indices,including NDVI(normalized difference vegetation index),RVI(ratio vegetation index),RSI(ratio shortwave infrared index)and DVI(difference vegetation index)and six textural features,that is ME(mean),HOM(homogeneity),DIS(dissimilarity),ENT(entropy),ASM(angular second moment)and RK(relative kurtosis)were developed and selected. Three topography characteristic factors related to spatial distribution of major forest types such as DEM elevation slope and aspect were also extracted and used in the hierarchical classification process. Threshold method, support vector machine(SVM), multiple classifier combination(MCC) and artificial neural network(ANN) classification methods were developed and applied in different levels of image. Classification results of different layers were combined into forest types fine classification result map of the whole research area. Finally, independent test samples of seven forest types based on stratified random sampling were selected and the confusion matrix and Kappa coefficient were examined to evaluate the accuracy of classification results. In order to further evaluate the validity of classification method and reliability of classification results on the whole, the performance of the classification method was discussed by comparing the statistic area about five main forest types based on classification results with the statistic results of forest resources inventory and image visual interpretation.[Result] The results showed that the method proposed in this paper had a good performance in forest type information extraction. Overall accuracy of seven forest types, including closed forest land, other types of forest land, nursery land and so on reached 92.28% and overall Kappa coefficient was 0.8996. Average relative accuracy of area statistic results of five main forest types of fine identification, including Pinus tabulaeformis, Pinus armandii, Larix kaempferi, Oak species dominant broad-leaved deciduous forest and other (hardwood species dominant) deciduous broad-leaved mixed forest, reached 92.4%.[Conclusion] The results of this study suggest that the hierarchical information extraction method based on multi-source data support is an effective approach of precise classification of forest land types, fine identification of forest types and accurate monitoring of forest resources, especially in mountainous area of complicated topography conditions. The proposed method in this paper have advantages in fine identification of forest land types with high accuracy and high reliability, and the detail degree of fine identification reaches dominant tree species, which could fully meet the needs of forestry applications such as forest resources investigation, forest land change monitoring and thematic map digital update, etc.

Key words: multi-source data, precise classification, hierarchical information extraction, multiple classifier combination, forest land type