With the Jiangle state-owned forest farm of Fujian Province as the study area, the potential of classification in tree species and age groups through GF-2 image were explored. First, the canopy spectral curve of main tree species were measured and the reflectance differences between them were analyzed. After image preprocessing and in combination with normalized difference vegetation index (NDVI) and topographic factors, multi band remote sensing images were constructed. Object-oriented multi-scale segmentation technology was applied to extracting the spectral and texture attributes, followed by attributes filter. On the basis of 7 kinds of schemes, Cunninghamia lanceolata (3 age groups),Pinus massoniana and Phyllostachys edulis were classified by random forest classifier. The role of spectrum, texture and auxiliary data in classification was quantitatively analyzed. The results show that the scheme of spectra combined with 4 directions of texture attributes has overall accuracy of 87.4% with Kappa coefficient being 0.85, and age groups in Cunninghamia lanceolate were effectively classified. Random forest classifier can achieve better classification results based on the optimal attribute set. GF-2 has great potential in tree species and age group classification and provides reliable data source for forest resources investigation and management.
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