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The paper explores the importance of phenological information of vegetation for vegetation classification. The time series of OLI images in different season are used to obtain the samples of different vegetation types. The importance of single OLI image and the multi-temperal OLI image combinations are separately calculated using Random Forest (RF). The results show that near infrared band (band5) or short-wave infrared band (band6) for single OLI image are important bands to distinguish vegetation types, and Coastal band (band1) also has potential information to distinguish vegetation types. For different time series of OLI image combinations, OLI images in May and July were optimal for vegetation types differentiation. Therefore, the phenological information of vegetation is important to improve the separability of vegetation, and the use of multi-sequence images can improve the classification accuracy of vegetation and achieve rapid and accurate remote sensing forest mapping.

Analysis of features importance based on different seasonal OLI images by Random Forest