Tree Species Classification Based on Fusion Images by GF-5 and Sentinel-2A

Forest ecosystem detection and assessment usually requires accurate spatial distribution information of forest tree species. Remote sensing technology has been confirmed as the most important method for tree species acquisition, and space-borne hyperspectral imagery, with the advantages of high spectral resolution, provides a better possibility for tree species classification. However, the present in-orbit hyperspectral imager has proved to be too low in spatial resolution to meet the accuracy needs of tree species classification. In this study, we firstly explored and evaluated the effectiveness of the Gram-Schmidt (GS) Harmonic analysis fusion (HAF) method for image fusion of GaoFen-5 (GF-5) and Sentinel-2A. Then, the Integrated Forest Z-Score (IFZ) was used to extract forest information from the fused image. Next, the spectral and textural features of the fused image, and topographic features extracted from DEM were selected according to random forest importance ranking (Mean Decreasing Gini (MDG) and Mean Decreasing Accuracy (MDA)), and imported into the random forest classifier to complete tree species classification. The results showed that: comparing some evaluation factors such as information entropy, average gradient and standard deviation of the fused images, the GS fusion image was proven to have a higher degree of spatial integration and spectral fidelity. The random forest importance ranking showed that WBI, Aspect, NDNI, ARI2, FRI were more important for tree species classification. Both the classification accuracy and kappa coefficients of the fused images were significantly greatly improved when compared to those of original GF-5 images. The overall classification accuracy ranged from 61.17% to 86.93% for different feature combination scenarios, and accuracy of the selected method based on MDA achieved higher results (OA = 86.93%, Kappa = 0.85). This study demonstrated the feasibility of fusion of GF-5 and Sentinel-2A images for tree species classification, which further provides good reference for application of in-orbit hyperspectral images.