## 4 Discussion
### Mapping and Spatial Distributions of Forest Types for the Different Models  
As shown in the visualisation map (Figure 7), RF showed severe salt-and-pepper noise. Object-based classification by U-Net and U-Net++ reduced this noise, but many patches remained where NBF was misclassified as CP and the edges were not sharp and clear (unlike MSG-GCN) (Figures 7 and 10). With MSG-GCN, misclassified forests clustered mainly along the transition zones of the forest boundary areas (Figure 8), similar to previous findings [30; 73], perhaps because of homogeneity in the spectral responses and shade due to the highly enclosed overlapping crowns [2], as well as the greater sensitivity of edges compared with internal areas. This is a longstanding problem in semantic segmentation. Unlike the regular morphology of well-defined urban land, the morphology and texture of forest conapies are heterogeneous and complex and vary according to species composition [66]. Thus, ground truths may not be recorded accurately given the inevitable subjective boundary errors and variation in remote sensing images caused by weather or the sensor type used. Weight or edge masks could be assigned to boundary area pixels [112] to effectively mine edge and neighbourhood information. Apart from edge misclassification, U-Net and U-Net++ misclassified internal forest areas (i.e., NBF as CP; Figure 8), perhaps because of the similar spectral values and crown sizes of certain species. However, MSG-GCN rarely made such misclassifications. Node transfer and aggregation yielded multiscale graphical information that highlighted both intra- and interclass differences, thereby improving the recognition of different categories [106]. modules) and GCN (multiscale graph convolution) are used to perform feature learning on small-scale regular areas and large-scale irregular areas, respectively, which aids decision making regarding boundary pixels. We also found that NMXs and CPs were more susceptible to misclassification than NBLs. Classification of forest types using only the similar and overlapping spectral DNs is not sufficient. The visualisation map of the entire area revealed that edge pixels were more likely to be misclassified as neighbourhood pixels by all networks and that the CNN approaches were associated with random misclassification patches in internal zones. NMF was more challenging to classify than other forest types, given the imbalances in datasets, the heterogeneous canopy texture, and the fact that edge regions are evident only in very high-resolution aerial RGB images. In future research, we will aim to combine a multisensor remote sensing dataset and a multimodal machine learning model [113; 114; 115; 116] to enhance the multidisciplinary nature of remote sensing images and deep-learning technology, to overcome the remaining problems with the MSG-GCNs method (such as how to make full use of multimodal [117] data to aid segmentation of edge pixels). Simultaneously, multisensor data will be applied to build concise efficient graph semantic models to improve the accuracy of forest type classification. Conceptualization, methodology, formal analysis, and writing (original draft preparation), H.P. ; resources, supervision, and writing (review and editing), T.O. and S.T. ; writing (methodology review and editing), Y.Z. All authors have read and agreed to the published version of the manuscript. This study was partially supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number [JP18K05742], Hainan Provincial Natural Science Foundation, No.322 MS019. This research is partially supported by Initiative on Recommendation Program for Young Researchers and Woman Researchers [2022-I-004], Information Technology Center, The University of Tokyo. Not applicable.