Exploring the Optimal Feature Combination of Tree Species Classification by Fusing Multi-Feature and Multi-Temporal Sentinel-2 Data in Changbai Mountain

Abstract

Tree species classification is crucial for forest resource investigation and management. Remote sensing images can provide monitoring information on the spatial distribution of tree species and multi-feature fusion can improve the classification accuracy of tree species. However, different features will play their own unique role. Therefore, considering various related factors about the growth of tree species such as spectrum information, texture structure, vegetation phenology, and topography environment, we fused multi-feature and multi-temporal Sentinel-2 data, which combines spectral features with three other types of features. We combined different feature-combinations with the random forest method to classify Changbai Mountain tree species. Results indicate that topographic features participate in tree species classification with higher accuracy and more efficiency than phenological features and texture features, and the elevation factor possesses the highest importance through the Mean Decrease in Gini (MDG) method. Finally, we estimated the area of the target tree species and analyzed the spatial distribution characteristics by overlay analysis of the Classification 3 result and topographic features (elevation, slope, and aspect). Our findings emphasize that topographic factors have a great influence on the distribution of forest resources and provide the basis for forest resource investigation.

Estimation of Forest Parameters in Boreal Artificial Coniferous Forests Using Landsat 8 and Sentinel-2A

“…• ), abbreviated as BTE, and then averages the results to provide a single texture unoriented metric, abbreviated as MTE [59,60]. Meanwhile, principal component analysis (PCA) was carried out for bands of L8 and S2A sensors, and the first principal component was selected for texture feature extraction, abbreviated as PTE [61,62]. The eight GLCM textures can be subdivided into three categories [63] (Table 7): contrast (contrast, dissimilarity, homogeneity), orderliness (second moment, entropy), and statistically (mean, variance, correlation).…”