Uncovering the Potential of Multi-Temporally Integrated Satellite Imagery for Accurate Tree Species Classification

Abstract

In this study, prior to the launch of compact advanced satellite 500 (CAS500-4), which is an agriculture and forestry satellite, nine major tree species were classified using multi-temporally integrated imageries based on a random forest model using RapidEye and Sentinel-2. Six scenarios were devised considering the composition of the input dataset, and a random forest model was used to evaluate the accuracy of the different input datasets for each scenario. The highest accuracy, with accuracy values of 84.5% (kappa value: 0.825), was achieved by using RapidEye and Sentinel-2 spectral wavelengths along with gray-level co-occurrence matrix (GLCM) statistics (Scenario IV). In the variable importance analysis, the short-wave infrared (SWIR) band of Sentinel-2 and the GLCM statistics of RapidEye were found to be sequentially higher. This study proposes an optimal input dataset for tree species classification using the variance error range of GLCM statistics to establish an optimal range for window size calculation methodology. We also demonstrate the effectiveness of multi-temporally integrated satellite imageries in improving the accuracy of the random forest model, achieving an approximate improvement of 20.5%. The findings of this study suggest that combining the advantages of different satellite platforms and statistical methods can lead to significant improvements in tree species classification accuracy, which can contribute to better forest resource assessments and management strategies in the face of climate change.