Exploitation of Time Series Sentinel-2 Data and Different Machine Learning Algorithms for Detailed Tree Species Classification

Abstract

The classification of tree species through remote sensing data is of great significance to monitoring forest disturbances, biodiversity assessment, and carbon estimation. The dense time series and a wide swath of Sentinel-2 data provided the opportunity to map tree species accurately and in a timely manner over a large area. Many current studies have applied machine learning (ML) algorithms combined with Sentinel-2 images to classify tree species, but it is still unclear, which algorithm is more effective in the automotive extraction of tree species. In this study, five ML algorithms were compared to identify the composition of tree species with multitemporal Sentinel-2 images in the JianShe forest farm, Northeast China. Three major types of deep neural networks [Conv1D, AlexNet, and long short-Term memory (LSTM)] were tested to classify Sentinel-2 time series, which represent three disparate but effective strategies to apply sequential data. The other two models are support vector machine (SVM) and random forest (RF), which are renowned for extensive adoption and high performance for various remote sensing applications. The results show that the overall accuracy of neural network models is better than that of SVM and RF. The Conv1D model had the highest classification accuracy (84.19%), followed by the LSTM model (81.52%), and the AlexNet model (76.02%). For non-neural network models, RF's classification accuracy (79.04%) is higher than that of SVM (72.79%), but lower than that of Conv1D and LSTM. Therefore, the deep neural networks combined with multitemporal Sentinel-2 images can efficiently improve the accuracy of tree species classification.