Forest Gap Extraction Based on Convolutional Neural Networks and Sentinel-2 Images

Abstract

As a type of small-scale disturbance, forest gap and its accurate extraction are of great significance to monitor forest long-term dynamics, to choose forest recovery mode and to predict forest recovery pace. Currently, airborne LiDAR and high-resolution multi-spectral data are commonly used to accurately classify forest gaps, but they are costly to acquire and have limited time and space availability. In contrast, the Sentinel-2 multi-spectral data with a 10 m spatial resolution overcomes these drawbacks in forest gap extraction. In this work, an integrated framework that combines multi-source remote sensing, machine learning and deep learning to extract forest gap in wide regions was proposed and tested in three sites. First, LiDAR, Sentinel series and random forest (RF) algorithm were synergized to produce a canopy height product in model training site. On this basis, samples for forest canopy, forest gap and non-such were identified from LiDAR-derived canopy height model (CHM) and Sentinel-based canopy height inversion (HI) data to train forest gap extraction models by applying the Deep Forest (DF) and Convolutional Neural Networks (CNN) algorithms, followed by a comparison of the accuracy and the transferability among the four models (DF-CHM, DF-HI, CNN-CHM and CNN-HI). The results indicated that the R2 and RMSE of Sentinel-based canopy height retrievals were estimated at 0.63, and 7.85 m respectively, the difference in the mean height and standard deviation between HI and CHM was 0.03 m and 4.7 m respectively. And there was a spatial agreement of about 98.60% between the HI-identified samples and the CHM-identified samples, with an agreement of 54.89% for the forest gap class. The CNN-HI model had the highest accuracy in both transfer learning test sites, with an overall accuracy (OA) of 0.85 and 0.87, Kappa coefficient at 0.78 and 0.81, respectively, proving that it has good transferability. Conversely, the DF-based models generally gave poorer accuracy and transferability. This study demonstrates that combining Sentinel-2 multi-spectral data and CNN algorithm is feasible and effective in forest gap extraction applications over wide regions.