A Learnable Model With Calibrated Uncertainty Quantification for Estimating Canopy Height From Spaceborne Sequential Imagery

Abstract

Global-scale canopy height mapping is an important tool for ecosystem monitoring and sustainable forest management. Various studies have demonstrated the ability to estimate canopy height from a single spaceborne multi-spectral image using end-to-end learning techniques. In addition to texture information of a single-shot image, our study exploits multi-temporal information of image sequences to improve estimation accuracy. We adopt a convolutional variant of a long short-term memory (LSTM) model for canopy height estimation from multi-temporal instances of Sentinel-2 products. Furthermore, we utilize deep ensembles technique for meaningful uncertainty estimation on the predictions and post-processing isotonic regression model for calibrating them. Our lightweight model (~ 320k trainable parameters) achieves mean absolute error (MAE) of 1.29m in a European test area of 79km2. It outperforms state-of-the-art methods based on single-shot spaceborne images as well as costly airborne images, while providing additional confidence maps that are shown to be well calibrated. Moreover, the trained model is shown to be transferable in a different country of Europe using a fine-tuning area of as low as ~ 2km2 with MAE = 1.94m.