Mapping Vegetation Height — Probabilistic Deep Learning for Global Remote Sensing

Abstract

Mapping vegetation properties globally is crucial to understand the role of terrestrial ecosystems in the global carbon cycle. Spatially explicit, high-resolution data are needed to manage terrestrial ecosystems so that climate change can be mitigated and biodiversity loss prevented. Since no current single data source can provide such data with global coverage and high spatial resolution, new solutions must be found. This thesis aims to develop novel data-driven tools based on state-of-the-art deep learning to advance the mapping of vegetation properties, in particular canopy height, at global scale. Two ongoing space missions, namely the Copernicus Sentinel-2 mission and NASA’s GEDI LIDAR mission, deliver publicly available data that form the basis of the methods presented in this thesis. While GEDI is a key climate mission that provides sparse vegetation structure measurements at global scale (between 51.6° N & S), Sentinel-2 delivers dense optical images with global coverage, but cannot directly measure vertical vegetation structure. The presented work is a holistic approach based on gradually extended methods towards the large-scale fusion of Sentinel-2 and GEDI for the global mapping of canopy top height with high spatial resolution. Furthermore, since transparency of the modelling limitations is critical to build trust and to inform downstream applications about the reliability of the estimates, probabilistic deep learning techniques are integrated to quantify the predictive uncertainty. In a first step, a novel approach based on deep convolutional neural networks (CNNs) was developed to estimate dense canopy height maps from Sentinel-2 optical images by training with local dense reference data from airborne measurement campaigns (LIDAR and photogrammetry) in Gabon and Switzerland. By exploiting textural image features, the model achieved low error, even for canopies up to 50 m height. However, its applicability is limited to regions represented by the available training data. The launch of the spaceborne GEDI full waveform LIDAR in December 2018 promised to provide sparse reference data of vegetation structure measurements at global scale. Since interpreting on-orbit GEDI LIDAR waveforms proved to be a difficult task due to unknown noise in the data, a novel probabilistic deep learning approach was developed to retrieve canopy top height globally and quantify the predictive uncertainty from GEDI. Given these footprint-level estimates, the Sentinel-2 based canopy height mapping approach could be extended to be trained with sparse supervision. After demonstrating that this approach allows to estimate canopy top height suitable to map indicative high carbon stocks in tropical Southeast Asia, a global probabilistic model was developed to retrieve canopy top height anywhere on Earth. Ultimately, the first global, wall-to-wall canopy top height map at 10 m ground sampling distance was computed for the year 2020.