Urban Green Infrastructure (UGI) provides ecosystem services such as cooling of temperatures and is majorly important for climate change adaptation. Green Volume (GV) describes the 3-D space occupied by vegetation and is highly useful for the assessment of UGI. This research uses Sentinel-2 (S-2) optical data, vegetation indices (VIs), Sentinel-1 (S-1) and PALSAR-2 (P-2) radar data to build machine learning models for yearly GV estimation on large scales. Our study compares random and stratified sampling of reference data, assesses the performance of different machine learning algorithms and tests model transferability by independent validation. The results indicate that stratified sampling of training data leads to improved accuracies when compared to random sampling. While the Gradient Tree Boost (GTB) and Random Forest (RF) algorithms show generally similar performance, Support Vector Machine (SVM) exhibits considerably greater model error. The results suggest RF to be the most robust classifier overall, achieving highest accuracies for independent and inter-annual validation. Furthermore, modelling GV based on S-2 features considerably outperforms using only S-1 or P-2 based features. Moreover, the study finds that underestimation of large GV magnitudes in urban forests constitutes the biggest source of model error. Overall, modelled GV explains around 79% of the variability in reference GV at 10 m resolution and over 90% when aggregated to 100 m resolution. The research shows that accurately modelling GV is possible using openly available satellite data. Resulting GV predictions can be useful for environmental management by providing valuable information for climate change adaptation, environmental monitoring and change detection.
Keywords: Climate change adaptation; Green volume; Machine learning; Remote sensing; Sentinel 2; Urban green infrastructure.
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.