A Decision Rule and Machine Learning-Based Hybrid Approach for Automated Land-Cover Type Local Climate Zones (LCZs) Mapping Using Multi-Source Remote Sensing Data.

This study presents a streamlined, automated classification method to map land-cover type Local Climate Zones (LCZs). Using a two-phase hybrid approach, we first generated training samples through universal decision rules and subsequently, a Machine Learning (ML) algorithm was trained on the generated samples to classify LCZs. The proposed model harnesses plant height data, combined with spectral bands and remote sensing indices, to accurately classify various land-cover types like dense forest, scattered trees, bush/scrub, low plant/agricultural land, bare rocks/paved surface and bare soil/sands. Targeting global applicability, we tested our method across six diverse locations spanning four continents: Fresno (California), Central Michigan, Western Phoenix (Arizona), Khulna (Bangladesh), Lagos (Nigeria), and Western Sydney. In each location, after generating training samples with the decision rules, a Random Forest algorithm was employed for LCZ classification. Results showcase that data from Sentinel 1 & 2, night-time light, and GEDI relative height are effective in characterizing land-cover type LCZs and decision-rules can be established. The decision-rules consistently auto-generate training samples, undeterred by varying geographical and climatic conditions. This automated system has achieved promising accuracy across all tested sites, suggesting its potential to map land-cover type LCZs and vegetation globally with higher accuracy.