Accurate vegetation detection, characterization, and canopy structure are essential for understanding ecosystem structure, productivity, disturbance, carbon storage, and electrical grid security. The fusion of complementary remote sensing observations is becoming central to vegetation science because it improves sensitivity to vegetation structure, composition, and function while expanding monitoring capability across weather, illumination, and spatial constraints. New opportunities are emerging from integrating SAR, visible and infrared imagery, LiDAR, hyperspectral observations, GEDI, ICESat-2, and solar-induced fluorescence.

This session invites abstracts on methodological and applied advances in vegetation detection, classification, structural characterization, phenology, canopy height estimation, and ecosystem monitoring through multimodal data fusion and multi-sensor analysis. We welcome contributions across terrestrial ecosystems and spatial scales, including studies focused on machine learning, time-series analysis, crown mapping, biomass and carbon dynamics, disturbance monitoring, electrical grid security, and validation methods.

• GC - Global Environmental Change
• IN - Informatics
• SY - Science and Society