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BG9.7 | Spatiotemporal Data Cubes and Machine Learning models for Earth resources monitoring
Spatiotemporal Data Cubes and Machine Learning models for Earth resources monitoring
Convener: Tomislav Hengl | Co-conveners: Luca Brocca, Leandro Leal ParenteECSECS, Davide ConsoliECSECS
We call researchers working with continental and global-scale dataset for producing time-series of predictions of environmental variables especially the ones focused on the essential variables. Radeloff et al. (2024) (the Landsat science team) have proposed 13 essential and many more desirable/ aspirational products using medium resolution imagery referred to as “Medium-resolution satellite image-based products that meet the identified information needs for sustainable management, societal benefits, and global change challenges”. The desirable products include: maps of crop types, irrigated fields, land abandonment, forest loss agents, LAI/FAPAR, green vegetation cover fraction, emissivity, ice sheet velocity, surface water quality and evaporative stress. The aspirational land monitoring products include: forest types, and tree species, urban structure, forest recovery, crop yields, forest biomass, habitat heterogeneity and winter habitat indices, net radiation, snow and ice sheet surface melt, ice sheet and glacier melt ponds, sea ice motion and evaporation and transpiration. We will discuss modeling approaches, hybrid data science / process-based models and methods for accuracy assessment and visualization of uncertainty. Once one produced time-series of predictions, these can be further used to analyze trends and detect potential ecosystem degradation of restoration.