Alfredo Huete of University of Technology Sydney

Phenology is the study of annual recurring biological life cycle events and the drivers and controls of their periodicity. Shifts in phenology, depict a plants’ integrated response to climate and environmental changes and have become an important source of information on how plants are responding to climate change. Satellite data, with its synoptic views and high repetitive sampling offer numerous opportunities to advance the study of phenology. Thus far, satellite products have primarily contributed to studies of 'landscape phenology', defined as the aggregate, multi-species, seasonal vegetation patterns sensed by satellites. In this study, we show how distinct species phenologies can be discriminated employing decomposition methods in the temporal domain.  For the case of grasses, we are able to separate ‘mixed’ cool season grasses (C3) from warm season grasses (C4) based on their unique temporal signatures.  This enables refined climate studies showing phenological advances and C3/ C4 geographical shifts with global warming.  We further employ machine learning approaches to formulate and better understand relationships between landscape phenology and seasonal flowering- induced pollen concentrations. We show that machine learning approaches significantly improved pollen prediction capabilities and provided key information to better attribute changes in pollen counts driven by shifting ecological landscapes from climate change drivers. 

Biography:

Distinguished Professor Alfredo Huete leads the Ecosystem Dynamics Health and Resilience research program within the School of Life Sciences, at the University of Technology Sydney, Australia.  He is a core member of the Centre for Advanced Modelling and Geospatial Information Systems.  His main research interests include the use of remote sensing to study ecosystem processes, health and functioning. He integrates ground, tower and satellite ecohydrology measures to study carbon and water cycling and analyse ecosystem responses and resilience to climate forcings and extreme events. He has over 20 years experience working on satellite mission teams, including the NASA-EOS MODIS Science Team, the New Millennium EO-1 Hyperion Team, and JAXA GLI team. His past research involved the development of the soil-adjusted vegetation index (SAVI) and the enhanced vegetation index (EVI), which became an operational satellite product on MODIS and VIIRS sensors.  Currently, he is involved with the Australian Terrestrial Ecosystem Research Network (TERN), helping to produce national operational phenology products; as well as the AusPollen network which couples satellite sensing to better understand and predict pollen phenology from allergenic grasses and trees. 

Address:University of Technology Sydney, , Sydney, Australia