Dr. Lien Pham is a scientist with expertise in environmental research, geospatial data analysis, and machine learning. She earned her Ph.D. in Physical Geography from the University of Waikato, New Zealand, in 2018. Following her doctorate, she pursued a research fellowship focused on understanding the causes of mangrove dieback in Australia. Later in 2018, she worked as a postdoctoral researcher in the United States, specializing in seagrass mapping using advanced technologies, including deep learning techniques. Currently based in New Zealand, Dr. Lien Pham is involved in research and development in the forestry sector at Timberlands Limited. Her focus is on enhancing forestry management through the application of deep learning and machine learning, utilizing a diverse array of remote sensing data sources.

Currently works at the University of Florence. PhD in sustainable forest management at University of Florence with a thesis entiteled Application of big data analytics in remote sensing supporting sustainable forest management. The main research interests are related to the use of remote sensing data for forestry applications. In particular spatialized estimates of forest variables with remotely sensed data such as satellites, lidar, UAVs

Maggie has been a scientific programmer and researcher in the Biospheric Sciences Laboratory at NASA Goddard Space Flight Center in Greenbelt, Maryland for ten years. Her interests include mapping and quantifying land cover change at various spatial scales and modeling with high resolution commercial data. Currently her research is focused on agricultural monitoring and food security in Africa and carbon dynamics in Arctic boreal forests.

Zhao Dan, Associate Researcher of the Institute of Space and Astronautical Information Innovation, Chinese Academy of Sciences (CAS), recipient of the National Key R&D Programme for Young Scientists of the Ministry of Science and Technology (MOST). He has proposed the non-destructive observation technology of "density × volume" and the remote sensing technology of "parameter-model-validation". He led the completion and release of the national 250-metre spatial resolution vegetation aboveground biomass dataset. He has successively undertaken projects and themes of the National Key Research and Development Programme, projects of the National Natural Funding Committee of China, the Pilot Project of the Chinese Academy of Sciences and STS projects, etc. He has published more than 50 academic papers and one monograph.

During his career, Nicolò has mainly focused on the use of remote sensing to map and monitor forest ecosystems. He favored the use of LiDAR sensors, both airborne (airplane and UAV) and terrestrial, to study forest structural complexity, to effectively monitor ecological restoration plantings (at the individual tree-level in Tasmania’s temperate forest) and in disturbed tropical ecosystems (at the plot level in the tropical lowlands of Sumatra, Indonesia). Nicolò has also worked with imaging spectroscopy (hyperspectral) sensors (mounted on satellite, airplanes, and UAVs), to successfully segregate between species and between genetic provenances of the same species (individual tree and provenance phenotyping). His current research is focusing on (i) the monitoring of forest health status and disease expression though satellite imagery, and (ii) on the use of regional level airborne laser scanning (ALS) to map, quantify and monitor the growth of the exotic forest estate of New Zealand.

Dr. Svetlana Saarela defended her doctoral thesis at the University of Helsinki in 2015. Currently, she is employed as a researcher at the Norwegian University of Life Sciences in Ås, Norway. Her main research interest is developing statistical frameworks for forest inventories by combining remotely sensed data from different sensors with field sample data.