Departmental Research Seminars Series
Quantitative Remote Sensing
Date: 18 DEC 2023 (Monday)
Time: 10:00-10:45 | 11:15-12:00 | 14:30-15:15 | 15:45-16:30 (HKT)
Mode: Hybrid Mode
Venue: CLL, Department of Geography, 10F, The Jockey Club Tower, Centennial Campus, HKU
Zoom: Zoom link will be provided upon successful registration
[ 10:00-10:45 ]
Water in Plants: The Achilles Heel of Global Carbon Storage
Forests play a pivotal role in global carbon dynamics by storing 70-90% of all terrestrial living biomass. However, they are increasingly vulnerable under recent climate change. Significant carbon loss may result from extreme climate events destabilizing forest ecosystems. Water availability to vegetation has become a critical limiting factor for sustaining forest carbon storage, however its representation in most land surface models is lacking. Better understanding plant hydraulics and accurately constraining the vegetation water stress are therefore crucial for global carbon storage and the carbon cycle. In this seminar, I will present our endeavors in both numerical simulations and observations to achieve quantitative understanding of plant hydrodynamics in the context of climate change. These include the development of a new plant hydraulic module for the ORCHIDEE land surface model to facilitate the simulation of drought-induced carbon loss, which is increasingly frequent and intense in recent years. Applying the model to the Amazon rainforest suggests it might partially transit from carbon sink to source if the climate keeps changing at its current pace. Additionally, I have been testing a GNSS-based technique to resolve plant canopy water availability down to sub-hour time scales. Combining these observations with simulations of the CliMA next-generation Earth system model, we can obtain better observational constraints of plant hydrodynamics. This exploration enriches our understanding of the vegetation carbon-water cycle, and offers crucial guidance for climate mitigation strategies.
Dr. Yitong Yao
Postdoctoral Fellow, California Institute of Technology, United States
Yitong Yao is currently a postdoc at California Institute of Technology. Dr. Yao earned her B.S. from Beijing Forestry University in 2015, M.S. from Peking University in 2018, and Ph.D. from Laboratoire des Sciences du Climat et de l'Environnement (LSCE) of Universite Paris Saclay in 2022. Her research focuses on vegetation carbon-water coupling and land-atmosphere interactions with leading research contributions on developing land surface models and improving remote sensing techniques. Her previous research also includes carbon flux estimation and prediction from regional to global scales. Dr. Yao has published 7 first-authored peer-reviewed articles and 3 more currently under review, as well as ~20 other contributing papers. She has been actively involved in several projects, including a France-funded research project CLand, a NASA research project “Bridging the gap between Carbon Cycle Models and Remote Sensing”, and a Schmit Future research project CALIPSO on the destabilization of the carbon cycle.
[ 11:15-12:00 ]
Multi-Source Satellite Remote Sensing for Wildfire Monitoring Using Deep Learning
In the face of escalating climate change, wildfires are becoming increasingly frequent, intense, and prolonged, posing significant environmental and societal challenges. Effective wildfire management hinges on the timely and accurate monitoring of these events and their impacts. Traditional optical multispectral satellite observations, while useful, often encounter limitations under adverse conditions such as cloud cover, smoke, and haze, leading to reduced observation efficacy in the visible/infrared spectrum. This seminar delves into innovative solutions to overcome these challenges through the integration of Synthetic Aperture Radar (SAR) technology and deep learning methodologies. SAR's ability to penetrate through clouds and smoke, coupled with its all-weather, day-and-night sensing capabilities, makes it an invaluable tool for enhancing wildfire detection and monitoring. This talk will cover our latest research findings on leveraging multi-source satellite remote sensing data, including Sentinel-1, Sentinel-2, and ALOS-2 PALSAR-2, for advanced wildfire monitoring. Our approach utilises deep learning techniques to synergize the strengths of both optical and SAR data, providing more robust and reliable wildfire monitoring capabilities. The seminar will showcase the potential of these technologies in improving our understanding and response to wildfire dynamics in a changing climate.
Research Scientist, KTH Royal Institute of Technology, Sweden
Puzhao Zhang is Research Scientist at KTH Royal Institute of Technology, who earned the B.S. in Intelligent Science and Technology (2013) and Ph.D. in Pattern Recognition and Intelligent Systems (2019) from Xidian University, China. He further enhanced his academic prowess with a second Ph.D. in Geoinformatics from KTH, Sweden, in 2021. Dr. Zhang's research navigates the intersection of multi-source satellite remote sensing, big data analytics, machine learning, and computer vision, focusing on Geospatial artificial intelligence (GeoAI) for large-scale Earth observation applications. Dr. Zhang's collaborative endeavours, notably co-editing the special issue “Multi-Source Data with Remote Sensing Techniques” in Remote Sensing with Prof. Dr. Shutao Li, IEEE Fellow, exemplifies his commitment to advancing his field. Dr. Zhang was awarded a Post-doctoral Fellowship (2M SEK) by Swedish Digital Futures, he also participated in the ESA SAR4Wilfire Project and EU horizon 2020 “Harmonia” Project. Currently, Dr. Zhang is engaged in several research projects that aim to develop GeoAI for sustainable global environmental monitoring, reflecting his profound commitment to addressing some of the most pressing environmental challenges of our time.
[ 14:30-15:15 ]
An Advanced Inversion Framework and the Global Vegetation Satellite Products
The primary challenge in quantitative remote sensing lies in transforming raw satellite data into high-level environmental variable products that can be employed by diverse communities. This presentation will encompass two key aspects: inversion methodology and global satellite product generation. The first segment of the presentation will unveil a cutting-edge, data assimilation-based, unified inversion framework capable of estimating multiple land surface variables from one or more integrated satellite datasets. This novel approach significantly deviates from the traditional "single-variable from single-sensor" method. It has undergone testing with various satellite data and displays immense potential for future applications. The latter portion will showcase an array of global vegetation satellite products we have developed using deep learning and other methods, including leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation by green vegetation (FAPAR). These products form part of the Global LAnd Surface Satellite (GLASS) suite, which has garnered widespread usage worldwide. These global vegetation products play a crucial role in land surface modeling, terrestrial carbon flux estimation, agricultural crop yield prediction, and numerous other applications.
Dr. Han MA
Research Assistant Professor, University of Hong Kong, HKSAR
Dr. Ma currently holds the position of Research Assistant Professor in the Department of Geography at the University of Hong Kong (HKU). She pursued her doctoral degree from Beijing Normal University and the University of Maryland, and worked at Wuhan University prior to joining HKU. Her primary research interest lies in converting satellite data into high-level products representing various essential environmental variables. This is achieved by developing sophisticated inversion methodologies and generating global land products. As the first or corresponding author, Dr. Ma has published 18 peer-reviewed articles in top-tier remote sensing journals. She also serves as a Thematic Editor for the Earth System Science Data journal and an Editorial Board Member for the Big Earth Data journal. Dr. Ma has developed several global products within the Global LAnd Surface Satellite (GLASS) product suite and is a key member of the Hi-GLASS product development team.
[ 15:45-16:30 ]
Quantitative Remote Sensing for Monitoring Terrestrial Ecosystem Functioning: Synergies, Opportunities, and Future Perspectives
Terrestrial ecosystems provide indispensable services to human society, including resource provision, climate regulation, and cultural enrichment. The vitality of these services hinges on water, energy, and carbon exchanges between terrestrial ecosystems the atmosphere, which constitute the primary terrestrial ecosystem functioning. The intricate interdependence between terrestrial ecosystems and human activities underscores the criticality of comprehensively understanding and monitoring the ecosystem functioning. Remote sensing, owing to its expansive spatial coverage and continual temporal extent, has become a pivotal tool for this purpose. It facilitates the mapping of essential land surface parameters, such as land surface temperature, evapotranspiration, and gross primary productivity. Recent advancements in satellite observation technology and data science have opened unprecedented avenues for effectively monitoring terrestrial ecosystem functioning. The amalgamation of multi-source satellite data with sophisticated physical and statistical models provides an unparalleled opportunity to gain insights into ecosystem dynamics and inform decision-making for sustainable resource management and conservation practices.
Dr. Tian Hu
Research & Technology Associate, Luxembourg Institute of Science and Technology, Luxembourg
Dr. Tian Hu holds PhD degrees from University of Chinese Academy of Sciences and Griffith University. He currently works as a Research & Technology Associate (equivalent to Associate Professor) at Luxembourg Institute of Science and Technology. His research focuses on monitoring water, energy, and carbon cycles in terrestrial ecosystems with a combination of ground measurements, remote sensing observations, and modelling. He has published over 30 research papers in internationally reputable journals, including Remote Sensing of Environment, Water Resources Research, IEEE Transactions on Geoscience and Remote Sensing, and Geophysical Research Letters. He has participated in multiple projects as PI/co-PI funded by European Space Agency (ESA) and Luxembourg National Research Fund. Meanwhile, Dr. Hu is involved in the preparation for the next-generation high-resolution thermal missions, including TRISHNA (CNES-ISRO) and LSTM (ESA). Furthermore, he serves as an Associate Editor for the AGU journal Earth and Space Science.