Topics Webinar | EO&GEO Series: Investigating State-of-the-Art Machine Learning Approaches in Vegetation Analysis Through Earth Observation Data
Topics Webinar | EO&GEO Series: Investigating State-of-the-Art Machine Learning Approaches in Vegetation Analysis Through Earth Observation Data
Part of the Topics Webinar series
Vegetation Height Mapping, Machine Learning, Weed Mapping
Welcome from the Chairs
Welcome to the Investigating State-of-the-Art Machine Learning Approaches in Vegetation Analysis Through Earth Observation Data Webinar!
Vegetation cover maps, whether they focus on structural attributes, ecological aspects, or biomass content, are invaluable for understanding Earth's ecosystems in a spatial context.
The advent of Earth Observation data has transformed vegetation mapping and trend analysis, offering data with various spatial and spectral resolutions on a global scale. Vegetation mapping and analysis provide critical insights into the distribution and density of vegetation, while also highlighting the impact of overall environmental changes on biodiversity and ecosystems. An accurate interpretation of this remote sensing data necessitates sophisticated analytical techniques to manage its complexity and vastness.
Join us for an insightful webinar that explores cutting-edge machine learning methodologies and their role in vegetation analysis using Earth Observation (EO) data. This webinar features four scholars presenting their latest research on how machine learning and EO data can enhance vegetation monitoring and mapping. It will facilitate the sharing of insights and ideas among participants while opening opportunities for collaborative efforts.
We are privileged to have esteemed research scientists and academics from recognized research institutions and universities in Australia. They will share their valuable insights and research findings on the effective application of machine learning technology in Earth observation data in obtaining essential information related to climate change, land use planning, ecosystem conservation, weed management, and agricultural management.
Date: 15 May 2025
Time: 7:00 a.m. CEST | 3:00 p.m. AEST | 1:00 p.m. CST
Webinar ID: 854 2945 1681
Webinar Secretariat: journal.webinar@mdpi.com
Event Chairs
Research Fellow, Transdisciplinary School, University of Technology Sydney (UTS)
Dr. Arnick Abdollahi is a research fellow at the University of Technology Sydney (UTS), specializing in Earth and space science informatics, artificial intelligence (AI), and environmental science. He completed his Ph.D. at UTS and was a postdoctoral fellow at the Australian National University’s Bushfire Research Centre, where he led AI initiatives to enhance bushfire resilience. His research has advanced the analysis of bushfire behaviour, promoted the responsible use of AI in environmental monitoring, and influenced national strategies for remote sensing and risk management. Currently, he manages a national initiative developing an AI-powered grazing planner to support sustainable livestock farming in the face of climate variability and bushfire threats. Dr. Abdollahi has received several grants and accolades, including recognition in the 2024 Australian AI Awards and the 2025 Australian Space Awards.
Experimental Scientist, Environment, CSIRO (Commonwealth Scientific and Industrial Research Organisation)
Chandrama has a background in remote sensing image analysis. She completed her Ph.D. in automated flood mapping using advanced machine learning techniques from the QUT Centre for Robotics. The objective of her research was to develop classification models utilizing deep feed-forward artificial neural networks that can differentiate permanent water bodies from flooded areas in real-time remote sensing images acquired by aerial or space-borne sensors. She joined CSIRO in 2021, working with the National Bushfire Intelligence Capability team as a remote sensing and GIS analyst. She has been working with the climate and vegetation team, helping in mapping and quality assurance of the products using geostatistics and machine learning methods. She is also involved with multiple projects related to multi-hazard risk assessment within the Enabling Resilience Investment group at the CSIRO. Research areas include remote sensing image analysis for quality assurance and risk and natural hazard analysis, network analysis using telecommunication data, analysis of the vulnerability of telecommunication towers to natural hazards, mapping and monitoring of natural hazards, and data integration and image processing methods, including programming in Python.
Invited Speakers
Research Fellow, Transdisciplinary School, University of Technology Sydney (UTS)
Harnessing Earth Observation Technology and Machine Learning for National Biomass Assessment in Australia.
Dr. Arnick Abdollahi is a research fellow at the University of Technology Sydney (UTS), specializing in Earth and space science informatics, artificial intelligence (AI), and environmental science. He completed his Ph.D. at UTS and was a postdoctoral fellow at the Australian National University’s Bushfire Research Centre, where he led AI initiatives to enhance bushfire resilience. His research has advanced the analysis of bushfire behaviour, promoted the responsible use of AI in environmental monitoring, and influenced national strategies for remote sensing and risk management. Currently, he manages a national initiative developing an AI-powered grazing planner to support sustainable livestock farming in the face of climate variability and bushfire threats. Dr. Abdollahi has received several grants and accolades, including recognition in the 2024 Australian AI Awards and the 2025 Australian Space Awards.
Senior Research Scientist, Environment, CSIRO (Commonwealth Scientific and Industrial Research Organisation)
Generating an Australia-Wide Vegetation Height Product by Combining Gedi Satellite Lidar With Optical, Radar and Climate Earth Observation Data in a Machine Learning Model.
Dr Catherine Ticehurst received her PhD in Geomatic Engineering (modelling SAR polarimetry in urban environments) at the University of NSW. She started working at the CSIRO in 1998 where she has been working in what is now the Landscape Observation group at CSIRO Environment, mapping and monitoring a variety of vegetation and wetland environments using a range of current and emerging airborne and satellite earth observation technologies. Research areas include: Mapping surface water dynamics at regional scales; Developing relationships between biophysical parameters of vegetation and remote sensing data (airborne and spaceborne SAR, hyperspectral and multi-spectral airborne and spaceborne optical data); NDVI time-series analysis for monitoring crops; Radar polarimetry, including polarisation signature decomposition, and its relationship with mangrove and rainforest environments; High-resolution crown delineation and classification in high-biodiversity vegetation environments; Data integration and image processing methods, including programming in python; Application of LiDAR altimetry for mapping vegetation height and surface water height; Extracting and using Analysis Ready Data within an Open Data Cube using Jupyter Notebooks.
Associate Professor, Sustainability Research Cluster, University of the Sunshine Coast, Queensland
Earth Observation Data Fusion for Improved Mapping of Weeds in Natural and Plantation Forests With Machine Learning Approaches.
Dr Sanjeev Kumar Srivastava, PhD Geographical Sciences Australian National University, works as an Associate Professor in Geospatial Analysis at the University of the Sunshine Coast. He uses geospatial analytics to conserve earth resources. His research interests include mapping wildfire fuel distribution, post-burn areas, vegetation regrowth, and ecosystem monitoring, utilising analysis of both field-based and remote sensing data. Within the School of Science, Technology, and Engineering, he leads a research cluster, ‘Geospatial Analytics for the Conservation and Management of Earth Resources’. While working at the University of the Sunshine Coast, he has led projects funded by SmartSat CRC, the Asia-Pacific Network for Global Change Research, the Queensland Fire Department, and local councils.
Senior Research Scientist, CSIRO (Commonwealth Scientific and Industrial Research Organisation)
Ecosystem Condition Modelling Using the Habitat Condition Assessment System (Hcas) and Its Applications in Australia.
Dr Roozbeh Valavi is a senior research scientist at CSIRO, specialising in spatial ecology and biodiversity modelling. He develops scalable scientific software for environmental applications, focusing on species distribution modelling and habitat mapping. Previously, he worked at Cesar Australia, applying predictive modelling to forecast agricultural pest invasions and pesticide resistance. During his PhD at The University of Melbourne, he advanced species distribution modelling methods and developed practical tools. He has extensive expertise in R, Python, and C++, as well as ecological modelling, geospatial analysis, machine learning, and scientific software development.
Research Scientist, Environment CSIRO
Ecosystem Condition Modelling Using the Habitat Condition Assessment System (Hcas) and Its Applications in Australia.
Dr Kate Giljohann is an ecologist and ecological modeller, specialising in research to support environmental decision-making. Her work focuses on the assessment and prediction of change in biodiversity and the impact of novel disturbance regimes and threatening processes on ecological dynamics. Kate’s research merges geospatial analysis, statistical modelling and decision science to address environmental challenges. She has conducted extensive research on fire ecology across southern Australia, including the development and testing of biodiversity resilience metrics, and coupling of biodiversity models with mechanistic fire regime simulations and scenario analysis to explore options for sustaining biodiversity.
Program
| Speaker/Presentation | Time in AEST | Time in CST (Asia) | Time in CEST |
|
Chair 1: Dr. Arnick Abdollahi Chair Introduction |
15:00 – 15:10 | 13:00 – 13:10 | 07:00 – 07:10 |
|
Speaker 1: Dr. Catherine Ticehurst Generating an Australia-Wide Vegetation Height Product by Combining Gedi Satellite Lidar With Optical, Radar and Climate Earth Observation Data in a Machine Learning Model |
15:10 – 15:30 | 13:10 – 13:30 | 07:10 – 07:30 |
| Q&A Session | 15:30 – 15:40 | 13:30 – 13:40 | 07:30 – 07:40 |
|
Speaker 2: Dr. Sanjeev Kumar Srivastava Earth Observation Data Fusion for Improved Mapping of Weeds in Natural and Plantation Forests With Machine Learning Approaches |
15:40 – 16:00 | 13:40 – 14:00 | 07:40 – 08:00 |
| Q&A Session | 16:00 – 16:10 | 14:00 – 14:10 | 08:00 – 08:10 |
|
Chair 2: Dr. Chandrama Sarker Chair Introduction |
16:10 – 16:20 | 14:10 – 14:20 | 08:10 – 08:20 |
|
Speaker 3: Dr. Arnick Abdollahi Harnessing Earth Observation Technology and Machine Learning for National Biomass Assessment in Australia |
16:20 – 16:40 |
14:20 – 14:40 |
08:20 – 08:40 |
| Q&A Session | 16:40 – 16:50 | 14:40 – 14:50 | 08:40 – 08:50 |
|
Speaker 4: Dr. Kate Giljohann and Dr. Roozbeh Valavi Ecosystem Condition Modelling Using the Habitat Condition Assessment System (Hcas) and Its Applications in Australia |
16:50 – 17:10 | 14:50 – 15:10 | 08:50 – 09:10 |
| Q&A Session | 17:10 – 17:20 |
15:10 – 15:20 |
09:10 – 09:20 |
|
Closing of Webinar Chair |
17:20 – 17:25 | 15:20 – 15:25 | 09:20 – 09:25 |
Relevant Special Issue and Papers
Relevant Special Issue:
Investigating State-of-the-Art Machine Learning Approaches in Vegetation Analysis through Earth Observation Data
Edited by Dr. Arnick Abdollahi and Dr. Chandrama Sarker
Deadline for submission: 25 May 2025
Relevant Papers:
Retrieval of Crop Canopy Chlorophyll: Machine Learning vs. Radiative Transfer Model
Authors: Mir Md Tasnim Alam; Anita Simic Milas; Mateo Gašparović; Henry Poku Osei
Remote Sens. 2024, 16(12), 2058; https://doi.org/10.3390/rs16122058
A Robust Dual-Mode Machine Learning Framework for Classifying Deforestation Patterns in Amazon Native Lands
Authors: Julia Rodrigues; Mauricio Araújo Dias; Rogério Negri; Sardar Muhammad Hussain; Wallace Casaca
Land 2024, 13(9), 1427; https://doi.org/10.3390/land13091427
Evaluating Land Surface Temperature Trends and Explanatory Variables in the Miami Metropolitan Area from 2002–2021
Authors: Alanna D. Shapiro and Weibo Liu
Geomatics 2024, 4(1), 1-16; https://doi.org/10.3390/geomatics4010001
A GIS-Based Framework to Analyze the Behavior of Urban Greenery During Heatwaves Using Satellite Data
Authors: Barbara Cardone; Ferdinando Di Martino; Cristiano Mauriello; Vittorio Miraglia
ISPRS Int. J. Geo-Inf. 2024, 13(11), 377; https://doi.org/10.3390/ijgi13110377
UAV-Based Wetland Monitoring: Multispectral and Lidar Fusion with Random Forest Classification
Authors: Robert Van Alphen; Kai C. Rains; Mel Rodgers; Rocco Malservisi; Timothy H. Dixon
Drones 2024, 8(3), 113; https://doi.org/10.3390/drones8030113
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