Overview
Background
I am specialised in remote sensing, Earth observation (EO), and geospatial data science. My current research centres on modelling canopy height by integrating GEDI LiDAR and Sentinel-2 satellite data. This work leverages high-performance computing (HPC), cloud platforms, and machine learning (ML) to produce scalable, reproducible workflows for environmental monitoring and decision support.
My current focus includes:
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EO-driven canopy height estimation for forest structure and biomass monitoring
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Scalable HPC/cloud workflows for processing LiDAR and satellite data
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AI/ML applications in EO for ecosystem modelling and climate resilience
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Supporting policy and land planning with EO-derived environmental indicators
Previously, I led research on satellite-based grassland curing to improve fire monitoring, addressing inter-satellite variability and product accuracy for use by fire managers. I also contributed to government-led programs focusing on environmental water planning and provide the framework for coordinating the development of flood works.
I collaborate with academic, government, and industry stakeholders nationally and internationally to ensure EO science translates into tools and insights that support environmental monitoring.
Availability
- Dr Lydia Li is:
- Available for supervision
Fields of research
Qualifications
- Doctor of Philosophy of Geography, Monash University
Research interests
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Remote Sensing
My research interests lie in the application of remote sensing, Earth observation, and geospatial analytics to monitor vegetation structure and ecosystem dynamics. I focus on integrating satellite and LiDAR data with machine learning and cloud computing to model canopy height and landscape change. I am particularly interested in scalable, reproducible EO workflows that support environmental monitoring, climate resilience, fire risk assessment, and sustainable land management. My work bridges science, and environmental decision-making.
Research impacts
My research contributes to environmental decision-making by delivering scalable, data-driven insights from satellite and LiDAR observations, supporting climate resilience, land management, and ecosystem health.
Currently, my work on canopy height estimation enhances Australia’s ability to monitor forest dynamics, contributing to carbon stock estimation, biodiversity accounting, and conservation planning. The methods I develop help agencies and researchers track vegetation change efficiently across broad landscapes using open-source, reproducible tools.
In previous work, I supported bushfire agencies with more reliable curing metrics derived from multi-sensor EO data. I also provided geospatial analyses for state government programs assessing environmental water delivery, riparian condition, and floodplain connectivity.
Key impacts include:
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Enabling scalable forest structure monitoring for national environmental assessments
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Improve grassland curing estimate for fire monitoring using remote sensing techniques
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Supporting environmental water planning through EO-based landscape indicators
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Building technical capacity in EO analytics across academic and public sectors
My research bridges scientific innovation and practical environmental management, with a strong focus on digital transformation, reproducibility, and public sector relevance.
Works
Search Professor Lydia Li’s works on UQ eSpace
2023
Journal Article
Assessing factors impacting inter-satellite variability of grassland curing estimates for fire monitoring in Victoria, Australia using remote sensing
Li, Sike (2023). Assessing factors impacting inter-satellite variability of grassland curing estimates for fire monitoring in Victoria, Australia using remote sensing. International Journal of Digital Earth, 16 (1), 3368-3383. doi: 10.1080/17538947.2023.2248966
2021
Journal Article
Very rapid forest cover change in Sichuan Province, China: 40 years of change using images from declassified spy satellites and Landsat
Song, Dan-Xia, Huang, Chengquan, He, Tao, Feng, Min, Li, Ainong, Li, Sike, Pang, Yong, Wu, Hao, Mohamed Shariff, Abdul Rashid and Townshend, John (2021). Very rapid forest cover change in Sichuan Province, China: 40 years of change using images from declassified spy satellites and Landsat. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10964-10976. doi: 10.1109/jstars.2021.3121260
2021
Journal Article
Evaluation and intercomparison of topographic correction methods based on landsat images and simulated data
Ma, Yichuan, He, Tao, Li, Ainong and Li, Sike (2021). Evaluation and intercomparison of topographic correction methods based on landsat images and simulated data. Remote Sensing, 13 (20) 4120, 4120-20. doi: 10.3390/rs13204120
2021
Journal Article
Improved modeling and analysis of the patch size–frequency distribution of forest disturbances in China based on a Landsat forest cover change product
Song, Dan-Xia, Huang, Chengquan, He, Tao, Sexton, Joseph O., Li, Ainong, Li, Sike, Wu, Hao and Townshend, John R. (2021). Improved modeling and analysis of the patch size–frequency distribution of forest disturbances in China based on a Landsat forest cover change product. International Journal of Digital Earth, 14 (2), 181-201. doi: 10.1080/17538947.2020.1810337
2021
Journal Article
Inter-satellite variability of grassland curing maps produced by different satellite sensors–Victoria, Australia
Li, Sike (2021). Inter-satellite variability of grassland curing maps produced by different satellite sensors–Victoria, Australia. International Journal of Digital Earth, 14 (7), 899-920. doi: 10.1080/17538947.2021.1900938
2020
Journal Article
Analysis of Landsat 8 detection of the interannual variability of grassland curing in Greater Melbourne, Australia
Li, Sike (2020). Analysis of Landsat 8 detection of the interannual variability of grassland curing in Greater Melbourne, Australia. International Journal of Digital Earth, 13 (11), 1321-1338. doi: 10.1080/17538947.2019.1710273
2020
Journal Article
Comparison of different multispectral sensors for photosynthetic and non-photosynthetic vegetation-fraction retrieval
Ji, Cuicui, Li, Xiaosong, Wei, Huaidong and Li, Sike (2020). Comparison of different multispectral sensors for photosynthetic and non-photosynthetic vegetation-fraction retrieval. Remote Sensing, 12 (1) 115, 115-1. doi: 10.3390/rs12010115
2018
Journal Article
Change detection: how has urban expansion in Buenos Aires metropolitan region affected croplands
Li, Sike (2018). Change detection: how has urban expansion in Buenos Aires metropolitan region affected croplands. International Journal of Digital Earth, 11 (2), 195-211. doi: 10.1080/17538947.2017.1311954
2017
Journal Article
Identifying the main contributors of air pollution in Beijing
Li, Sike, Feng, Kuishuang and Li, Mengxue (2017). Identifying the main contributors of air pollution in Beijing. Journal of Cleaner Production, 163 (Supplement), S359-S365. doi: 10.1016/j.jclepro.2015.10.127
Supervision
Availability
- Dr Lydia Li is:
- Available for supervision
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Media
Enquiries
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