Skip to menu Skip to content Skip to footer
Dr

Lydia Li

Email: 

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:

  • EO-driven canopy height estimation for forest structure and biomass monitoring

  • Scalable HPC/cloud workflows for processing LiDAR and satellite data

  • AI/ML applications in EO for ecosystem modelling and climate resilience

  • 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

Qualifications

  • Doctor of Philosophy of Geography, Monash University

Research interests

  • 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:

  • Enabling scalable forest structure monitoring for national environmental assessments

  • Improve grassland curing estimate for fire monitoring using remote sensing techniques

  • Supporting environmental water planning through EO-based landscape indicators

  • 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

9 works between 2017 and 2023

1 - 9 of 9 works

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

Assessing factors impacting inter-satellite variability of grassland curing estimates for fire monitoring in Victoria, Australia using remote sensing

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

Very rapid forest cover change in Sichuan Province, China: 40 years of change using images from declassified spy satellites and Landsat

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

Evaluation and intercomparison of topographic correction methods based on landsat images and simulated data

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

Improved modeling and analysis of the patch size–frequency distribution of forest disturbances in China based on a Landsat forest cover change product

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

Inter-satellite variability of grassland curing maps produced by different satellite sensors–Victoria, Australia

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

Analysis of Landsat 8 detection of the interannual variability of grassland curing in Greater Melbourne, Australia

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

Comparison of different multispectral sensors for photosynthetic and non-photosynthetic vegetation-fraction retrieval

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

Change detection: how has urban expansion in Buenos Aires metropolitan region affected croplands

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

Identifying the main contributors of air pollution in Beijing

Supervision

Availability

Dr Lydia Li is:
Available for supervision

Before you email them, read our advice on how to contact a supervisor.

Media

Enquiries

For media enquiries about Dr Lydia Li's areas of expertise, story ideas and help finding experts, contact our Media team:

communications@uq.edu.au