His research interests include:
- Biophysical remote sensing of terrestrial environments
- In situ observations (including spectral-radiometry)
- Scaling ground observations to the image and landscape level
- Spatial data uncertainty
|Attribution of active fire using simulated fire landscapes||Bryan Hally|
|Multi resolution, high temporal fire monitoring and intensity mapping using Himawari-8 Advanced Himawari Imager data||Chathura Wickramasinghe|
This project seeks to (1) optimize the use of earth observing systems for active fire monitoring by exploring issues of scale, accuracy and reliability, and (2) to improve the mapping and estimation of post-fire severity and fuel change through empirical remote sensing observations.
Understanding the utility of thermal remote sensing systems for active fire detection and monitoring. Exploring issues of scale, accuracy and reliability through simulations and field validation.
In the last decade A range of sensing technologies, techniques and platforms have emerged to capture 3D structural information. This project explores these systems as alternative quantitative solutions to traditional fuel hazard and fire severity evaluations.
Active fires are inscreasingly being identified using satellite remote sensing to determine their size and severity. Verifying the information derived from the wide variety of different sensors and their associated fire algorithms can be a challenging task.
Current methods of fire detection using remote sensing rely on contextual algorithms to characterise fire.
Himawari-8 presents exciting opportunities to map fires in near real time. Exploiting information across temporal and spatial domains enables a new paradigm in fire detection and surveillance.
Accurately estimating background temperatures is vital for identifying fire using remote sensing. New temporal-based methods for temperature estimation are harnessing the increased stream of imagery from new satellite sensors to improve our understanding of the diurnal cycle of the landscape.
The Fuels3D app provides a low cost data collection method for estimating fuel hazard metrics. Testing of the app has demonstrated that it provides significantly greater repeatability and improved quantification of metrics than visual assessments.
|Presentation-Slideshow||21 Mar 2014||Monitoring and prediction||Save (7.35 MB)||flood, modelling, multi-hazard|
|Presentation-Slideshow||05 Dec 2014||Thermal anomaly and hazard mapping||Save (670.97 KB)||fire, forecasting|
|Presentation-Slideshow||24 Oct 2016||Disaster landscape attribution, active fire detection and hazard mapping||Save (1.9 MB)||fire, fire impacts, remote sensing|
|HazardNoteEdition||28 Nov 2016||Monitoring and predicting natural hazards||Save (853.18 KB)||forecasting, modelling, severe weather|