Nicolas Younes is an engineer, a geospatial and remote sensing scientist, and an entrepreneur, who’s passionate about Earth Observation, knowledge transfer, education, and about understanding spatial and temporal changes in Australian forests. Nicolas works in local, continental, and global-scale projects that seek to further our understanding of the vegetation phenology and flammability through the use of remote sensing technologies.

 Currently, Nicolas is deeply involved in the development of the OzFuel satellite mission, the first satellite the world’s first satellite created specifically to monitor the flammability of eucalypt forests. Through the OzFuel project, he’s helping advance Australia’s space industry. His work ranges from using hyperspectral sensors, to data analysis, machine learning, and biochemical analysis of flammable components in eucalypt trees.

Nicolas holds an Environmental Engineering degree, a Master’s degree in Oil and Gas Exploration and Production, a Master’s degree in Natural Resource Management, and a PhD in Earth and Environmental Sciences.

Areas of current research:

• Vegetation phenology
• GAMs
• Oil Palm monitoring
• Forest loss in Upper Guinea Forest
• Intertiral ecosystems
• environmental economics

Remote sensing of Wetlands and Mangrove forests

• Landsat and Sentinel 2 time series
• Mangrove phenology
• Using Generalized Additive Models (GAMs) to differntiate mangrove zones and populations
• Global scale assessment of mangrove ecosystems

Remote sensing of leaf flammability traits in eucalypt forests

• Imaging spectroscopy of eucalypt trees
• Proximal remote sensing using hyperspectral remote sensing
• Biochemistry of eucalypt trees, including GC-MS, SPIME and other methods

Development of the OzFuel sensor

• Sensor design and input data
• End user requirements
• Image simulation
• Spectral and biophysical database development
• Field-based calibration and validation

Radiative Transfer Modelling of eucalypt forests

• Development and application of 1-D radiative transfer model based on the spectral and biophysical characteristics of eucalypt trees.

Development and usage of ML/AI methods for

I'm open to discussing potential Honors, Masters, and PhD projects.

Each project will be developed with the student, and tailored to provide each student with the skills they want to develop.

Discerning live and dead fuels using hyperspectral remote sensing

Bushfire management requires knowledge about how much fuel is available to burn, and its distribution. Fuel can be either dead (e.g. litter, mulch), or alive (i.e. vegetation), and this particular project focuses on detecting the proportion of dead/live fuel from a series of hyperspectral images collected in the National Arboretum Canberra and/or other sites across the ACT.
The scope of this project will include examining and merging spectral data sets from different spectral imaging devices, and using libraries available in the Python programming language. After loading the data sets (images), it is then required to perform data fusion on the data, as the data sets from the separate cameras have different spectral and spatial resolutions. Once merged, the datasets will aid in the delineation of live and dead fuels in the images. Creating a program which performs data fusion on the data sets so will also be useful for the future as it will streamline the data gathering process. Once the datasets are merged, classifying live and dead fuels form the images will take place.

This project aims to verify the spectral bands that are more sensitive to variations in fuel loads (i.e. lignin and cellulose), fuel moisture content, and oil contents. These components are critical determinants of fuel flammability and, therefore, will help narrow down the preliminary spectral specification of OzFuel indicated in the Pre-Phase A OzFuel end-user preliminary requirements document.
The student will use an existing spectral library and a series of hyperspectral images collected from the National Arboretum Canberra to fulfil the aims of the project. It is also expected that the student will collect their own data in the field. With these data, the student will create a series of simulated images that compare existing satellites (MODIS/Sentinel-2/Landsat) to OzFuel. At the conclusion of the project, the student will have gained experience programming, data visualization, data wrangling, statistics and data collection in the field. The project could also include the incorporation of cheaper, lighter computing devices.

1. Mady Hall. Australian National University. Honours. Primary Supervisor (2024 - ongoing). 
2. Estefania Erazo Mera. James Cook University. PhD. External advisor. (2020 - ongoing). 
3. Patrick Fernandez de Viana. Australian National University. Honours. Primary Supervisor (completed in 2023). 
4. Sydnie Graham. Brock University (Canada). Honours. Co-Supervisor (completed in 2022).
5. Sam De Rota. Australian National University. Honours. Primary Supervisor (completed in 2022). 
6. Kieran Aryal. Australian National University. Honours. Primary Supervisor (completed in 2022). 
7. Jackson Taylor. Australian National University. Honours. Co-Supervisor (completed in 2021). 
8. Mary K. Bennett. James Cook University. Master’s level. Co-Supervisor (completed in 2020). 
9. Monica Delgado. Universidad Internacional SEK. Master’s level. Co-Supervisor (completed in 2015).