EMSINA's 5 Capability Questions
Q1: What is the Project Scope / What is trying to be solved?
"In 2020 we held a series of workshops across CRC SmartSAT industry and research partners and with Bushfire and Natural Hazards CRC and AFAC. The following is the main activity we will start to build a number of co-funded projects. The responses to dot points below are based on how we will approach this. :
Fire Capability Development - Open call for CRC partners to participate in mission development for pre-fire (fuel load, moisture, flammability) using SWIR and other sensors, for all mission components from sensor, satellite, analytics and delivery systems, to deliver Phase A proposal by December 2021, to submit for funding in 2022.
Q2: What will be available to the EM community / decision makers at the end of your project?
The EM community will be engaged with resultant mission design teams in defining the forms of output information required for their use (environmental variables, area to be covered, smallest mapping units, update frequency, essential meta-data, file formats, required error and accuracy levels) . NOTE - THIS WILL NOT BE A DISCUSSION ABOUT SATELLITE DATA DETAILS e.g. pixel size etc.
Other information provided by the presenter.
The full list of potential projects we will investigate is:
1. Improved and up to date knowledge on fuel loads (fuel types, condition, structure, quantity, moisture) through the integration of multi-sensor EO spectral information
[Pre-fire / Fuel load]
• Problem addressed (to be developed)
• Research topic: fusion of multi-sensor EO spectral information for more up-to-date fuel load information. 2. Eyes on the ground: seeing through forest canopy 每 integrating EO (SAR, optical) and on-ground sensor data for improved risk knowledge
[Pre-fire / Risk Knowledge 每 Fuel load, fuel moisture content, flammability]
• Problem addressed: being able to characterise the fuel load and environmental conditions of vegetation below forest canopy
• Research topic: integration of SAR, optical and other data 每 potential use of AI. 3. Multi-sensor multi-platform (space, air, ground, social/call-in) data integration for improved fire detection and fire behaviour modelling
[During fire / Fire detection, Fire Behaviour]
• Problem addressed: fragmentation of systems and data across agencies and end-users resulting in sub-optimal and/or partial information on fire detection and behaviour changes
• Research topic: approach to integrate data from multiple sources (space, air, ground, social/call-in) and across multiple platforms to enable faster more comprehensive information on fire detection and fire behaviour. 4. Validation of fire risk assessment EO-derived products through the introduction of indigenous knowledge
[Pre-fire / Risk knowledge]
• Problem addressed: EO product validation is critical to ensure end users can rely on product outcomes for decision making, yet the amount of the on-the-ground validation data is very limited and particularly challenging to obtain in Australia 每 due to the vastness of our territory. Indigenous communities gather significant amount of knowledge on the state of the land, vegetation and waterbodies across the country, particularly in some of the most remote areas. This knowledge would be extremely valuable to support validation of fire risk EO-derived products. A project could look at developing a mutually beneficial approach and framework for near-real time knowledge sharing and transfer with EO-product developers and indigenous communities.
• Research topic: to be developed
Question and Answers
The following are overflow questions that the presenter was unable to answer during the meeting.