Mind the Gaps: Why High-Resolution Analysis is Crucial in a Wet Fire Season
A wet year brings growth. Across Northern Australia, the 2025 season has been defined by significant rainfall, leading to abundant, dense vegetation. While this green landscape is welcome, it presents a unique and heightened challenge for fire managers. How do you effectively conduct mitigation burns when the landscape is holding so much moisture?
In our last post, we showcased the 10-meter precision of FiSci's fire scar detection. Now, we'll explore why this technology is more critical than ever in a wet year, where understanding the complex mosaic within a burn is key to preventing future disaster. Early dry season burns are designed to reduce fuel, but in a landscape saturated with moisture, these burns are rarely uniform, leaving behind a crucial, and often overlooked, legacy: the gaps.
The Hazard Hiding in Plain Sight
Look at the fire scar map below, generated by FiSci Detect from prescribed burns conducted on a Northern Territory property over May and June 2025. The challenge of this exceptionally wet year is immediately apparent with the burn leaving a highly fragmented landscape. The red areas show what has burned, but the real story is in the gaps—the vast unburnt areas left behind.
Caption: FiSci Detect reveals the intricate, patchy reality of early dry season burns. The unburnt "gaps" can become significant hazards later in the season.
This patchiness isn't random. It's a direct result of the landscape conditions at the time of the burn. So, what causes these gaps?
Following the Moisture: The Predictive Power of GVMI
The primary factor determining whether vegetation will burn is its moisture content, a factor that becomes paramount in a wet year. We use the Global Vegetation Moisture Index (GVMI), a satellite-derived measure, to map water content in vegetation before a burn. The GVMI map from early May 2025 is striking; the extensive bright green areas reveal just how much water the landscape was holding. When we overlay the subsequent fire scars (dark patches), the link is undeniable: the fires simply burned around these moisture-saturated parts of the landscape.
Caption: The proof is in the overlay. The fire scars (dark areas) perfectly avoid the high-moisture (green) parts of the landscape, demonstrating a direct link between pre-burn conditions and fire behaviour.
Today's Firebreak is Tomorrow's Fuel
In the early dry season, these moist, unburnt patches act as natural firebreaks. However, as the season progresses, these areas cure and dry out. After a wet year, the fuel load in these gaps can be much higher than average. They transform into continuous, heavily-fueled corridors. An ignition in the late dry season can then exploit these gaps, allowing a high-intensity wildfire to move through areas of now-cured, dense grass, bypassing the earlier mitigation efforts with potentially devastating consequences.
Identifying the gaps is the first step. The critical next question for land managers is: which gaps matter most?
From Detection to Decision: Simulating Risk with FiSci Mitigate
This is where the workflow comes full circle. Once FiSci Detect has identified the precise fire scars, we import that data directly into our FiSci Mitigate platform. Here, we can simulate the behaviour of potential late-season fires under more extreme conditions.
The platform uses the exact boundaries of the unburnt gaps to model how a future fire might spread, effectively testing the integrity of the initial burn.
Caption: From detection to decision. FiSci Mitigate uses the detected fire scars to simulate the spread of a potential late-season wildfire, highlighting which gaps are most critical to address.
This powerful simulation moves beyond just identifying gaps to actively prioritising them. It shows land managers which unburnt areas pose the most significant risk of allowing a large-scale fire to escape. This enables them to focus their resources, targeting follow-up treatments with precision to close the most critical gaps before they become a threat.
It’s this end-to-end solution, from high-resolution detection to predictive simulation, that provides the clarity and foresight needed for effective, proactive savanna fire management, especially when facing the challenges of a wet year.
