Different environments will present completely different fire behaviour under the same conditions. For example, a eucalypt forest may burn more severely than a grassland because of the difference in fuel type and climate. In this video we walk through how to select (and define) different vegetation models, and take a closer look at accessing specific data from the Fire-EdUp Platform.
The Fire-EdUp Platform is compatible with several vegetation models. Each model calculates FBI in a different way - making some models more sensitive than others. That makes sense if you consider how some vetetation types might be more prone to fire risk - Like Eucalypt Forests where the oils in a Eucalypt tree can fuel very aggressive fires.
You can assign the following model names to the
- 'button grass'
- 'spinifex grasslands'
- 'grassy woodlands'
- 'wet eucalypt forests'
- 'pine plantations'
- 'dry eucalypt forests'
Observe how the FBI may change for different models even when the Environmental and Fuel Conditions remain the same.
Define your Own Model
The models we've implemented apply a simple multiplier to FBI. You can see the multipliers for each model defined in valid model list.txt file - we've chosen values between 1 and 1.5
Perhaps you want to define a model that is not already provided and define it with your own fire sensitivity weighting.
For that, you can use...
.create_model("name", weighting) accepts a string to name the model, and a number to use as the weighting.
Intuitively, fires burn more aggressively uphill because the fire can readily access the uphill fuel. The
.slope_degrees property allows us to describe the slope we are interested in, and once again this is just another variable that can affect how FBI is calculated.
Access Individual Data
.collect_data() method reads all the input sliders and packs the data into a Python Dictionary. There's a lot of information in here! When we print(data) it fills up the shell with text.
To access just one of the values stored in
data we can use it's key name. For example,
data['temperature']contains the Temperature in °C
data['humidity']contains the Relative Humidity in %
data['wind']contains the Wind Speed in km/h
data['fuel']contains the Fuel Load in tonne/ha
data['moisture']contains the Fuel Moisture Content in %
By accessing just one of the data values, we can perform logical operations like comparing values. For example, we can print a message when it's very hot, or dangerously windy:
if data['temperature'] > 30: print("It's too hot!") if data['wind'] > 63: print("Dangerous winds!")
Continue to the next guide, where we explore how to improve the Fire-EdUp platform with automatic data collection.