As our planet’s climate changes, scientists hunt for ways to assess, forecast, and mitigate the risks from fires and other hazards. Our current prediction methods, based on previous patterns, are falling short. Several researchers and scientist have turned to the planet itself to track and predict – using the planet’s flora.
This idea isn’t new. In fact, the USA National Phenology Network (USA – NPN) based at the University of Arizona was started in 2007. According to them phenology is “nature’s calendar”, the schedule of plant and animal development.
Farmers use phenology to decide when to plant and apply fertilizers or pesticides. It’s helps us know exactly when seasonal allergies will hit hardest. Phenological events are the most sensitive indicator to changes in our climate – the canary in the coal mine.
Because of this sensitivity, phenology could take the lead in climate change predictions. USA – NPN provides free resources to help everyone, from land management agencies to the public, make evidence-based decisions.
Predicting Fire Risk
Phenological events can be used to predict the wildfire risk – even predicting the timing, intensity, and size. Earlier spring phonologic events initiated by warm spring temperatures increases the wildfire activity later in the year.
The warm weather causes earlier and more rapid snowmelt, which increases the amount of vegetation that grows during the spring months. This provides more fuel for the fire season. An earlier and warmer spring also leads to a dryer summer.
Plant moisture content is one of the most important factors to wildfire risk. When this falls below 79%, the risk of a burn increases significantly. By tracking the moisture content, researchers can create fire risk estimates.
Traditionally, the moisture content was determined by collecting vegetation samples to assess in the lab or using remotely sensed imagery. Collecting samples is a difficult and slow process. Using remotely sensed imagery is quicker and easier but is imprecise. By visually looking for phonologic events, a third option is offered, that is quicker, easier, and more precise.
Satellite-Derived Phenology – Good or Bad?
Phenology analysis using remote sensing, named apparent phenology, is an option that has been used to predict the effects of climate change. However, a recent study done by the Centre for Tropical Environmental and Sustainability Science and School of Science and Engineering at James Cook University in Australia questions the validity of the models created based on remote sensing.
The researchers identified “1) areal extent; 2) site location; 3) frequency of observation; 4) spatial resolution; 5) temporal coverage; and 6) the number of clouds” as factors that could impact the accuracy of apparent phenology. This hypothesis makes sense – clouds obscure satellite images – but had never been studied before.
The study revealed that in almost every case the phenology detected remotely was not comparable to on the ground phenology results. Meaning, all the models are wrong. According to the authors of the study, the inability to zoom in on the ‘miniscule and specific changes that take place in the ecosystem, or when they take place, or if they take place at the same time throughout the landscape’ impairs the results. However, while these models are wrong, the authors stress that they are helpful to “examine seasonal variations and long term trends and eventually compare those variations to other biophysical drivers.”