Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series.

Fire risk mapping - mapping the probability of fire occurrence and spread - is essential for pre-fire management as well as for efficient firefighting efforts. Most fire risk maps are generated using static information on variables such as topography, vegetation density, and fuel instantaneous wetness. Satellites are often used to provide such information. However, long-term vegetation dynamics and the cumulative dryness status of the woody vegetation, which may affect fire occurrence and spread, are rarely considered in fire risk mapping. Here, we investigate the impact of two satellite-derived metrics that represent long-term vegetation status and dynamics on fire risk mapping - the long-term mean normalized difference vegetation index (NDVI) of the woody vegetation (NDVIW) and its trend (NDVIT). NDVIW represents the mean woody density at the grid cell, while NDVIT is the 5-year trend of the woody NDVI representing the long-term dryness status of the vegetation. To produce these metrics, we decompose time-series of satellite-derived NDVI following a method adjusted for Mediterranean woodlands and forests. We tested whether these metrics improve fire risk mapping using three machine learning (ML) algorithms (Logistic Regression, Random Forest, and XGBoost). We chose the 2007 wildfires in Greece for the analysis. Our results indicate that XGBoost, which accounts for variable interactions and non-linear effects, was the ML model that produced the best results. NDVIW improved the model performance, while NDVIT was significant only when NDVIW was high. This NDVIW-NDVIT interaction means that the long-term dryness effect is meaningful only in places of dense woody vegetation. The proposed method can produce more accurate fire risk maps than conventional methods and can supply important dynamic information that may be used in fire behavior models.

RGB plots as a tool for the simultaneous visualization of multiple data layers in a two dimensional space.

Visualization of multidimensional data helps in understanding complex systems and environments. We present here a red, green, blue (RGB) visualization method that can serve to display environmental properties. The saturation of each color is used to represent the concentration of a given property. The implementation of that figure is illustrated through visualization of three dissolved inorganic nutrient concentrations along a vertical transect of the Mediterranean, as well as through a vertical time series of three phytoplankton group cell numbers. The RGB figures show well known properties of the water column. In addition, they reveal some lesser-known properties, such as regions in shallow water in which the ratio of phosphorus and silica to nitrogen is high, and a deep eukariotic phytoplankton community. Visualization of such data is usually performed with three separate contour or surface plots, and occasionally two properties are presented as an overlay in a single figure. The RGB figure offers a better way to visualize the interactions among the three separate plots than is commonly available.