Key message Wildfire danger and burnt areas should increase over the century in southern
Europe, owing to climate warming. Fire-prone area expansion to the north and to …

Bayesian hierarchical models with latent Gaussian layers have proven very flexible in
capturing complex stochastic behavior and hierarchical structures in high-dimensional …

We develop a stochastic modeling approach based on spatial point processes of log-
Gaussian Cox type for a collection of around 5000 landslide events provoked by a …

Extreme wildfires continue to be a significant cause of human death and biodiversity
destruction within countries that encompass the Mediterranean Basin. Recent worrying …

Abstract Motivated by the Extreme Value Analysis 2021 (EVA 2021) data challenge, we
propose a method based on statistics and machine learning for the spatial prediction of …

Due to climate change and human activity, wildfires are expected to become more frequent
and extreme worldwide, causing economic and ecological disasters. The deployment of …

The growing human population and demand for food have significantly impacted soil
resources. Understanding the spatiotemporal change of soil conditions is important to …

Because most natural phenomena exhibit dependence at multiple scales like locations of
earthquakes or forest fire occurrences, spatio-temporal single-scale point process models …

Landscape heterogeneity affects population dynamics, which determine species
persistence, diversity and interactions. These relationships can be accurately represented …

In the last decades, eucalypt plantations are expanding across the Brazilian savanna, one of
the most frequently burned ecosystems in the world. Wildfires are one of the main threats to …