Artificial intelligence has been applied in wildfire science and management since the 1990s,
with early applications including neural networks and expert systems. Since then, the field …

Natural disturbances play a key role in ecosystem dynamics and are important factors for
sustainable forest ecosystem management. Quantitative models are frequently employed to …

With machine learning models being increasingly used to aid decision making even in high-
stakes domains, there has been a growing interest in developing interpretable models …

Topographic variation underpins a myriad of patterns and processes in hydrology,
climatology, geography and ecology and is key to understanding the variation of life on the …

Fire occurrence, which results from the presence of an ignition source and the conditions for
a fire to spread, is an essential component of fire risk assessment. In this paper, we present …

Fundamentals of Satellite Remote Sensing: An Environmental Approach, Third Edition, is a
definitive guide to remote sensing systems that focuses on satellite-based remote sensing …

Fire is a chaotic and extreme phenomenon. While the past few years have witnessed the
success of integrating machine intelligence (MI) to tackle equally complex problems in …

Topographical relief comprises the vertical and horizontal variations of the Earth's terrain
and drives processes in geomorphology, biogeography, climatology, hydrology and …

This paper provides insight into the use of Machine Learning (ML) models for the
assessment of human-caused wildfire occurrence. It proposes the use of ML within the …

The increasing global concern about wildfires, mostly caused by people, has triggered the
development of human-caused fire occurrence models in many countries. The premise is …