Remote Sensing of Post-fire Environmental Damage and Forest Recovery: New Challenges and Approaches

Dear Colleagues,

Understanding of forest fire regimes involves characterizing spatial distribution, recurrence, intensity, seasonality, size, and severity. In recent years, knowledge of damage levels can be directly related to the environmental impact of fire and, at the same time, it is a valuable estimator of fire intensity, when the data about it are not available. Remote sensing may be seen as a tool to accurately assess burn severity and to predict the potential effects of forest fires on ecosystems, thus making the prediction of the regeneration of the plant community and the effects on ecosystems easier. This information is basic to facilitate decision-making in the post-fire management of fire-prone ecosystems.

Nowadays, there has been intense research activity in relation to burned areas, burn severity, and vegetation regeneration because fires in many areas of the planet are becoming more severe and extensive. The environmental damage of affected ecosystems is also much more important, so their correct evaluation and follow-up pose great challenges to current scientists. The current advances in remote sensing and related sciences will allow us to evaluate the damage with greater precision and to know with greater reliability the dynamics of recovery.

This Special Issue aims at studies covering new remote sensing technologies, new sensors, data collections, and processing methodologies that can be successfully applied in burn severity mapping, vegetation revovery monitoring, and post-fire management of fire-prone ecosystems affected by large fires. We welcome submissions that cover but are not limited to:

• Global trends in mapping burned and burn severity in local and regional areas using the remote sensing approach;
• Wildfire severity evaluation and land monitoring with big data and artificial intelligence classification;
• Remote sensing-based assessment of post-fire forest patterns monitoring successional stages;
• 3D mapping by photogrammetry, LiDAR, and SAR in post-fire studies;
• New hyperspectral sensors applications in post-fire studies;
• Ultra-high spatial resolution using unmanned aerial vehicles (UAV) in post-fire studies;
• Improved methods of modeling image time-series for fire disturbance recovery;
• Understanding wildfire behavior and ecology behavior within and around the wildland–urban interface (WUI);
• Impact of climate change on forest fire severity and consequences for ecosystem recovery;
• Fire severity and recovery dynamics in Reducing Emissions from deforestation and degradation programs (REDD+).

Dr. Alfonso Fernández-Manso
Dr. Carmen Quintano
Guest Editors

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