The world we live in has experienced several ecological disasters, most of them caused by direct human actions, which have been intensifying particularly in the past few decades. In particular, forest fire is a natural hazard, whose intensity and frequency have recently been increased, thus affecting forest ecosystems with serious consequences on the environment, society and economy. In addition, drier, warmer weather caused by climate change creates conditions for extreme and widespread biomass burning. Spaceborne and airborne remote sensing Earth observation platforms with improved instruments (including optical, thermal infrared, microwave sensors) have rapidly evolved in the last decades, thus providing an invaluable data source, to understand, monitor and respond to wildfires, at various spatial and temporal scales. Optical and SAR data with global coverage and frequent revisits have become freely available, thus playing a critical role in wildfire monitoring. Moreover, LiDAR has the potential to penetrate the forest canopy, thus emerging as a powerful active sensing tool for assessing both landscape fire hazard and fire effects on vegetation at fine-grained scale. Together to remote sensing analyses, experimental analyses of fire effects are important to have a complementary information about the impact of fire.
This Research Topic aims at highlighting recent progress in remote sensing methodologies for studying forest fires, burned areas, damage, emissions, soil erosion, deforestation and land cover change, vegetation regeneration, also contributing to forest fire modeling and identification of areas at highest risk.
We solicit contributions describing innovative remote sensing-based approaches, data processing techniques and modelling tools, to improve understanding of fire and to support operational applications. In addition, we encourage contributions on the integration of remote sensing data and data collected on field to confirm and improve the existing models. We invite researchers to contribute Original Research articles as well as Review articles.
Potential topics include but are not limited to the following:
• Multispectral (e.g. Sentinel-2, Landsat-8 OLI) and Hyperspectral (e.g. PRISMA) data processing;
• Synthetic Aperture Radar (SAR) images processing (e.g., Cosmo-Skymed, Sentinel-1, ALOS PALSAR);
• SAR polarimetry;
• LiDAR processing in vegetation characterization (pre- and post-fire);
• Remote-sensing processing techniques in wildfire management;
• Remote sensing time series data and methods;
• Methods for multi-source data fusion and integration;
• Integration of satellite, aerial/drone, and in situ observation;
• Recent developments in artificial intelligence (AI) for satellite imagery processing;
• Analysis of forest fire at multiple spatial and temporal scales and Geostatistical Analysis;
• Burnt area mapping: Rapid Damage Assessment and High resolution burnt area mapping;
• Fire emissions;
• Fire monitoring, fire evolution simulation and fire severity assessment; and
• Identification of areas at risk and risk mitigation strategies.
The world we live in has experienced several ecological disasters, most of them caused by direct human actions, which have been intensifying particularly in the past few decades. In particular, forest fire is a natural hazard, whose intensity and frequency have recently been increased, thus affecting forest ecosystems with serious consequences on the environment, society and economy. In addition, drier, warmer weather caused by climate change creates conditions for extreme and widespread biomass burning. Spaceborne and airborne remote sensing Earth observation platforms with improved instruments (including optical, thermal infrared, microwave sensors) have rapidly evolved in the last decades, thus providing an invaluable data source, to understand, monitor and respond to wildfires, at various spatial and temporal scales. Optical and SAR data with global coverage and frequent revisits have become freely available, thus playing a critical role in wildfire monitoring. Moreover, LiDAR has the potential to penetrate the forest canopy, thus emerging as a powerful active sensing tool for assessing both landscape fire hazard and fire effects on vegetation at fine-grained scale. Together to remote sensing analyses, experimental analyses of fire effects are important to have a complementary information about the impact of fire.
This Research Topic aims at highlighting recent progress in remote sensing methodologies for studying forest fires, burned areas, damage, emissions, soil erosion, deforestation and land cover change, vegetation regeneration, also contributing to forest fire modeling and identification of areas at highest risk.
We solicit contributions describing innovative remote sensing-based approaches, data processing techniques and modelling tools, to improve understanding of fire and to support operational applications. In addition, we encourage contributions on the integration of remote sensing data and data collected on field to confirm and improve the existing models. We invite researchers to contribute Original Research articles as well as Review articles.
Potential topics include but are not limited to the following:
• Multispectral (e.g. Sentinel-2, Landsat-8 OLI) and Hyperspectral (e.g. PRISMA) data processing;
• Synthetic Aperture Radar (SAR) images processing (e.g., Cosmo-Skymed, Sentinel-1, ALOS PALSAR);
• SAR polarimetry;
• LiDAR processing in vegetation characterization (pre- and post-fire);
• Remote-sensing processing techniques in wildfire management;
• Remote sensing time series data and methods;
• Methods for multi-source data fusion and integration;
• Integration of satellite, aerial/drone, and in situ observation;
• Recent developments in artificial intelligence (AI) for satellite imagery processing;
• Analysis of forest fire at multiple spatial and temporal scales and Geostatistical Analysis;
• Burnt area mapping: Rapid Damage Assessment and High resolution burnt area mapping;
• Fire emissions;
• Fire monitoring, fire evolution simulation and fire severity assessment; and
• Identification of areas at risk and risk mitigation strategies.
