Integrating Multi-Sensor Remote Sensing Data for Comprehensive spatio-temporal WildfireAssessment In Campania Provinces – Italy

Wildfires present substantial threats to ecosystems and human settlements which increase the importance of monitoring for timely detection and assessment. This study was performed on the Campania provinces—Salerno, Avellino, Benevento, Caserta, and Napoli in Italy—employing a multi-sensor remote sensing approach to elevate wildfire analysis. The first objective is identifying fire patches through Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2001 to 2020. Although, generating Difference Normalized Burn Ratio (DNBR) and Difference Normalized Difference Vegetation Index (DNDVI) maps from Sentinel-2 images. Integration of MODIS and Sentinel-2 outcomes enhances pinpointing fire-affected areas. Subsequently, Incorporating Landsat 9 images for Land Surface Temperature (LST) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) for precipitation trends in five provinces from 2001 to 2020 in Hotspot Polygons generated via First Order Contiguity edges corners algorithm. Pearson correlation coefficients between burnt area, LST, and precipitation are computed. A high correlation coefficient was observed between the mentioned parameters. Wildfire analysis reveals peak burnt areas in 2001, 2007, and 2017 in Avellino and Salerno Province. Change detection maps illustrate significant land cover changes from Forest to Savannas and Shrubland to grasslands in 2001. Avellino province reveals a decreasing trend in Grassland and an increase in Savannas, as the same as observations in Salerno Province. This study considers the analysis of wildfires, connecting burnt areas, climate variables, and land cover changes across the Campania provinces.

Keywords: Wildfire, Fire and Vegetation Indices, Land Cover Changes, Climate Change