This study aims to analyze the territory of a specific municipality using Corine Land Cover data and Sentinel-2 satellite images. Corine Land Cover data allowed for a comprehensive classification of the area into categories such as urban, agricultural, and forested zones, helping to understand the spatial distribution of land use. Municipal boundaries provided by ISTAT were also used for the analysis. The surface areas of each land use category were calculated using QGIS 3.38. Additionally, Sentinel-2 satellite images were employed to calculate the Normalized Difference Vegetation Index (NDVI), which helps monitor changes in vegetation cover from 2019 to 2024. NDVI values were processed through Google Earth Engine, with data filtered for May and June each year. This analysis provided insights into vegetation health and dynamics over time. The NDVI data was then reclassified into five classes in a GIS environment, facilitating comparative analysis. Raster images were converted into polygons to quantify the areas corresponding to each NDVI class, allowing precise measurement of vegetation cover changes. The study highlights how satellite imagery and GIS tools are essential for analyzing ecological trends, particularly in tracking vegetation health and potential environmental degradation. The study highlighted a significant reduction in dense vegetation cover (NDVI class 5), decreasing from 35 to 2% between 2019 and 2022. At the same time, lower and moderate vegetation classes increased, suggesting potential ecological degradation. The results indicate that environmental events or climate changes may have impacted vegetation in the study area.
Climate Change and Satellite Data for Analysis of Impacts on Rural Areas
Teresa Amodio
;Daniel Signorelli
2025
Abstract
This study aims to analyze the territory of a specific municipality using Corine Land Cover data and Sentinel-2 satellite images. Corine Land Cover data allowed for a comprehensive classification of the area into categories such as urban, agricultural, and forested zones, helping to understand the spatial distribution of land use. Municipal boundaries provided by ISTAT were also used for the analysis. The surface areas of each land use category were calculated using QGIS 3.38. Additionally, Sentinel-2 satellite images were employed to calculate the Normalized Difference Vegetation Index (NDVI), which helps monitor changes in vegetation cover from 2019 to 2024. NDVI values were processed through Google Earth Engine, with data filtered for May and June each year. This analysis provided insights into vegetation health and dynamics over time. The NDVI data was then reclassified into five classes in a GIS environment, facilitating comparative analysis. Raster images were converted into polygons to quantify the areas corresponding to each NDVI class, allowing precise measurement of vegetation cover changes. The study highlights how satellite imagery and GIS tools are essential for analyzing ecological trends, particularly in tracking vegetation health and potential environmental degradation. The study highlighted a significant reduction in dense vegetation cover (NDVI class 5), decreasing from 35 to 2% between 2019 and 2022. At the same time, lower and moderate vegetation classes increased, suggesting potential ecological degradation. The results indicate that environmental events or climate changes may have impacted vegetation in the study area.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
