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Climate changes and their consequences on the environment and quality of life in Cisterna di Latina (LT)
Satellite data
Copernicus is the European Union's Earth observation programme coordinated: it allows the achievement of global, continuous, autonomous, high quality, wide range Earth observation capacity. Its accurate, timely and easily accessible information help to understand and mitigate the effects of climate change: service providers, public authorities and other international organizations improve the quality of life for the citizens of Europe.
"Tropics (?)" project referes to Sentinel_2 and Sentinel_3 satellites data
EO Browser makes it possible to browse and compare full resolution images from the a complete archive of Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, ESA’s archive of Landsat 5, 7 and 8, global coverage of Landsat 8, Envisat Meris, Proba-V, MODIS and GIBS products.
Scene classification map - Sentinel-2 L2A
Scene classification was developed to distinguish between cloudy pixels, clear pixels and water pixels of Sentinel-2 data and is a result of ESA's Scene classification algorithm. Twelve different classifications are provided including classes of clouds, vegetation, soils/desert, water and snow. It does not constitute a land cover classification map in a strict sense.
February scene classification map variation timelapse (2017- 2022)
August scene classification map variation timelapse (2017 - 2021)
The variation of the Scene Classification Map evidences:
an evident decrease of the vegetated surface from the winter to the summer period;
a general decrease of vegetated areas both in summer and in winter in the 2017-2022 periodo .
Normalized Difference Vegetation Index (NDVI) - Sentinel-2 L2A
The normalized difference vegetation index is a simple, but effective index for quantifying green vegetation. It is a measure of the state of vegetation health based on how plants reflect light at certain wavelengths.
The value range of the NDVI is -1 to 1.
Negative values of NDVI (values approaching -1) correspond to water.
Values close to zero (-0.1to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1).
We used NDVI Index data to evidence the climate change impact on cultivated areas and natural vegetation
February NDVI index variation timelapse (2017 - 2022)
August NDVI index variation timelapse (2017 - 2021)
The variation of the Normalized Difference Vegetation Index for the months of February (2017 to 2022) shows:
a limitate decrease in the health of the vegetation (February 2022 is part of a dry period that began in January 2022) in winter period;
a decrease in the health in summer period;
Normalized Difference Moisture Index (NDMI) - Sentinel-2 L2A
The normalized difference moisture Index (NDMI) is used to determine vegetation water content and monitor droughts. The value range of the NDMI is -1 to 1. Negative values of NDMI (values approaching -1) correspond to barren soil. Values around zero (-0.2 to 0.4) generally correspond to water stress. High, positive values represent high canopy without water stress (approximately 0.4 to 1).
We used NDMI Index data to confirm the decrease in soil moisture as a result of climate change.
February NDMI index variation timelapse (2017 - 2022)
August NDMI index variation timelapse (2017 - 2021)
The variation of the Normalized Difference Moisture Index for the months in 2017 - 2022 period:
shows, except 2017, a general decrease in soil moisture for winter period;
shows, except 2018, a general decrease in soil moisture for summer period.
Thermal IR fire emission bands - Sentinel-3 SLSTR
Sentinel-3 Sea and Land Surface Temperature Instrument (SLSTR) has two dedicated channels (F1 and F2) that aim to detect Land Surface Temperature (LST). F2 Channel, with a central wavelength of 10854 nm measures in the thermal infrared, or TIR. It is very useful for fire and high temperature event monitoring at 1 km resolution.
We used Land Surface Temperature data to confirm the variation in ground temperatures as a consequence of global warming
February LST Index variation timelapse (2017 - 2022)
August LST Index variation timelapse (2017 - 2021)
The variation of the Land Surface Temperature data aren't statistically valid cause their small quantity.
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