The Utilization of Supervised Classification Sampling for Environmental Monitoring in Turin (Italy)

In a world threatened by climate change, the need to observe the land transformation is crucial to set environmental policies. One of the most prominent issues of environmental monitoring is the availability of updated and reliable land use data. The last land-use release in Piedmont Region (Italy) is in 2010, while the most updated Normalized Difference Vegetation Index is in 2016. To overcome this limit, in this study, a supervised classification sampling has been applied on a Sentinel-2 image produced by the Copernicus Program on 29 September 2020, using Esri ArcGIS (ver.10.8 Redlands, California, US) by accessing via ONDA-DIAS services to L2A products. After land classification, three maps were generated-the Habitat Quality, the Habitat Decay, and the Normalized Difference Vegetation Index. This study aimed at classifying the environmental status in five classes ranging from critical to health with a double perspective-(i) to make a comparative metropolitan assessment between municipalities and (ii) to evaluate the quality of urban public green areas in the city of Turin while defining a different kind of intervention. Results indicate that products derived from supervised classification sampling can be applied in a wide range of applications while reaching seasonal monitoring of the environmental status and delivering just-in-time solutions.

Automated summary

By utilizing Sentinel-2 images and supervised classification sampling, this study aims to evaluate land use and environmental conditions in a region. The results indicate that this method can be applied to monitor recent environmental status and provide reference and support for future interventions.