## 2 Methods
It leverages spectral (remote sensing) data to identify these green areas by analysing a satellite image (Sentinel-2) through a normalised differential vegetation index (NDVI). The indicator is calculated based on the following equation:  
\[\text{green areas in the urban areas}=\frac{\text{sum of green areas based on NDVI}}{\text{urban areas of the city}}\ *\ 100\ [\%], \tag{2}\]  
\[\text{NDVI}=\frac{\text{NIR}-\text{RED}}{\text{NIR}+\text{RED}}, \tag{3}\]  
where NIR is canal 8 of the satellite image from Sentinel-2, RED is canal 2 of the satellite image from Sentinel-2. The index calculation involves identifying green areas within the city using Sentinel-2 satellite images and applying the NDVI index with a threshold value of 0.2, which means that all areas covered by vegetation within the city are considered, encompassing sparse greenery such as shrubs and meadows, as well as dense greenery, which includes forests. Whilst the total anthropogenic area in the city is extracted from Corine Land Cover (CLC) data. Anthropogenic areas expressed by CLC code are: 111--dense urban fabric, 112--dispersed urban fabric, 121--industrial and commercial areas, 122--transport areas, 123--ports, 124--airports, 131--open-pit mining sites, 132--dumps and heaps, 133--construction sites, 141--urban green spaces, and 142--sports and recreational areas. The indicator is derived as the average from three satellite images taken during the summer months (June, July, August). The retention potential factor based on green areas is calculated as:  
\[\text{biologically active index}=\frac{\text{sum of biologically active areas}}{\text{area of the city}}\ *\ 100\ [\%], \tag{4}\]  
The indicator offers insights into the extent of biologically active land that has the natural capacity for retention within the city boundaries. The sum of biologically active areas is determined by combining the following types of land use: surface water, forested and wooded areas, shrub vegetation, permanent crops, grass vegetation, and agricultural crops. Finally, the indicator for impervious areas is determined based on satellite images (Sentinel-2), with the Normalised Difference Built-Up Index (NDBI) calculated from an equation:  
\[\text{NDBI}=\frac{\text{SWIR1}-\text{NIR}}{\text{SWIR1}+\text{NIR}}\ *\ 100\ [\%] \tag{5}\]  
where SWIR1 is a shortwave-infrared band 1 and NIR is a near infrared band. This indicator, i.e., NDBI can be compared to the urban areas. The limitations of the methods used for assessing these indicators arise primarily from the resolution and temporal constraints of satellite data. The spatial resolution of Sentinel-2 imagery may not capture small, narrow, or fragmented green spaces, such as street trees, small urban parks, or green strips along roads. Additionally, the temporal resolution is restricted to the summer months (June, July, and August), which may not fully represent vegetation dynamics throughout the year. Increasing occurrences of drought during summer months can lead to vegetation stress, altering NDVI values and causing certain green areas to appear dormant or less extensive than they actually are. Moreover, vegetation located on vertical surfaces, such as green walls, rooftop gardens, and facade plantings, is often overlooked in traditional satellite-based assessments. Furthermore, the classification of urban green spaces relies on predefined thresholds (e.g., NDVI \(\geq\) 0.2), which may not be universally applicable across different climatic regions or vegetation types. Seasonal variations in vegetation phenology, soil moisture content, and local environmental conditions further influence classification accuracy. One of the outputs of the City with Climate project, was a ranking of the city's sub-areas/districts according to their vulnerability to climate change and thus the need for urgent adaptation and prevention measures. The ranking is based on a multi-criteria analysis.