Why should cities be 'green'?
For some time now, we have been seeing a trend where many people move from the countryside towards the cities. Reasons for this trend might be better employment options, easier access to health care and better schooling or cultural offers. When moving to the city people look for a healty and pleasant place to live. An important factor in both is the availability of green space.
The cities advantages often come with a price tag: more stress, noise or pollution and far less access to green space. Outside of cities we are closer to protected areas, forests, fields and farms. However, big cities try to compensate that with green parks and gardens. But how green are our cities, or how much vegetation surrounds us in the cities? This is a question we will try to answer here.
Question: Which capital in Europe offers the most green space for their inhabitants?
City vs. urban vs. metropolitan areaIn order to assess the greeness of a city we first need to know what the cities boundary is. The truth is, city boundaries are difficult to define. Where does the city have its exact limits or boundaries? Borders become fuzzy when a city extends to an urban agglomeration, with neighbouring suburbs or commuter belts where finally everything merges into a larger metropolitan area. In the search for Europe’s greenest capital, all cities will be treated equally. The analysis is limited to a 5.000 meter radius buffer from every capital’s city-centre, a distance a person can walk in one hour.
How to measure and compare greenness?
Here we discuss, in short, an easy way to find Europe's greenest capital. This will be followed by a critical look at the method and a suggestion for how you can improve on this method. Using this example as inspiration it will be up to you to extend or improve this method. For now, the following steps have been performed to create a dataset that allows comparison of Europe's capitals.
Every available Sentinel-2 satellite imagery was piled into a large image stack and filtered according to their aquisition day of the year (DOY’s between 166 & 258). For every pixel in the remaining images a vegetation index (NDVI) value was calculated (value range between -1 / 1). Then, the five least
cloudy NDVI images where aggregated into one median image (Fig. 3 - left). From all pixels within the single median image was a histogram calculated & one final median value derived (Fig. 3 - right). This procedure was automatically repeated for each European capital area (5.000 m buffer around its city-center) in the Google Earth Engine.
The capital greenness is comparable by the median NDVI value.
Latitudes & longitudes vs. population
In order to sort the capitals greeness levels into the European landscape, the capitals are lined up according to their latitudes (Fig. 4) and longitudes (Fig. 1) (inspired by this blog post) as well as their population levels. The population numbers used here refer only to the official capital area, and do not include the wider metropolitan/urban district.
Sarajevo - Greenest capital in Europe
Sarajevo, the capital and largest city of Bosnia and Herzegovina, is the greenest capital of Europe. Nestled within the greater Sarajevo valley of Bosnia, it is surrounded by the Dinaric Alps and situated along the Miljacka River in the heart of Southeastern Europe and the Balkans. With a population of 380.000, Sarajevo is among the smaller capitals in Europe.
Vaduz - 2nd greenest capital in Europe
Vaduz is the capital of Liechtenstein and has the smallest population (5248) among all capitals in Europe. Although Vaduz is the best known town internationally in the principality, according to wikipedia it is not the largest; neighbouring Schaan has a larger population.
The greenest capitals in Europe are relatively small in population numbers. In fact the greenest six capitals (Sarajevo, Vaduz, Ljubljana, Andorra la Vella, Bern and Luxembourg) have less than 1⁄2 M inhabitants. What about the bigger cities?
NDVI histograms & median values for all capitals
The last graph in this blog post shows you the entire NDVI histogram dataset for every capital. Cities near the ocean or sea have lots of negative NDVI values which correspond to water, e.g. Copenhagen, Monaco or Lisbon. NDVI values close to zero generally correspond to barren areas of rock or sand, e.g. Athens, Valetta. Positive NDVI values represent shrub and grassland (approximately 0.2 to 0.4), while high NDVI values indicate forests (values approaching 0.5), e.g. Sarajevo and Vaduz.
Only NDVI histogram values above 0 were included in the median calculation.
Using App-Lab to find the greenest city
The above describes an easy way to find the greenest capital in Europe, however, there are some points of discussion with this method. First of all, the choice to use a 5km radius around the city center proved to include areas which should arguably not be included in the city limits. Another point of discussion might be whether NDVI is the correct dataset to measure greenness or whether a different dataset like the Leaf Area Index (LAI) is more suitable. This section will discuss how to use the App-Lab tools to easily compare different datasets and their results and include multiple datasets to come to a more precise answer.
Exploration of suitable datasets can be done through the maps explorer which can be found in the Data & Analytics section, which allows direct viewing of the data layers. For further understanding of how to access and manipulate this data see our tutorials. In short, if you want to find the greenest Capital of Europe, you will need to do some subsetting over time and space to only extract the data that you require. The easiest way to do this is by finding the dataset you require, for example LAI and visiting the data selection tab allowing you to set a bounding box and a specific time range. For more detail on extracting exactly the data you need, see our tutorials.
The above shows you how to extract a subset of a dataset for a specific layer, however, finding the greenest capital of Europe, might not only be about the NDVI or the LAI but can include more datasets. In order to easily combine and compare multiple datasets, also from outside of the EO domain, the App-Lab uses Linked Data. In order to understand how to use this, and find a demo of how to combine datasets to better understand the greenness of Paris see our Linked Data section.