Smart Cities: Satellites over Amsterdam

 Smart Cities: Satellites over Amsterdam

Featured image by vwalakte on Freepik

One of the United Nations Sustainable Development Goals is SDG 11: “Make cities and human settlements inclusive, safe, resilient and sustainable”. Earth Observation (EO) data provides the data about cities that forms the basis for the solutions needed to meet this ambitious goal.

According to the European Commission, a smart city is “a place where traditional networks and services are made more efficient with the use of the technologies for the benefit of its inhabitants and business”, aiming at finding smart solutions to urban problems.

For any city to be a ‘smart city’ there are three critical requirements: open data and sharing of data, citizen engagement and availability of smart services for citizens. Earth Observation (EO) data is an important part of this open data sharing. Smart solutions for urban challenges require a combination of social, geographical, environmental, and technological data and data analysis skills.

Earth Observation Data for Cities

Satellite data supports decision makers, providing them with information about the health of the city. Nowadays, satellites provide very frequent, up-to-date and highly detailed data, allowing observastions of changes over time and in different parts of the city in great detail.

In previous articles we have seen several algorithms used to process and interpret satellite imagery. So let’s have a look how different instruments and different processing methods lead to a wealth of visible and less visible information about cities that is useful to decision makers.

A good example of these different instruments and interpretations is available in easy-to-use EO data analysis tools like the Sentinel-Hub EO Browser and Nimbo Maps.

Different views of the same city: Looking at Amsterdam

In this section we will look at the city of Amsterdam, as an example of the wealth of information that satellites are providing to urban decision makers for any city around the world.

1- Optical in True Colour

Sensors carried by satellites can image Earth in different regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Sentinel-2 has 13 bands. True colour composite uses visible light bands red, green and blue in the corresponding red, green and blue colour channels, resulting in a natural coloured product, that is a good representation of the Earth as humans would see it naturally.

Sentinel-2 True Colour map of Amsterdam on 13 August 2022 (image by author, made on Sentinel-Hub EO Browser)

2 – Scene Classification

See below a scene classification map of Amsterdam. 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.

In this map the three dominant surface classes include vegetation (green), non-vegetated, like buildings, built-up areas, roads and bare fields (yellow) and water (blue).

Sentinel-2 Scene Classification map of Amsterdam on 13 August 2022 (image by author, made on Sentinel-Hub EO Browser)

3 – NDMI – Moisture Index

See below the Normalized Difference Moisture Index (NDMI) image for Amsterdam, again for 13 August 2022. The 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, red) correspond to barren soil or flat water surfaces. Values around zero (-0.2 to 0.4, yellow, green and light blue) generally correspond to water stress (drought). High, positive values (blue) represent high canopy or low vegetation without water stress (approximately 0.4 to 1, dark blue).

Sentinel-2 based NDMI map of Amsterdam on 13 August 2022 (image by author, made on Sentinel-Hub EO Browser)

4 – NDWI – Water Index

The Normalized Difference Water Index (NDWI) is mostly used for water body mapping. Values of water bodies are larger than 0.5 (shown as blue). Vegetation has negative values (green). Built-up features have positive values between zero and 0.2 (shown as white).

Sentinel-2 based NDWI map of Amsterdam on 13 August 2022 (image by author, made on Sentinel-Hub EO Browser)

5 – SWIR – Short Wave Infrared

Short wave infrared (SWIR) measurements help analysts estimate how much water is present in plants and soil, as water absorbs SWIR wavelengths. Short wave infrared bands are also useful for distinguishing between cloud types (water clouds versus ice clouds), snow and ice, all of which appear white in visible light.

In this perspective, a band is a region of the electromagnetic spectrum. A satellite sensor can image Earth in different bands.

In below composite image vegetation appears in shades of green. Bare soil and built-up areas are in various shades of brown, and water appears black. Newly burned land reflects strongly in SWIR bands, making them valuable for mapping fire damage. Each rock type reflects shortwave infrared light differently, making it possible to map out geology by comparing reflected SWIR light.

Sentinel-2 based SWIR map of Amsterdam on 13 August 2022 (image by author, made on Sentinel-Hub EO Browser)

The real time monitoring obtained can give us information about land traffic, marine traffic, fire, air quality, water quality, landslide… permit us a better urban planning, green areas and land use, coastal areas, public transport… a better damage assessment after natural disasters, waste management… Deciding where to allocate resources and which areas of the city need more attention guarantees efficiency and effectiveness.

6 – SAR Radar Imagery

Unlike optical images, Synthetic Aperture Radar (SAR) satellites use active signals, that bounce back on the Earth’s surface, to produce satellite images. These signals can penetrate clouds, so they can be taken under any weather conditions. Radar signals reflect different from flat surfaces (water, fields) than from buildings and forests, while different materials reflect differently too. Hence, SAR images provide another wealth of information not obtained by optical wavelengths. See below a Sentinel-1A SAR image of Amsterdam on 15 August 2022.

As we have seen in previous articles, SAR images can also be used to measure very small, millimetre-scale displacements of the ground and buildings.

See also: Change Detection in Cities Using SAR Images

See also: Interpreting SAR Satellite Images

This displacement monitoring is a vital tool in the efforts to preserve over 200 kilometres of quay walls along Amsterdam’s historic inner-city canals. These canals are often adjacent to many historical buildings that are susceptible to damage by nearby construction activities and quay decay. Monitoring displacement is therefore a vital tool in the efforts to preserve the physical and historical assets of the city.

To check the state of bridges and canal walls in the Inner City, the municipality is using traditional measuring bolts that track structural movements, but also more innovative methods such as satellite data, sonar and 3D scans.

SAR data gives us information about what is going on in the urban area. It is vital to prevent critical situations and respond promptly to emergencies and, urban planning, planning about infrastructures and services.

Gabriela Quintana Sánchez

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