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Exploring Contributions from Satellites in Support of SDG Targets and Indicators
  SDG-2: Zero Hunger  
  SDG-6: Clean Water and Sanitation  
  SDG-11: Sustainable Cities and Communities  
  SDG-14: Life Below Water  
  SDG-15: Life on Land  

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spacer Goal 11: Sustainable Cities and Communities

Increasingly satellite monitoring is possible at spatial and temporal resolutions suitable for urban applications. Data can be accessed on a free and open basis, enabling products specifically derived for urban planners, and with supporting tools and platforms that greatly increase the accessibility and usability of observations. Two important urban management topics where satellites are making a growing contribution are urban growth and air quality.

Mapping urban growth

A number of global urban extent datasets derived from satellite observations have been developed such as the Global Human Settlement Layer (GHSL) and the World Settlement Footprint 2015 (WSF2015).

The GHSL provides global spatial information about human settlements over time (1975, 1990, 2000 and 2014), generated from Landsat data, including built-up area, population density, and settlement maps.

The WSF 2015 was made available in 2018 and is the first global layer generated at 10m spatial resolution based on both optical and radar imagery (i.e., Landsat-8 and Sentinel 1). It allows the precise delineation of human settlements in urban, peri-urban and rural areas over the entire globe. The WSF evolution dataset estimating the global settlement growth from 1985 and generated from Landsat-5/7 imagery will follow in the second half of 2019.

The WSF suite is freely and openly released for exploitation via the Urban Thematic Exploitation Platform (U-TEP), a Big Data infrastructure offering online processing and analytics services for urban applications. The U-TEP seeks to provide an end-to-end analysis platform for a broad spectrum of users – both expert and non-expert – to produce and extract urban information (e.g., indicators) needed for sustainable urban management.

These global datasets of urban extent, thanks to the use of Big Data analytics platforms like the U-TEP, enable the production of evidence-based knowledge on the properties of human settlements such as area, shape, imperviousness, greenness, pattern and network of settlements and in the future even volumes of building. When combined with information on population they constitute a major source of data to inform the SDG indicator 11.3.1 on land consumption rate.

World Health Organization Data Integration Model for Air Quality Monitoring

Air pollution represents a significant environmental risk to health, and is also linked to climate change and ecosystem damage (e.g., via acid rain) through the release of CO2, black carbon (soot), sulphur dioxide, nitrogen oxides, and other greenhouse gasses. Monitoring the release of this pollution and its impact on air quality in the urban environment are keys to better-informed policies and assessment of the sustainability of development decisions.

The World Health Organization (WHO) is the custodian agency for SDG Indicator 11.6.2, using a variety of observations, including ground and satellite measurements, as inputs to models to estimate human exposure to harmful particulate matter of a diameter less than 2.5 micrometres, known as PM2.5. The WHO maintains an air quality database to support reporting and has recently developed the Data Integration Model for Air Quality (DIMAQ) that incorporates data from a variety of sources in order to provide estimates of exposures to PM2.5 at 0.1° × 0.1° globally.

At the country level, the United States’ AirNow system provides the public with real-time air quality observations, forecasts and health information. The system started in 1998, when air quality data was not easily accessed and a national real-time dataset was unavailable, and has since encouraged and supported air quality monitoring efforts around the world. The system makes operational use of data from multiple satellite instruments to supplement measurements from ground-based monitors, which increases the accuracy of PM2.5 air quality forecasts.
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