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.
