[Amanda] Hi, and welcome to week seven
of Remote Sensing Foundations.
This week, we will continue to look
at using Google Earth Engine
to explore some applications of remote sensing,
particularly that of human-based questions and needs,
or in other words, some human dimensions.
Upon completion of this module,
I hope you'll be able to do some really fun things
like create an animated GIF
or generate a split panel to compare two time periods.
Also, I hope you'll learn how to do some analyses
to kind of further investigate
what we're looking at this week,
including implementing quantitative
and qualitative analyses
using preexisting urban classifications.
We're also gonna build our own classification
of built-up land cover,
quantify the extent of urbanization across space and time,
use the Sentinel-5P data set to quantify changes
in air pollutant concentrations over time,
also calculate population-weighted
air pollutant concentrations,
and finally, derive and generate land surface temperature
to calculate surface urban heat island intensity.
We've got a few associated readings and tutorials
to show us how to use GEE in this context.
I've also included some supplementary options
as well as a link to a talk by Ran Goldblatt
about using remote sensing for urban research.
These supplementary readings and viewings
are optional, of course, but if you have time,
I do recommend watching Ran's talk
because it's really interesting.
It's both interesting and also pertinent
to what we're learning this week.
Last week, we explored a number of terrestrial applications
for remote sensing, especially that of wildfire,
wildlife conservation, and deforestation
or forest degradation.
We started by reviewing six fundamental reasons
why remote sensing has become an important
and more popular data source.
Then we discussed how remote sensing
is now used in wildfire applications
to monitor active fires, fire recovery,
and fire prediction and mitigation.
Next, we discussed the main applications
of wildlife conservation activities
utilizing remote sensing,
often used in conjunction with GIS
and other geospatial data sets
like radio collars or citizen science apps.
Three main areas as applied
to both terrestrial and aquatic wildlife
include things like inventorying and monitoring,
also predictive modeling like range expansion or shift,
and also wildlife management.
Some purposes of wildlife management
include things like to protect
and enhance wildlife populations,
research wildlife populations' ranges and movements,
understand habitats and change to habitats,
and also promote opportunities for recreational research
and education related to wildlife.
Finally, in terms of monitoring forest change
using remote sensing applications,
we define the difference
between forest degradation and deforestation.
Deforestation is the removal of trees,
and degradation is the reduction in the capacity of a forest
to produce ecosystem services
such as carbon storage and wood products
as a result of anthropogenic and environmental changes.
As you can imagine,
each looks significantly different from space.
This week, we're going to focus some effort
on how remote sensing is used in human applications.
As you can see by this list,
which is by no means exhaustive,
remote sensing has a ton of applications
when it comes to human-altered landscapes,
including things like agricultural environments,
urban environments, built environments,
air pollution and population,
heat islands, health applications,
humanitarian applications, monitoring mining activity,
and many, many other possibilities.
In terms of these applications,
beyond the various capabilities
of mapping of remotely sensed data sets,
they can also be used for analysis.
Multitemporal remote sensing
can facilitate the monitoring of all sorts of ecosystems,
as we know, but also that of spatial urbanization over long
as well as short time periods.
Using satellite systems like Landsat,
which became available around 1972,
monitoring spatial urban growth becomes possible
for almost 50, 60 years.
Change detection from 1973
until sometime maybe in the mid-2010s
divided up into multiple different time intervals,
so maybe, say, like four different time intervals,
can be used to show spatial urbanization
for cities all over the world and compared.
Using methods like gradient analysis or landscape metrics,
spatial extent, the landscape configuration,
the direction of growth,
and a few other variables like that,
these things can all be quantified
and used to compare between cities and regions.
And then in terms of looking at shorter time intervals
and detecting or monitoring changes,
like something along the lines of a construction in areas
or also perhaps looking at natural disaster mitigation
or cleanup, aftermath, like if a building collapses
after an earthquake impact, for instance,
those are also examples of some valuable applications.
The figure at the right, by the way,
shows a couple of different parameters
that were calculated for reference.
And this was an application.
It was in an article
where the application was using a 3D city model.
So they had a whole bunch
of different other geospatial data sets,
but most of them were remote sensing-derived.
And then they sort of put them together and were able
to get to really, really high spatial resolutions.
And using that, they were able
to calculate physical parameters
describing and quantifying the urban structure,
like building density and the floor space.
They created a floor space index.
Also the percentage of impervious surfaces
and the vegetation fraction,
which were all also calculated
through different classification methods.
Okay, so back to our list.
While I'd like to be able to delve deeper
into all of these things,
this week, we're gonna focus on these three
that I've put here in red,
urban environments, air pollution, and heat islands.
First, let's talk about some applications of remote sensing
within urban environments.
So urbanization
has dramatically changed the Earth's surface.
In order to take a quantitative
or even a qualitative look at the impact urban expansions
has had on any landscape,
we must first consider which data sets exist
and are appropriate
to answer whatever questions we choose to ask.
Some of these questions might include things like,
which remotely sensed data sets
are useful for urban areas?
What are the capabilities and limitations
of remote sensing regarding mapping, subsequent analysis,
and indirect assessments for relevant products
to urban planning?
And then within the sort of sub-idea
of using remote sensing
as a tool in urban planning,
there's a whole suite of other questions which can be asked,
and remote sensing can shed light on it
in figuring out their answers.
A lot of them are sort of ongoing questions
that city planners might be asking themselves,
like what defines our city,
or how is the city arranged spatially?
How dynamic is the urban environment changing over time?
And where are traffic hotspots?
Also things like where are, climatologically and socially,
the best neighborhoods, and how many people live there?
So urban planners need answers
to these different types of questions,
and these are all very space-oriented questions.
So remote sensing has the unique capability
to support decision making with spatial quantitative data
and information products on various topics,
from the extraction of the urban morphology
to the detection of urban growth.
Also things likes surface temperatures,
which we'll talk about a little bit later
when we talk about heat islands.
All these things to monitor the population density
or growth, and also to do other things
like monitoring traffic as well.
So using pixel-based post-classification change detection,
spatial urban growth can be identified.
So it enables planners
to detect the spatial dimension of sprawl
and the dynamics independence of time.
Beyond that, it also is allowing for processes
such as the redensification
or different types of development growth patterns
to be detected and also measured and analyzed.
And with the advent of high-resolution satellite sensors
in the last decade or so,
urban monitoring approaches allow a higher thematic
and geometric level than before.
So with pixel sizes becoming smaller than the image objects,
so meaning in a city, it might be smaller than a building
or smaller than maybe a small park,
it enables the remote sensing scientists
to use object-oriented classification methodologies
that could be sub-pixel
or in these very, very small pixels
to kind of improve their estimations
of all of these different variables
that we've already talked about.
So to give you a couple more visual examples
of urban remote sensing applications.
On the left, you can see a GIPHY
from an airport construction,
and it's the same one that you will actually make also
in the tutorial this week.
In the middle is an assessment of sea-level rise effects.
And as you can see from the blues in the legend,
these are different places and grades of inundation,
so the height of the inundation that will occur
in the city, in this coastal city,
red being the buildings,
and it's the height of the sea-level rise
that will occur in the future.
And then also there at the right,
this was from a city planning website,
and it's showing growth mapping of two German cities.
And what they did was they basically used,
it was multi-sensor spatio-temporal growth mapping
of both Munich and Dresden since the 1970s.
So what they did was they divided the time periods up
into four different eras
between 1972 and 2009,
and then they colored these areas
based on which timeframe,
where it was when the growth occurred.
So you can kinda get a sense
of where growth was occurring
and when was it occurring for these two cities, and compare.
So this definitely sort of shows
a lot of the value of remote sensing to urban planning.
In the tutorial this week,
you're going to look
at three existing urban classification schemes
that were created already
by other remote sensing scientists.
So the tutorial will show you how to pull
and query these data sets
from the Earth Engine Data Catalog
and also show you how to visualize
and quantify urban classes and also compare them.
The three classifications vary
in both their spatial and temporal resolution.
So for example, the MODIS data set that you'll use
is a global data set,
while the NLCD, which is the second one you'll use,
it covers only the United States.
And then there's a third one,
the Korean data set only covers the European Union.
So you'll look at how these classifications can be used
to quantify urban areas
and close with instructions
on how to perform per-pixel supervised image classification
to map built-up land cover at any location on Earth
and at any point in time using Landsat 7 imagery.
So, okay, now we're gonna spend a few minutes
talking about monitoring air quality using remote sensing.
After high blood pressure and smoking,
air pollution is the third largest risk factor
for death globally.
In fact, it's estimated
that chronic exposure to air pollutants
is responsible for more deaths annually
than HIV/AIDS, malaria, and TB combined.
Air pollution rapidly increases the risk
of environmental health problems,
affecting society in all parts of the developed
and developing worlds.
Recent findings show
that if you live within 50 meters of a major road,
your risk of lung cancer can increase by up to 10%.
It's also been found that bad air quality impacts the heart,
leading to increased risks of coronary heart disease
and heart attacks.
There's been a lot more scientific understanding
about the effects of air pollution within the medical field,
but recently too, remote sensing has been utilized as a tool
to monitor and characterize changes
to air pollution across our globe,
especially in urban areas.
Some of the benefits of using remote sensing in this way
is that satellite-based data collection
provides a consistent coverage of large areas
where pollution can be monitored in near real time.
Algorithms have been developed
to process the imagery very quickly
for monitoring to be done rapidly as well.
Pollution in the air
has been measured using a variety of different methods
that essentially involve subtracting spectral signatures
of ground-based components of an image
out of the data collected
and then classifying the remaining aerosol composition
based on known spectral signatures of airborne pollutants
and ozone elements.
Four such methods that offer options
to measure aerosol thickness
as the first step in pollution characterization
are listed at the bottom left here.
The ocean method, the brightness method,
contrast-reduction method, and dark vegetation method.
At the bottom right is an example
of a comparison of nitrogen dioxide measurement
over five urban areas
from a study that estimated air pollution
with remote sensing data
and was set to reveal greenhouse gas emissions from space,
which used the same sensor that you'll be using
during the tutorial this week.
That sensor is the Sentinel-5 Precursor, or Sentinel-5P,
and it's the first mission that has been dedicated
to monitoring our atmosphere.
Operating in a sun-synchronous orbit,
the satellite maps a multitude of air pollutants
around the globe.
After launching in 2017 and subsequent calibration periods,
Sentinel-5P reached its routine operations phase
in early 2019.
Since then, the European Space Agency
has made the final array of Level-2 data products
of atmospheric gases available,
offering daily coverage of the entire globe
at seven-kilometer resolution.
These products, which are shown down at the bottom
and include methane, nitrogen dioxide,
carbon moxide, sulfur dioxide, formaldehyde, and ozone,
are all not measured directly.
Instead, the satellite's 8-band spectrometer instrument,
which is called TROPOMI
and stands for TROPOspheric Monitoring Instrument,
takes measurements in the ultraviolet, visible, near,
and short-wavelength infrared light spectrum.
In combination with auxiliary input data,
which includes things like air pressure
and snow and ice masks,
the concentration of each atmospheric gas
is then modeled based on their absorption characteristics
at specific wavelengths of light.
While orbiting the globe
at an altitude of around 800 kilometers,
Sentinel-5P continuously takes measurements
of a 26-kilometer-wide area.
So each day, it orbits the Earth 14 times.
And with each orbit slightly overlapping,
basically it enables Sentinel-5P
to take one full picture
of the entire Earth's surface per day.
In the tutorial on monitoring air pollution,
you'll use TROPOMI to quantify the changes
in air pollution concentrations over time
for Wuhan, China.
You'll also generate a split-panel map
to compare two time periods,
and then you'll calculate
population-weighted air pollutant concentrations.
So related to increasing urbanization
and increasing air pollution,
we've also seen an increase in urban heat islands.
Urban heat islands are metropolitan areas
that are hotter than their outlying regions,
with the impacts felt most during the summer months.
About 85% of the US population lives in metropolitan areas,
so this affects most of us.
In a study by Berkeley National Laboratory,
they found that the average air temperature
across an entire city with 1 million or more people
can be 1.8 to 5.4 degrees Fahrenheit warmer
and as much as 22 degrees Fahrenheit hotter at night
than surrounding areas
because of the urban heat island effect.
However, the temperature increases are not uniform,
as shown in the figure at the bottom.
Some neighborhoods in a highly developed city
can experience peak temperatures
that are 15 to 20 degrees hotter during the day even
than nearby areas with more trees
and less pavement in the same city.
Across the world,
heat is the number one weather-related cause of death
for humans.
As cities slowly pivot to face climate change,
heat-related deaths in some places
are still expected to increase in the coming decades.
The cities with the five most intense urban heat islands
in the US are New Orleans, Newark, New Jersey,
New York City, Houston, and San Francisco.
But how are heat islands formed,
and how can we measure them?
Heat islands are heavily influenced by albedo,
which measures whether a surface reflects sunlight
or absorbs and retains the sun's heat.
Low albedo causes warming
because more energy is absorbed,
whereas high albedo areas are linked to cooling
because more energy is reflected.
So pavement has extremely low albedo,
whereas ice and snow have very high albedo.
Other factors that influence the presence
and intensity of heat islands
include the amount of impermeable surface,
lack of greenery and trees,
building height because this causes a lessening
of the ability of air to circulate,
and heat created by human activities
like driving cars and operating construction equipment
and other heavy machinery.
In terms of measurement,
in one example that used a citizen science approach,
a professor of urban studies and planning
at Portland State University named Vivek Shandas
began a multi-city urban heat mapping venture in 2015
that spanned urban areas around the world.
Starting in Portland, Oregon, though,
Shandas developed a network of citizen volunteers
and local organizations to map the urban heat islands.
So citizen volunteers were outfitted with a small instrument
that mounted to their vehicle
that recorded temperature close to the ground.
They would drive a pre-selected route through their city
three times a day,
mapping the temperature at a leisurely 30 miles an hour.
This allowed for the collection of data
that is hyperspecific to each city,
and that information was used to extrapolate outward
for citywide mapping.
The study that they conducted
has continued to show a correlation
between low income neighborhoods
and industrial areas of cities
and above average temperatures.
Lower income neighborhoods are more likely
to have an abundance of paved surfaces,
fewer trees, and less green spaces
that all increase temperatures in that area.
A lack of air conditioning
can also influence the number of heat-related deaths
or health issues
that a neighborhood faces during heatwaves.
The data pointed to six factors
that can affect urban heat islands in their study,
the first being the volume of the tree canopy,
the second being the height of the tree canopy,
third being the ground-level vegetation,
fourth being volume of buildings,
fifth being difference in building height,
and sixth being the color of the buildings.
But of course,
we cannot measure heat islands everywhere in this way
and with this much detail,
so remote sensing offers us this capability
with thermal radiation products.
So here are a couple of examples
of instrumentation that is used
to measure heat islands.
So one is a newer NASA product called ECOSTRESS,
which is aboard the International Space Station.
ECOSTRESS measures the temperature on the ground itself,
which is very similar to the air temperature at night,
although the ground may be warmer than the air
in daylight hours in some places.
The instrument was launched
on the International Space Station in 2018,
and its primary mission
is to identify plants' thresholds for water use
and water stress, which is giving insight into their ability
to adapt to a warming climate.
However, ECOSTRESS also records other heat-related phenomena
like heatwaves and heat islands.
It has a pixel size of about 225 feet by 125 feet,
which is about 70 by 38 meters.
So it's a very, very high-resolution instrument
compared to other ones that are out there
which can serve as a really powerful tool
towards understanding aspects of both weather events
and things like urban heat islands
above traditional observation networks.
So the instrument, as I said,
is aboard the International Space Station.
And actually, earlier this year in May of 2022,
the ECOSTRESS recorded some really blistering temperatures
in urban areas around Delhi,
which was during a historic heatwave
which still hasn't actually abated even now.
The relentless heatwave blanketed India and Pakistan,
and it's been going since mid-March of this year.
So it's caused dozens of deaths.
It's given rise to increasing amounts of fires,
also increasing air pollution,
and has also reduced crop yields in that region.
And as you can see from the figure at the left,
it's a look at all of the areas around New Delhi.
And what you can see
is Delhi's down at the bottom right there in that figure,
and then all these other villages and small cities
that are peaking around 122,
or sorry, 102 degrees Fahrenheit, sometimes even at night,
while the nearby fields and agricultural areas
are as much as 40 degrees Fahrenheit cooler,
as shown by the blue and green areas.
Then if you look over at the right,
we can look a little bit at the MODIS LST,
or land surface temperature image.
This week in the tutorial, in the reading,
you'll learn how to understand
and derive land surface temperature using this instrument.
At the right
is showing a MODIS LST map for Uganda,
and the inset graph shows a monthly time series
of temperature values for the year 2015.
So the resolution for this data set is around one kilometer.
And in the tutorial, you'll use MODIS LST
to generate urban and rural reference values,
and then you'll use that
to calculate the surface urban heat island intensity.
All right, so that's about all I have for lecture this week,
and I hope that you can use these tutorials
to gain an understanding of the human dimensions
of how we shape our planet using remote sensing.
And I encourage you also to spend some time
with the supplementary tutorials
if this is an area that interests you
because there's a whole host of other applications
that I didn't have time to go over
but are covered pretty well in the book,
especially that of things like disease epidemic mapping
and other humanitarian aid
and relief type of projects.
So there's a lot of really, really cool new ways
that scientists are developing to use remote sensing
to help understand our planet.
Have a great week, and more soon.
Take care. Bye.