[Amanda] Hi, welcome to Week 6
of Remote Sensing Foundations.
This week, we will start to learn
about some applications of remote sensing,
particularly that of the landscape
are terrestrial applications.
Upon completion of this module,
I hope you'll be able to understand
the different types of data sets
used to monitor wildfire and recovery,
list three ways
in which remote sensing is used in wildlife conservation,
explain the difference
between forest degradation and deforestation,
and also to conceptualize how to measure
and monitor change in forests using remote sensing.
We have a few associated readings,
including an article from Jeremy Hance from 2014,
about remote sensing in the realm of conservation.
It's a little bit dated,
but provides a good perspective
on how remote sensing has been and is used in conservation.
All right, so given that many forest environmental variables
and other terrestrial variables
can be measured directly in the field,
why has remote sensing become an important data source?
So this might seem kind of like an obvious question,
but I think it's an important context to why and how
remote sensing has been developed and used in conservation,
and sort of greater understanding
of Earth's processes in general.
So there's six fundamental reasons for this.
And firstly,
remote sensing imagery provides a more synoptic view.
So the vantage provided by earth-observing sensors
ensures that imagery captures
a complete picture of the environment and its field of view.
Because of this complete survey,
remote sensing allows wall-to-wall mapping
and monitoring of important ecological variables,
such as land cover change.
Secondly, remote sensing data
are available over the entire planet
and often at a range of spatial and temporal scales.
So we've talked about this a lot before,
but I think it's just important to sort of remind ourselves,
so key environmental remote sensing systems,
such as those carried out by like Landsat,
and some of the other systems have been able to provide
pretty constantly updateable stream of imagery
for the entire planet since the 1970s or early 80s.
Thirdly, remote sensing imagery
has a high degree of ho homogeneity.
So, critically,
data from key environmental remote sensing systems
are acquired under relatively fixed conditions.
And the data captured relate to the way
in which the radiation interacts with the environment,
which is constant in space and time.
So there are no human-induced complications
such as differences in measurement practices
from one country or another,
or for instance, user error,
if you're out in the field and you're measuring tree height,
that can be a really difficult thing to do,
and also, difficult thing to do in a consistent way
across systems and across different types of ways
that we measure, for instance, tree height.
So with remote sensing,
while we've talked about some of the things
that we have to do to data
to get them to be readily usable
and applicable to what we're measuring, in general,
those algorithms that we develop for pre-processing,
like subtracting out atmosphere conditions
or accounting for things like sensor angles
and things like that,
all of those things are applied to the data set
in a very homogenous way.
So that's also an important sort of key factor
in why remote sensing
has become such an important data source.
So fourth, the image contains or can easily be converted
to digital numbers or digital images,
and because of this, it can be really easily integrated
with other spatial data sets in GIS.
So we've talked about this as well before,
but again, I think it's just a good reminder
in review of how widely roster data sets can be used
and applied and sort of joined
with other different types of data sets.
So fifth, per unit area or per gigabyte of data,
remote sensing is a really inexpensive way
to acquire large, large amounts of data.
So, of course, we can think about the financial costs
associated with something like developing
and building and launching
and operating satellite remote sensing platform.
That is definitely for sure expensive,
but when we sort of divide that out
by the amount of coverage both temporally and spatially,
it actually becomes really, really inexpensive.
And it's also sort of important to point out
that there's been this increasing trend
to make these data sets
for all of the different environmental science research
that we do with remote sensing freely open and available.
And in addition to that, things like,
resources like Google Earth Engine
are able to provide easy access
to all these freely available data sets.
So sixth, and finally,
not only are data more readily available,
but there also has been an increasing trend
towards the sharing of data products
as well as the image data itself.
So sort of related to the fifth one, of it becoming cheaper.
It's also important to note
that there's a whole bunch of data sharing
that has developed in the world of remote sensing,
and that is a benefit to all of us.
So, it greatly reduces the need
for both expert knowledge of remote sensing
and also of image analysis.
And it's sort of given rise
to a greater amount of communication
between experts and environmental scientists,
and sometimes, also with land managers as well,
has been the case, historically speaking.
So, environmental scientists
can now easily access science data quality products
obtained from other people
and obtained from remote sensing.
And platforms like Google Earth Engine
have given rise to this.
So, if you look through the different data
available on the catalog,
you can see a lot of what's called mature data products,
where people have already done
all of the different processing,
pre-processing of the imagery,
and then they've used that to calculate
these really great global products
that you can now use yourself.
So those were the six things
that I just wanted to kind of remind us and review us about
in terms of why we're here and what's important,
and why remote sensing's become
such an important data source.
So today, we're gonna talk about
three terrestrial applications for remote sensing,
wildfire, wildlife conservation,
and forest degradation and deforestation.
Okay, so starting with remote sensing of wildfires,
we know that the number frequency
and severity of wildfires worldwide
have rapidly increased in recent years,
being driven by warmer temperatures
caused by climate change.
Identifying where and when these fires might ignite
can prevent them from ravaging the surrounding environment,
protects infrastructure, and also can save lives.
So remote sensing is now used
for a couple of different areas around surrounding wildfire.
For instance, there's active fire monitoring,
which, different land management agencies
deploy a whole suite of different technologies,
including thermal imagery, drones,
Lidar monitoring systems, airborne platforms,
all to monitor the spread
and severity of fires that are occurring.
There's also fire recovery.
So there's a huge steady area of science
that is basically geared
towards understanding how vegetation regrows,
using things like classification, time-series analysis,
kind of combining Lidar and optical technologies as well.
And then finally,
there's a huge area of science and research
that's committed towards predicting fire
and also mitigating it.
So, concerning things like fuel-load tracking,
climatic conditions, identifying climatic conditions
that might increase the likelihood of fire occurring.
Also, identifying vegetation stress,
which can be done pretty readily
with remote sensing technologies.
So couple of examples, drones equipped with Lidar sensors
are being used by Spanish researchers
to help manage the nation's forests.
And instead of detecting fires themselves,
the drones have advanced mapping capabilities
to gather data that can be used
to help lower the risk of fires occurring,
and when they're occurring,
having them be absolutely devastating.
And they do this by identifying
where the most intense wildfires and rapidly spreading zones
are most likely to occur,
based on all these conditions that I just listed here.
So like fuel-load trafficking,
vegetation stresses, drought conditions, things like that.
On the other hand, in California, for instance,
which is a known hotspot for wildfires,
the US Department of Homeland Security
Science and Technology of Directorate
recently tested for prototype technologies
for early wildfire detection.
So among these was a sensor
that monitors particulates and air quality,
so, meaning smoke and air pollution,
alongside with optical remote sensors,
thermal imagery, and photoelectric sampling.
So, all of these kind of combined together
to try to predict wildfire
and detect the spread of wildfires when they do occur.
So by way of another example,
we can also talk about Australia's 2019-2020 fire season
as an example of governments
and other land management agencies as well as scientists
kind of quickly coming together
to sort of develop and use technologies using remote sensing
surrounding wildfire monitoring and mitigation.
So it was known as the Black Summer, and massive blazes
charred more than 186,000 square kilometers of land.
There were also large amounts of smoke
that billowed Southeast over the Tasmanian Sea.
And smoke was disrupting both air traffic
and also air quality in as far away places
as the Western coast of South America.
So it was obviously a really, really big blaze
or a bunch of different blazes,
sort of an interesting aside
that kind of involved the remote sensing community
and a little bit of a scandal.
There was a whole bunch of different graphics designers
that were sort of enhancing
and kind of sensationalizing remote sensing imagery
for the use of sort of sensational news stories
about just how bad the fires were.
So I put an example up here, at the lower left,
of a BBC news article,
and that's not actually a real remote sensing image,
if you look at it closely.
It might have been applied on a remote sensing image,
but that image has been doctored,
and those fires are not actually what was seen from space.
What's usually seen from space, optically speaking,
is a lot less bright when it comes to fires, in general.
So just sort of an interesting aside there,
but, in general, so I put up a couple of different examples,
but this was an example of
sort of operational and experimental satellite,
remote sensing systems, kind of providing a capability
to provide both regional and global monitoring of fires.
So the systems that were developed,
they provide different types of fire information
for estimation of fire danger,
also detecting of active fires
and estimating burn area afterwards.
They're also used to quantify emissions
from fires themselves, and used to estimate fire damage,
and they can also be used
to monitor post-fire ecosystem recovery.
So I put a couple of different examples here,
the Sentinel Bushfire Monitoring System and the Firewatch,
which are both developed by the Australian government
and scientists.
And these are examples of these capabilities,
that are, satellite sensing based systems
for accomplishing all of these different things
with respect to wildfire.
So in general too, as I've sort of said,
fire monitoring is definitely a good example
of land management and disasters communities
kind of coming together with the scientific community
to produce real-time or near real-time monitoring systems.
Wildfire is definitely one
of those examples of disturbance
that are highly critical for knowledge on the ground
in near real-time to be able to mitigate
and also warn existing human habitation and infrastructure.
And in the case of Australia,
there was a lot of wildlife that was lost as well.
So this is an example of kind of these communities
coming together to work together to produce these systems.
And they are mostly remote sensing based,
but with a huge element of GIS as well.
And that's kind of unique and new
in terms of the context of history,
and historically, governments and scientists,
and these land management and conservation communities
may not have come together as quickly
to be able to do this type of work together.
All right, so what types of satellite imagery do we use
to monitor active fires?
Different sources of satellite imagery
can be used to visualize fire conditions and progressions,
also calculate band ratios
reflecting disturbance and severity,
and map burned areas
with training informed classification algorithms.
There are a lot of different fire data sets
that have already been made and shared
in platforms like Google Earth Engine.
And they're readily available
for monitoring fire locations extends and progressions
on a global scale.
So this week in the tutorial,
your goals are gonna be to access
and visualize fire monitoring data sets
in Google Earth Engine.
So you're gonna look at four different types of data sets,
also adjust previously drafted code
to calculate fire characteristics for a fire of your choice,
somewhere around the globe.
And then finally, you're gonna also explore fire metrics
and sort of visualization possibilities
in Google Earth Engine.
All right, next for our terrestrial applications this week,
we're gonna talk a little bit about wildlife applications.
And while there isn't a chapter per se
in the textbook that you all are reading,
concerning wildlife applications with remote sensing,
there's a huge body of research and information out there
to kind of understand how remote sensing
and GIS technologies together, especially,
are typically used in the areas of research
and conservation for wildlife management.
So three main areas of research concerning wildlife
are inventory and monitoring,
predictive modeling,
which are things like range expansion or shifting of ranges,
and also, wildlife management itself.
So there's a whole bunch of different applications
for these three areas
in both terrestrial and aquatic wildlife studies.
So to delve a little deeper and provide some examples,
and within those three main areas,
basically, remote sensing and other geospatial tools
can provide things like an analysis of wildlife distribution
and population health,
assessment of physical and environmental conditions
to determine habitat suitability to species
and changes to habitat,
measurement of factors that determine habitat suitability,
like climate, vegetation, land use, other surface features,
analysis of habitat fragmentation,
the connectivity of wildlife corridors
in urban and suburban areas,
and also things like migration patterns and range shifts.
So as an example of that latter one, especially two,
at the bottom here, that first figure on the left
shows the location of test sites
and what their land cover composition looks like.
And these test sites were for a study
that was looking at the migration patterns
of white stork across Southwestern Europe.
So the graph at the right shows the relative distribution
of movement data points per class
based on a land cover data set that's from way back in 2012.
So the figure shows winter in blue and summer in orange,
migration for this white stork individuals
that were collared and measured in the study.
So the points were registered over agriculture,
and suggested that the animals spent a lot of time
that was recorded within that land cover type,
and so, they were sort of looking at
also where nesting sites were as well,
and kind of developing a greater understanding
of what the habits were of this white stork population
as it migrated and moved in different areas
throughout Southwestern Europe.
So this is an example
that covers sort of a few of these different topics,
including things like, maybe perhaps looking at rain shift
or also looking at the distribution
and sort of what their habitat,
what their conditions were looked like
and what their suitable habitats were.
So we can also talk
about each of these different applications that I mentioned
in a little bit more detail.
First, if we're talking about inventorying and monitoring,
scientists and conservationists and land managers as well
often accomplish these remote sensing-based inventories
in conjunction with GIS location records.
And sometimes, these location records
can also be crowdsourced as well.
They are usually species presence and absence measurements,
and they're usually overlaid with land cover class,
which can help then kind of inform habitat ranges.
They can also use surveys and inventories,
and scale them to the pixel level to create range maps,
and also calculate species richness and diversity
within these areas that they're found.
So the figure at the bottom shows a simple workflow,
where researchers overlaid animal movement data
with time-series, remote sense and imagery.
After joining them together, they extracted the pixel value
for each animal position through time for analysis.
And this was all done using R and also Google Earth Engine.
Some applications for wildlife management
using remote sensing,
including to protect and enhance wildlife populations,
research wildlife population ranges in movement,
also understand habitats and change to habitats,
and also to promote opportunities
for kind of the coordination of recreational research
and education kind of surrounding wildlife.
So can we protect areas
that also include protecting wildlife
as well as providing areas of recreation for humans
in addition?
So this is actually an example in the right of a workflow
that was basically creating a habitat suitability map,
and also then using that to monitor habitat change
for a particular species of bird that was of interest.
So also,
a little bit more about habitat suitability and change.
So habitat suitability and home ranges are, again,
also determined usually in conjunction with GIS
using remote sensing.
So they typically involve combining results by year
to add a temporal dimension,
to kind of understand range shifts as well.
And when we track and monitor them through time,
it can shed light on
how or why migration patterns may shift.
So here is a study by Crego et al in 2021,
which extracted NDVI values across a year of data
for range areas,
sort of in conjunction with a couple of different species,
including African Buffalo elephant and wilderbeest
that were being tracked using GPS telemetry units.
So the results sort of showed
a bunch of different discrepancies between annual mean NDVI,
which is in red of these graphs,
and the time match NDVI, which is in blue,
at the animal locations across the species and the regions.
So they were sort of using this
to kind of understand where these animals were ranging
and what the health of those ecosystems looked like
for the time that they were measuring.
So the third area of terrestrial application
that we're gonna talk about today
is forest degradation and deforestation.
Forests are fundamentally important ecosystems
on our planet.
They occupy 1/3rd of the planet's land mass,
covering nearly 4 billion hectares.
The largest forested areas
are located in the boreal and equatorial zones.
And the current distribution is not fixed,
but has a continuously changing sort of area over time
due to the influence of environmental change
and also human impacts.
So 1/3rd of a current forest areas
are made up of forest that is considered primary are intact.
And the remaining 2/3rds
are subjected to a lot of anthropogenic activity
and have, as a result, an uncertain future.
The global forest condition, as visualized here,
was developed using satellite-derived
and also modeled current and future
or potential, sorry, forest cover.
It's a map that was developed by Mitchell et al. in 2017.
And it was for the purpose of
sort of understanding carbon balance
across these different ecosystems.
All right, so in order to be able
to measure forest change from space,
it's really important to distinguish
between deforestation and degradation
and what we mean by those terms,
and especially, how we would measure the two of them
from remote sensing.
So each year, several million hectares of forest disappear,
and vast areas are subjected to degradation as well.
Deforestation is the complete removal of forest cover.
Conversion into agricultural and pasture lands
are the principle causes of deforestation.
But once certain areas are abandoned,
sometimes, forest cover can once again develop
leading to what is known as secondary forests.
Unlike deforestation,
degradation is partial destruction of forest cover.
It's defined as a loss of the forest capacity
to provide ecosystem services
like carbon storage or forest products,
following the anthropogenic activity,
which are things like unsustainable logging, agriculture,
sort of the encroachment of invasive species, fire,
fuel wood gathering, and livestock grazing.
So, degraded forests
include a large diversity of forest types
depending on the nature, intensity,
and frequency of the degradation.
And they can be also, as I'm sure you can imagine,
a bit more difficult to measure and characterize from space.
So an example of this is the map and graph at the bottom.
So on the left,
the map is showing degradation between the years 1987-2019
for the country of Georgia.
And those two insights show regions within the country
that are showing a large expansion of forest degradation.
The graph at the right is pixel counting for the entire map
of both deforestation and degradation
between those years.
And as you can see,
degradation accounts for many, many more pixels
than deforestation accounts for.
So, many of these areas,
degradation is much more of an issue or a problem
than deforestation.
So how do we characterize and measure degradation,
particularly from space?
Here is an example of the type of information
that we might try to collect.
So in the inset 'A,'
that's maybe known as moderate forest degradation,
there's cutting sites in there,
and in the process of regeneration.
And so, using a Landsat image with a color-composite,
with mid infrared, near infrared
in red and the blue channel respectively,
we could perhaps be able to gain a better understanding
of how degraded that inset is.
'B' shows more intense degradation,
with large portions of the ground visible,
following forest exploitation.
And 'C' shows the average degradation
with areas of exploitation
and also old roads which are growing in.
So all of these areas pictured are in Brazil.
And this was from a study
where they were talking about how to kind of differentiate
between these different patterns of degradation
and these different levels of recovery
from degraded areas as well.
So the most common vegetation indices that we use
are ones that differentiate between bare ground
and vegetation.
Those are sort of the main areas.
So I guess the next question is,
"Which images should be used for measuring deforestation?"
"Which images should be used
"for measuring deforestation and degradation?"
As with many of the other areas of remote sensing
that we've talked about,
there are trade-offs and benefits of using each type of data
to characterize both deforestation and degradation.
Optical data, for instance,
is acquired in the visible and near infrared at wavelengths,
and it's sensitive to operating variables of vegetation
like chlorophyll activity,
also structure and internal water content of leaves.
Radar, on the other hand,
which can be acquired at multiple frequencies
with longer wavelengths,
are sensitive to the structure of the vegetation,
and also can be sensitive to the structure of the ground.
And structural properties
include things like basal area stand structure
and the presence of water as well.
So this figure shows common remote-sensing platforms
and sensor combinations of remote sensing data
that we can use to measure deforestation and degradation.
On the left, we see these platforms
and the most commonly utilized sensors
for specific platforms,
and also the relative altitude at which they're deployed.
On the right, we show, at the top,
a true-color digital aerial photography
and false-color with near infrared sensing,
which is an example of an optical data set
in the middle of a Lidar point cloud
of vegetation near river.
And finally, SAR data, or Synthetic Aperture Radar Data,
for two polarizations from Sentinel one at the bottom.
So a key advantage of Lidar and SAR sensors overall,
and especially in this context,
is their ability to penetrate clouds and smoke
and operate at night.
SAR sensors can differentiate land cover features
according to the surface roughness
and 3D structure of the forest features,
as well as water content too.
So, depending on the wavelength of a sensor,
whether it's X band, L band, or C band,
so those are three different types of radar,
the signal can penetrate vegetation, canopy, and also soils.
Conversely, Lidar systems, as we know,
admit pulses from lasers,
and because of that,
their ability to measure the distance
and the reflective light,
it allows for the capability
for measuring or making 3D representations of the system.
And degraded systems
are able to be accurately captured by Lidar,
provided that they're flown at the proper resolution
for being able to capture changes and structural features.
So in recent years,
as a result of developments of sensor technology,
passive and active sensors now have versions
that can be mounted on all platforms.
So, including things like ground-based drone or UAV,
which is Unmanned Aerial Vehicles,
airborne or spaceborne platforms as well.
Although,
larger platforms such as satellites and larger planes,
they can herein carry heavier play loads
so they can allow for larger sensor systems
that have a higher quality and accuracy of data capture.
However, because of this sort of sensor miniaturization,
we can also find them now mounted
kind of in newly found combinations.
So such as mounting a synthetic aperture radar on a UAV,
which wasn't something that was available
maybe 10 years ago.
So we're able to start kind of providing more,
more options for data set capture.
And in addition, it's also becoming possible
to mount two sensor types on the same platform
and develop sort of analysis systems.
The user can process multiple different data sets
to produce higher quality maps
and do things like develop methods to unmix spectral data,
for instance.
So this approach considers pixels
made up of more than one type of soil, covered to be mixed.
This is sort of an example.
So for example, a pixel location of on the edge of a forest
that contains a portion of bare soil
and a portion of vegetation
might be contrasted to a pixel
that is considered to be more pure, is how it's termed,
where it might just be the entire pixel is made up of soil
or the entire pixel is made up of vegetation.
So spectrum on mixing methods are able to determine
the internal composition of mixed pixels
on the basis of spectral characteristics
of these pure pixels, which are also known as end members,
and the pure pixel end members are able to show
all the different classes of cover and change in one image.
So, kind of similar to this concept
of being able to fly more than one sensor,
to be able to accomplish collection
of sort of more spectorally-diverse data sets,
and sort of putting them together
and performing operations like spectral and mixing,
which I sort of went into a little bit
in the last slide there,
where you might have the ability to characterize pixels
that have a have mixed vegetation and soil
or vegetation and other type in them,
we've also started to think about
how we can apply hyperspectral data sets
or data sets that have a much larger spectral capture
to study things like forest degradation,
because as you can imagine,
looking at all the broccoli from space,
so sometimes, I have to refer to forest
or healthy intact forest as broccoli,
you're looking at the broccoli from space,
there's a whole bunch of different difficulty
that kind of arises
when you have all these different types of disturbances
or human interaction that's going on below the broccoli,
so, creation of roads and other things like that.
And some of it can be seen from space and some of it can't.
So how can we measure from the top down
all of these different things
that are sometimes occurring from the bottom up?
So hyperspectral data sets are one way
that we can also kind of approach
measuring forest degradation and deforestation,
and especially degradation.
So the figure at the top is kind of a reminder
of the different bands or channels
that are measured by common sensors.
So the two at the top show Landsat and Sentinel,
and at the bottom,
it shows the near continuous measurement
of hyperspectral data sets.
So the use of optical data like Sentinel, Landsat
to identify deforestation or forest degradation
takes into account both spatial
and temporal variations in reflectance,
but there's also an increasing trend
in the development of remote sensing
in many tropical countries to increase
from these optical or panchromatic wavelengths
to multispectral satellite systems,
and also to especially hyperspectral sensing technologies
on airborne platforms.
So like other remote sensing technologies,
hyperspectral offers certain advantages
over conventional multispectral data.
So hyperspectral data or imagery can be used
or can be considered as kind of a single-image data set
with a continuous spectrum of radiance or reflectance
as values associated with each pixel.
Research and development of hyperspectral imagery
has been an active area of research during the past decade
and even further back as well.
But sort of this application with forests
and disturbance in forest
has been on the rise in the past decade.
So, hyperspectral imagery,
it may be better suited
to resolve things like drought effects on tropical forest,
because they are highly sensitive
to canopy leaf water content and light use efficiency.
So the use of hyperspectral data
also allows the retrieval of specific biochemical compounds
associated with plant function and structure,
including things like foliar nitrogen,
carbon and nitrogen ratios, chlorophyll concentration,
and structural compounds like lignin or cellulose.
And in terms of monitoring degradation,
the derivation of functional types
has commonly centered on the use of fine spectral resolution
or hyperspectral data
in order to discriminate among different plant attributes.
So within a highly-heterogeneous landscape
like that of a top view of a degraded forest,
having more spectra means less possibility of overlap
when characterizing these vegetation types,
and also their health,
and other features like soils and non-tree vegetation.
Basically, with hyperspectral data,
spectral fingerprints are features
are able to be more finally detailed
to the remote-sensing specialist.
So, remote sensing can be used, as we know,
for mapping and measuring virtually
all types of landscape variables,
but especially key forest variables
like density and basal area.
So I wanted to include this table here,
because it highlights
really particularly common applications
for specific remote-sensing systems.
So the double check marks refer to more common applications
and the single check marks
refer to less common applications.
So it's important to note
that there are examples in the literature
for nearly all sensor and platform combinations.
So as you can see from here, some of the columns,
which are multispectral, fine spatial resolution,
multispectral medium or coarse spatial resolution,
hyperspectral, synthetic-aperture radar, and Lidar,
those are the different divisions that we're thinking about.
And then we're also looking at things
like land use and land cover change,
how they're applied, cover estimates,
and there's a whole bunch of subcategories for that.
Also, vegetation structure,
vegetation chemistry and moisture,
biodiversity, disturbance, and soil.
So I wanted to kind of
include all of these different attributes in here,
because I thought it was a good way
to kind of pictorially represent
what these different types of remote sensing platforms
that we've talked about a bit this semester,
how they're kind of used with respect to forests
and measurement of forest degradation and deforestation.
I'm sure similar tables might be out there
for thinking about how they might be applied
to things like wildlife management,
as well as buyers as well.
But, if you have a chance, I guess,
spend a few minutes reading these different slides
to kind of look at how they're put together,
and kind of what different examples of their uses might be,
which is in the column at the far right.
So finally, as kind of a case study example,
I wanted to bring up a study area
that kind of been on the rise in the past couple decades,
and that's the Democratic Republic of the Congo.
So, in many forest loss hotspots across our planet,
and the DRC is one of those,
accurate classification of forest cover and type
are needed before we can even measure loss.
There's many different places across the tropics
that's home to where all of the rainforest in the world are.
And many of the countries that contain the rainforest
don't necessarily have access
to sophisticated remote-sensing image processing systems,
and so they rely heavily, and increasingly,
they rely heavily on platforms like Google Earth Engine
to be able to interpret what the free imagery out there,
like Landsat, is measuring.
So this is a study from 2020, and it was basically a map
where the study region was at the forest edge
of the North Batéké Chiefdom.
And I kind of wanted to include this here
just to kind of give you an idea
of how complicated the workflows are
to get to be able to accurately classify forests
and also classify forest health as well.
So the resulting map is at the right side,
and the workflow to be able to get from,
using Sentinel 2 data,
and being able to understand what is included
in forest versus non forest areas,
and what the NDVI
of these different land cover classes might be,
as well as where communities and where roads
and things exist.
This is sort of modern map-making.
And it's important to kind of understand
that there's a lot of work
that goes into just making these maps,
and here, they were also, the study was also then
looking at areas where agriculture was on the rise
and deforestation by the agriculture was occurring.
Okay, so we've kind of gone through
these three different areas,
and just to kind of go back
to what we're doing with the readings,
there is a chapter on forest degradation and deforestation
in Google Earth Engine.
And the goals of that chapter are to,
at the end of the tutorials,
be able to detect deforestation and forest degradation
using spectral unmixing,
and interpreting the fraction of images and NDFI
using a temporal color composite,
implementing an NDFI-based change map
to monitor deforestation and forest degradation,
and finally, running a time-series change detection
to detect deforestation and forest degradation,
and also revegetation through time.
Concurrently to what you're gonna be doing
in the tutorial this week,
I also wanted to kind of highlight a website
that's out there, that's called globalforestwatch,
and it uses a whole bunch
of these different types of approaches
to detect forest cover, forest gain, and forest loss,
as a bunch of other different variables as well,
pertaining to forest worldwide.
And it has a really kind of fun
and interesting graphical user interface.
So for the Yellowdig assignment,
you're gonna be going to Global Forest Watch,
and you're going to be working with the map a little bit
to kind of figure out how to measure forest loss
in an area that you choose.
So just to kind of show you a couple of things
on the webpage,
so this is what occurs when you open up the map,
this is what you'll see.
You can also switch, I don't know if you can see my mouse,
but you can switch down to the planet satellite imagery
down on the bottom of the pop out that's on that map,
if you wanna look at it,
and it's sort of a satellite background,
which is what I'll be showing in the next slide.
So here, I've zoomed into an area of Madagascar
that is near and dear to my heart.
And so, I wanted to look at tree cover loss through time,
and what you can do,
if you see, these are the sort of baseline variables
that are included.
You can exclude them by pressing the X,
or you can include others
by clicking on forest change or land cover,
clicking on some of those different icons over there
at the far left.
There's also climate and biodiversity as well.
So you can include different variables
and you can exclude other ones.
And if you look at the cover loss,
there's a little start arrow,
and you can actually play a little movie for yourself
as to what cover loss has looked like
in the region that you choose between 2001 and 2021.
So here, I also wanted to kind of show you,
down at the left side, you can see,
there's a popup that I clicked on,
so you can look at, it says image type,
and if you click on it,
right now, it's natural color,
but you can also click on it
to show a false color, near infrared
of the satellite background.
So that's what that looks like if you do that.
And then, okay, so going back to just the natural color.
If you pop out the forest change,
you can see that a bunch of the other variables
will come up, things that you can click on.
And when you toggle them,
when you click on the grayed-out areas
and turn them on and off,
that's when they'll get included in your map
and sort of overlay it.
You can also play with the opacity
of the variable that you choose
if you wanna sort of dial it up or dial it back.
Right now, it's sort of all set at the defaults.
So here, if you see, I removed forest gain,
'cause I just wanted to focus on cover and loss.
And another interesting thing that you can do,
so if you turn on another data set,
so here I turned on tree cover loss by Dominant Driver,
which has a color-coordinated key,
and you'll notice that my map
suddenly became very pixelated,
and that's because not all of the data sets
are calculated using the 30-meter Landsat
or Sentinel data sets,
some of them are calculated using other data sets.
So this is a tree cover loss by Dominant Driver dataset.
And if you look at the metadata
down where the variables listed,
you can see that it's got a 10-meter resolution.
So that's why, it suddenly looks very pixelated on my map.
There's also some other ones in there
that are 500-meter Motus,
375-meter Veers, things like that.
So just be aware as you're clicking around and playing
that that's why things might get pixelated for you.
So finally, if you go back,
I took us back to just looking at what I was interested in,
in looking at, which was tree cover loss in the area.
If you click on the analysis tab at the top, next to legend,
what you'll be able to do is,
it'll ask you if you wanted to upload,
it'll ask you if you wanna upload a shape file
or you can actually draw one interactively.
So I drew a box,
you can sort of see the black box around the region.
And then you can click 'analyze,'
and it will calculate and produce a map for you
of what the forest loss looks like between 2001 and 2021.
So that's sort of along the lines
of what you will be doing for your Yellowdig assignment.
There's some questions that go along with that,
so just pay attention
and look forward to reading and interacting
with your discussions of sort of why you chose things
and what you're seeing there,
and how accurate you think it might be.
All right, well, thank you for your attention,
and let me know if you have any questions,
and we'll see you next week.