In this video, I'll be explaining to you what remote sensing is and what GIS is, in very simple
terms so that even if this is the first time you are coming across these technologies you will be
able to walk away with some useful information. In addition I'll also be providing some examples
of applications of remote sensing and gis and will be discussing the kinds of remote sensing
technologies we currently have. We will also be touch-basing some of the key terms associated with
gis towards the end. I have separated the video into chapters so that in case you are interested
in specific topic you can hop onto those sections and all the links mentioned in this video will be
included in the description section below. So that out of the way let's get started. Hey guys welcome
or welcome back to my channel and happy new year if you guys are new here, hi there, this is Mikey.
I'm a PhD student at UC Berkeley working at the intersections of remote sensing, forestry and
data science and in this channel, I'll be posting daily videos on topics ranging from drones, LiDAR,
trees, maps, data analysis, visualization, machine learning and everything else in between. The idea
is to make concepts associated with physical geography and data science simple enough so that
more people can apply it. So yeah this channel is gonna be a bridge between spatial science and data
science so if you're into any of this make sure to subscribe! okay let's start with remote sensing.
When we first hear about remote sensing what are those things that we associate it with? um well,
satellites and obviously NASA right? that's why here I got this makeshift drawing representing a
satellite drawing and also the confused earth. But remote sensing is much much more than just
satellites which we'll be getting into it in a bit. So, what is remote sensing? for answering
that let's split the words here! The first part is remote... this relates to something that is not
physically touching the object of our interest... and the second part sensing can be interpreted as
a feeling we get when we sense an object... so by feeling, in this case we mean the information
that we are extracting which can be something like a physical property of the object. And this
is exactly what remote sensing stands for! It is a science of measuring or obtaining information of
an object without making any physical contact. I repeat... remote sensing is a sense of measuring
or obtaining information of an object without making any physical contact. So, without making
any physical contact that's the important part here! and the term was coined in the early
1950s and nowadays it's a major branch of study within the realm of physical geography.
Now let's consider our day-to-day behavior and see whether we can relate it to the concept we
just discussed. For instance, imagine you are hanging out in your backyard
and working on some assignment on your laptop... so are you here remotely sensing any data?
If you consider you're typing on the keyboard it is not remotely sensing the data as you are making
physical contact here. This is the same when we take the case of lab work
where we are performing experiments and physically measuring things. Whereas imagine here you got
bored and you decide to relax and just look around and observe the trees and birds around the corner.
This can be considered as remote sensing as here you're not making any physical contact with the
object of interest. As I mentioned before even though satellite-based imagery is the most popular
topic related to remote sensing, remote sensing doesn't have to be always done from outer space!
Nowadays drones are another widely used platform for performing remote sensing. Also, note that the
platform carrying the sensor doesn't always have to be in the air. The laser scanning technology
now that's being used in the autonomous car is also an example of remote sensing... so
is the x-rays you take at the hospital! In case of the laser scanning technology which is also
known as lidar, we are able to detect objects and even create three-dimensional models of them. I'll
be creating a playlist entirely on later remote sensing in the coming days and the link will be
included in the description section in case you are interested in exploring more on the topic.
Among all the examples we have just mentioned note that there is always a sensor that collects
the data and there will be a platform for having the sensor - whether it be a satellite or drone or
car... and based on how the object under focus is illuminated, remote sensing can be classified into
two categories: active and passive remote sensing. In the case of passive sensors such as drones
taking photos here... it depends on external energy for illumination. They actually measure
the reflected sunlight emitted from the sun. Meanwhile, active sensors, they have their own
source of energy for illumination and lidar is a classic example of this. Another relatable example
can be when you take a photo on your phone at night using a flashlight... here you use your
own source of light for illuminating the target right? so this is also an example. Now if you
ask me whether active remote sensing or passive remote sensing is better... it totally depends
on your particular situation. In general, active remote sensing can be found to be more dependable
than passive remote sensing methodologies. As you can imagine we can't depend on satellite
imagery based on remote sensing during the nighttime as we need the sun's energy for
illumination. Also, the sensors are heavily affected by things like clouds and aerosols.
However active remote sensors are mostly more expensive than passive remote sensing technologies
and it's not always possible to acquire such data for large-scale or recurring studies! I think this
is a very broad topic and deserves a video of its own. Let me know in the comments section if
you guys would like me to make an in-depth video on this in the future. Well, we live in a world
overloaded with data, and apart from the personal level data collection happening... there are also
a lot of other global-scale active and passive sensors that are orbiting the globe and collecting
data 24 x 7. Even though we won't be going into a lot of detail here I would like to introduce
you to some other major types of remote sensing. All these classifications are primarily based on
the electromagnetic spectrum. You know the thing we all learned as we were kids... yeah the same
thing... where the visible light falls towards the center. Without a doubt, the most common and
widely available remotely sensed data is through optical remote sensing technologies. Optical
remote sensing makes use of visible near-infrared and short-wave infrared sensors for capturing
images. Another common remote sensing method is the lidar which stands for light detection and
ranging... and this is an active sensor where the target is illuminated with laser light... and the
reflection is measured with a sensor. Then we have thermal remote sensing... which uses the
infrared portion of the spectrum and measures emitted thermal energy... and another remote
sensing technology that is gaining popularity is microwave remote sensing... which draws inferences
based on the emitted microwave energy... and once again based on the source of energy all
these remote sensing technologies can be further grouped into either active or passive sensors!
Now after realizing that we have all these data so what's next? A lot of us are oftentimes left
with a bunch of question marks inside our head and it's totally normal... based on our interest
and research question we need to decide what kind of remote sensing data to choose from...
for instance, a person working on the agricultural sector might find microwave remote sensing very
useful as it helps quantify properties such as soil moisture. Same with optical remote
sensing technologies... which will allow you to monitor plant health and estimate crop yield.
On the other hand, a person working in urban designing might be more fascinated with lidar,
as this can be used to develop something like 3d models of buildings or bridges. Irrespective of
the fields you are in, I bet you can find some applications of remote sensing in your everyday
work... we just scratched the surface here... there is even more! We also have something called
sonar... which utilizes sound energy and make observations based on how sound waves bounce off
various objects... same way we also have radar... which we all know is something used by police for
speed enforcement. But once again make sure not to get overwhelmed with all this vast multitude
of data and information... I know this might be a bit too much for an intro video but I just wanted
to give you an idea of what all you can do using remote sensing! Nowadays we just see the success
stories of people coming up with all these amazing discoveries using the data available...
but we should also remember that all these are achievable for us as well... the thing that is
holding a lot of people back is not the lack of information... but the surplus of information!
Imagine you see something like satellite used to detect forest fires in amazon or something
like that... and now if you want to try doing the same... you are generally forced to read an
entire textbook or numerous research articles. All these long-winded research explanations are
for sure required... but we also need to make sure that the tools are easily accessible to the common
public and are easily understandable... so that more people can get involved... and that's the
major motto of this channel... so if you agree with my ideology hit that like button and make
sure to subscribe!!! Okay, so now let's move to the second part of this video which is GIS.
GIS stands for geographic information systems... at some places, I have even seen it expanded
as geographic information science. I drew this figure as layers are something that
you easily associate GIS with. However, the most important keyword regarding GIS is the LOCATION!
GIS helps us understand what has happened at a particular location, what is happening now,
and what can happen in the future! Let's say the layers here represent the vegetation cover,
water bodies, streets and political boundaries. This is an example of how we can split an area
using GIS. We separate a region into multiple layers so that it's easy to analyze the situation
and make decisions. For example, if you want to buy a new home in a given locality... using GIS
layers you can evaluate how much time it will take to reach the airport from this housing location...
are there any rivers nearby... are there any hiking trails in your area... or even something
like the population density or area of your respective county. You might not be recognizing
it, but you actually make use of all these concepts on a daily basis while using apps such as
google maps! so back to the real deal... what is GIS??? this is a very hard question to answer...
you know as there are 100 ways to answer this. However, in short, all you need to keep in mind is
that there are three main important things: which are data collection, data management and data
analysis!!! GIS can be referred to as a framework or a technology field or a computer system that
is capable of collecting, managing and analyzing spatial data. I want to once again stress on these
terms which are collecting, managing and analyzing spatial data. Here, by spatial data we mean
information on the location which is usually geographically geo-referenced... and all the
information collected can be visualized as a map as well. So how is the data collection done?
this can be using your phone or a GPS tracker or using surveying tools or maybe digitizing an
old map that you already have ...or just based on your knowledge of the area
you can add information to GIS. All these data can be classified further into two main categories...
vector and raster, which we'll be looking into towards the end of this video. We will also be
looking into various other key terms such as... shapefile and geodatabase because these are how
the data collected is managed within the GIS. The data management part is also a very crucial
element as a lot of information coming in might be from diverse sources. For instance, there might be
a part of your team out there working in the field collecting field data and you'll be at the office
working on the data collected in real time. For making this possible GIS makes use of hardware and
software capabilities and your at times might need really powerful computers and servers to perform
these operations. As you can imagine... apart from managing and storing the data.. this step can also
include enhancing the quality of collected data as well as sharing the data with a wider audience. So
once you have the data in a usable quality... then we can perform various analyses based
on our research questions... and then can further examine and refine results accordingly. If you
take the example from before regarding buying a new house and if you want to know how far the
nearest airport is... we can choose some analysis related to distance calculation in GIS and easily
come up with solutions... and based on the available traffic data we can further optimize the
route to reach airport depending on the time of the day. This is exactly how google maps provide
you with suggestions while driving during rush hour times. So... to summarize, GIS is a computer
system that is capable of collecting managing, and analyzing spatial data... and a few of the major
components of GIS that you need to keep in mind are hardware, software, data, people and methods.
Now that you have learned what GIS is... let's look into some key terms regarding this field of
study before we wrap up this video... but before that please make sure to smash that LIKE button
if you're finding value through this video :) Okay here I have come up with one two three
four five six seven :D key terms that you will come across again and again while stepping into
the GIS arena. Let's start with vector and raster data which are the two main types of data. Raster
data can be treated as an array of grid cells where each cell will be assigned a pixel value.
This can represent discrete entities such as population density or can be something like
a continuous variable such as temperature. The aerial and satellite imagery we bring
into GIS platforms are automatically treated as raster objects... and this is one reason why...
to me... raster data is more associated with the remote sensing sector... and I usually link GIS
with the vector data analysis because that's the real power of arcgis... and those kind of
platforms... at least from my understanding. While working with GIS it is also very likely that you
will be engaging with vector data most of your time. So vector is a type of spatial data that
uses x and y coordinates to define a location and can be further classified into three categories:
points, lines and polygons. This brings us to our next term, which is shapefile. Shapefile is
like the bread and butter of GIS. You will hear this every now and then... so what is shapefile?
shapefile is a geospatial vector data format for GIS and is developed and regulated by ESRI,
which is like NASA in the GIS sector. I'm sure that you would have heard about ESRI
since you are interested in GIS. They are a company based in redlands, California and is an
international supplier of GIS software, webgis and all those kind of things... and arcmap is the main
component of ESRI's arcgis suite of geospatial processing programs... and can be used to view,
edit, create, explore and analyze your special data and more. Other than arcmap you can also find
various other alternative open-source software. QGIS is one prominent example. GIS mapping refers
to the process of uploading data layers into a GIS software for producing specific maps that can
provide valuable and easily readable information that raw data can't present on its own...
and while generating maps and performing spatial analysis it is extremely important to keep a
check on your projection coordinate systems... as otherwise the accuracy of your processes will be
compromised... A geographic coordinate system is a way to define the locations on the surface of the
earth and it uses a three-dimensional spherical surface for this purpose. Well, within the GIS,
this coordinate system information provides you with a framework of reference for your data
and also helps with aligning your data with other existing data. Next is geodatabase which
we mentioned earlier... this is just a way to store large chunks of spatial data in a less
messy manner... these databases are optimized for storing and querying data... and the information
we put are hugely preserved and represented in the form of simple geometric objects such as
points lines or polygons. Finally, we have the 3d models which can be generated based on the spatial
information we had collected and processed within the GIS. This is a very interesting by-product of
GIS data and is extremely useful in sectors such as civil engineering and urban planning. Nowadays
ESRI even offers various interactive map options after creating 3d models to test different case
scenarios. In case you're interested I'll be including some related links in the description
section so that you can check it out and see whether these 3d models can be used for your
study. So... that's all for today... I think I really need to go drink some water :D anyways
thanks for stopping by! I'll see you guys again tomorrow with a new video... and also
don't forget to check out my playlist related to lidar remote sensing... and spatial analysis
in R... I shall include the respective links once the videos are up :) Till next time ciao!!!