Conclusion

This course has opened my eyes up to a whole new world of science, one which I was only acutely aware of and now has an incredible interest in its future potential.  Since its fruition from a hot air balloon, to the recent release of the Sentinel 2 project, the change that has occurred has been unprecedented and the potential it holds is truly remarkable. This blog has been a really useful catalyst in preparing myself for my dissertation which will likely use satellite imagery to aid my projections of the changes occurring along the Taiga-Tundra interface in Norway.

Applications

Nearing the end of the course now having learnt a lot about how it works and then utilising aspects of it, remote sensing potential is evidently endless and its applications are incredible varied.

One aspect that has really intrigued me, that coincided with my other geography module, is remote sensing application in natural disasters, such as flooding events. By using DEM models it is possible to calculate areas that are of risk and in 2011 Manchester created ‘The Strategic Flood Risk Assessment’, which assessed areas at most risk and created subsequent management plans to combat it, such as flood plains and building raised houses (MCC, 2017). Without remote sensing this wouldn’t be possible and thousands of people would subsequently be at risk.

MCC (2017). New development and flood risk | Flooding and drainage | Manchester City Council. [online] Manchester.gov.uk. Available at: http://www.manchester.gov.uk/info/100006/environmental_problems/5404/flooding_and_drainage/5 [Accessed 24 May 2017].

 

Putting it together

Understanding the processes that are involved in gathering remote sensing images, utilising them within the coursework has been incredible interesting so I thought I dedicate a post to how I have applied satellite imagery.

The first couple of practicals gave me an introduction to ENVI (ENvironment for Visualizing Images) and similar to GIS, took a while to get my head around. However, with hours spent playing, I now feel comfortable understanding the different reflectance of different land covers and utilising different image enhancement processes to aid image interpretation, such as contrast stretching.

One application was related to NDVI within western Sudan and its role in the conflict. NDVI is something which is relatively related to the ideas I have around my dissertation and has subsequently lead me into ideas surrounding vegetation health as a potential tracker of change but also gave me great insight into the factors that played a role in the Darfur conflict, a topic I was relatively unfamiliar with, however have since taken a great interest in understanding the many factors involved.

The final practical really opened my eyes to some of the potentials in the field of remote sensing, being able to classify different land covers based on spectral signatures really was fascinating, which opened up so many potential doors. The real question I wanted to know about this though was how possible was it to differentiate within land covers? A subject I have been consistently trying to understand since my earlier blog about spectral signatures.

However combining this with the previous post about sentinel 2, I was able to find several papers illustrating the potential of high-resolution images of the Taiga-Tundra interface:

-Multisensor NDVI-Based Monitoring of the Tundra-Taiga Interface

-Land Cover Mapping in Northern High Latitude Permafrost Regions with Satellite Data: Achievements and Remaining Challenges

-Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product

Launch of Sentinel-2!

Thought this brief post would be highly appropriate having just discussed how sensors work in the previous post and also the potential this brings which could be highly useful to my future plans for measuring vegetation cover.

The Sentinel-2 is a land monitoring constellation consisting of two high resolution multispectral optical imagers, providing resolution up 10m! Every 10 days with one satellite and 5 days with 2! Smashing temporal and spatial issues! The information that this has the potential to provide can revolutionise understandings of land use, modelling climate change extent and impacts, whilst having land cover mapping seeing a significant improvement (ESA, 2017).  The Sentinel 2 will carry on from Landsat measurements, producing timelines of changes like the deforestation near the northwestern Brazilian city of Rio Branco in the past 30 years:

http://www.esa.int/spaceinvideos/Videos/2017/03/Deforestation_in_Rio_Branco

Relating to dissertation ideas, this will help understand the potential changes within boreal forests and help projecting any interpretations of plant health in relation to growth (Majasalmi and Rautiainen, 2016).

References

ESA (2017). Sentinel-2. [online] European Space Agency. Available at: http://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Sentinel-2 [Accessed 23 May 2017].

Majasalmi, T. and Rautiainen, M. (2016). The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: a simulation study. Remote Sensing Letters, 7(5), pp.427-436.

Where are you getting this information?

One reason I chose remote sensing was because my basic understanding of it was that it heavily related to GIS, a subject I enjoyed last year and got a lot out off. Since taken the course for a while now I’ve begun to understand more why this is the case, with GIS being more greatly directed at using maps to illustrate processes within the world , whilst  remote sensing is more the provider of information which is then subsequently used. This interested me because it did make me wonder where the information I was using, actually came from? And even though ideas of resolution and types of sensors came up, my understanding was fairly primitive and I have since come across it quite frequently making me want to know more about it and where would I get the data I might need in the future.

Resolution spatially and temporally I had come across quite frequently throughout my studies, with notable academics such as Doreen Massey’s views on space and place being quite an iconic aspect of first year. Pixelating of images as a result of resolution is something that I have been made aware of since getting my first mobile phone and subsequent understandings from photography. However spectral resolution took a while to get my head around, with there being several different types of ways sensors achieved their data. It is the ability of a sensor to differentiate between wavelength intervals, with finer resolutions having narrower wavelength bands (NRC, 2017). The three main types panchromatic, multi-spectral and hyper-spectral resultantly have different spectral resolutions from one another as a result of the different widths (Campbell, 2011). Recently, advancing technologies has meant an unprecedented development in remote sensing, which has led to incredible potential regarding observation capabilities.

References

Campbell, J. (2011). Introduction to Remote Sensing. Taylor & Francis, London.

NRC (2017). Spectral Resolution | Natural Resources Canada. [online] Nrcan.gc.ca. Available at: http://www.nrcan.gc.ca/node/9393 [Accessed 23 May 2017].

Multispectral signatures of the earth

 

This post follows on from the previous post about radiation and interactions within the atmosphere, as most sensors measure the reflectance radiated back from sensors, understanding what different objects reflect absorb and transmit is key to understanding spectral signatures of the earth’s surface. Figure 1 illustrates the different signatures of the different land covers and some of the satellites that measure the bands of reflectance, which relates back to the idea of windows of light passing through the atmosphere. However, there are several difficulties with this measuring of spectral signatures as this suggests that nature performs uniformly, which is rarely the case, leading to the preferred terminology of “Spectral Response Pattern” (Aggarwhal, 2004) and this is something that has to be accounted for when using satellite imagery.

Figure 1 – Spectral signatures and satellite sensors (Geol-amu, 2017).

This relates quite significantly to the research I will likely be doing as part of my dissertation, measuring the spectral signatures of Picea abies (Norwegian spruce) within Tiga regions in Norway, so is an area I was particularly interested in and lead me on to methods of measuring the changes that are occurring.

Boreal forests are heavily influenced by indirect and direct climatic systems which have subsequent effects on the latitudinal and longitudinal distribution (Solberg, et al., 2002), making measuring the changes over time accessible from the use of migration of the vegetative spectral signatures, understanding this type of vegetation’s specific spectral reflectance will be important in differentiating between different land covers and potentially more difficultly, between different types of vegetation. Figure 2 illustrates the different reflections of different types of forest, evidently they are very similar and this is not yet specific to the species I am interested in, hence a greater understanding will need to be made when trying to decide boundaries of forests.

Figure 1, Spectral signatures of different forests (NASA, 2017)

References

Aggarwal, S. (2004). Principles of remote sensing. Satellite remote sensing and GIS applications in agricultural meteorology, 23

Geol-amu (2017). Spectral reflectance of land covers. [online] Available at: http://www.geol-amu.org/notes/m1r-1-8.htm [Accessed 22 May 2017].

NASA (2017). Remote Sensing : Feature Articles. [online] Earthobservatory.nasa.gov. Available at: https://earthobservatory.nasa.gov/Features/RemoteSensing/remote_04.php [Accessed 21 May 2017].

 

Interactions with the atmosphere

It is always really satisfying to be able to apply the knowledge learnt from education to real life. During reading week I captured the image above, it shows sunset and the various colours of the spectrum on the sky, why does this occur?  How come these are the colours? Are some of the questions that were answered in this week’s laboratory practical and subsequent readings.

There are interactions within the atmosphere that result in the different alterations of light and also on remote sensing systems, most notably absorption, refraction and scattering. Different wavelengths are absorbed as a result of the different gases that compose the atmosphere, these are varied across the energy emitted, causing ‘windows’ where radiation reaches the earth’s surface. Most remote sensing sensors operate by measuring the reflection of the radiation that is able to pass through the atmosphere and be reflected back, however some measure the absorption such as those from carbon dioxide (CO₂) and other gases (NASA, 2017). Refraction occurs when radiation interacts between mediums of different densities, which can pose issues when trying to calculate the return of radiation (Aggarwal, 2004). Finally scattering, is the redirection of paricles within the atmosphere (Campbell, 2011).

There are 3 types of scattering, each related to different particle sizes, Rayleigh scattering is a result of smaller particles than the associated wavelength, Mie scattering with those of the same or similar sizes and Non-selective a result of particles larger than the associated wavelength, it’s because of scattering that the sky is blue and changes colour as it rises and sets (Campbell, 2011).

At longer distances light travels across the sky during sunset as illustrated below, with the shorter waves being more readily absorbed by the atmosphere and scattered around, shorter wavelengths, like red and orange, are able to reach us hence giving sunset the varying colours (UoW, 2017).

Following this photo, I managed to capture the image below, which lead me to look into how Aurora Borealis phenomenon occurred within the atmosphere. Unlike the sky being blue, the reason auroras occur is because solar winds distort the earth’s magnetic field allowing for charged particles from the sun to enter the atmosphere and energise gases, making them glow (BBC, 2017) this tends to occur more frequently nearer the magnetic poles.

This aspect of remote sensing was incredibly insightful, it taught me several things about the Earth’s atmosphere I had witnessed but did not actually know the cause off, making me wonder what else do I see and can be answered by remote sensing?

References

Aggarwal, S. (2004). Principles of remote sensing. Satellite remote sensing and GIS applications in agricultural meteorology, 23

BBC (2017). What are the Northern Lights? – BBC News. [online] BBC News. Available at: http://www.bbc.co.uk/news/science-environment-26381685 [Accessed 22 May 2017].

Campbell, J. (2011). Introduction to Remote Sensing. Taylor & Francis, London.

NASA (2017). Remote Sensing : Feature Articles. [online] Earthobservatory.nasa.gov. Available at: https://earthobservatory.nasa.gov/Features/RemoteSensing/remote_04.php [Accessed 21 May 2017].

UoW (2017). What Determines Sky’s Colors At Sunrise And Sunset?. [online] ScienceDaily. Available at: https://www.sciencedaily.com/releases/2007/11/071108135522.htm [Accessed 13 Mar. 2017].

 

 

A Brief History of Remote Sensing

According to the United Nations Office for Outer Space Affairs (UNOOSA, 2016), there were 1419 operational satellites orbiting Earth in 2016 and around a quarter of these are Earth-observing.

Remote sensing arose with the development of flight and photography. The two together allowed for the balloonist G Tournachon to create the first aerial photographs of Paris in 1858. Along with this, there were several other methods deployed to produce aerial photographs, from pigeons to rockets, but these were quickly filtered out of practical use. Amongst many other technological developments, war acted as a catalyst to the development of Remote Sensing, with it becoming prolific in airborne surveillance and reconnaissance up until the Cold War. Figure 1 below shows how remote sensing was used in world war 1 to locate trenches for military use.

What I have come to realise is how recent remote sensing as a source of information actually is and how much has changed within such a short period of time, making me curious to where this course will lead and the opportunities remote sensing currently has and what it holds in the future.

References

UNOOSA (2017). UNOOSA. [online] Available at: http://www.unoosa.org/ [Accessed 23 May 2017].

What is remote sensing?

An idea that really stuck with me about Remote Sensing was that the data it gained involved no intervention, in comparison to most scientific measurements, which involve a type of manual application (Lillesand et al., 2015). Even the use of our eyes is a form of remote sensing and that is effectively how it works. It utilises electromagnetic radiation that is reflected or emitted from the Earth’s surface which can then be generated into a vast range of images. Many of these images produced are a result of radiation that is not visible by the human eye, unleashing incredible potential in understanding many of the Earth’s processes. Satellites record this information from different portions of the electromagnetic spectrum (Figure 1), which is measured by their wavelengths, and these invisible types of light are assigned visible colours to represent them.

The Larson C Ice sheet in Antarctica is currently on the brink of producing an ice berg the size of Delaware, which without infrared imaging would be a lot harder to see, however Figure 2 shows clearly the crack by representing water in the crack as black as a result of satellite imagery from the project MIDAS Sentinel-1 radar satellite, I currently don’t quite understand how the image differentiates between the crack and the ice cover. An aspect of remote sensing which I am planning on better understanding.

The crack through Larsen C ice shelf is visible as a dark line from bottom right to top left of this satellite image (MIDAS, 2017)

References

Lillesand, T., Kiefer, R.W. and Chipman, J. (2015) Remote sensing and image interpretation. Hoboken, NJ, United States: John Wiley & Sons.

MIDAS, P. (2017). Larsen C Ice Shelf rift continues to grow. [online] Project MIDAS. Available at: http://www.projectmidas.org/blog/larsen-c-rift-continues-to-grow/ [Accessed 23 May 2017].

Introduction

This blog will effectively act as a virtual timeline of my learning throughout the Remote Sensing course at the University of Manchester. It is going to be posted in a chronological order and guided by the arguments and information brought about from the lectures, tutorials and practicals.  The posts will likely be sporadic and varied, depending on the different tangents and events that occur throughout the 12 week period, but will hopefully be interlinked in a manner that augments my learning of the subject.

Initially, my interest in Remote Sensing came from its use in other subjects such as GIS and Green Planet, which gathered a lot of its data from Remote Sensing related techniques and then manipulated the data in a way that made it understandable. Intrigued by where this data came from then led me to research remote sensing and its wide range of applications, which has since sparked potential dissertation ideas that would utilise remote sensing. I also have a real interest in Chemistry and hope to utilise and further develop my understanding, through its use in Remote Sensing.

An interest I am specifically interested in is boreal forests and how their advancement northwards has altered the albedo of those regions and the subsequent effects this has on the surrounding environment. Entwining this with GIS and dendrochronology I believe could advance the understanding of these processes and would be a potentially produce a really interesting dissertation, hence this blog may also be heavily linked to this, which could lead to detailed blog posts.