Sample Research Paper on Change Analysis of Darien Connecticut

Darien is one of the oldest towns in the United States. Originally part of Stamford, Darien town, in
Fairfield County Connecticut was first settled in 1737 and incorporated as a town in 1820 (McCain, 2009).
The town is geographically located on the shores of Long Island and sits on the main route for people
moving between Boston and New York City (McCain, 2009). Its unique position makes it fit for
commuting to and from the cities of Boston and New York (Miller, Burgan & Fitzgerald, 2019). Darien is
of significant and interests not just because I could live there as I complete my post-graduate studies at
Stamford University, but also because it has one of the richest communities in the United States.
Understanding the historical significance and use of pictorial evidence to study the growth – or lack thereof
– of the town is stimulating.
Darien, with its landmass area of fewer than thirteen miles squared, has a population density of
1,615.8 people per square mile. One of the most interesting things about the town is that its population is
younger and it has the highest population of young non-college students in the state as well as a relatively
high average of children born in every household (Town Charts, 2020). Although a rich community, most
of its residents commute to their places of work using trains which eliminates the problem of traffic for the
town’s residents. With a crime rate of 12 per 1000 residents, Darien’s rates are average compared to
American cities and towns. The public education system in the town of Darien has consistently over years
ranked as one of the best in the country. The housing of the town is on a perfect trajectory making its
growing population to get decent housing and keep the town’s status. The town is really a great place for
young families to start and bring up their children.
Remote Sensing
Also known as earth sensing, remote sensing is a process through which researchers use
sensors mounted on satellites or aircraft to gather or ‘sense’ objects on the ground from far above

and measuring tier physical characteristics. Remote sensing capitalizes on the reflection ability
of structures and objects on the earth’s surface (Chouhan, 2017). Remote sensing falls under
passive and active remote sensing. According to Chouhan (2017), the difference between the
two is the source of light or radiation to be reflected. In passive remote sensing, the measuring
equipment measures the sun's reflection from the object sensed, or even the heat radiated by the
object. On the other hand, in active remote sensing, the sensing equipment emits its light and
directs it towards the object, and measures the reflection.
A proper understanding of remote sensing needs one to understand how the reflected
light or radiation is detected. There are four methods of measuring the physical objects on the
ground and concepts that one must understand to know how remote sensing works. The four are
Temporal Resolution, Spectral Resolution, Spatial Resolution, and Radiometric Resolution
(Aasen et al., 2018). Temporal resolution is the concept that refers to the difference in time
between two images of the same geographical location. Over time, temporal resolution of
satellites has significantly improved not just due to an increase in the number of satellites
orbiting earth but also because of developments in internet and technological capabilities.
Spectral resolution relates to the ability of a satellite to measure different wavelengths in
the electromagnetic spectrum. This is important because not all types of light are visible when
emitted or reflected. The ability to measure this type of light means that a satellite can ‘sense’
objects in darkness (Levin et al., 2020). On the other hand, spatial resolution is the size of the
smallest object that the satellite could detect on the ground that is the size of one pixel on the
ground. This is an important aspect as it dictates how much one could zoom into a satellite image
and view the objects on the ground. Radiometric Resolution is the ability of a digital sensor to

differentiate grey-scale value while capturing an image. Radiometric Resolution rakes images in
digital form and uses 8-bit technology.
Historical Imagery
The images on Darien dates back to December of 1984. There is a huge difference in
spatial resolution between the images of Darien taken in 1984 and those of October 2020. While
the resolution for October 2020 was so good that one could zoom to street level, the resolution
for the 1984 aerial image was poor and had a low spatial resolution, and zooming in any way
blurred the image. Between the 1934 and 2020 images, photography technology has significantly
increased. Specifically, high-resolution cameras have been developed. The developments were
informed by relevant technology and the need to use drones in covert and overt operations,
hence, a need for clear images. Below are the two images from 198 and 2020 respectively.


1934 Google Maps Pro Image of Darien (Google Maps Pro, 2020)

2020 Google Maps Pro image of Darien (Google Maps Pro, 2020)
Comparison of the Google Earth Pro Image with a 1934 air Photo
The primary difference between the 1934 air photo and the google pro images of Darien
is that while the google pro images are colored while the 1934 air photo is in black and white. In
the 1934 image, there are very few houses in 1934, a little more in the 1984 image while the
2020 image has the most houses. The change in housing as seen in the images reflects the
population change in the area over the same period.
The spatial resolution of the 1934 air image and that of 1984 is similar, the 220 google
pro photo, however, has a very high spatial resolution. In both the 1934 and the 1984 images, it
is only in the sky-view that one can view the objects on the ground. The 1934air image has a

higher spectral resolution than the two google pro satellite images. The Image, unlike the other
two, it capitalized on the reflective properties of the objects in the ground. The roads and other
bright objects are more clearly visible on the 1934 photo, this is because the 1934 aerial photo
‘sensed’ reflected light of different wavelengths. This property was able to make up for the lower
spatial resolution. See the 1934 air photo of Darien in below.

(UConn, 2020). UConn Air Photo Archive for Darien
Historical images like this are particularly important to geographers. It helps the
geographers understand how the terrain of a certain area has changed over time. This way, they
understand why and how some physical features came about. Therefore, using photos like this,
the geographers could predict how future generations could impact the environment. For
example how new buildings change the flow of water which could lead to flooding especially in

coastal towns such as Darien. The photos could also serve as evidence to show physical changes
of land e.g. due to mass graves being dug and make it easier to obtain hard evidence. To non-
geographers, the photos could help them understand how their towns have evolved giving them a
clear understanding of their surroundings.



Aasen, H., Honkavaara, E., Lucieer, A., & Zarco-Tejada, P. J. (2018). Quantitative remote
sensing at ultra-high resolution with UAV spectroscopy: a review of sensor technology,
measurement procedures, and data correction workflows. Remote Sensing, 10(7), 1091.
Chouhan, T. S. (2017). Remote Sensing and GIS GPS Based Resource Management. Scientific
Google Maps Pro (2020) Google Earth Web. Google.
Levin, N., Kyba, C. C., Zhang, Q., de Miguel, A. S., Román, M. O., Li, X., … & Wang, Z.
(2020). Remote sensing of night lights: A review and an outlook for the future. Remote
Sensing of Environment, 237, 111443.
McCain, D. R. (2009). Connecticut Coast: A Town-By-Town Illustrated History. Rowman &
Miller, D., Burgan, M., & Fitzgerald, S. (2019). Connecticut: The Constitution State. Cavendish
Square Publishing, LLC.
Town Charts (2020). Darien CDP, Connecticut Demographics Data. TownCharts.
UConn (2020). UConn Air Photo Archive. UConn.