The document analyzes changes in water level and soil moisture in the Aral Sea between 2000 and 2017 using satellite imagery. The researchers obtained Landsat 7 and Landsat 8 data from 2000 and 2017. They performed pre-processing steps to transform the raw data to reflectance. Next, they classified the images using supervised classification to identify seven land cover classes. Their analysis found decreases in water, shallow water, and dark bareland, and increases in salt, bareland, vegetation/wetland, and light bareland between 2000 and 2017. This indicates the Soviet decision to divert rivers for cotton farming degraded the Aral Sea over time.

1. The purpose of this poster is to quantify and locate the
changes of water level and soil moisture in the Aral Sea
between 2000 and 2017. In order to do this, we took
measurements of radiated energy from the Earth’s surface to
gather the required information and derived contemporary
data of the Aral Sea.
Aral Sea: Land Change Analysis
[2000 | 2017]
Brian Eitel (School of Arts and Sciences) AND Joel Foo (School of Arts and Sciences)
Pre-Processing: from DN to Reflectance
We obtained and downloaded two sets of data from the
online repository on the Global Land Cover Facility
website.The 1st set of data is from Landsat 7 Enhanced
Thematic Mapper Plus (ETM+).The 2nd set of data is
from Landsat 8 Operational Land Imager (OLI).We then
imported these layers into ERDAS Imagine and stacked
them. After which, we cropped the scene to our region
of interest, and came up with the images above.
We also transformed raw sensor data to reflectance for
image restoration, compensating for distortion, errors,
and noise during the data acquisition and recording
process.
Signature Mean Statistic
Signature Color Code and
Training Sites
Signature Separability
Classification Accuracy Assessment
A classification accuracy assessment was used to
compare our classified image to independent
geographical data.We did this by performing
using 30 randomly selected points and
comparing our images to a 2017 Google Earth
reference image of the same region.
Key Facts
The Aral Sea was once the 4th
largest lake in the world but
has now dried up.The cause of
this was caused by the Soviets
diverting the 2 rivers that
sustain it, the Amu Darya and
Syr Darya, in order to grow
cotton.The events that
happened in the Aral Sea
clearly shows how much
damage can be done to the
environment through man-
made activities.
Image Enhancement: Color Composites
This process is done to improve or increase the visual
appearance and quality of digital images by altering the
original brightness values.
Aral Sea
July 29, 2000 Oct 8, 2017
July 29, 2000 Oct 8, 2017
Image Classification
Image classification is the extraction of
information classes from a multiband
raster image. Pixels are assigned to
classes based on their spectral-
radiometric temporal responses.We used
the classification algorithm of supervised
classification which identifies the classes
by using samples of known training sites.
In order to do this, we created 7
informational classes in the Aral Sea
region - Salt, Bareland,Water, Shallow
Water,Vegetation/Wetland, Light
Bareland, Dark Bareland. After which, we
performed a signature evaluation where
the computer calculated the separability
of signatures for all possible combinations
of specified number of bands through
transformed divergence. ERDAS uses
training site data to characterize each
class by its mean position on each band.
To classify an unknown pixel it checks the
distance from that pixel to each class and
assigns it to the nearest class.This is done
by using Euclidean Distance
July 29, 2000
Oct 8, 2017
Land Change Analysis (2000 – 2017)
Objective
Tasseled Cap
Difference
The Tasseled Cap Analysis
(TASSCAP) is designed to
analyze and map moisture
changes detected by various
satellite sensor systems.
Conclusions
Sources
From our results, we found
decreases in water, shallow
water, and dark bareland.
Increases were in salt, bareland,
vegetation/wetland, and light
bareland.The dramatic decrease
in water and increase in bareland
shows the Soviet Union’s decision
to divert the water in the 2 rivers
in order to grow cotton in the
Aral Sea region. In addition to the
diversion of the two rivers,
increasing global temperatures
may also be a secondary factor
to its degradation.
1) Campbell. James B. Introduction to Remote
Sensing, NewYork: Guilford, 1996. Print
2) “Google Earth” Google Earth. Dec 2017
Survey, USGS - U.S. Geographical.
“EarthExplorer.”EarthExplorer, U.S. Gelogical
Survey, earthexplorer.usgs.gov/.
3) “Tasseled Cap Function.”Tasseled Cap
Function – Help | ArcGIS for Desktop,
desktop.arcgis.com/en/arcmap/10.3/manage-
data/raster-and-images/tasseled-cap-
transformation.htm.
July 29, 2000
Oct 8, 2017
8 km 8 km
8 km
8 km 8 km
8 km 8 km
8 km
8 km