Miranda Riemer

University of Minnesota

FR3262

FR3262Land Change and Desertification in the South Asembo Region

Lake Victoria

Land Change and Desertification in the South Asembo Region and Lake Victoria

The South Asembo region along Lake Victoria in Kenya is one of the most

underdeveloped, impoverished areas remaining in Africa. It is one of the hardest hit areas in

terms of AIDS and Malaria, and faces constant issues with access to clean, safe drinking water.

Pollution and runoff from livestock further pollute the limited water sources available, including

the nearby Lake Victoria. Villagers in the region survive off of subsistence farming, often cutting

down forested areas to access less degraded farming land. As a result of these practices, the

South Asembo region has faced a good amount of land use and land cover change throughout the

past, especially as population density increases around its shores.

Additionally, Lake Victoria itself has

faced change through desertification and

over-use of its resources. Desertification

is the process by which a combination of

climate and anthropogenic factors can

lead to a drying up of vast areas of land

or water bodies. Increased attention has

been given to the declining water levels

of Lake Victoria in recent years, as they

have been shown to have decreased an average of at least six feet since 2001. Years of severe

drought and changing climatic conditions have led to concern for the lakes of Africa,

surrounding land, and rivers that feed off of these water bodies, such as the Nile River which

begins its journey at Jinja, Uganda, the headwaters of the Nile. These waters, especially Lake

Figure 1 - Figure depicting changes in population density around Lake Victoria since 1960. Image courtesy of Wiki Commons (2012).

Victoria, are ecological havens for a diverse array of plant and animal species that exist nowhere

else in the world.

As these areas are faced with increased health and resource issues, it is important to look

at the extent to which the land and water surfaces are changing over time, particularly in

correlation with the changing population densities of the region. This project examines the land

cover classification of the South Asembo region and shoreline of Lake Victoria in Kenya,

particularly in correlation of the changing population density in the region. Two satellite images

are examined; one from 1984 and one from 2008, to further examine the changes that occurred

within the twenty-four year period. Remote sensing and the Imagine software allows for a large

scale investigation and classification of the land in this remote area.

Satellite images were obtained from the United States Geological Survey (USGS) website, using

the Glovis Global Visualization Viewer. I obtained approximate coordinates for the South

Asembo area and used the search tool to find a list of available images. I selected two images

Figure 2 - Satellite images of the Asembo Area from 1984 (left) and 2008 (right)

with less than 10% cloud cover from 1984 and 2008 for this comparison. The images were

analyzed using ERDAS Imagine Image Processing software.

To complete this analysis, I chose to do unsupervised classification of the two images,

since I do not have enough familiarity with the region and land type to create the training areas

necessary for a supervised classification model. ERDAS Imagine utilizes an ISODATA

clustering technique to complete unsupervised classifications, meaning it uses the statistics of the

image data to group the pixels into similar classes. For this project, I eventually decided on

setting the model to create twenty classes with fifty maximum iterations to reach the

convergence threshold of 0.950. This decision was made after repeatedly running the model with

various values to determine which ones created the best result. Under “Initializing Options”, I

chose the option to initialize means along the Principal Axis, rather than a forty five degree line.

The resulting classes were then identified and recoded based on aerial photography and

general knowledge of the land area. I chose to separate the twenty defined classes into water,

natural vegetation/forest, grasses/shrubs, bare soil, and agriculture/livestock pasture giving each

a unique color scheme. A thematic recode was then performed to combine similar classes into

five rather than twenty classes. At this point, it would have been ideal to perform a change

analysis of the two images to quantify the differences between the two time periods, but due to a

lack of resources this was not possible at this time. Additionally, there was a miscommunication

with my former project partner about the images being rectified with true coordinates and it turns

out this step was not done prior to the classifications being conducted. Therefore, a change

analysis would have been a difficult step to fit in, as it requires that the images be correctly

referenced to ground points and, more importantly, to each other.

The results of the classifications and visual comparison of the two images showed some

important differences in the land cover data between the two years. Under close inspection,

there is a slight decrease in the size of Lake Victoria, as can be seen in the receding shoreline in

the Asembo region. Additionally, there is a large change from agricultural land and pasture to

bare soil between 1984 and 2008. The forest and shrub areas fluctuate slightly between the two

years, and appear to actually increase in area during the twenty four year time period.

Figure 3 - Classified map of the South Asembo region in 1984

Figure 4 - Classified image of South Asembo region in 2008

Figures three and four show the resulting classified images, as well as the histograms, names,

and legends for the classes.

Overall, the results of this project were different than originally anticipated. While I did

expect to see a recession of the Lake Victoria shoreline, I also expected to see more concrete

visual results from deforestation and conversion to farmland in the region. Instead, the farmland

and pasture areas actually decreased in size between 1984 and 2008. This could be due to

differences in the month of the photo, however this problem was difficult to avoid if I wanted to

obtain a satellite image with little cloud cover of the area.

There were many unforeseen issues that arose while completing this project that led to

problems throughout the entire process. My partner for the project requested that we take an

incomplete, which we did, but then chose to drop the class. As a a result, I had to complete the

project on my own after considerable time had passed from when I took the course. Therefore, I

was a little rusty on the operations in ERDAS Imagine, and had to redo large portions of the

work. Additionally, there were some miscommunications about what steps had or had not been

completed, which led to problems later on (such as thinking that the image rectification step had

occurred when it had not).

Without these issues, the results of this project would likely have been more accurate.

However, I still gained a great deal of knowledge from completing the process, particularly

regarding the operations within ERDAS Imagine. Additionally, I learned valuable information

about the problems that can occur when working in teams, and how foresight and planning ahead

can help avoid these issues.