1. Extracting coastlines from Remote Sensing imagery
An Object Oriented methodology using Landsat TM and ETM+ data
Maputo Bay
Mozambique
CSIR

2. Landsat imagery are first selected &
downloaded free of charge from the
USGS GLOVIS archive (ADD URL!)
The geometrically corrected imagery
is then processed to correct for the
effects of the atmosphere and prepared
for Object Oriented (OO) Image Analysis
The image to the left shows Maputo
Bay captured in Red tones in the
image represent photosynthetically
active vegetation while grey and silver
depict urban areas, blue represents
ocean and inland water bodies. This
image is known as a False Colour
composite

3. The first step in the analysis is the
removal of unwanted data using
a mask prepared before hand. See
green areas bottom and top left.
Following this the image is segmented
into image objects using a segmentation
algorithm.

4. The image segments seen on the
left are the building blocks of OO
image analysis. Image object
statistics are then generated for
each segment and used to classify
the image.
Classification is typically undertaken
using segment statistics such as mean,
variance, standard deviation or area
and even measures of adjacency.
Segments that fulfil a pre-defined
criteria can then be assigned to the
class of interest. In this case the class
of interest is ocean

5. Segments which fulfil the criteria are
then assigned to the class and
given an appropriate colour.

6. Once the segments have been identified
they are merged into one image object.
Further analysis is then required to refine
the coastal boundary, a container class
is usually used.
In the present analysis a coastal zone
class is created to refine the location of
the coastline

7. The coastal zone class is seen here
as white image objects adjacent to
the ocean class. Once again image object
criteria were used to identify suitable
image objects not identified in the first
round of image object classification
Image objects within the coastal zone which fulfil the criteria for ocean are then
added to the ocean class creating a
binary image depicting ocean – non ocean

8. The ocean class can then be exported
to a format suitable for use in a
Geographical Information System.

9. The vector data created during the
Classification can then be compared
to vector coastlines extracted from
imagery captured in other years.
The aim being to identify any significant
accretion or erosion of the littoral zone.
The following slides show the Maputo
Bay coastline and its associated change
between 1984 and The Object
Oriented approach described above was
applied to each scene 1984, 1986, 1991,
2000 and The extracted coastlines
are then compared.
This work forms part of a coastal
vulnerability analysis for Mozambique.
The aim is to identify coastal areas
that are susceptible to adverse effects
assocaited with climate change and
sea-level rise.

10. 1984

11. 1986

12. 1991

13. 2000

14. 2003

15.
1984
2000
1986
2003
1991
CSIR