Extracting coastlines from Remote Sensing imagery An Object Oriented methodology using Landsat TM and ETM+ data Maputo Bay Mozambique 1984 - 2003 Copyright @ CSIR 2009 www.csir.co.za

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 1984. 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

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.

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

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

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

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

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

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 2003. The Object Oriented approach described above was applied to each scene 1984, 1986, 1991, 2000 and 2003. 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.

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1986

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2003

1984-2003 1984 2000 1986 2003 1991 Copyright @ CSIR 2009 www.csir.co.za