Seasonal multitemporal land-cover classification and change detection analysis of Bochum, Germany, using multitemporal Landsat TM data

The 40-year Landsat time series makes it possible to continuously map and examine land-cover changes. By using images from two dates in each classification year, we can improve the classification accuracy of monotemporal approaches for each year and reduce the misclassification problem between bare lands or impervious surface and vegetation cover types. Two pairs of multitemporal cloud-free Landsat Thematic Mapper images the first pair from 1 May and 9 June 1986 and the second from 4 June 2010 and 20 April 2011 were selected from the area of the city of Bochum, Germany. The multitemporal image sets were classified separately using the maximum likelihood classification algorithm. The overall accuracies of the monotemporal classifications for 1 May and 9 June 1986 were, respectively, 77.1 and 75.4% while the overall accuracy of the multitemporal classification for 1986 was 82.1%. The overall accuracies of the monotemporal classification for 4 June 2010 and 20 April 2011 were, respectively, 81.4 and 77.9%, while the overall accuracy of the multitemporal classification for 2010/2011 was 88.2%. Post-classification comparison change detection was used to determine change in land-cover type. The proportion of urban area increased from 55.3 to 61.1% for the whole area, while that of agricultural land decreased from 24.8 to 21.8% and bare land from 3.6 to 0.2%. Forest and water bodies remained almost unchanged between 1986 and 2011.