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Urban land use change analysis and modelling: a case study of Setubal-Sesimbra, Portugal
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Resumo(s)
In this paper urban land use change analysis and modeling of the Concelhos of
Setúbal and Sesimbra, Portugal is accomplished using multitemporal and
multispectral satellite images acquired in the years 2000 and 2006 and other vector
datasets. The LULC maps are first obtained using an object-oriented image
classification approach with the Nearest Neighbour algorithm in Definiens.
Classification is assessed using the overall accuracy and Kappa measure of
agreement. These measures of accuracies are above minimum standard accepted
levels. The land use dynamics, both for pattern and quantities are also studied using a post classification change detection technique together with the following selected spatial/landscape metrics: class area, number of patches, edge density, largest patch index, Euclidian mean nearest neighbor distance, area weighted mean patch fractal
dimension and contagion. Urban sprawl has also been measured using Shannon
Entropy approach to describe the dispersion of land development or sprawl. Results
indicated that the study area has undergone a tremendous change in urban growth
and pattern during the study period. A Cellular Automata Markov (CA_Markov)
modeling approach has also been applied to predict urban land use change between
1990 and 2010 with two scenarios: MMU 1ha and MMU 25ha. The suitability maps
(change drivers) are calibrated with the LULC maps of 1990 and 2000 using MCE
and a contiguity filter. The maps of 1990 and 2000 are also used for the transition
probability matrix. Then, the land use maps of 2006 are simulated to compare the
result of the “prediction” with the actual land use map in that year so that further
prediction can be carried out for the year 2010. This is evaluated based on the Kappa
measure of agreement (Kno, Klocation and Kquanity) and produced a satisfactory
level of accuracy. After calibrating the model and assessing its validity, a “real”
prediction for the year 2010 is carried out. Analysis of the prediction revealed that
the rate of urban growth tends to continue and would threaten large areas that are
currently reserved for forest cover, farming lands and natural parks. Finally, the
modeling output provides a building block for successive urban planning, for
exploring how and
Descrição
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies