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• A spatio-temporal urban expansion modeling a case study Teheran metropolis, Iran

  10-19

  Sassan Mohammady

  Views:

  131

  During the past decades, urban growth has been accelerating with the massive immigration of population to cities. Urban population in the world was estimated as 2.9 billion in 2000 and predicted to reach 5.0 billion in 2030. Rapid urbanization and population growth have been a common phenomenon, especially in the developing countries such as Iran. Rapid population growth, environmental changes and improper land use planning practices in the past decades have resulted in environmental deterioration, haphazard landscape development and stress on the ecosystem structure, housing shortages, insufficient infrastructure, and increasing urban climatological and ecological problems. In this study, urban sprawl assessment was implemented using Shannon entropy and then, Artificial Neural Network (ANN) has been adopted for modeling urban growth. Our case study is Tehran Metropolis, capital of Iran. Landsat imageries acquired in 1988, 1999 and 2010 are used. According to the results of sprawl assessment for this city, this city has experienced sprawl between 1988 to 2010. Dataset include distance to roads, distance to green spaces, distance to developed area, slope, number of urban cells in a 3 by 3 neighborhood, distance to fault and elevation. Relative operating characteristic (ROC) method have been used to evaluate the accuracy and performance of the model. The obtained ROC equal to 0.8366.

• Urban vegetation classification with high-resolution PlanetScope and SkySat multispectral imagery

  66-75

  Loránd Szabó

  Dávid Abriha

  Kwanele Phinzi

  Szilárd Szabó

  Views:

  619

  In this study two high-resolution satellite imagery, the PlanetScope, and SkySat were compared based on their classification capabilities of urban vegetation. During the research, we applied Random Forest and Support Vector Machine classification methods at a study area, center of Rome, Italy. We performed the classifications based on the spectral bands, then we involved the NDVI index, too. We evaluated the classification performance of the classifiers using different sets of input data with ROC curves and AUC values. Additional statistical analyses were applied to reveal the correlation structure of the satellite bands and the NDVI and General Linear Modeling to evaluate the AUC of different models. Although different classification methods did not result in significantly differing outcomes (AUC values between 0.96 and 0.99), SVM’s performance was better. The contribution of NDVI resulted in significantly higher AUC values. SkySat’s bands provided slightly better input data related to PlanetScope but the difference was minimal (~3%); accordingly, both satellites ensured excellent classification results.