The information on urban land cover distribution and its dynamics is useful for understanding urbanization and its impacts on the hydrological cycle, water management, surface energy balances, urban heat island, and b...The information on urban land cover distribution and its dynamics is useful for understanding urbanization and its impacts on the hydrological cycle, water management, surface energy balances, urban heat island, and biodiversity. This study utilizes machine learning, texture variables and spectral bands to quantify the urban growth annually. We used multi-temporal Landsat satellite image sets from 2007 to 2016 and Random Forest classification to map urban land-use in Dar es Salaam. We also applied Annual classification approach to detect the spatiotemporal patterns of urban areas. This approach improved classification accuracy and aided in understanding the urban land-use system dynamics operating in our study area. The results pointed out that, the total built-up areas have grown from 318 km2, 388.6 km2 and 634.7 km2 in 2007, 2012 and 2016 respectively. The built up areas growth rate is almost 8%, which makes Dar es Salaam be among the fastest growing cities in Africa. The results indicate that, combining spectral bands, texture variables (NDVI BCI, MNDWI) and annual classification map approach was sufficient to map the urban areas. The approach applied in this research provides a useful guide to the urban growth studies and may also serve as a tool for land management planners.展开更多
文摘The information on urban land cover distribution and its dynamics is useful for understanding urbanization and its impacts on the hydrological cycle, water management, surface energy balances, urban heat island, and biodiversity. This study utilizes machine learning, texture variables and spectral bands to quantify the urban growth annually. We used multi-temporal Landsat satellite image sets from 2007 to 2016 and Random Forest classification to map urban land-use in Dar es Salaam. We also applied Annual classification approach to detect the spatiotemporal patterns of urban areas. This approach improved classification accuracy and aided in understanding the urban land-use system dynamics operating in our study area. The results pointed out that, the total built-up areas have grown from 318 km2, 388.6 km2 and 634.7 km2 in 2007, 2012 and 2016 respectively. The built up areas growth rate is almost 8%, which makes Dar es Salaam be among the fastest growing cities in Africa. The results indicate that, combining spectral bands, texture variables (NDVI BCI, MNDWI) and annual classification map approach was sufficient to map the urban areas. The approach applied in this research provides a useful guide to the urban growth studies and may also serve as a tool for land management planners.
