Assessing sponge cities performance at city scale using remotely sensed LULC changes: Case study Nanjing

As a result of high-density urbanization and climate change, both the frequency and intensity of extreme urban rainfall are increasing. Drainage systems are not designed to cope with this increase, and as a result, floods are becoming more common in cities, particularly in the rapidly growing cities of China. To better cope with more frequent...

Mapping and quantifying the human-environment interactions in middle Egypt using machine learning and satellite data fusion techniques

Population growth in rural areas of Egypt is rapidly transforming the landscape. New cities are appearing in desert areas while existing cities and villages within the Nile floodplain are growing and pushing agricultural areas into the desert. To enable control and planning of the urban transformation, these rapid changes need to be mapped...

Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion

Invasive Spartina alterniflora (S. alterniflora), a native riparian species in the U.S. Gulf of Mexico, has led to serious degradation to the ecosystem and biodiversity as well as economic losses since it was introduced to China in 1979. Although multi-temporal remote sensing offers unique capability to monitor S. alterniflora over large...

On the accuracy of automated shoreline detection derived from satellite imagery: A case study of the Sand Motor mega-scale nourishment

Measured trends and variability in shoreline position are used by coastal managers, scientists and engineers to understand and monitor coastal systems. This paper presents a new and generic method for automated shoreline detection from the largely unexplored collection of publicly available satellite imagery. The position of the obtained...