Abstract
In the complex process of urbanization, retrieving its dynamic expansion trajectories with an efficient method is challenging, especially for urban regions that are not clearly distinguished from the surroundings in arid regions. In this study, we propose a framework for extracting spatiotemporal change information on urban disturbances. First, the urban built-up object areas in 2000 and 2020 were obtained using object-oriented segmentation method. Second, we applied LandTrendr (LT) algorithm and multiple bands/indices to extract annual spatiotemporal information. This process was implemented effectively with the support of the cloud computing platform of Earth Observation big data. The overall accuracy of time information extraction, the kappa coefficient, and average detection error were 83.76%, 0.79, and 0.57 a, respectively. These results show that Karachi expanded continuously during 2000–2020, with an average annual growth rate of 4.7%. However, this expansion was not spatiotemporally balanced. The coastal area developed quickly within a shorter duration, whereas the main newly added urban regions locate in the northern and eastern inland areas. This study demonstrated an effective framework for extract the dynamic spatiotemporal change information of urban built-up objects and substantially eliminate the salt-and-pepper effect based on pixel detection. Methods used in our study are of general promotion significance in the monitoring of other disturbances caused by natural or human activities.
Highlights
In the complex process of urbanization, retrieving its dynamic expansion trajectories with an efficient method is challenging, especially for urban regions that are not clearly distinguished from the surroundings in arid regions
With the development of remote sensing technology, time-series imagery combined with Earth Observation (EO) big data cloud computing platform can effectively solve these problems and achieve efficient and low-cost continuous monitoring of urban expansion
The United States Geological Survey has developed researchquality, application-ready science products derived from original Landsat data[13], which can be used to monitor, assess, and analyze urban expansion on the Google Earth Engine (GEE) platform
Summary
In the complex process of urbanization, retrieving its dynamic expansion trajectories with an efficient method is challenging, especially for urban regions that are not clearly distinguished from the surroundings in arid regions. We applied LandTrendr (LT) algorithm and multiple bands/indices to extract annual spatiotemporal information This process was implemented effectively with the support of the cloud computing platform of Earth Observation big data. With the development of remote sensing technology, time-series imagery combined with Earth Observation (EO) big data cloud computing platform can effectively solve these problems and achieve efficient and low-cost continuous monitoring of urban expansion. GEE is a representative remote sensing cloud computing platform that allows users to implement EO big data storage, management, and spatial analysis using Google infrastructure[14]. These advantages of cloud-based platforms provide an opportunity to monitor quickly and efficiently and continually land disturbances associated with urban expansion
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