Assessment of urban growth dynamics and threat to urban sustainability in Bahawalpur city: using google earth engine and machine learning
摘要
The study highlights the threats to urban sustainability shaped by the varying trends of urban growth dynamics. This understanding is helpful to mitigate the hostile impacts of urban development on urban sustainability. Hence, the research is focused to examine the varying trends of urban growth dynamics using Google Earth Engine (GEE) and machine learning classifiers. The urban growth was analyzed on the basis of variation in built-up land, vegetal cover, waterbody, and barren land from 1994 to 2024. Historical demographic profile of the city, historical google earth imagery and local knowledge of the study area were also a key source of information to support the results of supervised machine learning classifiers. The results of accuracy assessment confirmed that support vector machine performed better than other classifiers. The results of support vector machine classifier exhibit constant increase in built-up area. It showed a trend of progressive increase at the expense of degradation in vegetation and barren land. The built-up land was increased from 67 to 99 km2 from 1994 to 2024. The value of correlation heatmap (+ 0.98) also evident of strong correlation between built-up area and the increase in the population of the city. This strong correlation advocates the varying trends of urban growth dynamics which effects the urban sustainability of the area. The outputs of the research highlight the need of sustainable urban growth to support sustainable and resilient urban environment.