Cities are regions in which concentrated human activities occur, and because cities represent an important point of contact between natural and socio-economic systems, unsustainable urban development is creating serious ecological and environmental problems. Given that cities exchange materials and energy with their surrounding environment, whether cities can simulate the same processes in natural systems and whether the theoretical insights gained from natural systems can provide similar insights that would let urban managers mitigate the environmental problems of these hybrids of artificial and natural systems (Zhang et al., 2015).
Rapid economic development and population growth have both contributed to an unprecedented urban land expansion rate (Tayyebi et al., 2014), which further have boosted the economy and rural-urban migrant due to enhanced employment and incomes, educational opportunities, and living standards in urban areas (Liu and Li, 2017). Sustainable urban development was necessary among diverse benefits in multiple aspects, including the economy, society, and ecology (Fan and Qi, 2010; Tayyebi et al., 2016). Thus, appropriately managing the relationships between human activities and environmental change is critical if we can the sustainable development of urban systems. The driving factors, pressures, states, impacts, and responses framework proved effective for simplifying and manage the interactions between humans and the environment. Remote sensing (RS) techniques have widely applied to investigate land-use change as an enable rapid, accurate, recurrent, and consistent observations to be made at large scales throughout all times of the day (Liu et al., 2016; Tayyebi and Pijanowski, 2014). RS based available methods for monitoring urban land-use changes generally include visual interpretation and object-based approaches that use high-spatial-resolution images (Qian et al., 2015; Yu et al., 2016), and spectral index methods that use multispectral images (Estoque and Murayama, 2015). It is crucial to integrate the multi-dimensional spatial and spectral features of RS images if we are to acquire highly accurate spatiotemporal information on urban land expansion.
Traditional urban expansion research works mainly depend on historical statistical data, which is time-consuming and high-cost but shows low accuracy. Nowadays, remotely sensed imagery has become the primary data source for detecting the urban expansion process. The application of remote sensing on urban expansion becomes increasingly extensive with the monitoring scale ranging from small to large, monitoring frequency ranging from low to high, monitoring period ranging from short to long, data source ranging (Zhang et al., 2018).
A large number of studies have been reported by researchers to identify and monitor the changes in urban impervious surface by applying digital image analysis techniques (Deng et al., 2012; Sugg et al., 2014; Weng, 2012) used multi-temporal LANDSAT TM/ETM+ images for extraction and assessment of impervious surface areas using the spectral unmixing method for Pearl River Delta of China and concluded in their work that multi-temporal satellite images are a very useful database for impervious surface area estimation and can be used for water management in urban areas (Sarkar Chaudhuri et al., 2017).
Maimaiti et al (2017) analyzed the urban expansion characteristics of Korla City for the period 1995–2015 by using Landsat TM/ETM. Urban land-use types were classified using the supervised classification method in ENVI 4.5. Urban expansion indices, such as expansion area, expansion proportion, expansion speed, expansion intensity, compactness, and fractal dimension, were calculated. The results indicated that, over the past 25 years, the area and proportion of urban land increased substantially with an average annual growth rate of 15.18%.
Zubair et al (2019) classified SPOT (Satellite Pour observation de la Terre) satellite data of the Creek Missouri River sub-watershed using the supervised maximum likelihood classification method to assess the loss of prairies and croplands due to urban expansion in six sub-watersheds in the Kansas City metropolitan area of the States of Kansas and Missouri in the U.S. B Results from the 22-year study revealed that in all six sub-watersheds, croplands and grassland were depleted at alarming rates with no sustainable effort to conserve them.
Xin et al (2017) proposed a new calibration method for Defense Meteorological Satellite Program’s Operational Line-scan System (DMSP/OLS) data based on the Rational Function Model (RFM). Stable lit pixels were taken on to validate the effectiveness of the model. The deference of mean square error shows that RFM method is better than traditional quadratic polynomials method for 76% of the data. Urban areas from 1992 to 2013 were extracted based on the calibrated data. Correlation analysis and multiple linear regression analysis between socio-economic factors and DMSP/OLS data were performed for Wuhan (China). The results of the correlation coefficient between night-time lights and socio-economic factors were higher than 0.85. Zou et al (2017) used a neighborhood statistics analysis (NSA) method and a local-optimized threshold method was used to detect urban changes from 1992–2011 from the time-series DMSP/OLS nighttime light (NTL) images in the Middle Reaches of the Yangtze River, China. The results revealed that urban areas extracted from the NTL data were consistent with those extracted from the Landsat Thematic Mapper data, with an overall accuracy of 81.74% and a Kappa of 0.40. DMSP/OLS data offers stable NTL imagery with a good opportunity for characterizing the extent and dynamics of urban development at the global and regional scales.
Li et al (2016) adopted the methods of Mann-Kendall and linear regression to analyze urban dynamics from time series Vegetation Adjusted NTL Urban Index (VANUI) data from 1992 to 2013 in the Southeast United States of America (U.S.A.), which is one of the fastest-growing regions in the nation. The newly built urban areas were effectively detected based on the trend analysis. Besides, the VANUI-derived urban areas with an optimal threshold method were found highly consistent with the Landsat-derived National Land Cover Database. The total urbanized areas in large metropolitan areas in the southeastern U.S.A. increased from 8524 km2 in 1992 to 14,684 km2 in 2010, accounting for 5% and 9% of the total area, respectively. The results further showed that urban expansion in the region cannot be purely explained by population growth. The results suggested that the VANUI time series provided an effective method for characterizing the spatiotemporal dynamics of the urban extent at the regional scale.