Expansion of Urban Impervious Surfaces in Xining City Based on GEE and Landsat Time Series Data

Abstract

Urban expansion is often studied in large cities such as Beijing, Shanghai, and Guangzhou, while scant attention is paid to smaller cities such as Xining. However, Xining is the largest city on the Tibetan Plateau, and an important city in China's "Belt and Road Initiative". As its economy and society develops, Xining will play an increasingly important role in connecting the central and western regions. In order to quantify the impacts of rapid urbanization, it is extremely important to collect data on the time and space variations of impervious surfaces. As such, we collected Landsat long-term sequence data about Xining City from 1987-2019 using the random forest method, and then optimized the feature parameters to obtain the dataset. Our results demonstrated that the overall accuracy of land use classification in Xining city is 83.4% and that the urban impervious surface accuracy is 89.5%. Additionally, the overall accuracy improved by 2.4% after optimizing the characteristic parameters, while the urban impervious surface accuracy is 92.8%. In 27 of the 33 years we studied, the classification accuracy of impervious surfaces exceeded 90%. After correcting for the temporal consistency check, the accuracy of impervious surfaces improved by 2% compared to the original sequence. We analyzed the change of impervious surfaces in Xining based on the results of the final dataset and found that the impervious surface area of Xining increased from 55 km 2 in 1987 to 334 km 2 in 2019. Xining is a typical semi-open river valley city which shares spatial and temporal characteristics with other urban centers. The spatial and temporal characteristics of the expansion of urban spaces in the main urban area of Xining are obvious and are primarily spread around the central area toward tree branch shaped road, which help other cities located in river valleys better understand how urbanization progresses.

A Comparative Analysis of Index-Based Methods for Impervious Surface Mapping Using Multiseasonal Sentinel-2 Satellite Data

“…MPERVIOUS surfaces include man-made surfaces, such as roads, driveways, sidewalks, parking lots, rooftops, which prevent water from infiltrating into the soil [1]. Over time, impervious surface area (ISA) has been experiencing dramatic expansion with rapid urbanization [2], which potentially leads to negative ecological and environmental consequences, such as urban heat island [3]- [5] and waterlogging [6]. Therefore, timely and accurate ISA mapping plays a crucial role in urban planning and ecological environment assessment.…”

“…Commonly used remotely sensed data include MODIS [8], [9], Landsat [10], Sentinel-2 [11], [12], IKONOS [13], [14], to list a few. As medium-resolution images can provide reliable ISA estimations [1], 30m Landsat products have been widely used to extract and characterize the dynamics of impervious surfaces [2], [7], [9]. Nevertheless, ISA extracted from Landsat data is insufficient to describe urban biophysical components [15], [16].…”

Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data

“…Early studies focused mostly on extracting impervious surface information at local or regional scales, using medium or fine spatial resolution images [11,12]. For instance, Landsat time-series data are often used to map the impervious surface change for analyzing the impacts of urbanization or urban expansion [13][14][15]. Moreover, these small-area data were used as an evidence base to support urban planning, socio-economic, and environmental management in specific cities or local administrations [16].…”

Semantic Network-Based Impervious Surface Extraction Method for Rural-Urban Fringe From High Spatial Resolution Remote Sensing Images

“…Therefore, impervious surfaces are often used as an important indicator to measure the degree of urbanization and the urban ecological environment. The study of impervious surfaces is a great significance for monitoring urban ecological environment, hydrological environment, urban heat islands, and so forth [5]- [10].…”