Spatiotemporal Patterns of Impervious Surface Area and Water Quality Response in the Fuxian Lake Watershed

Abstract

The increase of urbanization level has led to the rapid increase of impervious surface area (ISA). The aim of this work is to clarify the relationship between the ISA and water quality and lay a foundation for the improvement and protection of the water quality in the basin. Taking the Fuxian Lake Basin in Yunnan Province as an example, based on the Landsat ETM+ remote sensing image and the Gram–Schmidt (GS) image fusion algorithm, the four-terminal model and the linear spectral mixture model (LSMM) were used to extract the impervious surface of the watershed from 2006 to 2015. And statistical methods were used to distinguish its relationship with water quality. The results show that the four-terminal model and the linear spectral mixture model can effectively extract the impervious surface information of the Fuxian Lake Basin. The average root mean square error (RMS) of the image decomposition results from 2006 to 2015 was less than 0.02. In the past 10 years, the ISA has changed significantly in the Fuxian Lake Basin. The ISA showed an overall upward trend from 2006 to 2015. It increased from 24.73 km2 in 2006 to 35.14 km2 in 2015, an increase of 10.81 km2. From the value anomaly, the ISA in 2006 and 2009 is lower than the multiyear average, and those in the other years are higher than the multiyear average. The percentage of ISA in the basin was significantly positively correlated with Chemical Oxygen Demand-Mn (CODMn) and total phosphorus (TP) (r is 0.772, 0.763), and the correlation in the flooding season was greater than that in the dry season. The ISA threshold for water quality deterioration is around 10% in the Fuxian Lake Basin. Reducing ISA coverage, controlling ISA to less than 10%, and preventing nonpoint source pollution during flooding season will be the best measures to effectively improve the water quality environment in the basin.

Investigation of long‐term river water quality variations using different urbanization indices and assessment of common scientific perspectives of urbanization on water quality

“…The built area is classified as buildings, rooftops, tarred roads, pavements, driveways, parking lots, stormwater drains, swimming pools, and sidewalks (Musakwa & Van Niekerk, 2014). This urbanization index is very comparable to the impervious surface area percentage which is defined as the ratio of impervious surface area to the total surface area of the catchment and has been used previously to study the dynamics between urban development and ecological conditions of streams and rivers (Kim, Jeong, Jeon, & Bae, 2016; Luo et al., 2018; Li, Hong, Jin, Zhou, & Peng, 2020). The built area fractions in three specifically chosen periods of time with similar time gaps (1988–1990, 1998–2000, and 2015–2017) were calculated using satellite images and survey maps issued by the Lands Department of Hong Kong.…”

Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers

“…ese surfaces prevent the absorption of water into the soil. Previous works have pointed out the impact of impervious surface areas on water quality and the frequency/intensity of downstream runoff [1][2][3][4][5]. erefore, they have been identified as a key indicator used in evaluating urbanization influences on surrounding natural environment and ecosystem [6].…”