The Impact of Impervious Surface on Water Quality and Its Threshold in Korea

Kim, Hakkwan; Jeong, Hyunjo; Jeon, Jihye; Bae, Seung-Jong

doi:10.3390/w8040111

Cited by 50 publications

(40 citation statements)

References 16 publications

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“…Impervious surfaces are usually defined as the entirety of impermeable surfaces such as roads, buildings, parking lots, and other urban infrastructures, where water cannot infiltrate through the ground (Sun et al 2011). Urbanization results in the increase in impervious surfaces, which in turn casts great impacts on urban environmental problems such as increased urban heat islands , surface runoff , water contamination (Kim et al 2016), and air pollution (Touchaei et al 2016). Facing rapid urbanization all over the world, these environmental concerns have triggered a surge of interest in impervious surface studies.…”

Section: Introductionmentioning

confidence: 99%

Extracting Urban Impervious Surface from WorldView-2 and Airborne LiDAR Data Using 3D Convolutional Neural Networks

Sun

Zhao

et al. 2018

J Indian Soc Remote Sens

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The urban impervious surface has been recognized as a key quantifiable indicator in assessing urbanization and its environmental impacts. Adopting deep learning technologies, this study proposes an approach of three-dimensional convolutional neural networks (3D CNNs) to extract impervious surfaces from the WorldView-2 and airborne LiDAR datasets. The influences of different 3D CNN parameters on impervious surface extraction are evaluated. In an effort to reduce the limitations from single sensor data, this study also explores the synergistic use of multi-source remote sensing datasets for delineating urban impervious surfaces. Results indicate that our proposed 3D CNN approach has a great potential and better performance on impervious surface extraction, with an overall accuracy higher than 93.00% and the overall kappa value above 0.89. Compared with the commonly applied pixel-based support vector machine classifier, our proposed 3D CNN approach takes advantage not only of the pixel-level spatial and spectral information, but also of texture and feature maps through multi-scale convolutional processes, which enhance the extraction of impervious surfaces. While image analysis is facing large challenges in a rapidly developing big data era, our proposed 3D CNNs will become an effective approach for improved urban impervious surface extraction.

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Section: Introductionmentioning

confidence: 99%

Extracting Urban Impervious Surface from WorldView-2 and Airborne LiDAR Data Using 3D Convolutional Neural Networks

Sun

Zhao

et al. 2018

J Indian Soc Remote Sens

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“…The most obvious one is soil erosion, and soil erosion is also the cause of water pollution. Relevant indicators involve forest coverage [29], green percentage, the proportion of salinization area and the proportion of soil erosion area [30].…”

Section: Ecological Subsystemmentioning

confidence: 99%

Evaluation of Sustainable Use of Water Resources in the Beijing-Tianjin-Hebei Region Based on S-Type Functions and Set Pair Analysis

Men

Liu

2018

Water

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Beijing-Tianjin-Hebei is an area with insufficient per capita water resources. This study evaluates the current status and development trend of sustainable use of water resources in the region, and identifies specific factors influencing sustainable development so as to provide a theoretical basis and data support for the management of water resources in the Beijing-Tianjin-Hebei region. Applying the compound system of social, economic and ecological environment, this study established an evaluation index system. The evaluation index data is obtained through the relevant calculations based on the National Economic and Social Development Announcement, the Water Resources Bulletin and the National Bureau of Statistics data. The index weights are calculated using a combination of rough set and fuzzy theory. The obtained weights were added to the set pair analysis method to acquire evaluation results. Based on the traditional set pair analysis method, this study proposes a new set pair analysis method (Method 4) using S-type functions (Formula (11)) instead of the addition weighted synthesis method (Formula (10)) in the calculation of weighted connections. In order to verify the feasibility of this method, the Spearman correlation coefficient method was used to calculate the correlation coefficient between evaluation results of Method 4 and evaluation results of other traditional methods. In addition, the set pair exponential potential is adopted to determine the impact indicators of the sustainable utilization of water resources in Beijing-Tianjin-Hebei in this study. The results show that in the Beijing-Tianjin-Hebei region, the overall level of sustainable use of water resources has been gradually raised in the past 12 years. The results of the set pair analysis method that cites S-type functions have obtained higher Spearman correlation coefficients than traditional methods. The values of the correlation coefficients are 0.9954, 0.9910, and 0.9928 respectively in Beijing, Tianjin and Hebei. Moreover, according to the results of set pair exponential potential, the indicators in the region are quasi-inverse potential or strong inverse potential, including per capita water resources and the ecological environment water use rate. Thus, a dense population with scarce water resources, and a lack of ecological water are the common problems that Beijing, Tianjin and Hebei have to face.

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“…Impervious surface area (ISA) is not only an indicator of urbanization process, but also a key sensitive factor for monitoring environmental problems in built-up areas such as high surface runoff [2], urban heat island effect [3], transport of water pollutants [4], degradation in water quality [5,6] and air pollution [7]. ISA only accounts for a small proportion of terrestrial (TM, ETM+, and OLI-TIRS) acquired in four seasons.…”

Section: Introductionmentioning

confidence: 99%

A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery

et al. 2017

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Impervious surface area (ISA) is a key factor for monitoring urban environment and land development. Automatic mapping of impervious surfaces has attracted growing attention in recent years. Spectral built-up indices are considered promising to map ISA distributions due to their easy, parameter-free implementations. This study explores the potentials of impervious surface indices for ISA mapping from Landsat imagery using a case study area in Boston, USA. A modified normalized difference impervious surface index (MNDISI) is proposed, and a Gaussian-based automatic threshold selection method is used to identify the optimal MNDISI threshold for delineating impervious surfaces from background features. To evaluate its effectiveness, comparison analysis is conducted between MNDISI and the original NDISI using Landsat images from three sensors (TM/ETM+/OLI-TIRS) acquired in four seasons. Our results suggest that built-up indices are sensitive to image seasonality, and summer is the best time phase for ISA mapping. With reduced uncertainties from automatic threshold selection, the MNDISI extracts impervious surfaces from all Landsat images in summer with an overall accuracy higher than 87% and an overall Kappa coefficient higher than 0.74. The proposed method is superior to previous index-based ISA mapping from the enhanced thermal integration and automatic threshold selection. The ISA maps from the TM, ETM+ and OLI-TIRS images are not significantly different. With enlarged data pool when all Landsat sensors are considered and automation of threshold selection proposed in this study, the MNDISI could be an effective built-up index for rapid and automatic ISA mapping at regional and global scales.

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The Impact of Impervious Surface on Water Quality and Its Threshold in Korea

Cited by 50 publications

References 16 publications

Extracting Urban Impervious Surface from WorldView-2 and Airborne LiDAR Data Using 3D Convolutional Neural Networks

Extracting Urban Impervious Surface from WorldView-2 and Airborne LiDAR Data Using 3D Convolutional Neural Networks

Evaluation of Sustainable Use of Water Resources in the Beijing-Tianjin-Hebei Region Based on S-Type Functions and Set Pair Analysis

A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery

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