Impervious surface extraction based on different methods from multiple spatial resolution images: a comprehensive comparison

Feng, Shanshan; Fan, Fenglei

doi:10.1080/17538947.2021.1936227

Cited by 22 publications

(7 citation statements)

References 75 publications

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“…The PISI (Perpendicular Impervious Surface Index) [1] was developed to extract ISA from Landsat images. The formula for PISI can be expressed as follows: PISI = 0.8192bBlue − 0.5735bNIR + 0.075 (2) where bBlue and bNIR are the reflectance of the Blue and NIR wavelengths, respectively. The values of ISA indices all range from -1 to +1.…”

Section: Calculate Pisi Indexmentioning

confidence: 99%

“…In order find the solution to manage and control the change, remote sensing technique by time-series satellite data was used. There are many indexes to calculate impervious surface from satellite data such as Biophysical Composition Index (BCI), Normalized Built-Up Area Index (NBAI), Combinational Build-Up Index (CBI), Perpendicular Impervious Surface Index (PISI), where the PISI index gives the best result [1] [2]. Therefore, the purpose of this study is to analyse of changes of impervious surface area (ISA) in Ho Chi Minh City and Ba Ria -Vung Tau province in the period of 2009-2020 by calculating the PISI index with Landsat satellite image data.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of changes of impervious surface area in Ho Chi Minh City and Ba Ria - Vung Tau province

Truong¹,

Pham²,

Dang³

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Rapid urban growth causes the increase of impervious surface which was converted from vegetation land. This affects the rate of water penetration into soils, the rise of surface runoff, and the lowering of groundwater levels. The expansion of the impervious surface gives rise to a series of changes in the physical environment, affecting the quality of life of people due to a change in the thermal energy balance. Therefore, the purpose of this study is to analyse of changes of impervious surface area Ho Chi Minh City and Ba Ria - Vung Tau province which is the marine national economic hub in the period of 2009-2020 by using Landsat satellite image data. The main methods used are NDWI, PISI indices and ISODATA algorithm. The results show that the area of impervious surface area of Ho Chi Minh City is very high with 18.5% in 2009 and 35% increasing rate from 2009-2020. There are 75% districts in Ho Chi Minh city having over 50% impervious surface area. Ba Ria-Vung Tau has lower impervious surface area with 5.8% in 2009 but having the incredible increasing rate– over 100%.

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Section: Calculate Pisi Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of changes of impervious surface area in Ho Chi Minh City and Ba Ria - Vung Tau province

Truong¹,

Pham²,

Dang³

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…The Trimble eCognition software provides various sets of feature options; besides image statistics, many remote sensing indices, including the normalized difference water index (NDWI) and normalized difference vegetation index (NDVI), are an option that can improve the classification results. Moreover, the perpendicular impervious surface index (PISI) is used as a feature in the OBIA method because of its good performance in mapping impervious surfaces in high spatial resolution images with great accuracy [28]. Experiments were performed during the feature selection step to determine the appropriate statistics, and the most widely used features including the shape and texture (derived from the grey-level cooccurrence matrices (GLCM)) were also extracted, including the homogeneity, dissimilarity, entropy, and correlation, while spectral statistics (mean value and brightness) were defined for the classifier.…”

Section: Feature Selectionmentioning

confidence: 99%

“…This method can effectively reduce the spectral variation present in a variety of land cover types, thereby improving the precision of classification [23][24][25]. Numerous investigations have indicated that the OBIA method exhibits high efficiency and precision in revealing the distribution of impervious surfaces [26][27][28]. The nearest neighbor (NN) classifier is popular when performing the OBIA method; however, the NN classifier may be less effective with high-dimensional data due to issues related to feature correlation [29,30].…”

Section: Introductionmentioning

confidence: 99%

Changes in Impervious Surfaces in Lhasa City, a Historical City on the Qinghai–Tibet Plateau

2023

Self Cite

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Impervious surface cover reflects the urban environment and urban expansion. Lhasa City is a historical city and one of the most populous on the Qinghai–Tibetan Plateau, and has been experiencing rapid urbanization in recent years. Analyzing the impervious surface distribution can reveal urban development characteristics and provide data for sustainable urban planning to protect the heritage. This study explored the spatial and temporal changes and expansion patterns of impervious surfaces in different zones of Lhasa City. Impervious surface maps (2014 and 2021) were extracted from Gaofen-1 images with a high spatial resolution (2 m) using an object-based image analysis method. Next, a gravity center, standard deviational ellipses and landscape indices were used to characterize impervious surface expansions in different zones. The result indicated that the impervious surface in Lhasa expanded from 51.149 km2 in 2014 to 63.299 km2 in 2021. The growth rates of impervious surfaces inside the Environmental Coordination zone were lower than in the zones outside. From 2014 to 2021, the impervious surface of Lhasa expanded in the southeast direction. Infilling and consolidation were the primary impervious surface development patterns. The expansion of the impervious surface was related to topography, population, and economic and policy factors.

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“…The low quality of the image caused noise and ambiguous spectral responses that complicate the SVM algorithm to distinguish between two classes. On the other hand, the overall misclassification occurred also due to a similar spectral response between built-up areas and bare soil, especially dry soil [81,82]. A wide area of impervious surfaces such as parking lots, which are categorized as a non built-up areas, also caused an error [83].…”

Section: Transformation Of the Built-up Areamentioning

confidence: 99%

Exploring Built-Up Indices and Machine Learning Regressions for Multi-Temporal Building Density Monitoring Based on Landsat Series

Suharyadi¹,

Umarhadi²,

Awanda³

et al. 2022

Sensors

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Uncontrolled built-up area expansion and building densification could bring some detrimental problems in social and economic aspects such as social inequality, urban heat islands, and disturbance in urban environments. This study monitored multi-decadal building density (1991–2019) in the Yogyakarta urban area, Indonesia consisting of two stages, i.e., built-up area classification and building density estimation, therefore, both built-up expansion and the densification were quantified. Multi sensors of the Landsat series including Landsat 5, 7, and 8 were utilized with some prior corrections to harmonize the reflectance values. A support vector machine (SVM) classifier was used to distinguish between built-up and non built-up areas. Regression algorithms, i.e., linear regression (LR), support vector regression (SVR), and random forest regression (RFR) were explored to obtain the best model to estimate building density using the inputs of built-up indices: Urban Index (UI), Normalized Difference Built-up Index (NDBI), Index-based Built-up Index (IBI), and NIR-based built-up index based on the red (VrNIR-BI) and green band (VgNIR-BI). The best models were revealed by SVR with the inputs of UI-NDBI-IBI and LR with a single predictor of UI, for Landsat 8 (2013–2019) and Landsat 5/7 (1991–2009), respectively, using separate training samples. We found that machine learning regressions (SVM and RF) could perform best when the sample size is abundant, whereas LR could predict better for a limited sample size if a linear positive relationship was identified between the predictor(s) and building density. We conclude that expansion in the study area occurred first, followed by rapid building development in the subsequent years leading to an increase in building density.

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Impervious surface extraction based on different methods from multiple spatial resolution images: a comprehensive comparison

Cited by 22 publications

References 75 publications

Analysis of changes of impervious surface area in Ho Chi Minh City and Ba Ria - Vung Tau province

Analysis of changes of impervious surface area in Ho Chi Minh City and Ba Ria - Vung Tau province

Changes in Impervious Surfaces in Lhasa City, a Historical City on the Qinghai–Tibet Plateau

Exploring Built-Up Indices and Machine Learning Regressions for Multi-Temporal Building Density Monitoring Based on Landsat Series

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