Subpixel urban impervious surface mapping: the impact of input Landsat images

Deng, Chengbin; Li, Chao‐Jun; Zhu, Zhe; Lin, Weiying; Li, Xi

doi:10.1016/j.isprsjprs.2017.09.015

Cited by 29 publications

(13 citation statements)

References 82 publications

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“…This indicates that the use of multi-seasonal SAR images can improve the classification accuracy of urban land cover because multi-season SAR can provide more abundant radar scattering information of ground objects. Deng et al also showed that urban land-cover classification can be improved by using multi-season imagery [27]. For all seasonal combinations, the F1 measure of FOR was the highest, followed by BIS and WAT, while the classification result of GRA was the poorest.…”

Section: Incremental Classification Results Using Multi-season Imagesmentioning

confidence: 98%

“…In Nanjing, the rainy season mainly occurs between March and August; from late June to early July, the climate segues into the plum-rains season, with abundant rainfall and heat. When using summer images for urban land-cover classification, water can be mistaken for urban impervious surfaces due to the spectral similarity between water and dark impervious surfaces [27]. By contrast, Nanjing's climate in winter is relatively dry with less rain.…”

Section: Analysis Of Temporal Variables Used For Classificationmentioning

confidence: 99%

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Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region

Zhou

Zhao

Sun

et al. 2017

IJGI

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The objective of this research was to investigate the impact of seasonality on urban land-cover mapping and to explore better classification accuracy by using multi-season Sentinel-1A and GF-1 wide field view (WFV) images, and the combinations of both types of images in subtropical monsoon-climate regions in Southeast China. We obtained multi-season Sentinel-1A and GF-1 WFV images, as well as the combinations of both data, by using a support vector machine (SVM) and a random forest (RF) classifier. The backscatter intensity, texture, and interference-coherence images were extracted from Sentinel-1A images, and different combinations of these Sentinel-1A-derived images were used to evaluate their ability to map urban land cover. The results showed that the performance of winter images was better than that of any other season, while the summer images performed the worst. Higher classification accuracy was achieved by using multi-season images, and satisfactory classification results were obtained when using Sentinel-1A images from only three seasons. The best classification result was achieved using a combination of all Sentinel-1A data from all four seasons and GF-1 WFV data from winter, with an overall accuracy of up to 96.02% and a kappa coefficient reaching 0.9502. The performance of textures was slightly better than that of the backscatter-intensity images. Although the coherence data performed the worst, it was still able to distinguish urban impervious surfaces well. In addition, the overall classification accuracy of RF was better than that of SVM. and is widely used in urban mapping [4][5][6][7]. However, because optical remote sensing is susceptible to the effects of cloudy and rainy weather, accurate mapping using optical images is limited. It has been demonstrated that by using all-weather, day-and-night imaging, as well as canopy penetration and high-resolution capabilities [8-10], Synthetic Aperture Radar (SAR) images effectively overcome these limitations in land-cover classification.Earlier studies that investigated LULC information via SAR data mostly used single-frequency and single-polarization images as data sources. However, the limited information derived from single-frequency and single-polarization SAR data leads to limited classification accuracy [11]. As a result of the continuous development of radar technology, ALOS-PALSAR, Terra-SAR, and RADARSAT-2 satellites were launched; some researchers used multi-frequency and multi-polarization SAR data for urban mapping [12,13]. Tan et al. [12] reported that multi-polarization achieved better classification results than single-polarization, and that HV contributed more than the other three polarizations. Pellizzeri et al. [13] used multi-temporal/multi-band SAR data for urban mapping and obtained satisfactory classification results. There are also some studies that show how the fusion of optical and SAR images improves the classification accuracy of urban LULC [14][15][16].In addition to backscatter-intensity information, some other information c...

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Section: Incremental Classification Results Using Multi-season Imagesmentioning

confidence: 98%

Section: Analysis Of Temporal Variables Used For Classificationmentioning

confidence: 99%

Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region

Zhou

Zhao

Sun

et al. 2017

IJGI

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“…Therefore, high-resolution images of the four subregions ( Figure 7) with 1.07 m resolution were selected to obtain the accurate proportions of ISA, vegetation, and soil in each pixel of the corresponding Landsat 8 images by visual interpretation (Figure 10). Third, studies show that the extraction precision of ISA is also influenced by factors such as season, and summer images are the most suitable for ISA extraction [50,51]. However, the criterion we chose for the experimental areas was large differences that exist in the geographic environment to test the performance of PISI under different circumstances, and four Chinese cities-Wuhan, Guangzhou, Shenyang, and Xining-were selected.…”

Section: Discussionmentioning

confidence: 99%

A Novel Index for Impervious Surface Area Mapping: Development and Validation

et al. 2018

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The distribution and dynamic changes in impervious surface areas (ISAs) are crucial to understanding urbanization and its impact on urban heat islands, earth surface energy balance, hydrological cycles, and biodiversity. Remotely sensed data play an essential role in ISA mapping, and numerous methods have been developed and successfully applied for ISA extraction. However, the heterogeneity of ISA spectra and the high similarity of the spectra between ISA and soil have not been effectively addressed. In this study, we selected data from the US Geological Survey (USGS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral libraries as samples and used blue and near-infrared bands as characteristic bands based on spectral analysis to propose a novel index, the perpendicular impervious surface index (PISI). Landsat 8 operational land imager data in four provincial capital cities of China (Wuhan, Shenyang, Guangzhou, and Xining) were selected as test data to examine the performance of the proposed PISI in four different environments. Threshold analysis results show that there is a significant positive correlation between PISI and the proportion of ISA, and threshold can be adjusted according to different needs with different accuracy. Furthermore, comparative analyses, which involved separability analysis and extraction precision analysis, were conducted among PISI, biophysical composition index (BCI), and normalized difference built-up index (NDBI). Results indicate that PISI is more accurate and has better separability for ISA and soil as well as ISA and vegetation in the ISA extraction than the BCI and NDBI under different conditions. The accuracy of PISI in the four cities is 94.13%, 96.50%, 89.51%, and 93.46% respectively, while BCI and NDBI showed accuracy of 77.53%, 93.49%, 78.02%, and 84.03% and 58.25%, 57.53%, 77.77%, and 64.83%, respectively. In general, the proposed PISI is a convenient index to extract ISA with higher accuracy and better separability for ISA and soil as well as ISA and vegetation. Meanwhile, as PISI only uses blue and near-infrared bands, it can be used in a wider variety of remote sensing images.

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“…For every classification test, 30 samples were used. In total, 120 random points were separated from each class for AA from 1993, 2000, 2010, and 2017 (used to determine the similarity of two images), and from Sentinel-2 satellite images for 2017 [25,55,56]. The mutual information from the error matrix was used to assess the accuracy of the supervised image classification [57].…”

Section: Accuracy Assessment and Data Validationmentioning

confidence: 99%

Expansion of Impervious Surfaces and Their Driving Forces in Highly Urbanized Cities in Kyrgyzstan

Omurakunova

Bao

et al. 2020

IJERPH

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The expansion of urban areas due to population increase and economic expansion creates demand and depletes natural resources, thereby causing land use changes in the main cities. This study focuses on land cover datasets to characterize impervious surface (urban area) expansion in select cities from 1993 to 2017, using supervised classification maximum likelihood techniques and by quantifying impervious surfaces. The results indicate an increasing trend in the impervious surface area by 35% in Bishkek, 75% in Osh, and 15% in Jalal-Abad. The overall accuracy (OA) for the image classification of two different datasets for the three cities was between 82% and 93%, and the kappa coefficients (KCs) were approximately 77% and 91%. The Landsat images with other supplementary data showed positive urban growth in all of the cities. The GDP, industrial growth, and urban population growth were driving factors of impervious surface sprawl in these cities from 1993 to 2017.Landscape Expansion Index (LEI) results also provided good evidence for the change of impervious surfaces during the study period. The results emphasize the idea of applying future planning and sustainable urban development procedures for sustainable use of natural resources and their management, which will increase life quality in urban areas and environments.

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Subpixel urban impervious surface mapping: the impact of input Landsat images

Cited by 29 publications

References 82 publications

Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region

Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region

A Novel Index for Impervious Surface Area Mapping: Development and Validation

Expansion of Impervious Surfaces and Their Driving Forces in Highly Urbanized Cities in Kyrgyzstan

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