Impervious Surface Mapping Research Articles

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

Studies have shown that Sentinel-2 images have advantages over Landsat images in impervious surface area (ISA) extraction. The performance of index-based methods can be affected by different binary methods and subject to seasonal variation. This study marks the first attempt to assess the performance of different spectral indices for ISA extraction using multiseasonal Sentinel-2 images. Specifically, five indices (i.e., the biophysical composition index calculated using the Gram-Schmidt orthogonalization method, biophysical composition index calculated using a principal component-based Procrustes analysis, Normalized Built-up Area Index (NBAI), combinational build-up index, and perpendicular impervious surface index (PISI)) and three impervious surface binary methods (i.e., Otsu's method, manual method, and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) classification method) were tested on multi-seasonal Sentinel-2 images in the main urban area of Wuhan, China. Results showed that PISI combined with the ISODATA classification method achieved the highest accuracy with 92.64% OA, and 0.8410 Kappa coefficient, and NBAI combined with Otsu's method achieved the lowest accuracy with 35.37% OA, and 0.013 Kappa coefficient. Regarding the seasonal sensitivity, PISI is relatively more stable than the other methods. The superior performance of PISI is largely due to its capability in separating ISA from soil and vegetation. In addition, summer is the best season to map ISA from Sentinel-2 images when the impervious surface is generally less confused with bare soil. This study serves as a reference for the selection of spectral indices for ISA extraction from Sentinel-2 images in relation to binary methods and seasonal effects.

Mapping Impervious Surface Distribution and Dynamics in an Arid/Semiarid Area-A Case Study in Ordos, China

The impervious surface area (ISA) has become an important parameter in the areas such as surface runoff, climate change, and socioeconomic analysis. However, using remote sensing data to accurately map ISA is still difficult due to its complexity in the arid and semi-arid areas with large amounts of bare soil, sparse vegetation, and a small ISA. In this study, we have selected Ordos city as study area and examined the spatial pattern and dynamics in the years of 1990, 1995, 2000, 2005, 2010, 2015, and 2019 by using multiple data sources with a combination method. The results indicate that the ISAs in 2010, 2015, and 2019 were extracted with overall accuracies of 90.6%, 89.2%, and 91.8% with kappa coefficients of 0.79, 0.76, and 0.82, respectively. The ISA extraction results of the Urban Index (UI), Normalized Difference Built-Up Index (NDBI), Index-based Built-Up Index (IBI), and Impervious Built-up Index from optical sensors (IBUIopt) were used for comparison. Meanwhile, a correlation analysis between socioeconomic factors and the ISA of the typical resource-based city of Ordos was conducted. The spatial pattern and dynamics of the ISA data in Ordos city are of great significance to the study of socioeconomic and environmental changes and provide a reference to examine the effect of ISAs in other resource-based cities.

Day and night synergy to improve subpixel urban impervious surface mapping in desert environments at 30-m Landsat resolution

ABSTRACT Few studies provide effective guidance for detecting subpixel urban impervious surface in desert environments. Such an environmental setting is substantially different from the study areas of most existing studies, especially with complicated desert landforms. To improve the accuracy of subpixel mapping of urban impervious surface in desert environments, an integrative approach is proposed to estimate fractional urban impervious surface at the 30-m resolution. This is done by the synergistic use of open-source datasets taken from day and night that characterize different aspects of human activities at a different time of a day. We carefully analyse the performance of three methods with model input combinations from different sources, i.e. the day and night image synergy, daytime images only, and georectified night-time International Space Station (ISS) photographs only. Three major findings are concluded from the analyses. First, the collective usage of remote sensing images derived from day and night yields reasonable results in all desert cities and outperforms any single-source data for subpixel urban impervious surface mapping. Second, among all the input features from multiple data sources, night light variables have a higher contribution than other variables of daytime images for subpixel urban impervious surface mapping in desert cities, regardless of lighting types. Third, since desert cities remain understudied in previous studies, the proposed synergy method fills this gap in the literature. This data fusion of multi-source remotely sensed data is promising, which can be employed for better subpixel urban mapping in support of various environmental and socioeconomic applications in the future.

Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform

Abstract. The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. The use of remote sensing techniques is the only means of accurately carrying out global mapping of impervious surfaces covering large areas. Optical imagery can capture surface reflectance characteristics, while synthetic-aperture radar (SAR) images can be used to provide information on the structure and dielectric properties of surface materials. In addition, nighttime light (NTL) imagery can detect the intensity of human activity and thus provide important a priori probabilities of the occurrence of impervious surfaces. In this study, we aimed to generate an accurate global impervious surface map at a resolution of 30 m for 2015 by combining Landsat 8 Operational Land Image (OLI) optical images, Sentinel-1 SAR images and Visible Infrared Imaging Radiometer Suite (VIIRS) NTL images based on the Google Earth Engine (GEE) platform. First, the global impervious and nonimpervious training samples were automatically derived by combining the GlobeLand30 land-cover product with VIIRS NTL and MODIS enhanced vegetation index (EVI) imagery. Then, the local adaptive random forest classifiers, allowing for a regional adjustment of the classification parameters to take into account the regional characteristics, were trained and used to generate regional impervious surface maps for each 5∘×5∘ geographical grid using local training samples and multisource and multitemporal imagery. Finally, a global impervious surface map, produced by mosaicking numerous 5∘×5∘ regional maps, was validated by interpretation samples and then compared with five existing impervious products (GlobeLand30, FROM-GLC, NUACI, HBASE and GHSL). The results indicated that the global impervious surface map produced using the proposed multisource, multitemporal random forest classification (MSMT_RF) method was the most accurate of the maps, having an overall accuracy of 95.1 % and kappa coefficient (one of the most commonly used statistics to test interrater reliability; Olofsson et al., 2014) of 0.898 as against 85.6 % and 0.695 for NUACI, 89.6 % and 0.780 for FROM-GLC, 90.3 % and 0.794 for GHSL, 88.4 % and 0.753 for GlobeLand30, and 88.0 % and 0.745 for HBASE using all 15 regional validation data. Therefore, it is concluded that a global 30 m impervious surface map can accurately and efficiently be generated by the proposed MSMT_RF method based on the GEE platform. The global impervious surface map generated in this paper is available at https://doi.org/10.5281/zenodo.3505079 (Zhang and Liu, 2019).

Impervious Surface Expansion: A Key Indicator for Environment and Urban Agglomeration—A Case Study of Guangdong-Hong Kong-Macao Greater Bay Area by Using Landsat Data

Impervious surface (IS) is a key indicator to measure the urbanization process and ecological environment. Many studies have observed an urbanization process based on IS at the city scale. Understanding the changes in the IS over a period at a regional level offers an alternative and effective approach to characterize and quantify the spatial process of urban agglomeration. This study focuses on the urban agglomeration of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) by utilizing the sensor-based Landsat data during 1987-2017 and investigates the spatiotemporal distribution of IS expansion at both regional and city scales. The modified linear spectral mixture analysis (MLSMA) method is used to extract the IS of the GBA. Then, the IS mapping accuracies were assessed after comparison with high-resolution historical data. The spatiotemporal and directional changes of IS surfaces for GBA are analyzed by using Gravity Center (GC) and Standard Deviational Ellipse (SDE). Finally, Shannon’s Diversity Index (SHDI) is used to analyze the overall characteristics of landscape level, and the Patch Density (PD) and Landscape Shape Index (LSI) are used to describe the characteristics of different classes of the IS. The results show that the IS of the whole region experienced rapid and massive expansion during the past 30 years and exhibited a distinct characteristic along the Pearl River Delta (PRD) and the coastline. Furthermore, the IS area increased rapidly in the PRD, while it is relatively stable in Hong Kong and Macao. We believe that the findings of this study can help policy makers to better understand and maintain the sustainable development of the GBA.

Mapping Impervious Surfaces Precisely—a GIS-Based Methodology Combining Vector Data and High-Resolution Airborne Imagery

Impervious surfaces are fragments of the earth where the natural soil function has been altered considerably, for instance, regarding the infiltration of water. The methodology proposed in this study combines vector datasets and aerial imagery to map impervious areas precisely with a spatial resolution of 10 cm and focuses on the practicality and simplicity of the approach. The study is carried out in the German city of Mannheim. Vegetation is classified by NDVI calculation from airborne imagery, while vector data, based on municipal cadastres and OpenStreetMap, is used to superimpose impervious surfaces, such as buildings and traffic areas. Only data that is usually available and free of charge to municipal administrations is used to ensure the method’s repeatability. This data combination is proposed to benefit from the individual advantages of each data source. The uncertainties that arise are discussed, emphasizing areas that are particularly challenging to analyse. According to an extensive validation, the achieved results of the proposed method are valid with a correctness of 93%. Therefore, the proposed methodology can be suggested for statistical comparison purposes as a standardized practise.

Mapping impervious surface distribution in China using multi-source remotely sensed data

ABSTRACTImpervious surface area (ISA) data are required for such studies as urban environmental modeling, hydrological modeling, and socioeconomic analysis, but updating these datasets in a large area remains a challenge due to the complex urban landscapes consisting of different materials and colors with various spatial patterns. This research explores the integration of multi-source remotely sensed data for mapping China’s ISA distribution at 30-m spatial resolution. The integration of Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were used to extract initial ISA with spatial resolution of 250 m using a thresholding approach. The Landsat-derived NDVI and Modified Normalized Difference Water Index (MNDWI) were used to remove vegetation and water areas from the mixed pixels that existed in the initial ISA data. The spectral signatures of these ISA data were further extracted from Landsat multispectral images and used to refine the ISA data using expert knowledge. The results indicate that the integration of multi-source data can successfully map ISA distribution with 30-m spatial resolution in China with producer’s and user’s accuracies of 83.1 and 91.9%, respectively. These ISA data are valuable for better management of urban landscapes and for use as an input in other studies such as socioeconomic and environmental modeling.

Automated detection of impervious surfaces using night-time light and Landsat images based on an iterative classification framework

ABSTRACTA novel workflow for automated detecting of impervious surface by using night-time light and Landsat images at the individual city scale is proposed. This approach is composed by of three steps. In the beginning, urban, peri-urban and rural regions are detected from the night-time light image by a contour line algorithm. Then, using Landsat TM image, region-specific spectral index analysis is employed to generate initial training samples of urban land covers. Finally, an iterative classification framework is applied to select new training samples by integrating spectral and spatial information and to obtain the final mapping result. Experimental results of two cities show that the proposed method produces higher classification accuracy than the ones using the manual-sampling methods. Moreover, further validations suggested that the spatial information is able to effectively increase the producer’s accuracy of impervious surface. This automated approach is potentially important for large-scale regional impervious surface mapping and application.

Extracting impervious surfaces from full polarimetric SAR images in different urban areas

ABSTRACTAccurate mapping of impervious surface in urban areas is of great demand in environmental and socio-economic studies since impervious surface growth is recognized as an indicator of urbanization. To demonstrate the potential of full polarimetric Synthetic Aperture Radar (SAR) in impervious surface detection in different urban areas, this study focused on the exploitation of only SAR data. Three cities with different levels of urbanization – Tehran, Kordkuy, and Arak – have been selected to reduce the effect of input data on achieved results. Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) images have been classified by support vector machine (SVM) with the help of training data from high-resolution satellite images. Quantitative assessment of classification accuracy revealed that Kordkuy, a not fully developed city (i.e. 84.2%) has the lowest accuracy and Arak, a medium urbanized city, has the highest accuracy (i.e. 90.0%). To further explore the efficiency of full polarimetric SAR, grey level co-occurrence matrix (GLCM) texture of polarized bands has been extracted and put into the classification procedure. The texture information of SAR data provided positive contribution to the impervious surface estimation in three study cases. The improvement is especially noted in dark impervious surface class. All three study areas show an increase of about 6–8% in classification accuracy. The results prove that single use of full polarimetric SAR images holds high potential in identifying impervious surfaces in urban areas. The findings are of great importance in frequent urban impervious surface mapping and monitoring especially in cloud-prone area, where the use of optical data as well as the fusion of optic and SAR data are limited.

URBAN IMPERVIOUS SURFACE EXTRACTION BASED ON REMOTE SENSING IMAGES

Abstract. Urban planning and constructions affect spatial patterns of urban impervious surfaces, which in turn modify the urban environment and affect human-environment interactions. Impervious surfaces can redistribute precipitation patterns, and the perviousness–imperviousness ratio is considered as one important indicator for assessing the degree of urbanization and the quality of urban eco-environment. The spatial distribution and dynamics of impervious surfaces contribute to better understand urbanization and its impacts on regional or urban hydrological environment, surface temperature balance and biodiversity, etc. Hengqin new area is located in Hengqin island, south of Zhuhai city, adjacent to Hong Kong and Macao. It was officially established as a free trade zone in 2009. Due to the rapid development of Hengqin in recent years, this paper discusses Landsat8 imagery of time series in mapping impervious surfaces, and analysis the changes of impervious surface in Hengqin from 2013 to 2018. Support vector machine (SVM) is a classical classifier that is supervised learning models and that use related learning algorithms to analyze data for classification and regression analysis (Vapnik, 1995). In this paper, we obtain the impervious surface distribution via SVM and get good accuracy. The impervious surface distribution of Hengqin in six years show that the quickly improve of urbanization level. However, with the development of urbanization, the impervious surface has not changed dramatically, which shows that the decision-making of urban managers is correct. After the urbanization construction in Hengqin, it is still an ecological island.

A Comparative Study of various Indices for extraction urban impervious surface of Landsat 8 OLI

Impervious surface is one of the major land cover types of urban and suburban environment. Conversion of rural landscapes and vegetation area to urban and suburban land use is directly related to the increase of the impervious surface area. The impervious surface expansion is straight-lined with decreasing green spaces in urban areas. Impervious surface is one of indicator for detecting urban heat islands. This study compares various indices for mapping impervious surfaces using Landsat 8 OLI imagery by optimizing the different spectral characteristics of Landsat 8 OLI imagery. The research objectives are (1) to apply various indices for impervious surface mapping and (2) identifies impervious surfaces in urban areas based on multiple indices and provide recommendations and find the best index for mapping impervious surface in urban areas. In addition to utilizing the index, land use supervised classification method, maximum likelihood classification used for extracting built-up, and non-built-up areas. Accuracy assessment of this research used field data collection as primary data for calculating kappa coefficient, producer accuracy, and user accuracy. The study can also be extended to find the land surface temperature and correlate the impervious surface extraction data with urban heat islands.

Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.

Improving Impervious Surface Estimation by Using Remote Sensed Imagery Combined With Open Street Map Points-of-Interest (POI) Data

As a key indicator of urban environments, the accurate mapping of impervious surface is essential. With the availability of high spatial resolution remote sensing imagery, such as Sentinel-2, it is feasible to produce fine-scale impervious surface maps. For mapping high-resolution impervious surfaces, object-based image analysis (OBIA) classification method demonstrated its efficiency and accuracy by combining spectral information and spatial information. Although with some success, impervious surface estimation remains challenging because different land cover types share similar spectral information. With the emergence of affordable GPS-enabled devices (such as a smartphone) and web 2.0 development, more and more people are getting involved in sharing their locations with others or posting on the Internet. These volunteered geographical information (VGI) data provide a brand-new prospect for mapping urban impervious surfaces. This research proposes an optimized method for impervious surface mapping based on Sentinel-2 multispectral imagery and open street map (OSM) points-of-interest (POI) data. The proposed method was tested in Milwaukee county, US, and the results show that the overall accuracy of the proposed OBIA increases from 82.57% to 87.02% compared with the conventional OBIA. Thus, this study provides an effective means of combining OBIA and the relatively new VGI POIs data to extract impervious surface with higher spatial resolution.

A Nighttime Lights Adjusted Impervious Surface Index (NAISI) with Integration of Landsat Imagery and Nighttime Lights Data from International Space Station

Accurate mapping of impervious surface is essential for both urbanization monitoring and micro-ecosystem research. However, the confusion between impervious surface and bare soil is the major concern due to their high spectral similarity in optical imagery. Integration of multi-sensor images is considered to offer a better capacity for distinguishing impervious surface from background. In this paper, a new impervious surface index namely nighttime light adjusted impervious surface index (NAISI), which integrates information from Landsat and nighttime lights (NTL) data from International Space Station (NTL-ISS), is proposed. Parallel to baseline subtraction approaches, NAISI integrate the information from the first component of principal component (PC) transformation of NTL-ISS, the Soil Adjusted Vegetation Index (SAVI) and the third component of tasseled cap transform (TC3) of the Landsat data. Visual interpretation and quantitative indices (SDI, Kappa and overall accuracy) were adopted to elevate the accuracy and separability of NAISI. Comparative analysis with NTL derived light intensity, optical indices, as well as existing optical-NTL indices were conducted to examine the performance of NAISI. Results indicate that NAISI achieves a more promising capability in impervious surface mapping. This demonstrates the superiority of integration of optical and nighttime lights information for imperviousness detection.

Mapping impervious surface fractions using automated Fisher transformed unmixing

Spatial explicit monitoring of impervious surface fractions provides essential information for urban planning, urban disaster prevention and mitigation. Moderate spectral and spatial resolution satellite observation has become a tool to perform such monitoring across different spatial and temporal scales. Yet, one major challenge in urban remote sensing is the high spatial heterogeneity causing the presence of image pixels constituting a mixture of different land cover materials. Here we apply an automated unmixing procedure specifically designed to optimally account for the pronounced spectral variability (i.e. high within-class and low between-class variability) prevailing in urban settings. The approach is based on Fisher Discriminant Analysis (FDA), a data dimensionality reduction technique in which between-class variability is maximized and within-class variability is minimized while transforming spectra from the reflectance to the Fisher feature space. We integrated the FDA transformation in the widely-applied Multiple Endmember Spectral Mixture Analysis (MESMA) approach in an attempt to more effectively address the endmember variability problem. We made use of online spectral libraries to train the Fisher transformation parameters and steer the automated selection of image endmembers. Our Fisher transformed MESMA algorithm (F-MESMA) was tested on medium spatial resolution Landsat scenes and impervious fraction maps were generated for five major cities located in different regions of the world. The output of F-MESMA was compared to the output of state-of-the-art spectral mixture analysis approaches which were specifically designed to reduce endmember variability issues. We could demonstrate that compared to other spectral transformation approaches, the ratio of within- vs between-class variability of the endmembers was most strongly reduced after applying the Fisher transformation. As a direct consequence, across all five cities F-MESMA consistently provided the most accurate impervious surface fraction estimates (RMSEF-MESMA = 0.13 vs. RMSEalternative approaches = [0.16–0.17]).

A Hierarchical Extraction Method of Impervious Surface Based on NDVI Thresholding Integrated With Multispectral and High-Resolution Remote Sensing Imageries

Research on extraction of impervious surface has developed for decades, but it is still quite challenging to obtain impervious surface information with high accuracy, especially from multispectral remote sensing imageries. Linear spectral mixture analysis (LSMA) is a major method for estimating impervious surface areas. When LSMA is conducted, an important phenomenon is often neglected: the spectral signature is frequently interfered by the dominant component (impervious surface, vegetation, or soil) within a mixed pixel, namely the effects of mixed-pixel spectral interference. The primary objective of this study is to design a hierarchical extracting method based on normal difference vegetation index (NDVI) thresholding to eliminate these effects and improve mapping accuracy. In this method, NDVI was used and divided into different values within the range of 0.1–0.9 at intervals of 0.05. Every NDVI value was used to divide the Landsat OLI data into two segmented layers that were respectively unmixed using LSMA. The critical step was to seek out an optimal NDVI threshold by accuracy assessment, through which, a segmented layer that produced impervious surface extraction with best mapping accuracy could be identified. The results demonstrated that NDVI = 0.70 was the optimal threshold, indicating that Landsat image with NDVI ≤ 0.70 was not affected by spectral interference. Therefore, LSMA was implemented to extract impervious surface based on the Landsat image with NDVI ≤ 0.70, and the remaining area was classified by Gaofen-2 imagery to produce a complete map using object-based image analysis. This hierarchical method is effective to remove or minimize the effects of mixed-pixel spectral interference with a promising accuracy improvement of 29.3% on average. Furthermore, it provides a novel idea to enhance urban impervious surface mapping by LSMA based on NDVI thresholding.

40-Year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing

Impervious surfaces are the most significant feature of human settlements. Timely, accurate, and frequent information on impervious surfaces is critical in both social-economic and natural environment applications. Over the past 40 years, impervious surface areas in China have grown rapidly. However, annual maps of impervious areas in China with high spatial details do not exist during this period. In this paper, we made use of reliable impervious surface mapping algorithms that we published before and the Google Earth Engine (GEE) platform to address this data gap. With available data in GEE, we were able to map impervious surfaces over the entire country circa 1978, and during 1985–2017 at an annual frequency. The 1978 data were at 60-m resolution, while the 1985–2017 data were in 30-m resolution. For the 30-m resolution data, we evaluated the accuracies for 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Overall accuracies reached more than 90%. Our results indicate that the growth of impervious surface in China was not only fast but also considerably exceeding the per capita impervious surface area in developed countries like Japan. The 40-year continuous and consistent impervious surface distribution data in China would generate widespread interests in the research and policy-making community. The impervious surface data can be freely downloaded from http://data.ess.tsinghua.edu.cn.

Mapping Impervious Surfaces in Town–Rural Transition Belts Using China’s GF-2 Imagery and Object-Based Deep CNNs

Impervious surfaces play an important role in urban planning and sustainable environmental management. High-spatial-resolution (HSR) images containing pure pixels have significant potential for the detailed delineation of land surfaces. However, due to high intraclass variability and low interclass distance, the mapping and monitoring of impervious surfaces in complex town–rural areas using HSR images remains a challenge. The fully convolutional network (FCN) model, a variant of convolution neural networks (CNNs), recently achieved state-of-the-art performance in HSR image classification applications. However, due to the inherent nature of FCN processing, it is challenging for an FCN to precisely capture the detailed information of classification targets. To solve this problem, we propose an object-based deep CNN framework that integrates object-based image analysis (OBIA) with deep CNNs to accurately extract and estimate impervious surfaces. Specifically, we also adopted two widely used transfer learning technologies to expedite the training of deep CNNs. Finally, we compare our approach with conventional OBIA classification and state-of-the-art FCN-based methods, such as FCN-8s and the U-Net methods. Both of these FCN-based methods are well designed for pixel-wise classification applications and have achieved great success. Our results show that the proposed approach effectively identified impervious surfaces, with 93.9% overall accuracy. Compared with the existing methods, i.e., OBIA, FCN-8s and U-Net methods, it shows that our method achieves obviously improvement in accuracy. Our findings also suggest that the classification performance of our proposed method is related to training strategy, indicating that significantly higher accuracy can be achieved through transfer learning by fine-tuning rather than feature extraction. Our approach for the automatic extraction and mapping of impervious surfaces also lays a solid foundation for intelligent monitoring and the management of land use and land cover.

Synergistic Use of Optical and Dual-Polarized SAR Data With Multiple Kernel Learning for Urban Impervious Surface Mapping

Accurate mapping of impervious surface distribution is important but challenging. Integrating optical and SAR data to improve urban impervious surface estimation has recently shown promising performance. Further investigation and development on this multisensory approach are conducted in this study. A novel multiple kernel learning (MKL) framework is proposed to integrate heterogeneous features from Landsat-8 and Sentinel-1A data effectively. A linearly weighted combination of basic kernels built using each group of features is learned as the optimal kernel, while the hyperparameters and the weight of each basic kernel are determined simultaneously by using the differential evolution algorithm. Then, the optimal kernel is embedded into the support vector regression algorithm, and the impervious surface abundance of the study area is estimated by applying the developed multiple kernel support vector regression (MKSVR) model. The impervious surface ground truth at a subpixel level is derived from a high-resolution image by means of object-oriented classification. The experimental results indicate that the synergistic use of optical and dual-pol SAR data by employing MKSVR achieves a noteworthy improvement for impervious surface estimation compared to that using optical image alone, the root mean square error is decreased by 4.30%, and the coefficient of determination ( R 2) is increased by 9.47%, and that the incorporation of optical and SAR does not guarantee the improved performance, simply stacking all features of multisource data into a vector is not a good choice, and the MKL is a powerful tool to apply as demonstrated by the experiments conducted in this study.

Application of remote sensing and GIS for assessing the urbanization trend in Can Tho city

This paper presents the method of integrating remote sensing and GIS to analyze the urbanization trend through the impervious surface change in Can Tho City. The impervious surface maps were created from the multi-temporal LandSat images in 1997, 2005, 2010, 2016 and were overlaid in GIS to extract the urban expansion from 1997 to 2016. The results showed the urban area of Can Tho increased from 1506,638 ha in 1997 to 5611,114 ha in 2016, average growth rate of 14,3%/year. The integration of remote sensing and GIS was found to be effective in monitoring and analyzing urban growth patterns.