Urban Area Extraction Research Articles

Extraction of urban built-up areas based on Sentinel-2Aand NPP-VIIRS nighttime light data

Extraction of urban built-up areas based on Sentinel-2Aand NPP-VIIRS nighttime light data

Using Multi-Sensor Satellite Images and Auxiliary Data in Updating and Assessing the Accuracies of Urban Land Products in Different Landscape Patterns

Rapid and accurate updating of urban land areas is of great significance to the study of environmental changes. Although there are many urban land products (ULPs) at present, such as GlobeLand30, Global Urban Footprint (GUF), and Global Human Settlement Layer (GHSL), these products are all static data of a certain year, and are not able to provide high-accuracy updating of urban land areas. In addition, the accuracies of these data and their application value in the update of urban land areas need to be urgently proven. Therefore, we proposed an approach to quickly and accurately update urban land areas in the Kuala Lumpur region of Malaysia, and assessed the accuracies of urban land products in different urban landscape patterns. The approach combined the advantages of multi-source data including existing ULPs, OpenStreetMap (OSM) data, Landsat Operational Land Imager (OLI), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images. Three main steps make up this approach. First, the urban land training samples were selected in the urban areas consistent with GlobeLand30, GUF, and GHSL, and samples of bare land, vegetation, water bodies, and road auxiliary data were obtained by GlobeLand30 and OSM. Then, the random forest was used to extract urban land areas according to the object’s features in the OLI and PALSAR images. Last, we assessed the accuracies of GlobeLand30, GUF, GHSL, and the results of this study (ULC) by using point and area validation methods. The results showed that the ULC had the highest overall accuracy of 90.18% among the four products and could accurately depict urban land in different urban landscapes. The GHSL was the second most accurate of the four products, and the accuracy in urban areas was much higher than that in rural areas. The GUF had many omission errors in urban land areas and could not delineate a large area of complete spatial information of urban land, but it could effectively extract scattered residential land with small patches. GlobeLand30 had the lowest accuracy and could only express rough, large-scale urban land. The above conclusions provide evidence that ULPs and the approach proposed in this study have a great application potential for high-accuracy updating of urban land areas.

A New Fusion Approach for Extracting Urban Built-up Areas from Multisource Remotely Sensed Data

Recent advances in the fusion technology of remotely sensed data have led to an increased availability of extracted urban information from multiple spatial resolutions and multi-temporal acquisitions. Despite the existing extraction methods, there remains the challenging task of fully exploiting the characteristics of multisource remote sensing data, each of which has its own advantages. In this paper, a new fusion approach for accurately extracting urban built-up areas based on the use of multisource remotely sensed data, i.e., the DMSP-OLS nighttime light data, the MODIS land cover product (MCD12Q1) and Landsat 7 ETM+ images, was proposed. The proposed method mainly consists of two components: (1) the multi-level data fusion, including the initial sample selection, unified pixel resolution and feature weighted calculation at the feature level, as well as pixel attribution determination at decision level; and (2) the optimized sample selection with multi-factor constraints, which indicates that an iterative optimization with the normalized difference vegetation index (NDVI), the modified normalized difference water index (MNDWI), and the bare soil index (BSI), along with the sample training of the support vector machine (SVM) and the extraction of urban built-up areas, produces results with high credibility. Nine Chinese provincial capitals along the Silk Road Economic Belt, such as Chengdu, Chongqing, Kunming, Xining, and Nanning, were selected to test the proposed method with data from 2001 to 2010. Compared with the results obtained by the traditional threshold dichotomy and the improved neighborhood focal statistics (NFS) method, the following could be concluded. (1) The proposed approach achieved high accuracy and eliminated natural elements to a great extent while obtaining extraction results very consistent to those of the more precise improved NFS approach at a fine scale. The average overall accuracy (OA) and average Kappa values of the extracted urban built-up areas were 95% and 0.83, respectively. (2) The proposed method not only identified the characteristics of the urban built-up area from the nighttime light data and other daylight images at the feature level but also optimized the samples of the urban built-up area category at the decision level, making it possible to provide valuable information for urban planning, construction, and management with high accuracy.

Mapping Urban Areas Using a Combination of Remote Sensing and Geolocation Data

Urban areas are essential to daily human life; however, the urbanization process also brings about problems, especially in China. Urban mapping at large scales relies heavily on remote sensing (RS) data, which cannot capture socioeconomic features well. Geolocation datasets contain patterns of human movement, which are closely related to the extent of urbanization. However, the integration of RS and geolocation data for urban mapping is performed mostly at the city level or finer scales due to the limitations of geolocation datasets. Tencent provides a large-scale location request density (LRD) dataset with a finer temporal resolution, and makes large-scale urban mapping possible. The objective of this study is to combine multi-source features from RS and geolocation datasets to extract information on urban areas at large scales, including night-time lights, vegetation cover, land surface temperature, population density, LRD, accessibility, and road networks. The random forest (RF) classifier is introduced to deal with these high-dimension features on a 0.01 degree grid. High spatial resolution land cover (LC) products and the normalized difference built-up index from Landsat are used to label all of the samples. The RF prediction results are evaluated using validation samples and compared with LC products for four typical cities. The results show that night-time lights and LRD features contributed the most to the urban prediction results. A total of 176,266 km2 of urban areas in China were extracted using the RF classifier, with an overall accuracy of 90.79% and a kappa coefficient of 0.790. Compared with existing LC products, our results are more consistent with the manually interpreted urban boundaries in the four selected cities. Our results reveal the potential of Tencent LRD data for the extraction of large-scale urban areas, and the reliability of the RF classifier based on a combination of RS and geolocation data.

Developing a new cross-sensor calibration model for DMSP-OLS and Suomi-NPP VIIRS night-light imageries

Night-time light (NTL) data provides a great opportunity to monitor human activities and settlements. Currently, global-scale NTL data are acquired by two satellite sensors, i.e., DMSP-OLS and VIIRS, but the data collected by the satellites are not compatible. To address this issue, we proposed a method for generating long-term and consistent NTL data. First, a logistic model was employed to estimate and smooth the missing DMSP-OLS data. Second, the Lomb-Scargle Periodogram technique was used to statistically examine the presence of seasonality of monthly VIIRS time series. The seasonal effect, noisy and unstable observations in VIIRS were eliminated by the BFAST time-series decomposition algorithm. Then, we proposed a residuals corrected geographically weighted regression model (GWRc) to generate DMSP-like VIIRS data. A consistent NTL time series from 1996 to 2017 was formed by combining the DMSP-OLS and synthetic DMSP-like VIIRS data. Our assessment shows that the proposed GWRc model outperformed existing methods (e.g., power function model), yielding a lower regression RMSE (6.36), a significantly improved pixel-level NTL intensity consistency (SNDI = 82.73, R2 = 0.986) and provided more coherent results when used for urban area extraction. The proposed method can be used to extend NTL time series, and in conjunction with the upcoming yearly VIIRS data and Black Marble daily VIIRS data, it is possible to support long-term NTL-based studies such as monitoring light pollution in ecosystems, and mapping human activities.

Extraction of urban built-up areas from nighttime lights using artificial neural network

The spatial distribution of urban areas at the national and regional scales is critical for urban planners and governments to design sustainable and environment-friendly future development plans. The nighttime lights (NTL) data provide an effective way to monitor the urban at different scales however is usually achieved by using empirical threshold-based algorithms. This study proposed a novel Artificial Neural Network (ANN) approach, using moderate resolution imageries as NTL, MODIS NDVI and land surface temperature data, to map urban areas. Both random and maximum dissimilarity distance algorithm sampling methods were considered and compared. The validation of the urban areas extracted from MDA-based ANN against the 2011 US national land cover data showed a reasonable quality (overall accuracy = 97.84; Kappa = 0.74) and achieved more accurate result than the threshold method. This study demonstrates that ANN can provide an effective, rapid, and accurate alternative in extracting urban built-up areas from NTL.

Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns

The most commonly used model for analyzing satellite imagery is the Support Vector Machine (SVM). Since there are a large number of possible variables for use in SVM, this paper will provide a combination of parameters that fit best for extracting green urban areas from Copernicus mission satellite images. This paper aims to provide a combination of parameters to extract green urban areas with the highest degree of accuracy, in order to speed up urban planning and ultimately improve town environments. Two different towns in Croatia were investigated, and the results provide an optimal combination of parameters for green urban areas extraction with an overall kappa index of 0.87 and 0.89, which demonstrates a very high classification accuracy.

Modification of urban built-up area extraction method based on the thematic index-derived bands

This research aims to develop new automatic and quicker spectral signature analysis tools to separate urban built-up area and determines urban area changes. Nowadays, researcher uses thematic index-derived bands for automatic urban data extraction. The extraction of urban built-up land can be automatically done with New Built-Up Index (NBUI) although it has a limitation on separating built-up land and water body. This study attempts to obtain the maximum accuracy of the extraction by merging several indices including Enhanced Built-Up and Bareness Index (EBBI), Soil Adjusted Vegetation Index (SAVI), Modified Normalized Difference Water Index (MNDWI), New Water Index (NWI) and Normalized Different Pond Index (NDPI) and compared its accuracy with NBUI. The results showed that merging EBBI, SAVI, MNDWI and NDPI produces the highest accuracy of 98.21% by addition and subtraction. The combined application of EBBI, SAVI and NWI also gives a good effect for extracting urban built-up areas and has 94.64% mapping accuracy.

Mapping Urban Green Spaces Based on an Object-Oriented Approach

The advent of technology and its implications on especially remote sensing image processing using High Resolution Satellite Images (HRSI) to map land cover provide researchers to monitor land changes, make landscape analyses, and manage land transformation. One of land dynamics that should be mapped for the sustainability of urban area is green spaces. Urban green spaces, such as parks, playgrounds, and residential greenery may promote both mental and physical health. Besides, they contribute to ecosystem services such as reducing heat island effect and carbon storage, aiding water regulation etc. Therefore, mapping urban green infrastructure from a high-resolution satellite image provides an important tool to conduct studies, researches, and projects for sustainable development of urban areas. As the material of this research, one of the orthophotos of Aydin urban area exemplifies the park, the green cover in the agricultural area, the playground, and the residential garden, was used. For classifying land cover from the orthophoto with Object-Based Image Analysis (OBIA), eCognition Developer 9.0 software was utilized. To combine spectral and shape features, multiresolution segmentation was implemented. Additionally, features as brightness and ratio green were used for the extraction of urban green areas. In this research, urban green areas were successfully extracted from the orthophoto and accuracy assessment was performed on the classified image. OBIA of high resolution imagery enables to extract detailed information of various targets on urban areas. The result of accuracy assessment of the classification achieved 84.68% overall accuracy. To increase the accuracy via manual interventions, manual classification tool of eCognition Developer 9.0 may be used if needed.

Extraction and monitoring approach of dynamic urban commercial area using check-in data from Weibo

Urban hot spot detection and commercial area analysis for city economic planning and dynamic urban planning is of vital importance. However, it is difficult to obtain a more accurate commercial area boundary in the past. Check-in data obtained by a social networking service (SNS) and/or a location-based service (LBS) is a type of crowd-sourced geographic data that can reveal mass daily life activities, which provides a new big data source for urban hot spot detection and commercial area analysis. In this paper, a dynamic urban commercial area extraction and monitoring approach is proposed using SINA Weibo (a social network) check-in data. First, a check-in data pre-process model is proposed to simplify the amount of check-in data and improve the efficiency of cluster analysis. The spatial autocorrelation validation is implemented to validate the significant patterns of the spatial clustering of check-in data. Then, an exploratory spatial analysis and hot spot clustering method based on check-in data is proposed to detect urban hot spots and extract commercial areas using a geographic distribution metric with urban commercial hot spots. Second, the hot spot cluster analysis results are taken to determine the center of the commercial area and calculate the distribution of an ellipse, which is adopted to obtain the rough boundary of the commercial area. A planar Delaunay iterative triangulation algorithm is presented to determine the exact boundary of the commercial area. Then, the time sequence extraction result of the commercial area is presented to analyze the evolutionary trend in the city business space. Finally, the Weibo check-in data from 2012 to 2014 of Wuhan city are taken as an example dataset for the commercial area extraction and detection with the proposed approach. The results show that the method can accurately determine the boundary of and changes within the commercial area in Wuhan city. This study provides a new method for the monitoring of hot spots and the geographical situations of city commercial areas.

Urban Built-Up Area Extraction From Log- Transformed NPP-VIIRS Nighttime Light Composite Data

Accurate information on urban areas at regional and global scales is required for various socioeconomic and environmental applications. The nighttime light (NTL) composite data have proven to be an effective data source for extracting urban areas. Various urban mapping methods have been proposed in the literature to extract urban built-up areas from the Defense Meteorological Satellite Program’s Operational Linescan System NTL data with a variable accuracy. However, most of the previous methods cannot be directly applied to the NTL data derived from the Suomi National Polar-orbiting Partnership Satellite with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor onboard. In this letter, we introduced a logarithmic transformation to preprocess the NPP-VIIRS NTL composite data. Then, four popular methods for urban built-up area extraction were tested using the original and log-transformed NTL data, respectively. The selected methods included the thresholding technique, Sobel-based edge detection, neighborhood statistics analysis, and watershed segmentation. The accuracy of the results was evaluated through validating the urban areas derived using each method against the referenced urban areas obtained from the National Land Cover Database for the U.S.. The results indicated that logarithmic transformation is an effective procedure for enhancing the difference between urban built-up areas and nonurban areas. The selected methods for urban built-up area extraction were found to perform better on the log-transformed NTL data than the original NTL data.

Earthquake/Tsunami Damage Assessment for Urban Areas Using Post-Event PolSAR Data

Analyses of single-post-event polarimetric synthetic aperture radar (PolSAR) data permit fast and convenient post-disaster damage assessment work. By analyzing valid features, damaged and undamaged buildings can be quickly classified. However, the presence of oriented buildings in the disaster area makes the classification work more challenging. Many previous works extract the damage information of the disaster area by considering oriented buildings and undamaged parallel buildings as survived buildings. However, after-effect debris may create structures with random orientation angles. In our study on the Tohoku earthquake/tsunami disaster event, we found that some damaged buildings with large building orientation angles (with respect to the satellite flight path) are grouped as oriented buildings (undamaged buildings). In this paper, we propose a new earthquake/tsunami damage assessment method, particularly for urban areas, that takes this complex situation into consideration. The proposed method solves the problems of both urban-area extraction and damaged-building identification. For urban-area extraction, the proposed combined thresholding and majority voting method can accurately discriminate between urban and foreshortening mountain areas. Meanwhile, for damaged-building identification, the proposed new unsupervised damage assessment method classifies the buildings in a disaster area according to four conditions, and it outperforms the techniques used in existing works. The analysis results and the comparison with the supervised support vector machine (SVM) classification technique show that our proposed method can produce more accurate results for damage assessment using single-post-event PolSAR data.

Building visual green index: A measure of visual green spaces for urban building

In the context of rapid urbanization, balancing the development and conservation of green space is a challenging task for urban planning and urban administrative management. Accurate measurement of green spaces in urban areas is an important source of information when evaluating the effect of urban development on green space. In this study, we used multispectral remote sensing images to extract urban buildings and green areas and developed a Building Visual Greenness Index (BVGI) to estimate the green space views from buildings. Instead of considering a single value for the entire building, BVGI values for each floor above the second floor were calculated to take into account of the dependence of BVGI on the building height. The results show that BVGI can reflect the actual green space view for residents on each floor, and BVGI is a useful index for green space measurement at a community scale. Furthermore, BVGI may provide a virtual visualization index of green space in urban planning and replace the traditional green indexes at a community scale for urban administrators.

Eigenvalue-Based Urban Area Extraction Using Polarimetric SAR Data

Urban area extraction using polarimetric synthetic aperture radar (PolSAR) data has become a potential mean to urban studies because it holds the promise that the radar returns from specific scattering characteristics may be emphasized. Due to the high variability of urban land-scape and the existing misdetection of buildings as vegetation, urban area extraction is still a challenging problem. In this paper, an eigenvalue-based urban area extraction method is proposed. First, similar to the entropy/anisotropy plane, a two-dimensional RVI/PA plane is put forward to construct the extractor of buildings with small orientation angles. Second, coupled with the parameters, a robust extractor is introduced to elevate the scattering characteristics of buildings with large orientation angles but to suppress those of others. Finally, data-driven thresholds are investigated and ascertained for the extractors, thus urban areas are extracted. In addition, a change detection-based prescreening method is applied to refine the extraction result. The performance of the proposed method is demonstrated and validated with spaceborne and airborne fully PolSAR data over different test sites. The outputs show that the proposed method provides an overall accuracy of over 90%, as well as better visual results of the extracted buildings.

Residential carbon dioxide emissions at the urban scale for county-level cities in China: A comparative study of nighttime light data

Abstract Carbon emissions from residential energy consumption in urban areas play a pivotal role in meeting emission targets. It is more beneficial to decomposing the emission reduction target to subnational units. This study aims to map urban residential carbon emissions at a finer spatial resolution to offer reference to disaggregating the carbon-reduction targets down to each sub-unit. With the launch of Suomi National Polar-orbiting Partnership (NPP) satellite, the day-night band of Visible Infrared Imaging Radiometer Suite (VIIRS) onboard represents a major advancement compared with Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) in terms of radiometric accuracy, spatial resolution and geometric quality. Therefore, the useful proxy was utilized to model the spatial distribution of residential carbon emissions at the urban scale for county-level cities in China. Firstly, the dynamic optimal threshold method was used to extract urban built-up areas based on NPP-VIIRS data and the statistical data. Secondly, comparison was conducted between the NPP-VIIRS data and both original and saturation-corrected DMSP-OLS data to explore the performance of NPP-VIIRS data. Finally, the spatial characteristics were analyzed in detail at the regional, provincial and prefectural level, respectively. The results show that the NPP-VIIRS data performed better in both statistic regression and spatial comparison. The spatial patterns indicated that there was an obvious north-south differentiation, especially the carbon emission density (defined as carbon emissions per grid), which was significantly higher in Northeastern China than that of other regions. The climate elements positively contributed to the increase of carbon emissions in residential sector, especially stimulating emissions related to building heating.

A multi-layer Classification Technique for High Resolution Satellite Images Using Radiometric Calibration Modelling

Recent developments in satellite sensors tend to the availability of highspatial and spectral resolution images. The motivation of this researchpaper is to get maximum benefits of different bands in high resolutionsatellite images. In this research paper, a novel classification technique isintroduced where the shared texture features properties problem isaddressed. Atmospheric correction is applied on a high resolution WorldView 2 (WV2) image to produce reflectance value for all spectral bands.Reflectance image is produced by knowing the environmental parametersof the images at the capturing time, which can be extracted from auxiliaryfiles associated with the input image. A multi-layer classification treeanalysis is applied on a reflectance image to extract urban area featuresbased on investigated thresholding values. The proposed technique isinvestigated through MATLAB environment. The results of the proposedtechnique are assessed versus classification results of MaximumLikelihood classification technique that is applied through ENVI software.The assessment of classification results is represented in confusion matrixformat and determination of Kappa Coefficient. The investigatedtechnique succeeded in classifying urban area features up to 90%. Theproposed technique is fast, automated and suitable for any image withsame spectral bands as WV2 satellite image.

Step-wise Land-class Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity

The extraction of urban built-up areas is an important aspect of urban planning and understanding the complex drivers and biophysical mechanism of urban climate processes. However, built-up area extraction using Landsat data is a challenging task due to spatio-temporal dynamics and spatially intermixed nature of Land Use and Land Cover (LULC) in the cities of the developing countries, particularly in tropics. In the light of advantages and drawbacks of the Normalized Difference Built-up Index (NDBI) and Built-up Area Extraction Method (BAEM), a new and simple method i.e. Step-wise Land-class Elimination Approach (SLEA) is proposed for rapid and accurate mapping of urban built-up areas without depending exclusively on the band specific normalized indices, in order to pursue a more generalized approach. It combines the use of a single band layer, Normalized Difference Vegetation Index (NDVI) image and another binary image obtained through Logit model. Based on the spectral designation of the satellite image in use, a particular band is chosen for identification of water pixels. The Double-window Flexible Pace Search (DFPS) approach is employed for finding the optimum threshold value that segments the selected band image into water and non-water categories. The water pixels are then eliminated from the original image. The vegetation pixels are similarly identified using the NDVI image and eliminated. The residual pixels left after elimination of water and vegetation categories belong either to the built-up areas or to bare land categories. Logit model is used for separation of the built-up areas from bare lands. The effectiveness of this method was tested through the mapping of built-up areas of the Kolkata Metropolitan Area (KMA), India from Thematic Mapper (TM) images of 2000, 2005 and 2010, and Operational Land Imager (OLI) image of 2015. Results of the proposed SLEA were 95.33% accurate on the whole, while those derived by the NDBI and BAEM approaches returned an overall accuracy of 83.67% and 89.33%, respectively. Comparisons of the results obtained using this method with those obtained from NDBI and BAEM approaches demonstrate that the proposed approach is quite reliable. The SLEA generates new patterns of evidence and hypotheses for built-up areas extraction research, providing an integral link with statistical science and encouraging trans-disciplinary collaborations to build robust knowledge and problem solving capacity in urban areas. It also brings landscape architecture, urban and regional planning, landscape and ecological engineering, and other practice-oriented fields to bear together in processes for identifying problems and analyzing, synthesizng, and evaluating desirable alternatives for urban change. This method produced very accurate results in a more efficient manner compared to the earlier built-up area extraction approaches for the landscape and urban planning.

Urban Area Extraction by Regional and Line Segment Feature Fusion and Urban Morphology Analysis

Urban areas are a complex combination of various land-cover types, and show a variety of land-use structures and spatial layouts. Furthermore, the spectral similarity between built-up areas and bare land is a great challenge when using high spatial resolution remote sensing images to map urban areas, especially for images obtained in dry and cold seasons or high-latitude regions. In this study, a new procedure for urban area extraction is presented based on the high-level, regional, and line segment features of high spatial resolution satellite data. The urban morphology is also analyzed. Firstly, the primitive features—the morphological building index (MBI), the normalized difference vegetation index (NDVI), and line segments—are extracted from the original images. Chessboard segmentation is then used to segment the image into the same-size objects. In each object, advanced features are then extracted based on the MBI, the NDVI, and the line segments. Subsequently, object-oriented classification is implemented using the above features to distinguish urban areas from non-urban areas. In general, the boundaries of urban and non-urban areas are not very clear, and each urban area has its own spatial structure characteristic. Hence, in this study, an analysis of the urban morphology is carried out to obtain a clear regional structure, showing the main city, the surrounding new development zones, etc. The experimental results obtained with six WorldView-2 and Gaofen-2 images obtained from different regions and seasons demonstrate that the proposed method outperforms the current state-of-the-art methods.

A novel method for urban area extraction from VIIRS DNB and MODIS NDVI data: a case study of Chinese cities

ABSTRACTMapping urban areas at the regional and global scales has been used in ecology, environment, sociology, and other subjects. Recently, it has become increasingly popular to extract urban areas from night-time light remote-sensing data. In this article, we reported an alternative method to extract information of urban areas from VIIRS Day/Night Band (DNB) and MODIS normalized differential vegetation index (NDVI) data based on the adaptive mutation particle swarm optimization (AMPSO) algorithm and the Support Vector Machine (SVM) classification algorithm. This method was validated using the urban areas of nine Chinese cities classified from Landsat Enhanced Thematic Mapper (ETM+) images by object-oriented image classification technology. We demonstrated that this new method for urban area extraction had a good classification coherency with the Landsat8 OLI result. In addition, it is more robust than other classification methods, and can be used to characterize the inter-urban texture as well.

Effective Mapping of Urban Areas Using ENVISAT ASAR, Sentinel-1A, and HJ-1-C Data

This letter presents a methodology for urban area mapping with density-based spatial clustering of applications with noise (DBSCAN) using the Advanced Synthetic Aperture Radar (ASAR), Sentinel-1A, and HuanJing-1C data. Urban areas have a diversity of shapes, including circles, squares, strips, and other irregular shapes, and the DBSCAN clustering algorithm is suitable for identifying clusters of arbitrary shapes. Exploiting DBSCAN to extract urban areas is a key aspect of this method, and improvements via the incorporation of synthetic aperture radar data preprocessing and postprocessing also play important roles in optimizing the extractions. Different test site sizes were chosen to demonstrate the effectiveness and feasibility of the proposed method, and the validation results showed that the method is efficient and accurately extracts urban areas ranging from small towns to super metropolitan areas.