Urban Area Extraction Research Articles

Monitoring and Analysis of Urban Sprawl Based on Road Network Data and High-Resolution Remote Sensing Imagery: A Case Study of China's Provincial Capitals

The primary prerequisite for sustainable urban development is to accurately grasp the development of the city. The dynamic changes in the urban area can reflect the urban expansion process and spatial development model. Carrying out urban expansion monitoring and extracting urban areas is of great importance for grasping the law of urban development and promoting the sustainable development of cities. However, the related research reveals several problems such as insufficient accuracy and low intelligence of urban boundary extraction. In response to these problems, this paper proposes a new method for urban area extraction based on the progressive learning model. By combining prior knowledge and image features, the number of training samples required for machine learning is reduced, and the problem of using high-level semantic information expression in the process of urban areas is avoided by using the classification method, and thus the accuracy of urban area extraction is improved. The method uses urban road network data to divide the city into blocks. It applies the scene classification method to extract the urban areas and uses the pyramid layer-by-layer to carry out the space constraint method to integrate the urban extraction principle into the machine learning process, which can be obtained and kept artificial under a small sample condition. Extracting the effect of the urban area is very close, greatly reducing the workload and providing a new solution for high-precision automatic extraction of urban areas. Through the analysis of urban expansion, the following results were obtained: (1) from 2000 to 2015, China's provincial capital cities maintained a high-speed growth trend with a total area increased by 90%; (2) urban expansion showed significant regional differences. The eastern expansion rate gradually slowed down, the western and northeast regions accelerated their expansion, and the central region expanded steadily; (3) 61% of the provincial capital cities expanded exponentially; (4) the development of China's provincial capital cities was highly correlated with national urban development policies and regional development strategies.

A Novel Approach for Automatic Urban Surface Water Mapping with Land Surface Temperature (AUSWM)

The principal difficulty in extracting urban surface water using remote-sensing techniques is the influence of noise from complex urban environments. Although various methods exist, there are still many sources of noise interference when extracting urban surface water, and automatic cartographic methods with long time series are especially scarce. Here, we construct an automatic urban surface water extraction method from the combination of traditional water index, urban shadow index (USI), and land surface temperature (LST) by using the Google Earth Engine cloud computing platform and Landsat imagery. The three principal findings derived from the application of the method were as follows. (i) In comparison with autumn and winter, LST in spring and summer could better distinguish water from high-reflection ground objects, shadows, and roads and roofs covered by asphalt. (ii) The overall accuracy of Automated Water Extraction Index (AWEIsh) in Zhengzhou was 77.5% and the Kappa coefficient was 0.55; with consideration of the USI and LST, the overall accuracy increased to 96.0% and the Kappa coefficient increased to 0.92. (iii) During 1990–2020, the area of urban surface water in Zhengzhou increased, with an evident trend in expansion from 11.51 km2 in 2008 to 49.28 km2 in 2020. Additionally, possible omissions attributable to using 30m-resolution imagery to extract urban water areas were also discussed. The method proposed in this study was proven effective in eliminating the influence of noise in urban areas, and it could be used as a general method for high-accuracy long-term mapping of urban surface water.

TEMPORAL AND SPATIAL ANALYSIS OF CLASSIFICATION TREE FOR IMPERVIOUS SURFACE MAPPING FROM SENTINEL-2 MSI DATA

Abstract. For studies of urban development, it is an important method for obtaining the distribution of impervious surface (IS) areas and their dynamic change from remote sensing data. The dilemma of the same spectrum for different features and different spectrums for the same features, posed by the complexity of the IS objects, is the fundamental obstacle encountered in the extraction of urban IS areas. In this study, an automatic extraction method for urban IS areas is proposed and analyzed, based on classification and regression tree (CART) and ensemble learning strategies. The Sentinel-2 MSI data of 30 cities in China from 2018 to 2020 were selected for IS extraction experiments. We perform temporal and spatial modeling of the splitting threshold offset in the classification model to explore the effect of time and space on IS extraction. The obtained offset models show that the temporal variation is not significant, while the spatial offsets have more obvious linear relationships.

Spatial Expansion of Built-Up Areas in the Beijing-Tianjin-Hebei Urban Agglomeration Based on Nighttime Light Data: 1992-2020.

Built-up areas are one of the most intuitive and important indicators used to assess urbanization, the spatial expansion of which is of great significance in depicting the evolution of urban spatial structures. Based on the harmonized Defense Meteorological Satellite Program (DMSP) nighttime light dataset, this paper extracts the spatial distribution of built-up areas and explores the spatial expansion patterns and spatiotemporal evolution regularity of the Beijing–Tianjin–Hebei urban agglomeration from 1992 to 2020. The results show that the spatial comparison method, comparing the extracted area with the government’s statistical area, can accurately determine the optimal threshold of nighttime light and extract urban built-up areas. According to the spatial comparison method, the built-up areas of the Beijing–Tianjin–Hebei urban agglomeration are expanding rapidly from 1992 to 2020, and both expansion speed and expansion intensity have experienced an inverted “U-shaped” growth process. As the core cities of the Beijing–Tianjin–Hebei urban agglomeration, Beijing and Tianjin have been in the later stage of spatial expansion with slower expansion speed but better quality. In contrast, prefecture-level cities and other node cities have rapid expansion speed. The urban space structure of the Beijing–Tianjin–Hebei urban agglomeration has changed from a “monocentric model” to a “polycentric model” to a “metropolitan model”. High-tech industry parks around node cities have become important strongholds of urban space development, leading cities to evolve from monocentric structures to polycentric structures of downtown and industrial parks. The radiation range of core cities expands and spreads to surrounding districts and counties, which inevitably lead to the formation of metropolitan areas.

Evaluating the Ability of NOAA-20 Monthly Composite Data for Socioeconomic Indicators Estimation and Urban Area Extraction

The new visible infrared imaging radiometer suite (VIIRS) onboard the National Oceanic and Atmospheric Administration-20 satellite (NOAA-20) is capable of imaging the Earth during both daytime and nighttime. The NOAA-20 VIIRS’ day/night band (DNB) enables a new generation of nighttime imaging applications. However, few studies investigated the ability of NOAA-20 DNB nighttime light data in modeling socioeconomic indicators [such as the gross domestic product (GDP) and electric power consumption (EPC)] and extracting urban areas. In this article, we first used a simple linear regression model to investigate the potential of NOAA-20 nighttime light data for estimating GDP and EPC at multiple scales; we then extracted urban areas from NOAA-20 nighttime light data by using support vector machines. Taking mainland China as the study area, we found that the correlation coefficient of determination <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> between NOAA-20 nighttime light data and GDP/EPC at both provincial and prefectural scales are higher than 0.7. By comparing the results with NPP-VIIRS nighttime light data, similar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values were obtained at both two scales, indicating that NOAA-20 nighttime light data and NPP-VIIRS data are comparable in estimating socioeconomic indicators. Moreover, NOAA-20 also shows a similar detection ability with NPP-VIIRS in extracting urban areas. This article demonstrated that NOAA-20 and NPP-VIIRS are comparable in economic statistics estimation and urban area extraction. The NOAA-20 nighttime light data can be a useful data source for enlightening more applications in the fields of socioeconomic and urban studies.

Research on the Extraction Method of Urban Built-Up Areas With an Improved Night Light Index

Nighttime light (NTL) data has been widely used in the research of urban built-up areas extraction. Due to the low resolution and the overflow characteristics of NTL data, it is difficult to extract urban built-up areas in small cities with undeveloped economy and small areas. How to extract them accurately becomes an important problem. Based on Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Land Surface Temperature NLST), we proposed an improved night light index, named Vegetation, Building and Temperature Enhanced NTL Urban Index (VBTANUI) to accurately extract built-up areas in small cities by the marker-based watershed segmentation algorithm. Taking Jiayuguan, Ezhou and Wuzhishan, China as three examples, we extracted their urban built-up areas. Comparing with the four traditional NTL indexes, such as VANUI, EANI, BANUI65 and BANUI43, the results showed that: (1) the VBTANUI could effectively enhance the light intensity of the built-up boundaries; (2) F1 scores of VBTANUI in three cities are of 89.60%, 95.70% and 82.75% respectively, which are 6.04%, 7.12%, 8.10% and 8.47% higher in average than that of traditional four indexes; (3) VBTANUI is feasible and effective in the extraction of built-up areas in small cities with undeveloped economy and small areas.

基于夜间灯光数据的甘青宁城市扩展及其建成区植被变化特征

PDF HTML阅读 XML下载 导出引用 引用提醒 基于夜间灯光数据的甘青宁城市扩展及其建成区植被变化特征 DOI: 10.5846/stxb202005141227 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 甘肃农业大学大学生创新创业计划项目（202102045）；国家自然科学基金青年项目（41907406）；甘肃农业大学科技创新基金（GAU-KYQD-2018-23） Characteristics of urban expansion and vegetation change in built-up areas of Gan-Qing-Ning based on nighttime light data Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:开展针对黄河上游甘青宁地区城市建成区空间发展模式及其植被变化特征的研究，可为构建科学合理的城市发展体系、维护区域生态安全屏障提供重要的参考依据。以DMSP/OLS和统计数据为基础，运用统计数据比较法提取了甘青宁地区21座地级市（州）的城市建成区边界，并从城市空间扩展特征、扩展速度、动态度、紧凑度和重心迁移5个方面对城市扩展特征进行分析；在此基础上，引入SPOT/VEGETATION NDVI数据，以年均NDVI作为表征城市建成区植被状况的指标，从NDVI总和、均值、比值及其变化量、年际变化率及其变化百分率和稳定性5个方面对研究区2002-2013年城市建成区的植被变化进行研究。结果表明：1）采用统计数据比较法提取获得的研究区城市建成区精度较高，误差率在6.7%以内；2）城市用地围绕河西走廊及黄河上游地区形成不连续的"几"字型空间形态，并具有点状、线状和面状3种不同的分布模式；3）城市扩展年均速及动态度大体呈上升趋势，且2010-2013年的城市扩展速度（4.53 km2/a）是2006-2010年的2倍；4）除酒泉、海西、白银、天水、石嘴山和兰州外，其他城市建成区紧凑度指数均较高；5）城市重心在第一（2002-2006）和第二阶段（2006-2010）均向北发生迁移，在第三阶段（2010-2013）则向南发生迁移；6）城市建成区NDVI总和随建成区面积的增加呈上升趋势；7）城市建成区植被整体呈向好趋势，从NDVI均值看，2002-2006年植被状况变差，而2006-2013年又有所改善，且NDVI均值分布具有明显的空间差异，中西部地区普遍小于东部；从NDVI年际变化率和变化百分率来看，各城市建成区均大于外围背景区；8）城市建成区植被状况普遍较背景区差，且2002-2013年近4/7的城市建成区NDVI比值及其变化量均呈下降趋势；9）城市建成区植被稳定性普遍较差，半数以上城市的变异系数在12年间均有不同程度的增加。 Abstract:Carrying out research on the spatial development pattern and vegetation change characteristics of urban built-up areas in the Gan-Qing-Ning region on the upper reaches of the Yellow River can provide an important reference for building a scientific and reasonable urban development system and maintaining regional ecological security barriers. Based on DMSP/OLS and statistical data, this paper extracted the urban built-up area boundaries of 21 prefecture-level cities (prefectures) in Gan-Qing-Ning region by using statistical data comparison method, and analyzed the urban expansion characteristics from five aspects:urban spatial expansion characteristics, expansion speed, dynamic degree, compactness and center of gravity transfer. On this basis, the SPOT/VEGETATION NDVI (Normalized Difference Vegetation Index) data was introduced, and the annual average NDVI was used as an indicator to represent the vegetation status in urban built-up areas. The vegetation change of urban built-up areas in the study area from 2002 to 2013 was studied from five aspects:sum, mean, ratio and its change amount, interannual change rate, percentage and stability of NDVI. The results showed that:1) As one of the effective methods to extract urban built-up area, the statistical data comparison method had high accuracy in our study area, and the error rate was within 6.7%. 2) The urban land formed a discontinuous spatial pattern of "几" around the Hexi Corridor and the upper reaches of the Yellow River, and had three different distribution patterns:point, line and plane. 3) The annual average rate and dynamic degree of urban expansion showed an upward trend, and the rate of urban expansion during 2010-2013 (4.53 km2/a) was twice as high as that during 2006-2010. 4) Except for Jiuquan, Haixi, Baiyin, Tianshui, Shizuishan and Lanzhou, the compactness index of other urban build-up areas was relatively high. 5) In the first stage (2002-2006) and the second stage (2006-2010), the center of gravity shifted northward, and in the third stage (2010-2013), the center of gravity shifted southward. 6) The total NDVI of urban built-up area increased with the increase of built-up area. 7) The vegetation in urban built-up areas showed a positive trend on the whole. From the perspective of the mean NDVI, the vegetation condition got worse from 2002 to 2006, and improved from 2006 to 2013. Moreover, the mean NDVI distribution showed obvious spatial differences, and the central and western regions were generally smaller than the eastern ones. From the perspective of the interannual variation rate and percentage of NDVI, the built-up area of each city was larger than the peripheral background area. 8) The vegetation status in urban built-up areas was generally worse than that in background areas, and the NDVI ratio and its variation in nearly 4/7 urban built-up areas showed a downward trend from 2002 to 2013. 9) Vegetation stability in urban built-up areas was poor, and the variation coefficients of more than half of the cities increased to varying degrees in the past 12 years. 参考文献 相似文献 引证文献

Research on the change of Spatio-temporal characteristics of Urban built-up area based on remote Sensing-- A case study of Yulin City, Shaanxi Province

Since the reform and opening up, the process of urbanization in China has been at a high level. In order to accurately obtain the boundary of urban built-up area and the spatiotemporal change characteristics of urban built-up area in recent years, this paper takes Yulin City as an example. With the help of ArcGIS, ERDAS and other platforms, we combine spatial comparison method and index method to extract urban built-up area from DMSP-OLS night light data and Landsat image data. At the same time, the confusion matrix is used to verify the extraction boundary. The research shows that: (1) Using DMSP-OLS night light data and Landsat data to extract the built-up area, the overall accuracy can reach 99%; (2) The urban built-up area of Yulin City expanded at a high speed from 2005 to 2018; (3) The urbanization process in the northeast of Yulin City is obviously higher than that in the south in recent years. This study has a certain reference value for the extraction of urban built-up areas and the development of urbanization in Yulin City by using DMSP-OLS night light data and Landsat data.

An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI

Timely and accurate extraction of urban built-up areas is crucial to addressing environmental problems related to fast changes in urban land cover, which is fundamental for optimizing land use patterns and supporting global sustainable development. Nighttime light (NTL) from the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) offer a new data source for extracting urban information. However, this kind of data suffer from drawbacks of blooming effects. To address this problem, in this study, the Enhanced Nighttime Light Urban Index (ENUI) approach, which involves the combination of NPP-VIIRS NTL with the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI), is proposed and tested. This approach was used to rapidly monitor the urban built-up areas in the Guangdong-Hong Kong-Macao Greater Bay Area in 2012, 2015, and 2018. The average overall accuracy and Map-level Image Classification Efficacy (MICE) for the extraction results are 93.56% and 0.77, respectively, while those of the Local-Optimized Thresholding (LOT) are 86.48% and 0.54, respectively; meanwhile, the average F-score values, user's accuracy and producer's accuracy for urban areas using the proposed approach increased by 9.98%, 10.90% and 8.67%, respectively, compared with the LOT. These findings suggest that this approach has a higher extraction accuracy than the LOT; this is primarily ascribed to the integration of NTL data with the NDVI, NDWI, and NDBI, which increases the variability of nighttime light in the urban core area and adequately alleviates the blooming effects of nighttime light brightness in water bodies and vegetated areas. The proposed approach shows great potentials to accurately and effectively monitor multi-temporal urban information and address environmental issues using NPP-VIIRS NTL data in global urban agglomerations.

Extraction of urban built-up area based on the fusion of night-time light data and point of interest data.

The accurate extraction of urban built-up areas is an important prerequisite for urban planning and construction. As a kind of data that can represent urban spatial form, night-time light data has been widely used in the extraction of urban built-up areas. As one of the geographic open-source big data, point of interest (POI) data has a high spatial coupling with night-time light data, so researchers are beginning to explore the fusion of the two data in order to achieve more accurate extraction of urban built-up areas. However, the current research methods and theoretical applications of the fusion of POI data and night-time light data are still insufficient compared with the dramatically changing urban built-up areas, which needed to be further supplemented and deepened. This study proposes a new method to fuse POI data and night-time light data. The results before and after data fusion are compared, and the accuracy of urban built-up area extracted by different data and methods is analysed. The results show that the data fusion can avoid the shortage of single data and effectively improve the extraction accuracy of urban built-up areas, which is greatly helpful to supplement the study of data fusion in urban built-up areas, and also can provide decision-making guidance for urban planning and construction.

The Modified Normalized Urban Area Composite Index: A Satelliate-Derived High-Resolution Index for Extracting Urban Areas

The accurate and efficient extraction of urban areas is of great significance for better understanding of urban sprawl, built environment, economic activities, and population distribution. Night-Time Light (NTL) data have been widely used to extract urban areas. However, most of the existing NTL indexes are incapable of identifying non-luminous built-up areas. The high-resolution NTL imagery derived from the Luojia 1-01 satellite, with low saturation and the blooming effect, can be used to map urban areas at a finer scale. A new urban spectral index, named the Modified Normalized Urban Areas Composite Index (MNUACI), improved upon the existing Normalized Urban Areas Composite Index (NUACI), was proposed in this study, which integrated the Human Settlement Index (HSI) generated from Luojia 1-01 NTL data, the Normalized Difference Vegetation Index (NDVI) from Landsat 8 imagery, and the Modified Normalized Difference Water Index (MNDWI). Our results indicated that MNUACI improved the spatial variability and differentiation of urban components by eliminating the NTL blooming effect and increasing the variation of the nighttime luminosity. Compared to urban area classification from Landsat 8 data, the MNUACI yielded better accuracy than NTL, NUACI, HSI, and the EVI-Adjusted NTL Index (EANTLI) alone. Furthermore, the quadratic polynomial regression analysis showed the model based on MNUACI had the best R2 and Root-Mean Square Error (RMSE) compared with NTL, NUACI, HSI, and EANTLI in terms of estimation of impervious surface area. It is concluded that MNUACI could improve the identification of urban areas and non-luminous built-up areas with better accuracy.

A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

A Modified Built-up Index (MBI) for automatic urban area extraction from Landsat 8 Imagery

Automatic extraction of built-up areas utilizing built-up indices has increased attention due to their use in monitoring urban growth and environment related problems. In this research study, a new Modified Built-up Index (MBI) is proposed utilizing Landsat 8 Imagery. This technique is developed by combining advanced slope based indexing technique built-up index (ASIT_BI), normalized difference water index (NDWI), soil adjusted vegetation index (SAVI) and brightness temperature (TB). For this study, the optimal threshold values are obtained using Otsu’s thresholding method and modified two-stage multi-threshold Otsu (TSMO) method. Built-up area mapping/extraction using MBI is also compared with two other built-up indices namely ASIT_BI and MNDISI in term of accuracy assessment using recent Landsat 8 images. The results indicate that built-up indices are greatly affected by seasonality of image. The proposed Modified Built-up Index (MBI) results in highest mapping accuracy for all the four seasons with highest overall agreement of 97.8% for spring season. The proposed MBI method is more effective than previous built-up indexing methods because of the improved ability to separate built-up land from barren land.

An Improved Method for Urban Built-Up Area Extraction Supported by Multi-Source Data

Urban built-up areas, where urbanization process takes place, represent well-developed areas in a city. The accurate and timely extraction of urban built-up areas has a fundamental role in the comprehension and management of urbanization dynamics. Urban built-up areas are not only a reflection of urban expansion but also the main space carrier of social activities. Recent research has attempted to integrate the social factor to improve the extraction accuracy. However, the existing extraction methods based on nighttime light data only focus on the integration of a single factor, such as points of interest or road networks, which leads to weak constraint and low accuracy. To address this issue, a new index-based methodology for urban built-up area extraction that fuses nighttime light data with multisource big data is proposed in this paper. The proposed index, while being conceptually simple and computationally inexpensive, can extract the built-up areas efficiently. First, a new index-based methodology, which integrates nighttime light data with points-of-interest, road networks, and the enhanced vegetation index, was constructed. Then, based on the proposed new index and the reference urban built-up data area, urban built-up area extraction was performed based on the dynamic threshold dichotomy method. Finally, the proposed method was validated based on actual data in a city. The experimental results indicate that the proposed index has high accuracy (recall, precision and F1 score) and applicability for urban built-up area boundary extraction. Moreover, this paper discussed different existing urban area extraction methods, and provides an insight into the appropriate approaches selection for further urban built-up area extraction in cities with different conditions.

A Digital Terrain Modeling Method in Urban Areas by the ICESat-2 (Generating precise terrain surface profiles from photon-counting technology)

The ICE, Cloud and land Elevation Satellite-2 (ICES at-2) can provide new measurements of the Earth's elevations through photon-counting technology. Most research has focused on extracting the ground and the canopy photons in vegetated areas. Yet the extraction of the ground photons from urban areas, where the vegetation is mixed with artificial constructions, has not been fully investigated. This article proposes a new method to estimate the ground surface elevations in urban areas. The ICES at-2 signal photons were detected by the improved Density-Based Spatial Clustering of Applications with Noise algorithm and the Advanced Topographic Laser Altimeter System algorithm. The Advanced Land Observing Satellite-1 PALSAR –derived digital surface model has been utilized to separate the terrain surface from the ICES at-2 data. A set of ground-truth data was used to evaluate the accuracy of these two methods, and the achieved accuracy was up to 2.7 cm, which makes our method effective and accurate in determining the ground elevation in urban scenes.

An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI

Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and promoting sustainable urban development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective for extracting urban land. However, the saturation and blooming within the DMSP-OLS NTL hinder its capacity to provide accurate urban information. This paper proposes an optimized approach that combines NTL with multiple index data to overcome the limitations of extracting urban land based only on NTL data. We combined three sources of data, the DMSP-OLS, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), to establish a novel approach called the vegetation–water-adjusted NTL urban index (VWANUI), which is used to rapidly extract urban land areas on regional and global scales. The results show that the proposed approach reduces the saturation of DMSP-OLS and essentially eliminates blooming effects. Next, we developed regression models based on the normalized DMSP-OLS, the human settlement index (HSI), the vegetation-adjusted NTL urban index (VANUI), and the VWANUI to analyze and estimate urban land areas. The results show that the VWANUI regression model provides the highest performance of all the models tested. To summarize, the VWANUI reduces saturation and blooming, and improves the accuracy with which urban areas are extracted, thereby providing valuable support and decision-making references for designing sustainable urban development.

Extraction and Evolution Analysis of Urban Built-Up Areas in Beijing, 1984–2018

Accurate extraction of the boundaries of urban built-up areas (UBA) based on remote sensing and GIS technology is extremely important for predicting urban spatial evolution. Therefore, this study proposes a method for extracting urban built-up areas based on the impervious surface aggregation density (ISAD) of an object and uses the long-time series of the built-up area extraction results to analyze the spatial and temporal evolution characteristics of Beijing cities. The results show that the precision for extracting UBA using this method is 91.3%, which is better than that of existing methods based on supervised classification. The extraction results can also accurately depict the urban form and maintain the integrity of the built-up areas. Built-up areas in Beijing expanded from 1984 to 2018, with the most significant expansion in 2014–2018. In these 5 years, the expansion speed and intensity of Beijing respectively reached 270.70 km2/a and 1.65%, which were 2.43 times that of the entire research period. Built-up areas in Beijing have been expanding past their original limits over time in a ‘centralized concentric circle’ expansion model. The focus of the UBA moved northeast by 11.56 km. In the past 35 years, the contours of Beijing’s urban borders have become increasingly complex, and the spatial form of built-up areas has become increasingly discrete. The analysis of the spatial evolution of UBA is beneficial for more sustainable, compact and coordinated urban development.

Object-Based Method for Urban Extraction through Using Quick Bird Satellite Imagery, LiDAR Data and Digital Urban Geomatics Techniques

Urban extraction mapping has become increasingly important in recent years and particularity extraction urban features based on remotely sensed data such as high-resolution imagery and LiDAR data. Though the researchers used the high spatial resolution image to extract urban area but he methods are still complexand still there are challenges associated with combining data that were acquired over differing time periods using inconsistent standards. So, this study will focus on the extraction of urban area based on an object-based classification method with integration of Quickbird satellite image and digital surface elevation (DSM) extracted from LiDAR data for the Rusafa city of Baghdad, Iraq. All the processes were done in eCognition and ArcGIS software for feature extraction and mapping, respectively. The overall methodological steps proposed in this research for the extraction of urban area using object-based method. In addition of that both the image data and LiDAR-derived DSM were integrated based on the eCognition software for extraction urban map of Rusafa city, Baghdad. Finally, the results indicated that the Artificial Neural Networks (ANN) model achieved the highest training and testing accuracies and performed the best compared to RF and Support Vector Machines (SVM) methods. And also, the results showed that the Artificial Neural Networks (ANN) had capability to extract the boundaries of the buildings and other urban features more accurately than the other two methods. This could be interpreted as the Artificial Neural Networks (ANN) model can learn complex features by the optimization process of the model and its multi-level feature extraction property.

The Extraction of Urban Built-Up Areas by Integrating Night-Time Light and POI Data—A Case Study of Kunming, China

With the urban built-up area becoming one of the most prominent forms of urban expansion, accurately extracting the urban built-up area is becoming more and more important to judge the urbanization process and evaluate the urban environment. Since it is difficult to significantly improve the accuracy of single satellite data in the extraction of urban built-up areas, this study proposes a method integrating POI (Point of Interest) data and Luojia-1A data to improve the extraction accuracy of urban built-up areas. In this study, integrated Density Graph, OSTU and geometric mean are used to extract urban built-up areas respectively. The highest precision of urban built-up areas extracted before data integration is 74.3% with the highest Kappa value of 0.54; while the highest precision of urban built-up area extracted after data integration is 91.4%, with the highest Kappa value of 0.91. Therefore, it can be concluded that compared with the existing widely used night-light-based methods, this method can integrate the advantages of POI data and Luojia-1A data, that is, it can not only solve the long-term oversaturation phenomenon of night light data, but also can extract urban built-up areas in a more refined way. The method used in this study, which integrates POI data and Luojia-1A data, can not only provide a new method for urban built-up area extraction, but also can play an active guiding role in urban planning and construction.

Remote sensing image urban built-up area extraction and optimization method based on PSPNet

Remote sensing image urban built-up area extraction and optimization method based on PSPNet