Multi-level monitoring of subtle urban changes for the megacities of China using high-resolution multi-view satellite imagery

Huang, Xin; Wen, Dawei; Li, Jiayi; Qin, Rongjun

doi:10.1016/j.rse.2017.05.001

Cited by 168 publications

(78 citation statements)

References 98 publications

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“…Sensors on a satellite can capture the brightness of cities, farms, industrial areas, fishing vessel lights, forest fires, and other human activity areas at night and form a nighttime light image [6,7]. Nighttime light data can make up for the deficiency of statistical data for urban research in some respects, and can be applied to studies related to human activities due to the strong correlation between human activities and the lightmaps of population, GDP, or power consumption [8].At present, nighttime light data are widely used in research on urban expansion [9], urban morphology and structure [10,11], estimation of socioeconomic status [12][13][14][15], fisheries [16,17], and energy [18,19]. It has been found that population distributions and light intensities have significant correlations [20].…”

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confidence: 99%

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Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou

Lou

Chen

et al. 2019

Remote Sensing

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The worldwide development of multi-center structures in large cities is a prevailing development trend. In recent years, China's large cities developed from a predominantly mono-centric to a multi-center urban space structure. However, the definition and identification city centers is complex. Both nighttime light data and point of interest (POI) data are important data sources for urban spatial structure research, but there are few integrated applications for these two kinds of data. In this study, visible infrared imaging radiometer suite (NPP-VIIRS) nighttime imagery and POI data were combined to identify the city centers in Hangzhou, China. First, the optimal parameters of multi-resolution segmentation were determined by experiments. The POI density was then calculated with the segmentation results as the statistical unit. High-high clustering units were then defined as the main centers by calculating the Anselin Local Moran's I, and a geographically weighted regression model was used to identify the subcenters according to the square root of the POI density and the distances between the units and the city center. Finally, a comparison experiment was conducted between the proposed method and the relative cut-off_threshold method, and the experiment results were compared with the evaluation report of the master plan. The results showed that the optimal segmentation parameters combination was 0.1 shape and 0.5 compactness factors. Two main city centers and ten subcenters were detected. Comparison with the evaluation report of the master plan indicated that the combination of nighttime light data and POI data could identify the urban centers accurately. Combined with the characteristics of the two kinds of data, the spatial structure of the city could be characterized properly. This study provided a new perspective for the study of the spatial structure of polycentric cities.2 of 20 especially relatively objective and rational methods, to evaluate whether the implementation results are consistent with the planning intention.The study of a polycentric urban spatial structure always relies on statistical data, such as a population census or socio-economic indicators. However, to some extent, traditional methods relying on socio-economic and statistical data may have the following problems:Access to spatial disaggregated data. • Insufficient background and prior knowledge of the research area.Compared with traditional methods, remote sensing data, especially nighttime light remote sensing data, have been widely used in urban studies [3] because of their free availability, global coverage, and high temporal resolution [4,5]. Sensors on a satellite can capture the brightness of cities, farms, industrial areas, fishing vessel lights, forest fires, and other human activity areas at night and form a nighttime light image [6,7]. Nighttime light data can make up for the deficiency of statistical data for urban research in some respects, and can be applied to studies related to human activities due to the strong correlatio...

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confidence: 99%

“…At present, nighttime light data are widely used in research on urban expansion [9], urban morphology and structure [10,11], estimation of socioeconomic status [12][13][14][15], fisheries [16,17], and energy [18,19]. It has been found that population distributions and light intensities have significant correlations [20].…”

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confidence: 99%

Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou

Lou

Chen

et al. 2019

Remote Sensing

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“…Qin et al (2016) gave a fairly complete review on the recent development and applications of 3D change detection, who classified 3D change detection into two rationales: 1) only by geometric comparison; 2) geometric and spectral analysis, which includes the computation of both geometric and spectral information for change analysis. Huang et al (2017) applied multi-view ZY-3 satellite imagery to precise urban change analysis in a multi-level (pixel, grid, and city block) approach, and Tian et al (2014) evaluated accuracy of DSM produced from IKONOS stereo imagery and propose a change detection method on height changes and Kullback-Leibler divergence, which confirmed that DSM can be used to improve the accuracy of change detection. Thus, in this research we will use the height difference from geometric comparison for change detection.…”

Section: Introductionmentioning

confidence: 88%

Uav-Based 3d Urban Environment Monitoring

Boonpook

Tan

Liu

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Unmanned Aerial Vehicle (UAV) based remote sensing can be used to make three-dimensions (3D) mapping with great flexibility, besides the ability to provide high resolution images. In this paper we propose a quick-change detection method on UAV images by combining altitude from Digital Surface Model (DSM) and texture analysis from images. Cases of UAV images with and without georeferencing are both considered. Research results show that the accuracy of change detection can be enhanced with georeferencing procedure, and the accuracy and precision of change detection on UAV images which are collected both vertically and obliquely but without georeferencing also have a good performance.

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“…Over the past two decades, China's Earth observation satellites have developed rapidly, with optical remote sensing satellites as the main contributors, which are mainly used for natural resource monitoring, land mapping, meteorological observations, and more. At the 2-to 5-m image resolution scale, the positioning accuracy of Chinese remote sensing satellite images represented by ZiYuan3 (ZY3) has reached a high level, and its geometric accuracy can completely satisfy the accuracy of 1:50,000 mapping (Xin et al, 2017;Xin et al, 2018). As a follow-up development, the GF-7 satellite is included in the Medium and Long Term Development Plan for China's Civil Space Infrastructure (2015-2025Gu & Tong, 2015).…”

Section: Introductionmentioning

confidence: 99%

Overview of the GF‐7 Laser Altimeter System Mission

Tang

Xie

Liu

et al. 2020

Earth and Space Science

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The Gao Fen 7 (GF-7) satellite is China's first submeter, high-resolution Earth observation and remote sensing satellite for natural resource monitoring, land surveying, and other industrial applications in China. The GF-7 laser altimeter system is the successor of the ZiYuan3-02 (ZY3-02 satellite) laser altimeter. The key objective of the GF-7 laser altimetry system is to provide high-precision ground elevation control points that assist in optical imaging to achieve 1:10000 mapping in China. This paper introduces the GF-7 laser altimeter system payload specifications, mission objectives, in-orbit calibration plan, data products, and service policy. In the future, the GF-7 laser altimeter system will also serve multidisciplinary and professional applications through its precise elevation measurement capabilities. In addition, it will play a vital role in Earth observation, climate change monitoring, and environmental protection.

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Multi-level monitoring of subtle urban changes for the megacities of China using high-resolution multi-view satellite imagery

Cited by 168 publications

References 98 publications

Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou

Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou

Uav-Based 3d Urban Environment Monitoring

Overview of the GF‐7 Laser Altimeter System Mission

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