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Author (up) Li, H.; Jia, Y.; Zhou, Y.
Title Urban Expansion Pattern Analysis and Planning Implementation Evaluation Based on using Fully Convolution Neural Network to Extract Land Range Type Journal Article
Year 2018 Publication NeuroQuantology Abbreviated Journal
Volume 16 Issue 5 Pages 814-822
Keywords Remote Sensing
Abstract In recent years, due to the rapid development of China’s urban, it is significant for effective implementation of urban science development and planning that grasp the process of urban development, analyze the potential of subsequent development, and evaluate the matching degree of the development status and the planning. Thereinto, an effective way we exercise today is to evaluate urban expansion pattern analysis and planning implementation. According to research results of the urban land range extraction method based on the support vector machine (SVM) and fully convolution neural network (FCN) of the depth learning method for the night light image data, this paper describes an integration of remote sensing (RS) and geographic information system (GIS) and analyzes the urban expansion pattern of Beijing based on the computed results of landscape pattern indices. The results unveil that from 1990s to 2010s, Beijing took on a circle expansion mode on the ground the spatial agglomeration degree gradually increases and the expansion potential has spatial distinctions, which basically meets the requirements of the overall planning.
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Notes Approved no
Call Number NC @ ehyde3 @ Serial 2088
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Author (up) Li, K.; Chen, Y.
Title A Genetic Algorithm-Based Urban Cluster Automatic Threshold Method by Combining VIIRS DNB, NDVI, and NDBI to Monitor Urbanization Type Journal Article
Year 2018 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 10 Issue 2 Pages 277
Keywords Remote Sensing
Abstract Accurate and timely information related to quantitative descriptions and spatial distributions of urban areas is crucial to understand urbanization dynamics and is also helpful to address environmental issues associated with rapid urban land-cover changes. Thresholding is acknowledged as the most popular and practical way to extract urban information from nighttime lights. However, the difficulty of determining optimal threshold remains challenging to applications of this method. In order to address the problem of selecting thresholds, a Genetic Algorithm-based urban cluster automatic threshold (GA-UCAT) method by combining Visible-Infrared Imager-Radiometer Suite Day/Night band (VIIRS DNB), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI) is proposed to distinguish urban areas from dark rural background in NTL images. The key point of this proposed method is to design an appropriate fitness function of GA by means of integrating between-class variance and inter-class variance with all these three data sources to determine optimal thresholds. In accuracy assessments by comparing with ground truth—Landsat 8 OLI images, this new method has been validated and results with OA (Overall Accuracy) ranging from 0.854 to 0.913 and Kappa ranging from 0.699 to 0.722 show that the GA-UCAT approach is capable of describing spatial distributions and giving detailed information of urban extents. Additionally, there is discussion on different classifications of rural residential spots in Landsat remote sensing images and nighttime light (NTL) and evaluations of spatial-temporal development patterns of five selected Chinese urban clusters from 2012 to 2017 on utilizing this proposed method. The new method shows great potential to map global urban information in a simple and accurate way and to help address urban environmental issues.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2340
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Author (up) Li, K.; Chen, Y.; Li, Y.
Title The Random Forest-Based Method of Fine-Resolution Population Spatialization by Using the International Space Station Nighttime Photography and Social Sensing Data Type Journal Article
Year 2018 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 10 Issue 10 Pages 1650
Keywords Remote Sensing
Abstract Despite the importance of high-resolution population distribution in urban planning, disaster prevention and response, region economic development, and improvement of urban habitant environment, traditional urban investigations mainly focused on large-scale population spatialization by using coarse-resolution nighttime light (NTL) while few efforts were made to fine-resolution population mapping. To address problems of generating small-scale population distribution, this paper proposed a method based on the Random Forest Regression model to spatialize a 25 m population from the International Space Station (ISS) photography and urban function zones generated from social sensing data—point-of-interest (POI). There were three main steps, namely HSL (hue saturation lightness) transformation and saturation calibration of ISS, generating functional-zone maps based on point-of-interest, and spatializing population based on the Random Forest model. After accuracy assessments by comparing with WorldPop, the proposed method was validated as a qualified method to generate fine-resolution population spatial maps. In the discussion, this paper suggested that without help of auxiliary data, NTL cannot be directly employed as a population indicator at small scale. The Variable Importance Measure of the RF model confirmed the correlation between features and population and further demonstrated that urban functions performed better than LULC (Land Use and Land Cover) in small-scale population mapping. Urban height was also shown to improve the performance of population disaggregation due to its compensation of building volume. To sum up, this proposed method showed great potential to disaggregate fine-resolution population and other urban socio-economic attributes.
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ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2038
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Author (up) Li, X.; Liu, S.; Jendryke, M.; Li, D.; Wu, C.
Title Night-Time Light Dynamics during the Iraqi Civil War Type Journal Article
Year 2018 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 10 Issue 6 Pages 858
Keywords Remote Sensing
Abstract In this study, we analyzed the night-time light dynamics in Iraq over the period 2012–2017 by using Visible Infrared Imaging Radiometer Suite (VIIRS) monthly composites. The data quality of VIIRS images was improved by repairing the missing data, and the Night-time Light Ratio Indices (NLRIs), derived from urban extent map and night-time light images, were calculated for different provinces and cities. We found that when the Islamic State of Iraq and Syria (ISIS) attacked or occupied a region, the region lost its light rapidly, with the provinces of Al-Anbar, At-Ta’min, Ninawa, and Sala Ad-din losing 63%, 73%, 88%, and 56%, of their night-time light, respectively, between December 2013 and December 2014. Moreover, the light returned after the Iraqi Security Forces (ISF) recaptured the region. In addition, we also found that the night-time light in the Kurdish Autonomous Region showed a steady decline after 2014, with the Arbil, Dihok, and As-Sulaymaniyah provinces losing 47%, 18%, and 31% of their night-time light between December 2013 and December 2016 as a result of the economic crisis in the region. The night-time light in Southern Iraq, the region controlled by Iraqi central government, has grown continuously; for example, the night-time light in Al Basrah increased by 75% between December 2013 and December 2017. Regions formerly controlled by ISIS experienced a return of night-time light during 2017 as the ISF retook almost all this territory in 2017. This indicates that as reconstruction began, electricity was re-supplied in these regions. Our analysis shows the night-time light in Iraq is directly linked to the socioeconomic dynamics of Iraq, and demonstrates that the VIIRS monthly night-time light images are an effective data source for tracking humanitarian disasters in that country.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2339
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Author (up) Li, X.; Zhao, L.; Li, D.; Xu, H.
Title Mapping Urban Extent Using Luojia 1-01 Nighttime Light Imagery Type Journal Article
Year 2018 Publication Sensors (Basel, Switzerland) Abbreviated Journal Sensors (Basel)
Volume 18 Issue 11 Pages
Keywords Instrumentation; Remote Sensing
Abstract Luojia 1-01 satellite, launched on 2 June 2018, provides a new data source of nighttime light at 130 m resolution and shows potential for mapping urban extent. In this paper, using Luojia 1-01 and VIIRS nighttime light imagery, we compared several methods for extracting urban areas, including Human Settlement Index (HSI), Simple Thresholding Segmentation (STS) and SVM supervised classification. According to the accuracy assessment, the HSI method using LJ1-01 data had the best performance in urban extent extraction, which presented the largest Kappa Coefficient value, 0.834, among all the results. For the urban areas extracted by VIIRS based HSI method, the largest Kappa Coefficient value was 0.772. In contrast, the largest Kappa Coefficient values obtained by STS method were 0.79 and 0.7512 respectively when using LJ1-01 and VIIRS data, while for SVM method the values were 0.7829 and 0.7486 when using Landsat-LJ and Landsat-VIIRS composite data respectively. The experimented results demonstrated that the utilization of nighttime light imagery can largely improve the accuracy of urban extent extraction and LJ1-01 data, with a higher resolution and more abundant spatial information, can lead to better identification results than its predecessors.
Address Key Laboratory of the Ministry of Land and Resources for Law Evaluation Engineering, Wuhan 430074, China. xuhuimin1985_2008@163.com
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ISSN 1424-8220 ISBN Medium
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Notes PMID:30380616 Approved no
Call Number GFZ @ kyba @ Serial 2056
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