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Identifying Urban Sprawl by night lights a pending issue

机译:夜间识别城市蔓延灯征用待定问题

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Nighttime light imagery of the earth are a useful way to study the urbanization process. Satellite nocturnal images have been used to identify metropolitan areas as well as urban growth. However, the study of the extent and internal structure of urban systems by nighttime lights has had a fundamental limitation to date: the low spatial resolution of satellite sensors. DMSP Operational Linescan System (OLS), with its 2.7 km/pixel footprint, and Suomi National Polar Partnership (SNPP) satellite, with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on board, with a spatial resolution of 742 m/pixel, still have considerable limitations for the in-depth study of the internal structure of urban systems. The launch of Luojia 1-01 in June 2018 has increased expectations. Its high-resolution nocturnal images (130 metres/pixel) allows a better in-depth study of the landscape impacted by the urbanization. Nevertheless, the areas resulting from urban sprawl process are characterized by weak night lighting, which makes identification extremely difficult. Breaking the rigid boundary that historically distinguished the urban from the rural, the topological inversion of the landscape produced by urban sprawl, makes difficult to identify the territories impacted by dispersed, fragmented and low density urbanization processes. The identification of the sprawled zones and their segmentation of the agricultural covers as well as the rest of the open spaces is especially complex, given the spatial resolution of Luojia 1-01. In this sense, the consideration of the NDVI, altitude, orientation, slope as well as the information provided by the thermal bands of Landsat8 can help to carry out a finer identification of the different urban landscapes, and specifically of the Urban Sprawl. The aim of this paper is to analyse the capacity of Luojia 1-01 to identify different types of urban landscapes, especially the results of the urban dispersion process known as Urban Sprawl. The case study is Barcelona Metropolitan Area (636 km2 and 3.2 million inhabitants).
机译:地球的夜间光图像是研究城市化进程的有效途径。卫星图像夜间已经用于识别大都市地区以及城市的增长。然而,程度和城市系统的内部结构的由夜间灯光的研究已经有了最新的基本限制:卫星传感器的空间分辨率低。 DMSP操作线扫描系统(OLS),以其2.7公里/像素的足迹,并且芬兰语国家极伙伴关系(SNPP)卫星,与上板的可见红外成像辐射计套件(VIIRS)传感器,与742米/像素的空间分辨率,仍然有城市系统的内部结构的深入研究相当的局限性。在2018年6月推出珞珈1-01的增加的预期。它的高清晰度图像夜间(130公尺/像素)允许由城市化影响的景观更好的深入研究。然而,从城市扩张过程中产生的区域由弱夜间照明,这使得鉴定非常困难的表征。打破了刚性边界,从历史上看区分城镇从农村,通过城市扩张产生的景观的拓扑反转,使得难以确定由分散,片段化和低密度城市化过程影响的地区。该趴区域的识别及其农业盖的分割以及开放空间的其余部分是特别复杂,因为珞珈1-01的空间分辨率。在这个意义上,NDVI,高度,取向,倾斜的考虑以及由Landsat8的热波段中提供的信息可以帮助进行不同的城市景观的更精细的识别,并且特别是城市扩张。本文的目的是分析珞珈1-01的能力,以识别不同类型的城市景观的,特别是被称为城市蔓延城市分散处理的结果。案例研究是巴塞罗那大都会区(636平方公里和3.2万居民)。

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