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Delineating Spatial Patterns in Human Settlements Using VIIRS Nighttime Light Data: A Watershed-Based Partition Approach

机译:使用VIIRS夜间光数据描述人类住区的空间格局:一种基于分水岭的分区方法

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As an informative proxy measure for a range of urbanization and socioeconomic variables, satellite-derived nighttime light data have been widely used to investigate diverse anthropogenic activities in human settlements over time and space from the regional to the national scale. With a higher spatial resolution and fewer over-glow and saturation effects, nighttime light data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument with dayight band (DNB), which is on the Suomi National Polar-Orbiting Partnership satellite (Suomi-NPP), may further improve our understanding of spatiotemporal dynamics and socioeconomic activities, particularly at the local scale. Capturing and identifying spatial patterns in human settlements from VIIRS images, however, is still challenging due to the lack of spatially explicit texture characteristics, which are usually crucial for general image classification methods. In this study, we propose a watershed-based partition approach by combining a second order exponential decay model for the spatial delineation of human settlements with VIIRS-derived nighttime light images. Our method spatially partitions the human settlement into five different types of sub-regions: high, medium-high, medium, medium-low and low lighting areas with different degrees of human activity. This is primarily based on the local coverage of locally maximum radiance signals (watershed-based) and the rank and magnitude of the nocturnal radiance signal across the whole region, as well as remotely sensed building density data and social media-derived human activity information. The comparison results for the relationship between sub-regions with various density nighttime brightness levels and human activities, as well as the densities of different types of interest points (POIs), show that our method can distinctly identify various degrees of human activity based on artificial nighttime radiance and ancillary data. Furthermore, the analysis results across 99 cities in 10 urban agglomerations in China reveal inter-regional variations in partition thresholds and human settlement patterns related to the urban size and form. Our partition method and relative results can provide insight into the further application of VIIRS DNB nighttime light data in spatially delineated urbanization processes and socioeconomic activities in human settlements.
机译:作为对一系列城市化和社会经济变量的一种有益的替代措施,源自卫星的夜间光数据已被广泛用于调查人类住区在区域和国家范围内随时间和空间的各种人为活动。 Suomi国家极地轨道合作伙伴卫星上的夜光数据具有较高的空间分辨率,并且具有较少的过发光和饱和效应,其夜间光线数据来自具有昼/夜波段(DNB)的可见红外成像辐射计套件(VIIRS)仪器( Suomi-NPP),可能会进一步增进我们对时空动态和社会经济活动的理解,尤其是在地方范围内。然而,由于缺乏空间明确的纹理特征(通常对于常规图像分类方法至关重要),从VIIRS图像捕获和识别人类住区中的空间模式仍然具有挑战性。在这项研究中,我们通过结合用于人类住区空间描绘的二阶指数衰减模型与VIIRS衍生的夜间光图像,提出了一种基于分水岭的分区方法。我们的方法在空间上将人类住区划分为五种不同类型的子区域:高,中高,中,中低和低光照区域,这些区域具有不同程度的人类活动。这主要基于局部最大辐射信号的局部覆盖(基于分水岭)以及整个区域夜间辐射信号的等级和大小,以及遥感的建筑密度数据和社交媒体衍生的人类活动信息。通过比较夜间夜间各种亮度水平的子区域与人类活动之间的关系以及不同类型的兴趣点(POI)的密度的比较结果,表明我们的方法可以基于人工方法来区分不同程度的人类活动夜间辐射和辅助数据。此外,对中国10个城市群中99个城市的分析结果表明,分区阈值和人类居住模式的区域间差异与城市规模和形式有关。我们的划分方法和相关结果可以为VIIRS DNB夜间光数据在空间定型的城市化过程和人类住区的社会经济活动中的进一步应用提供见识。

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