The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity
The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity
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DOI：
10.1016/j.rse.2012.10.022
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发表时间：
2013-02
影响因子：
13.5
通讯作者：
Qingling Zhang;C. Schaaf;K. Seto
中科院分区：
文献类型：
--
作者：
Qingling Zhang;C. Schaaf;K. Seto
科学界和政策界越来越需要及时了解全球城市在形态、基础设施和能源使用方面的城市间差异。来自国防气象卫星计划/操作行扫描系统（DMSP/OLS）的夜间照明（NTL）数据能够提供夜间光度的信息，这是建筑环境和能源消耗的相关性。虽然NTL数据被用于绘制城市地区的总体测量，如总面积范围，但由于数据值的饱和，特别是在城市核心区，它们描述城市间变化的能力有限。在这里，我们提出了一种新的光谱指数，即植被调整NTL城市指数（VANUI），它将MODIS NDVI与NTL相结合，以实现三个关键目标。首先，该指数降低了NTL饱和的影响。其次，该指数增加了NTL信号的变化，特别是在城市地区。第三，该指数符合生物物理和城市特征。此外，该指数直观，易于实现，并能够快速表征夜间亮度的城市间变化。对瓦努阿图非关税区的评估表明，它大大降低了非关税区的饱和度，并增加了核心城市地区数据值的变化。因此，VANUI可以用于城市结构，能源使用和碳排放的研究。
The science and policy communities increasingly require information about inter-urban variability in form, infrastructure, and energy use for cities globally and in a timely manner. Nighttime light (NTL) data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) are able to provide information on nighttime luminosity, a correlate of the built environment and energy consumption. Although NTL data are used to map aggregate measures of urban areas such as total area extent, their ability to characterize inter-urban variation is limited due to saturation of the data values, especially in urban cores. Here we propose a new spectral index, the Vegetation Adjusted NTL Urban Index (VANUI), which combines MODIS NDVI with NTL, to achieve three key goals. First, the index reduces the effects of NTL saturation. Second, the index increases variation of the NTL signal, especially within urban areas. Third, the index corresponds to biophysical and urban characteristics. Additionally, the index is intuitive, simple to implement, and enables rapid characterization of inter-urban variability in nighttime luminosity. Assessments of VANUI show that it significantly reduces NTL saturation and increases variation of data values in core urban areas. As such, VANUI can be useful for studies of urban structure, energy use, and carbon emissions.