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The Tibetan Plateau (TP) is undergoing rapid urbanization. To improve urban sustainability and construct ecological security barriers, it is essential to quantify the spatial patterns of urbanization level on the TP, but the existing studies on the topic have been limited by the lack of socioeconomic data. This study aims to quantify the urbanization level on the TP in 2018 with Luojia1-01 (LJ1-01) high-resolution nighttime light (NTL) data. Specifically, the compounded night light index is used to quantify spatial patterns of urbanization level at multiple scales. The results showed that the TP had a low overall urbanization level with a large internal difference. The urbanization level in the northeast, southeast and south of the TP was relatively high, forming three hotspots centered in Xining City, Lhasa City and Shangri-La City, while the urbanization level in the central and western regions was relatively low. The analysis of influencing factors, based on the random forest model, showed that transportation and topography were the main factors affecting the TP's spatial patterns of urbanization level. The comparison analysis with socioeconomic statistics and traditional NTL data showed that LJ1-01 NTL data can be used to more effectively quantify the urbanization level since it is more advantageous for reflecting the spatial extent of urban land and describing the spatial structure of socioeconomic activities within urban areas. These advantages are attributed to the high spatial resolution of the data, appropriate imaging time and unaffected by saturation phenomena. Thus, the proposed LJ1-01 NTL-based urbanization level measurement method has the potential for wide applications around the world, especially in less-developed regions lacking statistical data. Using this method, we refined the measurement of the TP's urbanization level in 2018 for multiple scales including the region, basin, prefecture and county levels, which provides basic information for the further urban sustainability research on the TP.

Nighttime light images are valuable indicators of regional economic development, and nighttime light data are now widely used in town monitoring and evaluation studies. Using the nighttime light data acquired through Luojia1-01 and the geographic information system spatial analysis method, this study analyzed the spatial vitality pattern of 402 characteristic towns in six geographic divisions of China. The average DN (Digital Number) value of Guzhen, having the highest vitality level, was 0.05665221, whereas that of Xin’an, having the lowest vitality level, was 0.00000186. A total of 89.5% of towns have a low level of vitality. The regional differences were significant; high vitality towns are concentrated in economically developed coastal areas, mainly in two large regions of east China and south central. The average lighting densities of the towns in east China and south central were 0.004838 and 0.003190, respectively. The lighting density of the towns in west central was low, and the vitality intensity was generally low. A spatially significant positive correlation of small-town vitality was observed, and “high–high” agglomeration was primarily distributed in the Yangtze River Delta, Pearl River Delta, and Fujian coastal areas in east and south China. The towns with high vitality intensity had similarities in their geographical location, convenient transportation conditions, and profound historical heritage or cultural accumulation along with many industrial enterprises. This research empirically demonstrates the feasibility of using the 130-m-high resolution of the nighttime lighting data of Luojia1-01 to evaluate the vitality at the town scale, and the vitality evaluation focuses on the spatial attributes of the town, which is meaningful to guide the development of the town in each region given the vast area of China and the large differences in the development of different regions.

Nighttime light images are valuable indicators of regional economic development, and nighttime light data are now widely used in town monitoring and evaluation studies. Using the nighttime light data acquired through Luojia1-01 and the geographic information system spatial analysis method, this study analyzed the spatial vitality pattern of 402 characteristic towns in six geographic divisions of China. The average DN (Digital Number) value of Guzhen, having the highest vitality level, was 0.05665221, whereas that of Xin'an, having the lowest vitality level, was 0.00000186. A total of 89.5% of towns have a low level of vitality. The regional differences were significant; high vitality towns are concentrated in economically developed coastal areas, mainly in two large regions of east China and south central. The average lighting densities of the towns in east China and south central were 0.004838 and 0.003190, respectively. The lighting density of the towns in west central was low, and the vitality intensity was generally low. A spatially significant positive correlation of small-town vitality was observed, and "high–high" agglomeration was primarily distributed in the Yangtze River Delta, Pearl River Delta, and Fujian coastal areas in east and south China. The towns with high vitality intensity had similarities in their geographical location, convenient transportation conditions, and profound historical heritage or cultural accumulation along with many industrial enterprises. This research empirically demonstrates the feasibility of using the 130-m-high resolution of the nighttime lighting data of Luojia1-01 to evaluate the vitality at the town scale, and the vitality evaluation focuses on the spatial attributes of the town, which is meaningful to guide the development of the town in each region given the vast area of China and the large differences in the development of different regions. [ABSTRACT FROM AUTHOR]