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.

With the gradual expansion of benefits provided by islands to human wellbeing, it has become increasingly important to quantify the cultural ecosystem service functions of islands. In this study, the zone travel cost method (ZTCM) and individual travel cost method (ITCM) are used to assess the recreational and cultural ecosystem services value of the island region of Changhai County, China, and the recreational value of Xiaochangshan, Dachangshan, and Guanglu Islands based on questionnaire survey data. The results are as follows: (1) The overall recreational and cultural ecosystem services value of Changhai County in 2019 was 533.14 million USD, including a traveling cost of 395.71 million USD and consumer surplus of 137.43 million USD. (2) Further, based on the choice and consumption behaviors of tourists, multiple recreational values of the county were also inferred: the aesthetic experience presented the greatest contribution, while educational knowledge accounted for a lower percentage. (3) The recreational and cultural ecosystem services value 294.16, 60.03, and 143.26 million USD for Xiaochangshan, Dachangshan, and Guanglu Islands, respectively. Based on the findings of this study, future research will focus on the planning and development of Xiaochangshan Island tourism to maximize consumer surplus.

The objective assessment of ecological systems forms the basis of solving ecological environmental problems. Evaluating the ecosystem status of each county through the gross ecosystem product (GEP) can reveal the value of each ecosystem. In this study, we used the eco-economic method to calculate the GEP and the green gold index (GGI) of 13 counties in Hulunbuir City between 2015 and 2020. The results show that: (1) The GEP of Hulunbuir City in 2020 was 980.025 billion yuan. The GGI was 8.36, which was much higher than the national average. (2) Forestry and pastoral regions were the main contributors to the regulation service. (3) Hulunbuir City had the largest forest value, while the farmland value was the lowest. The most important sources of forest, grassland, wetland, water, and farmland value were Oroqen, Xin Right Banner, Xin Left Banner, Xin Right Banner, and Morin Banner, respectively. Based on our analysis, we found significant results through the transformation of the “Two Mountains” in Erguna, Genhe, and Zhalantun. The other counties in our study must optimize ecological research with respect to the traditional economic model. Our results provide a scientific reference for the application of the “Two Mountains” base in each county.