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Long-term sequence-high spatial-temporal resolution urban landscape dynamic remote sensing monitoring
[ Instrument network instrument research and development ] About 3% of the world’s urban surface supports 55% of human life. Complex and high-intensity human activities have drastically changed the urban landscape, but the current academic world lacks a long-term continuous sequence-high-temporal-spatial resolution urban landscape Dynamic data, the driving mechanism leading to the evolution of the urban landscape and its ecological effects are still unclear, which restricts the improvement of urban residents' well-being and sustainable urban development.
Based on the Google Earth Engine (GEE) cloud platform, researchers from the Urban Forest and Wetland Discipline Group of the Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences developed the Landsat image sharpening method for the urban landscape for the first time, retaining the original optical information and improving the spatial resolution of the image , Combined with vegetation phenology, surface optical information, and maximum variance method, proposed the urban landscape multi-level decision classification system (MDR), which proved the year-by-year landscape dynamics of Changchun City with a spatial resolution of 15m from 2000 to 2019. Based on the year-by-year landscape dynamics, the researchers determined the sudden change year of the landscape dynamics, combined with yearbook data, remote sensing data, and meteorological data to reveal the driving mechanism of urban landscape dynamics and the ecological effects produced by scientific quantification.
The results show that: (1) The accuracy of the urban landscape multi-level classification method proposed in this study is high, up to 92%, and the MDR classification system is usable and universal. After testing, it can draw the world's large city landscape coverage map with high accuracy (more than 0.85±0.01); (2) Between 2000 and 2019, the landscape dynamics of Changchun City increased by built-up area (34.0×103 ha to 47.5 ×103 ha), arable land decreased (22.0×103 ha to 5.7×103 ha), and the urban green area continued to increase. 2009 was a turning point in land dynamics. After 2009, the growth rate of built-up areas decreased from 2.24% per year to 1.30% per year, the rate of cultivated land loss decreased from 9.19% per year to 5.29% per year, and the growth rate of green land decreased from 2.66% per year to each year. 0.43%; (3) In the past 20 years, despite the continuous increase in built-up areas, the proportion of urban green space and built-up areas has remained at about 40%. GDP growth, population mobility, and industrial development promote changes in the urban landscape, while the increase in the proportion of built-up areas and the increase in the proportion of green space are related to the slowdown of urban wind speed, resulting in the increase of fine particulate matter in the urban area.
The research was completed by associate researcher Ren Zhibin, doctoral student Yulin Dong, and doctoral student Fu Yao of the Northeast Institute of Geography. Researcher He Xingyuan was the corresponding author of the paper. The relevant results were published on Remote Sensing. The research was funded by the member project of the Youth Promotion Association of the Chinese Academy of Sciences, the National Natural Science Foundation of China, and the Science and Technology Development Project of Jilin Province.