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The spatial distribution of night-time lights (NTL) provides a new perspective for studying the range and influence of human activities. However, most studies employing NTL time series are based on monthly or annual composite data, and time series studies incorporating sliding windows are currently lacking. Therefore, using National Polar-Orbiting Partnership's visible infrared imaging radiometer suite (NPP-VIIRS) night-time light remote sensing (NTLRS) data, VNP46A2, toponym, and Yunnan census statistical data, this study proposes a sliding-window-based NTLRS time series detection and analysis method. We extracted ethnic minority areas on the PyCharm platform using ethnic minority population proportion data and toponym and excluding data representing interference from urban areas. We used a sliding window approach to analyze NTLRS time series data of each ethnic group and calculated the cosine similarity between the NTL brightness curve of original data and the sliding window analysis result. The cosine similarity was greater than 0.96 from 2018 to 2020; we also conducted a field trip to the 2019 Torch Festival to demonstrate the applicability of the employed method. Finally, the temporal and spatial pattern of the Torch Festival was analyzed using the festival in Yunnan Province as an example. Results showed that the Torch Festival, mostly celebrated by the Yi ethnic group, was usually held on the 24th (and ranged from the 22nd to 26th) day in the sixth month of the lunar calendar (LC) every year. We found that during the Torch Festival, the greater the increase in the percentage of NTL brightness reduction in the main urban area of Kunming, the greater the percentage of ethnic minorities' NTL brightness. The width of the sliding window can be adjusted appropriately according to the research objective, with these results showing good continuity. Our study presents a new application of the sliding window approach in the field of remote sensing, suitable for research into festivals related to night lights and fire all over the world. [ABSTRACT FROM AUTHOR]

Impervious surface is a key factor to measure the urban ecological environment. It is of great significance for urban development planning to grasp the dynamic changes of impervious surfaces timely and accurately. Taking the Chenggong District of Kunming City as an example' based on the Landsat images in 2007' 2011' 2015' and 2019' the comparative study of normalized difference impervious surface index ( NDISI) and modified soil adjusted vegetation index ( MSAVI) was carried out to analyze the spatial and temporal evolution characteristics of impervious surface. The results showed that: !As the extraction accuracy and Kappa coefficient of NDISI were 87. 01% and 0. 81' respectively' which were beUer than MSAVI's 81. 78% and 0. 75 ' this paper selected the NDISI method to extract impervious surfaces in the Chenggong District the impervious surface area extracted in this paper increased from 46. 12 km2 in 2007 to 72. 64 km2 in 2011' 146. 94 km2 in 2015 and 164. 42 km2 in 2019' especially from 2011 to 2015' the impervious surface area had the fastest growth rate and nearly doubled. The changes to the impervious surface in Chenggong District are mainly influenced by such factors as national policies' urban planning' topographic factors' and traffic development. The impervious surface area along the Dianchi Lake in the west of Chenggong District and several administrative regions in the middle of Chenggong District developed rapidly' which brings certain preesure on the prevention and control of waterlogging in urban areas and the Dianchi Lake area. In the process of future urban planning' the expansion scope and speed of impervious surfaces should be wen controlled to avoid ecological and environmental problems caused by the unreasonable spatial patterns of impervious sur&aces. [ABSTRACT FROM AUTHOR]

Because of climate change and rapid urbanization, urban impervious underlying surfaces have expanded, causing Chinese cities to become strongly affected by flood disasters. Therefore, research on urban flood risks has greatly increased over the past decade, with studies focusing on reducing the risk of flood disaster. From 2012 to 2020, the impervious underlying surface has increased, and the permeable underlying surface has decreased annually in Kunming City. This study was conducted to investigate the impact of continuous changes in the urban underlying surface on flood disasters in the Runcheng area south of Kunming City from 2012 to 2020. We constructed a two-dimensional flood model to conduct flood simulations and flood risk analysis for this area. The relationship between the permeability of the underlying surface and urban flood risk was simulated and analyzed by varying the urban underlying surface permeability (0–60%). The simulation results show that the model can accurately simulate urban waterlogging, and the increase in urban waterlogging risk is related to the underlying surface permeability. Urban flood risk decreases with the increase in permeable underlying surface. The increase rate of flood risk in the part with permeability of 0–35% is greater than that the part with permeability of 35–60%, that is, when the permeability of underlying surface is lower than 35%, the flood risk rate will be higher. We demonstrated the impact of the urban underlying surface permeability on the risk of urban flood disasters, which is useful for urban planning decisions and urban flooding risk controls. [ABSTRACT FROM AUTHOR]