Forest phenology is sensitive to climate change, and its responses affect many land surface processes, resulting in a feedback effect on climate change. Human activities have been the main driver of climate change’s long-term shifts in temperature and weather patterns. Forest phenology, understood as the timing of the annual cycles of plants, is extremely sensitive to changes in climate. Quantifying the responses of temperate forest phenology under an elevational range of topographic conditions that mimic climate change is essential for making effective adaptive forest ecosystem management decisions. Our study utilized the Google Earth Engine (GEE), gap filling, and the Savitzky–Golay (GF-SG) algorithm to develop a long-time series spatio-temporal remote sensing data fusion. The forest phenology characteristics on the north slope of Changbai Mountain were extracted and analyzed annually from 2013 to 2022. Our study found that the average start of the growing season (SOS) on the north slope of Changbai Mountain occurred between the 120th–150th day during the study period. The end of the growing season (EOS) occurred between the 270th–300th day, and the length of the growing season (LOS) ranged from the 110th–190th day. A transect from the northeast to southwest of the study area for a 10-year study period found that SOS was delayed by 39 d, the EOS advanced by 32 d, and the LOS was gradually shortened by 63 d. The forest phenology on the north slope of Changbai Mountain showed significant topographic differentiations. With an increase of 100 m in altitude, the mean SOS was delayed by 1.71 d (R² = 0.93, p < 0.01). There were no obvious trends in EOS variation within the study area altitude gradient. LOS decreased by 1.23 d for each 100 m increase in elevation (R² = 0.90, p < 0.01). Forests on steep slopes had an earlier SOS, a later EOS, and a longer LOS than forests on gentle slopes. For each degree increase in slope, SOS advanced by 0.12 d (R² = 0.53, p = 0.04), EOS was delayed by 0.18 d (R² = 0.82, p = 0.002), and the LOS increased by 0.28 d (R² = 0.78, p = 0.004). The slope aspect had effects on the EOS and the LOS but had no effect on the SOS. The forest EOS of the south aspect was 3.15 d later than that of the north aspect, and the LOS was 6.47 d longer. Over the 10-year study period, the phenology differences between the north and south aspects showed that the LOS difference decreased by 0.85 d, the SOS difference decreased by 0.34 d, and the EOS difference decreased by 0.53 d per year. Our study illustrates the significance of the coupling mechanism between mountain topography and forest phenology, which will assist our future understanding of the response of mountain forest phenology to climate change, and provide a scientific basis for further research on temperate forest phenology.

As a major coastal economic province in the east of China, it is of great significance to clarify the temporal and spatial patterns of regional development in Shandong Province in recent years to support regional high-quality development. Nightlight remote sensing data can reveal the spatio-temporal patterns of social and economic activities on a fine pixel scale. We based the nighttime light patterns at three spatial scales in three geographical regions on monthly nighttime light remote sensing data and social statistics. Different cities and different counties in Shandong Province in the last 10 years were studied by using the methods of trend analysis, stability analysis and correlation analysis. The results show that: (1) The nighttime light pattern was generally consistent with the spatial pattern of construction land. The nighttime light intensity of most urban, built-up areas showed an increasing trend, while the old urban areas of Qingdao and Yantai showed a weakening trend. (2) At the geographical unit scale, the total nighttime light in south-central Shandong was significantly higher than that in eastern and northwest Shandong, while the nighttime light growth rate in northwest Shandong was significantly highest. At the urban scale, Liaocheng had the highest nighttime light growth rate. At the county scale, the nighttime light growth rate of counties with a better economy was lower, while that of counties with a backward economy was higher. (3) The nighttime light growth was significantly correlated with Gross Domestic Product (GDP) and population growth, indicating that regional economic development and population growth were the main causes of nighttime light change.