Land use changes affect the surface radiative budget and energy balance by changing the surface albedo, which generates radiative forcing, impacting the regional and global climate. To estimate the effect of land use changes on the surface albedo and climate change in a mountainous area with complex terrain, we obtained MODIS data, identified the spatial–temporal characteristics of the surface albedo caused by land use changes, and then calculated the radiative forcing based on solar radiative data and the surface albedo in the Qinling-Daba mountains from 2000 to 2015. The correlation between the land use changes and the radiative forcing was analyzed to explore the climate effects caused by land use changes on a kilometer-grid scale in the Qinling-Daba mountains. Our results show that the primarily land use changes were a decrease in the cultivated land area and an increase in the construction land area, as well as other conversions between six land use types from 2000 to 2015. The land use changes led to significant changes in the surface albedo. Meanwhile, the radiative forcing caused by the land use had different magnitudes, strengths, and occurrence ranges, resulting in both warming and cooling climate change effects.

Land surface temperature (LST) variations are very complex in mountainous areas owing to highly heterogeneous terrain and varied environment, which complicates the surface urban heat island (SUHI) in mountain cities. Previous studies on the urban heat island (UHI) effect mostly focus on the flat terrain areas; there are few studies on the UHI effect in mountainous areas, especially on the influence of elevation on the SUHI effect. To determine the SUHI in the Qinling–Daba mountains (China), MODIS LST data were first preprocessed and converted to the same elevations (1500 m, 2000 m, 2500 m, 3000 m, and 3500 m) using a digital elevation model and the random forest method. Then, the average LSTs in urban land, rural land, and cultivated land were calculated separately based on the ranges of the invariable urban, rural, and cultivated areas during 2010–2018, and the urban, rural, and cultivated land LST difference were estimated for the same elevations. Results showed that the accuracy of LST estimated using the random forest method is very high (R² ≥ 0.9) at elevations of 1500 m, 2000 m, 2500 m, 3000 m and 3500 m. The difference in urban, rural, and cultivated lands’ LST has a trend of decrease with increasing elevation, meaning that the SUHI weakens at higher elevations. The average LST of urban areas is 0.52–0.59 °C (0.42–0.57 °C) higher than that of rural and cultivated areas at an elevation of 1500 m (2000 m). The average LST of urban areas is 0.10–1.25 °C lower than that of rural and cultivated areas at elevations of 2500 m, 3000 m, and 3500 m, indicating absence of the SUHI at those elevations.

The altitude of alpine timberline elevation has been considered to correlate with certain climatic factors. Many related isotherms (e.g., warmest month 10 °C isotherm) have been proposed to explain the altitudinal distribution of alpine timberline at the global scale. However, any climatic index actually has a wide range at the alpine timberline position worldwide. The altitudinal position of the alpine timberline is related to far more than just one climatic factor. Therefore, we developed a multivariable model for timberline elevation variability by collecting data from 473 timberline sites on the Eurasian continent. We analyzed 12 climatic variables that potentially account for timberline variation. Principal component and regression analyses were used to mine four climatic variables. The mean temperature of the warmest month (MTWM), mean temperature of the coldest month (MTCM), climatic continentality (K), and annual precipitation (AP) explained 95% of the variability of timberline elevation. MTWM, MTCM, K, and AP contributed 18%, 41.28%, 34.9%, and 5.82%, respectively, to the altitudinal distribution of alpine timberline on the whole continent; 20%, 44%, 28.86%, and 7.14% in the eastern continent; and 17.71%, 39.79%, 40.21%, and 2.29% in the western continent. We showed that MTWM, MTCM, K, and AP are deterministic factors for the altitudinal distribution of alpine timberline in the Eurasian continent. MTCM and K contributed to explaining the altitudinal distribution of timberline both in the entire, eastern, and western parts of the Eurasian continent. Our research highlights the significance of MTCM for the altitudinal distribution of timberline.