The spatial distribution of potential vegetation types in Qinghai-Tibet Plateau presents a significant vertical zonation. Explicating the vertical differences of potential vegetation distribution under future climate change in Qinghai-Tibet Plateau is an important issue for understanding the response of terrestrial ecosystem to climate change. Based on the observed climate data in 1981-2010 (T0), the scenario data of RCP 2.6, RCP 4.5 and RCP 8.5 released by CMIP5 in 2011-2040 (T1), 2041-2070 (T2) and 2071-2100 (T3), and the digital elevation model (DEM) data, the Holdridge life zone (HLZ) model has been improved to simulate the scenarios of potential vegetation distribution in the different gradient zones of Qinghai-Tibet plateau. The shift model of mean center has been improved to calculate the shift direction and distance of mean center in the potential vegetation types. The ecological diversity index was introduced to compute the ecological diversity change of potential vegetation. The simulated results show that there are 17 potential vegetation types in Qinghai-Tibet Plateau. Wet tundra, high-cold moist forest and nival are the major potential vegetation types and cover 56.26% of the total area of Qinghai-Tibet Plateau. Under the three scenarios, the nival would have the largest decreased area that would be decreased by 3.340 × 10⁴ km² per decade, and the high-cold wet forest would have the greatest increased area that would be increased by 3.340 × 10⁴ km² on average per decade from T0 to T3. The potential vegetation types distributed in the alpine zone would show the fastest change ratio (11.32% per decade) and that in low mountain and other zone would show the slowest change ratio (7.54% per decade) on average. The ecological diversity and patch connectivity of potential vegetation would be decreased by 0.108% and 0.290% per decade on average from T0 to T3. In general, the potential vegetation types distributed in the high elevation area generally have a higher sensitivity to climate change in Qinghai-Tibet plateau in the future.

Impact of land use and land cover change on soil erosion is still imperfectly understood, especially in northeastern China where severe soil erosion has occurred since the 1950s. It is important to identify temporal changes of soil erosion for the black soil region at different spatial scales. In the present study, potential soil erosion in northeastern China was estimated based on the Revised Universal Loss Equation by integrating satellite images, and the variability of soil erosion at different spatial scales following land use changes in 1980, 1990, 2000, 2010, and 2017 was analyzed. The regionally spatial patterns of soil loss coincided with the topography, rainfall erosivity, soil erodibility, and use patterns, and around 45% of soil loss came from arable land. Regionally, soil erosion rates increased from 1980 to 2010 and decreased from 2010 to 2017, ranging from 3.91 to 4.45 Mg ha⁻¹ yr⁻¹ with an average of 4.22 Mg ha⁻¹ yr⁻¹ in 1980–2017. Areas with a rate of soil erosion less than 1.41 Mg ha⁻¹ yr⁻¹ decreased from 1980 to 2010 and increased from 2010 to 2017, and the opposite changing patterns occurred in higher erosion classes. Arable land continuously increased at the expense of forest in the high-elevation and steep-slope areas from 1980 to 2010, and decreased from 2010 to 2017, resulting in increased areas with erosion rates higher than 7.05 Mg ha⁻¹ yr⁻¹. At a provincial scale, Liaoning Province experienced the highest soil erosion rate of 9.43 Mg ha⁻¹ yr⁻¹, followed by Jilin Province, the eastern Inner Mongolia Autonomous Region, and Heilongjiang Province. At a county scale, around 75% of the counties had a soil erosion rate higher than the tolerance level. The county numbers with higher erosion rate increased in 1980–2010 and decreased in 2010–2017, resulting from the sprawl and withdrawal of arable land.

Explicitly identifying the desertification changes and causes has been a hot issue of eco-environment sustainable development in the China–Mongolia–Russia Economic Corridor (CMREC) area. In this paper, the desertification change patterns between 2000 and 2015 were identified by operating the classification and regression tree (CART) method with multisource remote sensing datasets on Google Earth Engine (GEE), which has the higher overall accuracy (85%) than three other methods, namely support vector machine (SVM), random forest (RF) and Albedo-normalized difference vegetation index (NDVI) models. A contribution index of climate change and human activities on desertification was introduced to quantitatively explicate the driving mechanisms of desertification change based on the temporal datasets and net primary productivity (NPP). The results show that the area of slight desertification land had increased from 719,700 km² to 948,000 km² between 2000 and 2015. The area of severe desertification land decreased from 82,400 km² to 71,200 km². The area of desertification increased by 9.68%, in which 69.68% was mainly caused by human activities. Climate change and human activities accounted for 68.8% and 27.36%, respectively, in the area of desertification restoration. In general, the degree of desertification showed a decreasing trend, and climate change was the major driving factor in the CMREC area between 2000 and 2015.