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Regional land use change is the main cause of the ecosystem carbon storage changes by affecting emission and sink process. However, there has been little research on the influence of land use changes for ecosystem carbon storage at both temporal and spatial scales. For this study, the Qihe catchment in the southern part of the Taihang Mountains was taken as an example; its land use change from 2005 to 2015 was analyzed, the Markov-CLUES composite model was used to predict land use patterns in 2025 under natural growth, cultivated land protection and ecological conservation scenario, and the land use data were used to evaluate ecosystem carbon storage under different scenarios for the recent 10-year interval and the future based on the carbon storage module of the InVEST model. The results show the following: (1) the ecosystem carbon storage and average carbon density of Qihe catchment were 3.16×10 7 t and 141.9 t/ha, respectively, and decreased by 0.07×10 7 t and 2.89 t/ha in the decade evaluated. (2) During 2005-2015, carbon density mainly decreased in low altitude areas. For high altitude area, regions with increased carbon density comprised a similar percentage to regions with decreased carbon density. The significant increase of the construction areas in the middle and lower reaches of Qihe and the degradation of upper reach woodland were core reasons for carbon density decrease. (3) For 2015-2025, under natural growth scenario, carbon storage and carbon density also significantly decrease, mainly due to the decrease of carbon sequestration capacity in low altitude areas; under cultivated land protection scenario, the decrease of carbon storage and carbon density will slow down, mainly due to the increase of carbon sequestration capacity in low altitude areas; under ecological conservation scenario, carbon storage and carbon density significantly increase and reach 3.19×10 7 t and 143.26 t/ha, respectively, mainly in regions above 1100 m in altitude. Ecological conservation scenario can enhance carbon sequestration capacity but cannot effectively control the reduction of cultivated land areas. Thus, land use planning of research areas should consider both ecological conservation and cultivated land protection scenarios to increase carbon sink and ensure the cultivated land quality and food safety.

Topographical relief is a key factor that limits population distribution and economic development in mountainous areas. The limitation is especially apparent in the mountain-plain transition zone. Taking the transition zone between the Qinling Mountains and the North China Plain (i.e. the mountainous area in western Henan Province) as an example and based on the 200-m resolution DEM data, we used the mean change-point analysis to determine the optimal statistical unit for topographical relief, and thereafter extracted the relief degree. Taking the 1:100,000 land use data, township population and county-level industrial data, population and economic spatial models were constructed, and 200-m resolution grid population and economic density maps were generated. Afterwards, statistical analysis was carried out to quantitatively reveal the impact of topographical relief on population and economy. In addition, the impacts of other topographical factors were discussed. The results showed the following. (1) The relief degree in western Henan is generally low, where 58.6% of the regional topography does not exceed half the height of a reference mountain (relative elevation ≤250 m). Spatially, the relief degree is high in the west while low in the east, and high in the middle while low in the north and south. There is a positive correlation between relief degree and elevation, and a much stronger correlation between relief degree and slope. (2) The linear fitting degree between the population and economic validation data and the corresponding simulation data are 0.943 and 0.909, respectively, indicating that the spatialized results can reflect the actual population and economic distribution. (3) The impact of topographical relief on population and economy was stronger than that of other topographical factors. The relief degree showed a good logarithmic fit relationship with population density (0.911) and economic density (0.874). Specifically, 88.65% of the population lives in areas where the topographical relief is ≤0.5 and 88.03% of the gross regional product was from areas where the relief is ≤0.3. Compared with the population distribution, the economic development showed an obvious agglomeration trend towards low relief areas.

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Climate change has caused substantial shifts in the geographical distribution of many species. There is growing evidence that many species are migrating in response to climate change. Changes in the distribution of dominant tree species induced by climate change can have an impact not only on organisms such as epiphytes and understory vegetation, but also on the whole ecosystem. Cyclobalanopsis glauca is a dominant tree species in the mingled evergreen and deciduous broadleaf forests of China. Understanding their adaptive strategies against climate change is important for understanding the future community structure. We employed the Maxent framework to model current suitable habitats of C. glauca under current climate conditions and predicted it onto the climate scenarios for 2041–2060 and 2081–2100 using 315 occurrence data. Our results showed that annual precipitation was the most critical factor for the distribution of C. glauca. In the future, increasing precipitation would reduce the limitation of water on habitats, leading to an expansion of the distribution to a higher latitude and higher altitude. At the same time, there were habitat contractions at the junction of the Jiangxi and Fujian Provinces. This study can provide vital information for the management of C. glauca, and serve as a reminder for managers to protect C. glauca in the range contraction areas.

Kedong County is typical of the black soil region of northeast China in being highly susceptible to accelerated soil erosion by gullying. Using data sourced from Corona satellite imagery for 1965, SPOT5 for 2005 and GF-1 for 2015, the spatial distribution of gullies in the research area was mapped. Land use data for 1965, 2005, and 2015 were obtained from the topographic map of 1954, and from Landsat images for 2005 and 2015. Over the last 50 years, the extent of gully erosion in the study area has increased markedly, most notably on cultivated land, while gully density rose from 2,756.16 m2/km2 to 14,294.19 m2/km2. Cultivating land on slopes, especially on slopes greater than ∼4°, may rapidly aggravate gully erosion. The greatest increases in gully density occurred in situations when cultivated land and other/degraded land were transformed, which gully erosion density increased by 49,526.69 m2/km2. Other/degraded land is the most vulnerable land in the study area, with the highest gully erosion density. In these cases, gully density initially increases and, although the “Grain for Green” project has been implemented, gully erosion density has not always declined in the recent past.

This paper reports the phenological response of forest vegetation to climate change (changes in temperature and precipitation) based on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time-series images from 2000 to 2015. The phenological parameters of forest vegetation in the Funiu Mountains during this period were determined from the temperature and precipitation data using the Savitzky-Golay filter method, dynamic threshold method, Mann-Kendall trend test, the Theil-Sen estimator, ANUSPLIN interpolation and correlation analyses. The results are summarized as follows: (1) The start of the growing season (SOS) of the forest vegetation mainly concentrated in day of year (DOY) 105-120, the end of the growing season (EOS) concentrated in DOY 285-315, and the growing season length (GSL) ranged between 165 and 195 days. There is an evident correlation between forest phenology and altitude. With increasing altitude, the SOS, EOS and GSL presented a significant delayed, advanced and shortening trend, respectively. (2) Both SOS and EOS of the forest vegetation displayed the delayed trend, the delayed pixels accounted for 76.57% and 83.81% of the total, respectively. The GSL of the forest vegetation was lengthened, and the lengthened pixels accounted for 61.21% of the total. The change in GSL was mainly caused by the decrease in spring temperature in the region. (3) The SOS of the forest vegetation was significantly partially correlated with the monthly average temperature in March, with most correlations being negative; that is, the delay in SOS was mainly attributed to the temperature decrease in March. The EOS was significantly partially correlated with precipitation in September, with most correlations being positive; that is, the EOS was clearly delayed with increasing precipitation in September. The GSL of the forest vegetation was influenced by both temperature and precipitation throughout the growing season. For most regions, GSL was most closely related to the monthly average temperature and precipitation in August.