The hilly and mountainous regions of China are characterized by unique features such as small plots of land, steep slopes, fragmented fields, and high soil viscosity, which result in a decline in the efficiency of conventional agricultural machinery, or even render its use impractical. To address this issue, this study developed a micro universal chassis adapted to hilly terrains. First, a four-wheel-drive multifunctional electric micro chassis was designed, considering the terrain characteristics of hilly regions and the agronomic requirements of maizesoybean strip intercropping. Second, the kinematics of the chassis were modeled and analyzed to determine optimal posture control strategies, and a fuzzy RBF neural network-based PID control algorithm was designed to enable dynamic adjustment of the chassis. Then, extensive testing was conducted on the prototype chassis, including straight-line driving tests, steering tests, climbing tests, and passability tests, which demonstrated its excellent operational performance. The straight-line driving tests showed an average lateral deviation of 30 mm and a maximum deviation of 60 mm, while the in-situ steering tests recorded a deviation of 20 mm. Finally, the prototype was applied to field weeding operations, where results indicated that its performance, including travel speed, weeding efficiency, and seedling damage rate, significantly outperformed existing traditional models. The findings suggest that the designed multifunctional micro universal chassis is highly effective for use in hilly and mountainous regions, with superior performance particularly under intercropping systems.

Non-grain production of cultivated land (NGPCL) threatened food security. Therefore, scholars have begun study this area in China and other countries, but most of the studies have focused on large scales, and few studies have focused on plot scale analysis. This study presents an analytical framework to shed light on the causes of NGPCL in the hilly mountainous regions of southern China. First, we categorized NGPCL into severe damage class and slight damage class according to the degree of damage of NGPCL to cultivated soils and the difficulty of restoring food production capacity. Then, we revealed the characteristics of spatial differentiation and causes of NGPCL in the southern hilly areas by using methods such as binary logistic regression model and spatial correlation analysis. Finally, the results in the study showed that: (1) the overall NGPCL areal in 2020 was 11288.46 hm2, accounts for 38.14%, of which the areas of NGPCL in the minor damage class and the serious damage class were 27.32% and 10.82%, respectively. (2) The spatial differentiation of NGPCL was obvious, which showed a clustered distribution pattern, with NGPCLs of the minor damage class clustered at high values in the topographically flat areas of the east-central zone, and NGPCLs of the severe damage class clustered at high values in the vicinity of the central urban area. (3) The levels of NGPCL for each type were significantly correlated with the three dimensions of natural, locational, and socio-economic factors, while topography, cultivated land infrastructure conditions and the degree of centralized and contiguous cultivated land were the important drivers of spatial differentiation of NGPCL. This paper reveals the distribution and influencing factors of NGPCL at the plot scale, that can provide theoretical reference and categorized governance suggestions for NGPCL governance in similar regions in China and even in the world.

With the global climate change, glaciers on the Qinghai-Tibet Plateau (QTP) and its adjacent mountainous regions are retreating rapidly, leading to an increase in active rock glaciers (ARGs) in front of glaciers. As crucial components of water resources in alpine regions and indicators of permafrost boundaries, ARGs reflect climatic and environmental changes on the QTP and its adjacent mountainous regions. However, the extensive scale of rock glacier development poses a challenge to field investigations and sampling, and manual visual interpretation requires substantial effort. Consequently, research on rock glacier cataloging and distribution characteristics across the entire area is scarce. This study statistically analyzed the geometric characteristics of ARGs using high- resolution GF-2 satellite images. It examined their spatial distribution and relationship with local factors. The findings reveal that 34,717 ARGs, covering an area of approximately 6873.54 km2, with an average area of 0.19 +/- 0.24 km2, a maximum of 0.0012 km2, and a minimum of 4.6086 km2, were identified primarily in north-facing areas at elevations of 4300-5300 m and slopes of 9 degrees-25 degrees, predominantly in the Karakoram Mountains and the Himalayas. Notably, the largest concentration of ARGs was found on north-facing shady slopes, constituting about 42 % of the total amount, due to less solar radiation and lower near-surface temperatures favorable for interstitial ice preservation. This research enriches the foundational data on ARG distribution across the QTP and its adjacent mountainous regions, offering significant insights into the response mechanisms of rock glacier evolution to environmental changes and their environmental and engineering impacts.

Soil erosion is a severe issue posing a number of adverse effects on the environment. It is a prominent hazard damaging the fertile agricultural land. Therefore, in this study, a spatio-temporal assessment of soil erosion was carried out in the Swat River Basin, Pakistan by employing the Revised Universal Soil Loss Equation (RUSLE). The parameters of the RUSLE model are rainfall erosivity, soil erodibility, slope length and steepness, land management and support practice. These factors were developed from monthly mean rainfall data obtained from the Regional Metrology Department Peshawar, FAO soil database, land use data prepared from Landsat-5 and 8 satellite imageries, topographic data obtained from the ALOS PALSAR Digital Elevation Model (DEM). The analysis discovered that 13% of the study area experienced severe erosion. Results of the spatial distribution and vulnerability to erosion within the Swat River Basin have been categorized into different zones such as very low (59.7%), low (19.5%), moderate (5.37%), high (6.86%), and very high (5.96%). These results accentuate the necessity for mitigation measures in the study area to mitigate the loss of valuable topsoil. This research possesses the potential to offer valuable insights into decision-making and planning to reduce the risk of erosion.

Hydrological processes in mid-latitude mountainous regions are greatly affected by changes in vegetation cover that induced by the climate change. However, studies on hydrological processes in mountainous regions are limited, be-cause of difficulties in building and maintaining basin-wide representative hydrological stations. In this study, a new method, remote sensing technology for monitoring river discharge by combining satellite remote sensing, un-manned aerial vehicles and hydrological surveying, was used for evaluating the runoff processes in the Changbai Mountains, one of the mid-latitude mountainous regions in the eastern part of Northeast China. Based on this method, the impact of vegetation cover change on hydrological processes was revealed by combining the data of hydrological processes, meteorology, and vegetation cover. The results showed a decreasing trend in the monitored river discharge from 2000 to 2021, with an average rate of -5.13 x 105 m3 yr-1. At the monitoring mainly influenced by precipitation, the precipitation-induced proportion of changes in river discharge to annual average river discharge and its change significance was only 6.5 % and 0.23, respectively, showing the precipitation change was not the cause for the decrease in river discharge. A negative impact of evapotranspiration on river discharge was found, and the decrease in river discharge was proven to be caused by the increasing evapotranspiration, which was induced by the drastically increased vegetation cover under a warming climate. Our findings suggested that increases in vege-tation cover due to climate change could reshape hydrological processes in mid-latitude mountainous regions, leading to an increase in evapotranspiration and a subsequent decrease in river discharge.

Mountains are the water towers of the world, so it is critical to obtain accurate precipitation data for mountainous areas. Due to the complex topography of high mountainous areas, precipitation ground stations are sparse and unevenly distributed in such areas, so precipitation products such as remote sensing and reanalysis products are used to obtain gridded precipitation data for these areas. However, no single precipitation product performs best in all areas of mountainous regions. Therefore, this study first evaluated the performance of 12 precipitation products in estimating precipitation in the Qilian Mountains at the station scale and sub-basin scale, and then compared the performance of precipitation estimates for the Qilian Mountains generated by 8 multimodel averaging methods. The evaluation results for 29 meteorological stations in the Qilian Mountains showed that the China Meteorological Forcing Dataset product was the best-performing precipitation product, while the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record product was the worst-performing precipitation product. The evaluation results for 18 sub-basins showed that at these sub-basins, the WorldClim was the best-performing precipitation product, while the High Asia Refined analysis was the worst-performing precipitation product. Thus, station-scale evaluations may not necessarily be applicable to the basin scale. Multi-model averaging methods effectively improved the accuracy of precipitation estimates both at station scale and at sub-basin scale. The Granger-Ramanathan variant C was the best multi-model averaging method for estimating precipitation at station scale. As the Granger-Ramanathan methods allow negative weights, they are not recommended to interpolate the Granger-Ramanathan weight values of stations to grids. The Bayesian model averaging (BMA) was found to be the most suitable multi-model averaging method for estimating precipitation in the Qilian Mountains by interpolation of weight values of stations to grids. The precipitation estimates generated by BMA show that the mean annual precipitation in the Qilian Mountains from 2001 to 2018 was approximately 336.1 mm, and the annual precipitation during this period increased linearly by 2.4 mm per year.