The groundwater table in the piedmont plain was only about 1-2 m in depth in the 1950s and 1960s, but it lowered dramatically afterwards to about 25-27 m in depth (currently 21-23 m above sea level) due to overpumping of groundwater and drought in the region. This change has adversely affected the sustainable development and food supply of this important agricultural area. The groundwater table at Luancheng Experimental Station of the Chinese Academy of Sciences, located in the piedmont, dropped from 39.36 m in 1975 to 21.47 m above sea level in 1999, at an average rate of 0.72 m/year. Water balance components, such as daily rainfall, pan-evaporation, and evapotranspiration (by lysimeter after 1995) have been recorded since the 1970s, and they were used as variants to simulate monthly water table change based on a physically based statistical model. Groundwater samples were collected during the period 1998-2001, and tritium was measured in the laboratory to trace the groundwater flow from the Taihang Mountains to the piedmont. A reasonable exploitation rate of 150 mm/year was obtained from the model by assuming the annual water table is constant. The recharge and groundwater flow from the Taihang Mountains plays an important role in the water balance of the piedmont area, and it was estimated to be about 112.5 mm/year by using the variation of tritium with the depth, which followed a good exponential function. The simple water balance calculation indicated that the water table could recede at a rate of 0.8 m/year, which is close to the actual situation.

Abstract:The lower reaches of the Yellow River are basically a discharge zone with a high salt content, and the study area of Yucheng in Shandong Province became arable only after the water diversion project from the Yellow River was implemented in 1972. The sustainability of agriculture in this area is examined through the redistribution of soil moisture and solutes in the vertical profile based on the measurement of soil moisture, potential and solute content in a maize field at the Yucheng Experimental Station. Diurnal moisture fluctuations appear in the surface layers at 30 and 50 cm depths, and the daily water content at 90 cm depth decreases about a month after planting, due mainly to the effect of root water extraction, even reaching a level lower than that at 70 cm depth. Soil moisture obviously increases for the three layers at 30, 50, 90 cm depth, and the relevant peak-time shifts from the surface 30 cm depth to the deep layer at 120 cm depth with a varied time lag in response to rainfall events, but there is little or no signal for the other layers due to the effects of soil properties, roots, and soil storage. The existence of a convergent zero flux plane may explain to some extent the accumulation of moisture and solutes in the layer at 120 cm depth. Though the chemical facies along the profile from the unsaturated surface to the deep saturated zone generally evolves in a direction of decreasing SO 4 2 and Cl , a strong driving force upward and the accumulation of solute at 120 cm may change the redistribution pattern and three groups of this pattern were classified according to the evolution and concentration distribution profiles. The main factors affecting the moisture, solute and their distributions for the three groups are varied: rainfall, irrigation and evapotranspiration for the surface layer till 70 cm depth, root extraction for the accumulation layer of 70-120 cm depth, and the fluctuation of the groundwater table for the deep layer at 120-200 cm depth. The agriculture appears sustainable as long as diverted water from the Yellow River is available, but the high content of solute accumulation in the layer at about 120 cm depth is a potential risk.

<p><strong>Abstract.</strong> UAV LiDAR systems have unique advantage in acquiring 3D geo-information of the targets and the expenses are very reasonable; therefore, they are capable of security inspection of high-voltage power lines. There are already several methods for power line extraction from LiDAR point cloud data. However, the existing methods either introduce classification errors during point cloud filtering, or occasionally unable to detect multiple power lines in vertical arrangement. This paper proposes and implements an automatic power line extraction method based on 3D spatial features. Different from the existing power line extraction methods, the proposed method processes the LiDAR point cloud data vertically, therefore, the possible location of the power line in point cloud data can be predicted without filtering. Next, segmentation is conducted on candidates of power line using 3D region growing method. Then, linear point sets are extracted by linear discriminant method in this paper. Finally, power lines are extracted from the candidate linear point sets based on extension and direction features. The effectiveness and feasibility of the proposed method were verified by real data of UAV LiDAR point cloud data in Sichuan, China. The average correct extraction rate of power line points is 98.18%.</p>