The spacing of PVDs is an essential factor affecting the consolidation effect of vacuum preloading. For exploring the influence of spacing of PVDs on the impact of sludge drainage consolidation, FeCl3, a commonly used inorganic coagulant, was used to pretreat the sludge. In the experiment a vacuum filtration test was carried out to determine the optimal addition amount of FeCl3, and then the landfill sludge was pretreated according to the FeCl3 optimal addition amount. And two different spacing of PVDs were used to carry out a vacuum preloading contrast test. Then, the drainage and settlement were recorded, and water content and vane shear strength (VSS) were measured after the experiment. Finally, Mercury intrusion porosimetry (MIP) was carried out to explore the pore characteristics of the sludge further. The main conclusions are as follows: After conditioning by FeCl3, the sludge's flocculent structure was destroyed, the intracellular water was released, and the effect of drainage capacity was significantly improved. After the spacing of PVDs is halved, the average volume reduction ratio and shear strength increases, and the effect of advanced dewatering and volume reduction of sludge improved significantly, leading to a better consolidation effect. In the progress of vacuum drainage consolidation, halving the spacing of PVDs results in large pores transformation into small pores, and the range of drainage consolidation is greatly expanded.
Beijing’s One Million Mu Plain Afforestation Project involves planting large areas with the exotic North American tree species Fraxinus pennsylvanica Marsh (ash). As an exotic tree species, ash is very vulnerable to infestations by the emerald ash borer (EAB), a native Chinese wood borer pest. In the early stage of an EAB infestation, attacked trees show no obvious sign. Once the stand has reached the late damage stage, death occurs rapidly. Therefore, there is a need for efficient early detection methods of EAB stress over large areas. The combination of unmanned aerial vehicle (UAV)-based hyperspectral imaging (HI) with light detection and ranging (LiDAR) is a promising practical approach for monitoring insect disturbance. In this study, we identified the most useful narrow-band spectral HI data and 3D LiDAR data for the early detection of EAB stress in ash. UAV-HI data of different infested stages (healthy, light, moderate and severe) of EAB in the 400–1000 nm range were collected from ash canopies and were processed by Partial Least Squares–Variable Importance in Projection (PLS-VIP) to identify the maximally sensitive bands. Band R678 nm had the highest PLS-VIP scores and the most robust classification ability. We combined this band with band R776 nm to develop an innovative normalized difference vegetation index (NDVI(776,678)) to estimate EAB stress. LiDAR data were used to segment individual trees and supplement the HI data. The new NDVI(776,678) identified different stages of EAB stress, with a producer’s accuracy of 90% for healthy trees, 76.25% for light infestation, 58.33% for moderate infestation, and 100% for severe infestation, with an overall accuracy of 82.90% when combined with UAV-HI and LiDAR.
Pine wilt disease (PWD) is a global destructive threat to forests, having caused extreme damage in China. Therefore, the establishment of an effective method to accurately monitor and map the infection stage by PWD is imperative. Unmanned aerial vehicle (UAV)-based hyperspectral imaging (HI) and light detection and ranging (LiDAR) technique is an effective approach for forest health monitoring. However, few previous studies have used airborne HI and LiDAR to detect PWD and compared the capability for predicting PWD infection stage at the tree level. In this paper, PWD infection was divided into five stages (green, early, middle, heavy, and grey), and HI and LiDAR data were integrated to detect PWD. We estimated the power of the hyperspectral method (HI data only), LiDAR (LiDAR data only), and their combination (HI plus LiDAR data) to predict the infection stages of PWD using the random forest (RF) algorithm. We obtained the following results: (1) The classification accuracies of HI (OA: 66.86%, Kappa: 0.57) were higher than those of LiDAR (OA: 45.56%, Kappa: 0.27) for predicting PWD infection stages, and their combination had the best accuracies (OA: 73.96%, Kappa: 0.66); (2) LiDAR data had higher ability for dead tree identification than HI data; and (3) The combined use of HI and LiDAR data for estimation of PWD infection stages showed that LiDAR metrics (e.g., crown volume) were essential in the classification model, although the variables derived from HI data contributed more than those extracted from LiDAR. Therefore, we proposed a new approach combining the merits of HI and LiDAR data to precisely predict PWD infection stages at the tree level, allowing better PWD monitoring and control. The approach could also be employed for mapping and monitoring other forest disturbance issues.
The traditional chemical vacuum preloading method for landfill sludge (LS) often has problems of pollution, deformation, and blockage of the drainage plate. Consequently, we proposed a freeze–thaw process with vacuum preloading via prefabricated horizontal drains (PHDs). First, the LS was pretreated by freeze–thaw. Then, a comparative model test of prefabricated vertical drains (PVDs) and PHDs was carried out to investigate the anticlogging mechanism and vacuum consolidation effect of PHDs from macro- and microaspects. The results showed that the water discharge of PHD was 13.4% higher than that of PVD, the settlement of PHD was 16% higher than that of PVD, the volume reduction ratio of PVD was 58.2%, and that of PHD was 67.5%. The initial water content of LS was 86%, the minimum for PVD was 61%, and the minimum for PHD was 56%. The particles of PVD were mainly transported in the radial direction, PHD was transported primarily in the vertical direction, and there were more small particles in the center of the PVD. PHD had a high degree of consolidation, mainly with micropores and mesoporous distribution, and the PVD consolidation degree was low, primarily the distribution of small pores. The study found that freezing, thawing, and vacuum preloading with PHDs may efficiently minimize the amount of LS.Practical ApplicationsAs a special biological colloid, landfill sludge (LS) has the characteristics of high moisture content, limited permeability, and a very low load-bearing capacity, resulting in low efficiency of vacuum drainage and consolidation, and construction personnel and heavy machinery are often unable to carry out direct operations, which greatly reduces the efficiency of vacuum preloading construction operations. In view of this, this study proposed a more environmentally friendly and efficient method of freeze–thaw combined with vacuum preloading using prefabricated horizontal drains (PHDs) for LS treatment. Using PHD can effectively improve the efficiency of drainage and consolidation and significantly improve construction efficiency. At the same time, the freeze–thaw method can effectively avoid the secondary pollution caused by the use of chemicals, reduce the cost of later sewage treatment, and improve the way of sludge resource utilization. The method of freeze–thaw combined with vacuum preloading using PHDs can effectively achieve in situ volume reduction treatment of LS, which is a guideline for the treatment of LS.
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