Abstract:Filtering is one of the core post-processing steps for airborne LiDAR point cloud. In recent years, the morphology-based filtering algorithms have proven to be a powerful and efficient tool for filtering airborne LiDAR point cloud. However, most traditional morphology-based algorithms have difficulties in preserving abrupt terrain features, especially when using larger filtering windows. In order to suppress the omission error caused by protruding terrain features, this paper proposes an improved morphological algorithm based on multi-level kriging interpolation. This algorithm is essentially a combination of progressive morphological filtering algorithm and multi-level interpolation filtering algorithm. The morphological opening operation is performed with filtering window gradually downsizing, while kriging interpolation is conducted at different levels according to the different filtering windows. This process is iterative in a top to down fashion until the filtering window is no longer greater than the preset minimum filtering window. Fifteen samples provided by the ISPRS commission were chosen to test the performance of the proposed algorithm. Experimental results show that the proposed method can achieve promising results not only in flat urban areas but also in rural areas. Comparing with other eight classical filtering methods, the proposed method obtained the lowest omission error, and preserved protruding terrain features better.

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Dendroclimatic techniques were used to assess the climate-growth relationships of refugial Meyer spruce (Picea meyeri Rehd. et Wils.) on a sandy substrate in semi-arid grassland of north China. Statistical analysis of the tree-ring data showed a mean series intercorrelation of 0.47, a signal-to-noise ratio of 14.44, and a mean sensitivity of 0.18, indicating suitability for climatic analysis. Radial growth was positively correlated with precipitation in February and May of the current year, and in September of the preceding year. However, radial growth of Meyer spruce also correlated negatively with mean monthly temperature in current May, of which mean maximum temperature explained most of the observed variation. In addition, radial growth negatively correlated with solar radiation over most of the year. Rainfall appeared to be the dominant growth-limiting factor in this semi-arid grassland, with temperature and solar radiation being of lesser importance. This study suggests that Meyer spruce in this stand is promising for dendroclimatic and ecological studies because of good cross-dating characteristics and high sensitivity to climate.

A novel aliphatic polycarbonate from renewable resource was prepared by copolymerization of furfuryl glycidyl ether and CO2 using rare earth ternary catalyst; its number-average molecular weight (M n) reached 13.3 × 104 g/mol. The furfuryl glycidyl ether and CO2 copolymer (PFGEC) was easy to become yellowish at ambient atmosphere due to postpolymerization cross-linking reaction on the furan ring; the gel content was 17.2 wt % after 24 h exposure to air at room temperature. PFGEC could be stabilized by addition of antioxidant 1010 (tetrakis[methylene (3,5-di(tert-butyl)-4-hydroxyhydrocinnamate)]methane) in 0.5−3 wt % after copolymerization. The Diels−Alder (DA) reaction between N-phenylmaleimide and the pendant furan ring was also effective for the stabilization of PFGEC by reducing the amount of furan ring and introducing bulky groups into PFGEC. The cyclization degree could reach 72.1% when the molar ratio of N-phenylmaleimide to furan ring was 3:1, and no gel was observed after 24 h exposure to air. The glass transition temperature (T g) of PFGEC was 6.8 °C, and it increased to 40.3 °C after DA reaction (molar ratio of N-phenylmaleimide to furan ring was 3:1). A third way was also conducted to solve the air instability of PFGEC, where tetrahydrofurfuryl glycidyl ether, a hydrogenated furfuryl glycidyl ether, was used instead of furfuryl glycidyl ether for air-stable polycarbonate, and a copolymer with M n of 7.7 × 104 g/mol and T g of −5.7 °C was synthesized.

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The standard forward transformation equation plays a major role in coordinate transformation between global and local datums. Thus, it is a prerequisite step in the forward conversion of geodetic coordinates into cartesian coordinates in coordinate transformation from global to local datum and vice versa. Numerous studies have been carried out on converting cartesian coordinates to geodetic coordinates (reverse procedure) through the application of iterative, approximate, closed form, vector-based and computational intelligence algorithms. However, based on literature covered pertaining to this study, it was realized that the existing researches do not fully address the issue of applying and testing alternative techniques in the case of the forward conversion. Hence, the purpose of this present study was to explore the coordinate conversion performance of two different artificial neural network approaches (backpropagation artificial neural network (BPANN) and radial basis function neural network (RBFNN)) and multiple linear regression (MLR). The statistical findings revealed that the BPANN, RBFNN and MLR offered satisfactory prediction of cartesian coordinates. However, the RBFNN compared to BPANN and MLR showed better stability and more accurate prediction results. Furthermore, in terms of maximum three-dimensional position error, the RBFNN attained 0.004 m while 0.011 and 0.627 m were achieved, respectively, by MLR and BPANN. By virtue of the success achieved in this study, the main conclusion drawn here is that RBFNN provides a promising alter-B Hu Youjian 123 Math Geosci native in the forward conversion of geodetic coordinates into cartesian coordinates. Therefore, the capability of artificial neural network as a powerful tool for solving majority of function approximation problems in geodesy has been demonstrated.