Unmanned aerial vehicles (UAVs) can obtain high-resolution topography data flexibly and efficiently at low cost. However, the georeferencing process involves the use of ground control points (GCPs), which limits time and cost effectiveness. Direct georeferencing, using onboard positioning sensors, can significantly improve work efficiency. The purpose of this study was to evaluate the accuracy of the Global Navigation Satellite System (GNSS)-assisted UAV direct georeferencing method and the influence of the number and distribution of GCPs. A FEIMA D2000 UAV was used to collect data, and several photogrammetric projects were established. Among them, the number and distribution of GCPs used in the bundle adjustment (BA) process were varied. Two parameters were considered when evaluating the different projects: the ground-measured checkpoints (CPs) root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2) distance. The results show that the vertical and horizontal RMSE of the direct georeferencing were 0.087 and 0.041 m, respectively. As the number of GCPs increased, the RMSE gradually decreased until a specific GCP density was reached. GCPs should be uniformly distributed in the study area and contain at least one GCP near the center of the domain. Additionally, as the distance to the nearest GCP increased, the local accuracy of the DSM decreased. In general, UAV direct georeferencing has an acceptable positional accuracy level.

Biomass is important in monitoring global carbon storage and the carbon cycle, which quickly and accurately estimates forest biomass. Precision forestry and forest modeling place high requirements on obtaining the individual parameters of various tree species in complex stands, and studies have included both the overall stand and individual trees. Most of the existing literature focuses on calculating the individual tree species’ biomass in a single stand, and there is little research on calculating the individual tree biomass in complex stands. This paper calculates the individual tree biomass of various tree species in complex stands by combining multispectral and light detection and ranging (LIDAR) data. The main research steps are as follows. First, tree species are classified through multispectral data combined with field investigations. Second, multispectral classification data are combined with LIDAR point cloud data to classify point cloud tree species. Finally, the divided point cloud tree species are used to compare the diameter at breast height (DBH) and height of each tree species to calculate the individual tree biomass and classify the overall stand and individual measurements. The results show that under suitable conditions, it is feasible to identify tree species through multispectral classification and calculate the individual tree biomass of each species in conjunction with point-cloud data. The overall accuracy of identifying tree species in multispectral classification is 52%. Comparing the DBH of the classified tree species after terrestrial laser scanning (TLS) and unmanned aerial vehicle laser scanning (UAV-LS) to give UAV-LS+TLS, the concordance correlation coefficient (CCC) is 0.87 and the root-mean-square error (RMSE) is 10.45. The CCC and RMSE are 0.92 and 1.41 compared with the tree height after UAV-LS and UAV-LS+TLS.

A controlled-source audio-frequency magnetotelluric (CSAMT) survey was used to detect geological structures beneath the thick quaternary formation in Taiyuan, Shanxi Province, northern China. Two main CSAMT survey lines with 182 survey sites were recorded. A two-dimensional (2D) inversion technique was used to interpret the CSAMT data. The inversion results suggested that: 1) there are four main buried faults named F1, F2, F3, and F4 with dip angles about 65° across the survey line from west to east, the fault displacements of these faults are about 230 m, 180 m, 220 m and 200 m, respectively; 2) the depth of the bedrocks decrease from 1600 to 500 m along the survey lines; and 3) from top to bottom, there are four major layers in the survey area that include the upper layer with the resistivity less than 40 ohm-m represents unconsolidated sediments in the Quaternary formation, a second layer with the resistivity range from 40 to 120 ohm-m represents mudstone and sandstone, a third layer with the resistivity range from 120 to 280 ohm-m represents coal measure strata in the Permian and Carboniferous and a bottom layer with the resistivity higher than 280 Ω·m represents limestone in Ordovician. The CSAMT method is an effective technique for exploring buried fault of several hundred meters deep in metropolitan environment.

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