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Camera-enabled unmanned aerial vehicles (UAVs) provide a promising technique to considerably speed up the inspection and visual data collection from regions that may otherwise be inaccessible. In addition, the technology of image-based 3D reconstruction can generate a point cloud model using images captured by UAVs. However, the performance of the point cloud modeling may be affected by multiple factors, such as the modeling software, ground control points (GCPs), and UAV flight modes. In this study, three common software packages were compared, and Pix4Dmapper was considered a suitable software for point cloud modeling for earthquake-damaged buildings. The accuracy and resolution of point cloud models are usually evaluated by root mean square error (RMSE) and ground sampling distance (GSD). The effects of the main factors, including the number of GCPs, distribution of GCPs, flight manner of the UAV, and distance from the UAV to the target, were investigated on the basis of two real-world multistory earthquake-damaged structures. The influence rules of the main factors revealed that a close range, automatic flight mode of the UAV, a large number of GCPs, and a relatively wide distribution of the GCPs may generate a point cloud model with low computational costs, high accuracy, and high resolution. In the particular illustration example here, the RMSE is 6.78 mm while the GSD is 1.60 mm. Finally, rapid structural damage inspection was demonstrated using an accurate point cloud model and compared with the inspection results of a total station and terrestrial laser scanner point cloud models. The comparison of different inspection results showed that the relative errors were relatively acceptable within 4%.

Conservation and efficient use of water resources are key to combating global climate change. The development of low-carbon industries plays an important role in promoting the protection of water resources. Based on this, this paper combines the measures of green production and consumption, and uses the concept of low-carbon packaging design to study the impact of water resource protection. First of all, this paper studies the impact of the packaging industry on the ecological environment, and combines the concept of global low-carbon development to implement a low-carbon packaging industry structure. Secondly, adhering to the concept of sustainable development, from green packaging design to green packaging consumption, this paper uses the water resources protection analysis impact model to empirically analyze the impact of the low-carbon packaging industry on water resources protection. The experimental results showed that after the implementation of the low-carbon packaging industry structure model in Zhejiang Province, China, the impact efficiency of the region on water resources was as high as 90%, and the sewage discharge showed a decreasing and stable trend, sewage discharge in the light industrial zone was reduced from 120,000 to 80,000 cubic meters, and two water pollutants, chemical oxygen demand (COD) and biochemical oxygen demand (BOD), were reduced by 4,600 and 2,200 tons respectively. Studies have shown that low-carbon packaging design can reduce carbon emissions from packaging production to a certain extent and reduce pollutant emissions, thereby improving the efficiency of water resource protection and management.

The issue of target drift prediction based on ocean current from high-frequency (HF) ground-wave radar was studied in this paper. A large number of unpowered drift observation experiments for a class of South China Sea characteristic offshore fishing vessel was carried out in the range of radar radiation. The calculation of sub-grid velocity in the test area was based on the comparison between the HF radar data and the meteorological observation data of the buoy at sea. The two observation data also have good consistency in the matching of time and space. To analyze the temporal characteristics of diffusion velocity, the sample autocorrelation and the partial autocorrelation function of a sub-grid velocity were calculated separately, and a sub-grid model based on the ARMA model was proposed. Firstly, trajectory simulation based on the Runge-Kutta method was used to preliminarily verify the improvement of target drift prediction accuracy by ARMA model. In addition, the Monte-Carlo method was used to calculate and compare the prediction range of different sub-grid velocity models, and the ARMA model with different continuous prediction time steps was studied. Compared with the traditional sub-grid velocity model, the ARMA model with short-term continuous prediction can significantly improve the performance of target drift prediction model in terms of mean prediction error and separation. However, ARMA model is also limited by its continuous prediction step size to a large extent. On the one hand, this study verifies that the ARMA model with short-term continuous prediction improves the performance of target drift prediction model. On the other hand, the results further demonstrated that measured data provided by the HF radar, combined with other continuous ocean observation data, are of value for trajectory analysis of drift targets.

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