The occurrence of drought is a complex process and is caused by the interaction of multiple drought-causing factors. The construction of traditional drought models and indexes seldom considers multiple drought-causing factors. This study integrated the precipitation, soil water and heat balance, and crop growth during drought. From the beginning of the process of agricultural drought, the atmosphere, soil, and crops that characterize drought are considered, through the principal component analysis method to construct a comprehensive drought monitoring index (CDMI). This index was verified by using the areas covered by drought, areas affected by drought, relative soil moisture, and crop yield. The annual average CDMI had negative correlations with areas covered and affected by drought. The correlation coefficients were –0.68 and –0.73. Moreover, the CDMI value had positive correlations with relative soil moisture and crop yield. The maximum correlation coefficient between CDMI and relative soil moisture was 0.91, and the correlation coefficient with maize yield was 0.52. Subsequently, the CDMI was applied to long-term drought monitoring in agricultural areas during the summer maize growing season (June to September) in Henan Province. Results showed that the most severe years of agricultural drought in Henan Province were 2000, 2001, 2004, 2006, 2008, and 2014. The most severe agricultural drought occurred in July and August 2014. Statistics found that Henan Province had high frequencies of severe drought. This study proved that CDMI calculated by multisource remote sensing data is a reliable and effective indicator for monitoring and assessing agricultural drought.

The mobile laser scanning (MLS) system can quickly collect the point cloud data of power lines and power line corridors that lay along urban road. Accurate and efficient extraction of power lines from the point cloud is the basis of power line inspections and maintenance. This paper presents a complete algorithm for power line extraction, modelling, and potential hazard analysis from MLS point clouds. The algorithm first extracted candidate power line points from non-ground points based on the analysis of linear feature. The catenary model is used to model and densify power lines. Power line corridor data are identified based on the horizontal distance between the points and the power lines. The individual trees in the power line corridor are divided into three zones: clearance, buffer, and safety zone. The sample data provided by the International Society for Photogrammetry and Remote Sensing Working Group (ISPRS WG) III/5 was used to test the performance of the method, and the completeness and correctness were 98.06% and 99.83%, respectively. A total of 78 individual trees were detected from the power line corridor, of which 26 trees were in the buffer zone, and 23 trees were in the clearance zone. Experiment results show clearly that this proposed method can effectively extract power lines and model them. Besides, by establishing clearance and buffer zones, trees posing potential hazards to power lines can be analysed.

Mobile Laser Scanning (MLS) point cloud data contains rich three-dimensional (3D) information on road ancillary facilities such as street lamps, traffic signs and utility poles. Automatically recognizing such information from point cloud would provide benefits for road safety inspection, ancillary facilities management and so on, and can also provide basic information support for the construction of an information city. This paper presents a method for extracting and classifying pole-like objects (PLOs) from unstructured MLS point cloud data. Firstly, point cloud is preprocessed to remove outliers, downsample and filter ground points. Then, the PLOs are extracted from the point cloud by spatial independence analysis and cylindrical or linear feature detection. Finally, the PLOs are automatically classified by 3D shape matching. The method was tested based on two point clouds with different road environments. The completeness, correctness and overall accuracy were 92.7%, 97.4% and 92.3% respectively in Data I. For Data II, that provided by International Society for Photogrammetry and Remote Sensing Working Group (ISPRS WG) III/5 was also used to test the performance of the method, and the completeness, correctness and overall accuracy were 90.5%, 97.1% and 91.3%, respectively. Experimental results illustrate that the proposed method can effectively extract and classify PLOs accurately and effectively, which also shows great potential for further applications of MLS point cloud data.

The total and individual aerosol direct radiative effects (ADREs) were estimated for clear-sky conditions using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) observations and radiative transfer model. In this study, a parametric sensitivity analysis was performed for the Santa Barbara DISORT Atmospheric Radiative Transfer model using a global sensitivity analysis method. Uncertainties in the ADREs due to aerosol optical properties and surface albedo errors were evaluated. The single-scattering albedo and asymmetry factor for marine, dust, pollution, and smoke aerosols required for the model calculations were obtained from the classified AErosol RObotic NETwork observations. The estimated average global ADREs and errors at the top-of-atmosphere (TOA) and the surface were −2.36 ± 0.54 and −4.78 ± 2.2 Wm⁻², respectively. In regions with higher dust, pollution, and smoke aerosol loading, the ADREs exhibited significant seasonal variability. In the Sahara and Arabian deserts, during the June-July-August season with higher dust aerosol loading, the seasonal average ADREs in the region were −3.23 and −21.37 Wm⁻² at the TOA and surface, respectively. In the Indian region, during the March–April–May season with higher pollution aerosol loading, the ADREs were −10.33 and −28.04 Wm⁻² at the TOA and surface, respectively. In Southern Africa, the smoke aerosol with a single-scattering albedo of 0.87 caused negative radiative effects at the TOA, and during the September-October-November season with higher smoke aerosol loading, the seasonal average ADREs were −2.34 and −6.36 Wm⁻² at the TOA and surface, respectively.