We report on studies of the lidar and the depolarization ratios for cirrus clouds. The optical depth and effective lidar ratio are derived from the transmission of clouds, which is determined by comparing the backscattering signals at the cloud base and cloud top. The lidar signals were fitted to a background atmospheric density profile outside the cloud region to warrant the linear response of the return signals with the scattering media. An average lidar ratio, 29 +/- 12 sr, has been found for all clouds measured in 1999 and 2000. The height and temperature dependences ofthe lidar ratio, the optical depth, and the depolarization ratio were investigated and compared with results of LITE and PROBE. Cirrus clouds detected near the tropopause are usually optically thin and mostly subvisual. Clouds with the largest optical depths were found near 12 km with a temperature of approximately -55 degrees C. The multiple-scattering effect is considered for clouds with high optical depths, and this effect lowers the lidar ratios compared with a single-scattering condition. Lidar ratios are in the 20-40 range for clouds at heights of 12.5-15 km and are smaller than approximately 30 in height above 15 km. Clouds are usually optically thin for temperatures below approximately -65 degrees C, and in this region the optical depth tends to decrease with height. The depolarization ratio is found to increase with a height at 11-15 km and smaller than 0.3 above 16 km. The variation in the depolarization ratio with the lidar ratio was also reported. The lidar and depolarization ratios were discussed in terms of the types of hexagonal ice crystals.
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http://dx.doi.org/10.1364/ao.41.006470 | DOI Listing |
Plants (Basel)
January 2025
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Precision pesticide application mainly relies on canopy volume, resulting in varied application effectiveness across different density areas of orchard trees. This study examined pesticide application effectiveness based on the spray wind, canopy volume, and leaf area within the canopy, providing variable bases for precise regulation of spray wind and pesticide dosage. The study addresses the knowledge gap by utilizing laser detection and ranging (LiDAR) to measure the thickness and leaf area of orchard tree canopies.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Guangqiao Load, Shenzhen, 518132, CHINA.
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January 2025
Research Institute of Forest Policy and Information, Chinese Academy of Forestry, Beijing, China.
The processing of LiDAR point cloud data is of critical importance in the context of forest resource surveys, as well as representing a pivotal element in the realm of forest physiological and ecological studies.Nonetheless, conventional denoising algorithms frequently exhibit deficiencies with regard to adaptability and denoising efficacy, particularly when employed in relation to disparate datasets.To address these issues, this study introduces DEN4, an unsupervised, deep learning-based point cloud denoising algorithm designed to improve the accuracy of single tree segmentation in LiDAR point clouds.
View Article and Find Full Text PDFFront Robot AI
January 2025
Department of Materials and Production, Aalborg University, Aalborg, Denmark.
Object pose estimation is essential for computer vision applications such as quality inspection, robotic bin picking, and warehouse logistics. However, this task often requires expensive equipment such as 3D cameras or Lidar sensors, as well as significant computational resources. Many state-of-the-art methods for 6D pose estimation depend on deep neural networks, which are computationally demanding and require GPUs for real-time performance.
View Article and Find Full Text PDFSci Total Environ
January 2025
Department of Geosciences, Atmospheric Science Division, Texas Tech University, Lubbock, TX, USA; National Wind Institute, Texas Tech University, Lubbock, TX, USA. Electronic address:
Understanding the kinematics of aerosol horizontal transport and vertical mixing near the surface, within the atmospheric boundary layer (ABL), and in the overlying free troposphere (FT) is critical for various applications, including air quality and weather forecasting, aviation, road safety, and dispersion modeling. Empirical evidence of aerosol mixing processes within the ABL during synoptic-scale events over arid and semiarid regions (i.e.
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