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Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR) data in an object-based image analysis (OBIA) paradigm. The aims of this work were the following: (i) to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation; (ii) to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure; and (iii) to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species' separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI) with a 1 m spatial resolution and the Canopy Height Model (CHM) data derived from the LiDAR data acquired by the airborne OPEN ACCESSRemote Sens. 2015, 7 923Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR), VHR/CHM, and minimum noise fractional transformed data (MNF)/CHM) were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that incorporating the crop height information into the hyperspectral extracted features provided a substantial increase in the classification accuracy. The combination of MNF and CHM produced higher classification accuracy than the combination of VHR and CHM, and the solely MNF-based classification results. The textural and geometric features in the object-based classification could significantly improve the accuracy of the crop species classification. By using the proposed object-based classification framework, a crop species classification result with an overall accuracy of 90.33% and a kappa of 0.89 was achieved in our study area.

Due to the improvement of the wear property, rolling contact fatigue including shattered rim and shelling are the main failure causes of the high-speed railway wheels. In this paper, shattered rim and shelling occurred on the service wheels of the China Railway High-speed (CRH) trains were systematically investigated. The recorded data of the last ten years CRH operation indicated that all shattered rims and shelling were detected with serving > 10 6 km (corresponding to the fatigue life 10 7 -10 9 cycles) which is very-high-cycle fatigue (VHCF). The crack initiation region of shattered rim located at the depth of 10-25 mm from the tread, while that of shelling located at the depth < 10 mm from the tread. The VHCF features under rolling contact loading were observed on the opening crack surfaces, i.e., similar VHCF features in uniaxial loading including the defect, fish-eye, and crack propagation region and unique VHCF features of the three dimensional crack surface feature, beach bands uniformly distributed in the crack propagation region, absence of fine granular area (FGA). The VHCF model considering the stress distribution, defect size and hardness were applied to discuss the failure mechanism of the shattered rim and shelling. effects of material defects and the contact shear stress profile with a maximum shear stress below surface are responsible for the https://doi.