It is difficult and time consuming to use traditional measurement methods to estimate the physical properties of snow. However, the emergence of hyperspectral imagery for estimating the physical properties of snow provides a powerful tool. Snow albedo, grain size, and temperature are important factors for evaluating the surface energy balance. Using the spectrum-reflection curves of the different grain sizes of snow measured in the fields of the Binggou watershed of the Heihe River Basin, China, we analyzed the spectral reflection characteristics of snow. A statistical detection method was used to choose the most sensitive bands in the field spectra and find the corresponding band (band 89) in the Hyperion imagery. The bands near 1033 nm were sensitive to the snow grain size. According to the relationship between the snow grain size and the measured spectrum, we built a snow grain-size estimation model. The results showed that the snow reflectance had a good linear and exponential relationship with the snow grain size. The correlation coefficients R of the two models were 0.81 and 0.84, respectively. We obtained the location of the absorption valley at the near-infrared wavelength, and the results showed that 6.9% of the pixels were affected by the snow water content. The locations of the absorption valley moved 1-4 bands from band 89 to shorter wavelengths. The accuracy of the snow grain size estimates based on the Hyperion imagery was relatively high.
ABSTRACT:In the discovering, identifying and mapping work of heritage objects in forest or desert areas, LiDAR ensures work efficiency and can provide the most complete and accurate 3D data. In the field of heritage documentation in China, the integration of LiDAR and small UAV is highly desirable. However, due to issues on the vibration of flying platform, load capacity, safety and other factors, not all UAVs can be used as LiDAR carriers. Therefore, the selection and design of suitable UAVs are very important. Little research has been done in this area and related experiments, complete test data and clear conclusions are hard to find. After long-term selection, design, trial-manufacturing and testing, the authors compare the vibration, capacity, reliability, stability of many UAV types, and finally develop two UAV platforms which are most suitable for carrying LiDAR for heritage mapping projects.
Due to the strengths and weaknesses of the airborne LIDAR data and QuickBird multispectral data, an improved classification method is presented for extracting vegetation information, roads, and buildings. A plot located in San Francisco was selected as the study site. Firstly, ground points were extracted from the LIDAR data and resampled to build DEM and DSM, and then derived nDSM by subtracting DEM from DSM. Secondly, the intensity information derived from LiDAR data was processed to be distributed evenly, and then generated an intensity clustering image, which classified LiDAR points into two basic clusters. Finally, add nDSM and intensity clustering images to QuickBird image as two extra bands, and then we can extract vegetation information, roads, and buildings using their height, intensity and spectral information. The results showed that the method combined airborne LIDAR-derived metrics and QuickBird multispectral data has higher classification accuracy. The proposed method in the paper could be applied to larger research area and other fields.
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