Abstract: Provided is an individual-tree segmentation method of UAV LiDAR point cloud based on canopy morphology. The method uses woodland data obtained by a UAV LiDAR to: extract initial canopies from a CHM based on a region growing algorithm, determine whether each initial canopy is a correct segmentation canopy according to the number of local density maximum points in each initial canopy, finely segment each wrong segmentation canopy according to canopy morphology to obtain an updated set of tree tops, and finally use each of the updated set of tree tops as a seed point for performing the region growing algorithm to thereby obtain a final individual-tree segmentation result. The method make full use of height information and density information contained in a tree point cloud; and under the guidance of the density information, a wrong segmentation tree can be more accurately identified.

Abstract: A rice-crop intensity identification method based on radar time series observation and temperature analysis is provided. Capturing of diversified periodic characteristics of time series backscatter and detection of backscatter troughs are achieved through time series reconstruction and trough identification; potential phenological phases corresponding to the backscatter troughs are determined through potential rice phenological phase estimation; and through temperature limitation of rice phenological phase, temperature suitability of potential rice phenological phases is evaluated, a backscatter trough that does not satisfy a temperature condition is removed by combining a rice growth mechanism and a regulation of rice-crop intensity, thereby realizing the identification of the rice troughs and the correction of the overestimation of the rice-crop intensity, and finally realizing the identification of rice-crop intensity.