Individual Tree Parameters Extraction Based On Lidar And Hyper-Spectrum Data

Following the rapid development of remote sensing techniques, traditional passive optical remote sensing is applied in forestry more and more broadly. However, its relatively simple spatial and spectrum information can't satisfy the requirement of forest resource investigation. Fortunately, Light Detection and Ranging (LiDAR) and hyper-spectrum techniques are applied in various fields including forestry quickly by the high quality3-dimension spatial information and high spectrum resolution respectively. High density LiDAR point cloud data, as well as the high spatial resolution hyper-spectrum image, can provide not only forest parameters in stand scale, but also in individual tree scale. To obtain individual tree parameters using LiDAR and hyper-spectrum data, following topics were studied:(1) A concept, named crown control, was proposed for the invalid values which were regularly existed in canopy height model (CHM). The crown control determined the real tree crown areas in CHM approximatively, neglected existing of the invalid values. Relatively exact crown areas were provided for the following CHM optimization and individual tree isolation. The crown contrl was carried out using morphological closing operation. The crown areas were determined by a threshold, which was decided by the crown base height of target area, based on the CHM after closing operation.(2) Filling Invalid Values in CHM with Morphological Crown Control was studied. The quality of CHM directly or indirectly influences the accuracy of various forest parameters extraction based on CHM. As a result, it was necessary to optimize CHM. The existing of the invalid values was one of the main factors which influenced the CHM quality. A CHM optimization algorithm with crown control was proposed to fill the invalid values, and retain canopy gaps. The algorithm firstly found potential invalid values in CHM using Laplacian operator. Then these potential invalid values located in canopy area by crown control, they were invalid values, otherwise canopy gaps. Finally the invalid values in CHM were replaced by corresponding pixels in global smoothed CHM. (3) Isolating individual trees in closed forest using watershed with crown control. The optimized CHM was suitable for the forest parameters extraction in individual tree scale. However, isolatiog individual trees in closed forest was a difficult problem. This paper proposed a watershed with crown control method for this issue. The method imported crown control to determine outer markers (crown areas) and assist find inner markers (treetop). Then simplified marker controlled watershed transform was applied to CHM with outer and inner markers. At last, the position, height and crown radius were gained from the watershed results and height normalized point cloud.(4) Individual tree species identification based on LiDAR and hyper-spectrum data. Once the individual tree position, height and crown radius were gained, individual tree species identification based on LiDAR and hyper-spectrum data should be an important step to improve the accuracy of subsequent tree parameters estimations. The method firstly extracted tree spectrum from hyper-spectrum image based on the individual tree isolation results, and then combined them. Then the classification was carried out with the combined individual tree spectrums using Supporting Vector Machine (SVM) and Spectrum Angle Mapper (SAM) respectively. Lastly, the tree species were obtained.Through the experiments in Heilongjiang Liangshui sample plot and the supplement experiment in Gansu Dayekou sample plot, some conclusions were concluded. The crown control recovered real crown areas in CHM effectively. The CHM optimization with crown control filled the most invalid values in CHM, as well as retained canopy gaps. The individual tree isolation with crown control found the most dominant and subdominant trees, and some medium trees in the plot well, and the accuracy of the tree height estimation was greater than90%. The individual trees species identification based on LiDAR and hyper-spectrum data identified dominant tree species in the plot with correctness of over90%, and sub dominant tree species with correctnes of over70%.In summary, LiDAR and hyper-spectrum data provide good forest parameters in individual tree scale. Higher quality individual tree position, height and crown radius can be extracted using optimized CHM with crown control. And the hyper-spectrum data can provide individual tree species, which is useful for the subsequent forest parameters estimations. As a result, these studies have high pratical values and application prospects in forest resource investigations.