Tree Species Classification And 3D Visualization Based On Airborne LiDAR And Hyperspectral Data

Forest resource is a natural and renewable resource,and is one of the most important resources on the earth.It provides material guarantee for the sustainable development of forestry.Forest plays an important role in regulating climate,maintaining biodiversity and providing ecological products.The accurate identification of tree species is the basis for research and utilization of forest resources.In this paper,the dense forest in Genhe experimentation area with complicated topography is taken as the research object.Based on the airborne Li DAR point cloud data and AISA EAGLE II hyperspectral image acquired in 2016,the point cloud features and hyperspectral features for different tree species are established,and the support vector machine(SVM)classifier is applied to classify the main tree species.A prototype system for tree species classification and 3D visualization in dense forest has been developed which realized the function of tree query,management,classification and three-dimensional visualization with C# programming language and control and interface provided by Skyline software,it can provide data and technical support for the ecological monitoring and sustainable management of forests.The main contents of this paper are as follows:(1)Feature-extraction from hyperspectral and airborne Li DAR data.Hyperspectral image has abundant spectral information and texture information.Airborne LiDAR can acquire the vertical structure characteristics of forest trees.In view of the complex conditions with undulating topography and dense forests in the study area,this paper applied point cloud segmentation algorithm(PCS algorithm)to separate individual trees after terrain correction.On the single tree scale,the hyperspectral and point cloud features for different tree species in the study area were established to form a multi-source database.(2)SVM classifier was applied to the classification of tree species in dense forest,the data was divided into training and verification samples.Radial Basis Function(RBF)kernel was selected,the penalty factor was set to 3,and the kernel parameter was set to 0.008,the main tree species were classified into larch,white birch and other eventually.The classification accuracy and Kappa coefficient were 81.14% and 0.61,respectively,which were 7.74%,0.16,15.44%,0.2 higher than the overall accuracy and Kappa coefficient of single Hyperspectral image and LiDAR data classification respectively.Finally,the taxonomic features and parameters were used to classify the tree species in the large area of the study area and the tree species classification map was obtained.(3)A prototype system for tree species classification and 3D visualization in dense forest was developed.Simultaneously,taking the Genhe experimental area as an example,the system was tested and applied practically.The analysis and conversion of attribute data were realized by GDAL to provide location and attribute information for 3D visualization.The DEM data of the study area and the orthophoto CCD image were used to generate the MPT to provide the terrain landscape scene.Using LOD(Levels of Detail)technology,the geometry and texture of the tree 3D model were processed with multi details and hierarchical sampling.Finally,according to the actual needs and corresponding design principles,a prototype system for tree species classification and 3D visualization in dense forest was developed with C# programming language and control and interface provided by Skyline software.This paper has changed the defects of previous forest simulation,and realized a three-dimensional scene with strong sense of reality.According to the tree location and attribute information,each tree showed different size and shape,the distribution of birch and larch was visualized;the operation of zoom,rotate,pan,view switch and other three-dimensional display functions were achieved;the query and management of tree attributes have been realized in the system.