The Research On Forest Characteristics Based On Lidar

Forest resources as the largest terrestrial resources, it changes not only affect the social and economic development, but also has a huge impact on the ecological environment. In recent years, increased environmental awareness of the importance of human resources in the forest to get a broader awareness. Therefore, efficient access to forest resources has become an inevitable trend. The forest is constituted by a single plant trees. Accurate forest tree parameters ensure getting detailed forest resources information, so accurately obtain high-precision single tree parameters profound. Although traditional optical remote sensing is widely used in the field of forestry, but only to provide simple spatial and spectral information, far unable to meet the needs of the forest resources of the investigation. Airborne laser radar technology, as one of the modern new technology for earth observation, compared with other methods of remote sensing mapping, the main advantage of this technology is capable of fast, direct access to the three-dimensional coordinate data of the feature surface. Thus, in recent years in the forest resources survey, three-dimensional mapping and other urban areas Lidar are on a wide range of applications. In the area of forest vegetation, because the airborne laser beam emitted by the laser radar system has a certain penetration, penetrate vegetation to reach the surface. Therefore, using this technology, you can get a three-dimensional data canopy surface and ground surface.In this paper, we use LiDAR data to get single trees parameters, carry out the following research work as follows:Get canopy height models. In this paper,using the irregular triangle net filtering algorithm to filter the LiDAR point, separate by filtering the ground point, vegetation points, outliers, house points, etc..Achieved a good classification results.Then,take classification of ground points and vegetation LiDAR point to generate digital surface model and digital elevation models, then make difference operation to digital surface model and digital elevation models to get canopy height models(CHM).Canopy height model optimization algorithm. Canopy height model includes a lot of elevation vulnerability that directly or indirectly affect the extraction accuracy of various forest parameters based on canopy height model. This paper presents a new approach to solve this problem. First, obtain a smooth canopy height model by morphological closing operation, and then normalize, binary, convolute canopy height model matrix. Finally replace the canopy height model abnormal elevation point with a smooth elevation values and retain the value between the low canopy and canopy, which make a continuous canopy to be repaired and aligned.Take multi-scale separation to Canopy height models optimized. Object-oriented multi-scale segmentation is merging technique of starting a pixel object which start from the bottom of a region. A small image objects can be merged into a slightly larger object. In this study, using multi-scale object-oriented segmentation method, the canopy height model generate an image object prototype, then achieve regional growth algorithm by setting different scale parameters, completed the study area canopy height model division.The division achieved the desired effect.Finally, estimate a linear regression to the relationship between the measured characteristics of forest trees and eigenvalues through the establishment of LiDAR.Inversion single tree height and crown.