Study On The Estimation Method Of Forest Parameters Using Airborne LIDAR

Forests are the integrity of multi-resource and multi-function. It is very important to take the reins of developing rule of forest growth and consumption. Traditional methods of ground survey of forest resources can obtain detailed data, but require longer time. With the emerging and fast developing of remote sensing technology, it is possible to quickly and precisely acquire large area data of forest inventories. Lighting detection and ranging (LIDAR) is an active remote sensing, which can precisely acquire 3D feature information of earth objects. The main objective of this dissertation is to reveal the LIDAR detection principle of 3D structure of forest canopy and seek the effective estimation methods of forest parameters. Specially, the research topics include:(1) Established the solid scatterer model according to the back scattering feature of earth objects.It was found that the current LIDAR equation had limitation as explaining extended returned waveforms by analyzing interactive relations between laser pulses and forest canopy,. Thus, the solid scatterer model was established on the basis. The scatterers were classified as three types of simple, solid and complex. LIDAR equation of simple scatterer is kept as the same. LIDAR equation of solid scatterer extended the existing convolution function, which was defined as the extended convolution function, to explain back scattering feature of laser pulse for the solid scatterer. LIDAR equation of complex scatterer could be expressed as simple addition of two equations of the other scatterers.(2) Developed an analysis method of waveform feature in term of the waveform features of different scatterers.On the basis of analyzing transmitted and returned waveforms of laser pulses of different scatterers, I discovered that the distributions of returned energy had different properties. In the other words, it was easy to judge the scatterer types and extract the relative value of waveform features according to these properties. For simple scatterer, the distance between LIDAR and scatterer could be computed from value of waveform feature. For solid scatterer, distances between LIDAR and different position of scatterer could be obtained; also, including the depth of scatterer. For complex scatterer, distances between LIDAR and different position of scatterer (including sub-scatterers) could be obtained; depth of scatterer (including solid sub-scatterers) could be also obtained.(3) Proposed an analysis method of relative back scattering cross section refer to the back scattering cross section of different scatterersThe back scattering cross section of different scatterers could be deduced from the corresponding LIDAR equations. It was difficulty to obtain all the parameters as resolving the back scattering cross section. Such that, the paper defined the relative back scattering cross section that was easy to be resolved by selecting a kind of scatterer as the reference scatterer. Considering the illumination area extent of laser pulse, relative back scattering ratio was defined as relative back scattering cross section of per unit area. In addition, equivalent relative back scattering cross section and equivalent relative back scattering ratio of solid and complex scatterer were defined.(4) Presented a transformation method from waveform to point cloud in tern of the feature values of waveforms of different scatterersThe feature values of waveforms include the distances between LIDAR and scatterers, the depths of scatters et al. The location of scatters could be calculated in combined with the location vector of LIDAR and direction vector of transmitted pulse. The cloud point data that was transformed from waveform data only contained the feature value of different scatterers, which could effectively reduce redundancy information that was not needed for the relative analysis.(5) Optimized the flow and relative algorithms of data processing in the preprocessing of cloud point dataAccording to sampling feature of horizontal space of laser pulse, the paper proposed a method of calculating sample density. According to sample density of laser pulse, proposed a judgment rule of pixel size, which was 1/2 of average point space, for rasterizing cloud point data. This would keep sample point information while reduce redundancy information in the whole way. In order to interpolate the zero pixels of digital surface model (DSM) raster data, a neighbor interpolation algorithm were developed, which used for feature analysis of the hole in LIDAR data. In order to smooth canopy height model (CHM) raster data, an effective algorithm was also developed for smoothing concave point of crown surface.(6) Discovered a double tangent crown edge recognition (DTCER) algorithm adapt to the crown features of individual tree described by CHM.The crown features of individual tree include crown top, edge et al. The local maximum search algorithm with fixed or variable window was used to recognize crown tops; the DTCER algorithm with both constant and relative running mode was developed to recognize crown edges. In case of continuous crowns, the DTCER algorithm introduced judgment rule of equal proportion, which was that the boundaries between continuous crowns were divided by proportion of corresponding tree heights. In addition, the DTCER algorithm adopted judgment rule of disjoint sets to partition different crowns. The crown edge vectorization made use of one four directions algorithm.(7) Found the optimal estimation methods of parameters of individual tree according to the recognized crown features of individual tree.The parameters that could be directly estimated from the recognized crown feature of individual tree included the tree height, crown diameter, and crown base height (CBH) et al. The tree height was estimated from the detected height value at the crown top position. The crown diameter was calculated from crown edge, for which main directions algorithm and area algorithm have been developed. The CBH was extracted from the height value at the lowest position of crown edge. The result shows that the estimation accuracy of tree heights of individual trees is the highest, the lower is that of crown diameters of individual trees, and the lowest is the crown base heights (CBHs) of individual trees. The indirect parameters of individual tree were consisted of diameter at breast height (DBH), and biomass et al., which were estimated by the allometric growth equation. In order to establish the allometric growth equation, regression analysis was performed between directly estimated parameters and field-measured diameters at breast height (DBHs), including regression analysis after natural logarithm operation of parameters. The result shows that the linear regression equation is the most optimal between the natural logarithm operation of field-measured DBHs and the natural logarithm operation of estimated tree heights and estimated crown diameters. The estimated biomass could be calculated from estimated parameters by already existed allometric growth equation of biomass.(8) Sought the effective estimation methods of stand parameters through the estimated parameters of individual tree.The direct parameters of stand included average height of stand, stem density et al., which were directly estimated from the estimated parameters of individual tree. The result shows that the most optimal relation is that between estimated average heights of stand weighted by crown areas and field-measured average heights of stand weighted by DBHs. The estimated accuracy of stem density is strongly influenced by the distribution feature of trees. The proportion of stem numbers between overstory and understory would contribute to variation of estimated stem density. The indirect parameters of stand included basal areas, stand biomass et al., which were directly estimated from the estimated parameters of individual tree. The result shows that the estimated accuracies of basal areas and stand biomass are easily influenced by stem density in case that the estimated accuracies of individual tree parameters and allometric growth equation are given.In a word, it concludes that the airborne LIDAR with high sample density could describe 3D structure feature of forest canopy in detail; the crown feature of individual trees could be precisely recognized by definite flow and relevant algorithm of LIDAR data processing, which can also be used for estimating relative parameters estimation of individual trees and forest stands.