轻小型航空遥感森林几何参数提取研究
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摘要
本研究以高精度轻小型航空遥感系统为平台,集激光扫描测距仪、数码相机、POS系统于一体,利用激光探测和测距技术和数字摄影测量技术,获取研究区高密度LiDAR点云数据和高空间分辨率的航空遥感影像,通过对点云数据和航空影像处理和分析,结合地面样地调查数据建立起的林木参数估测模型及航空立木材积表,估测研究区林分因子。主要结论包括：
(1)根据地面调查数据建立的马尾松胸径估测模型具有较高的精度。
通过地面样地调查的数据,参考前人研究的5个主要候选的胸径-冠径关系模型、10个胸径-树高关系的模型,建立起商城地区马尾松最优的胸径-冠径、胸径-树高模型,以及胸径-冠径、树高综合模型,实现以冠径、树高估测胸径,其最高决定系数达到0.615。另外,参考了商城地区马尾松树种的一元、二元材积表,建立起马尾松的航空立木一元、二元材积表,作为后期根据轻小型机载LiDAR点云数据及高精度航空影像反演林木参数的依据。
(2)轻小型机载LiDAR数据能有效提取林分平均高、单木树高、林分密度等林木参数。
利用GIS的空间分析功能,研究轻小型机载LiDAR点云数据提取林分平均高、单木树高、每公顷株数等林木参数的方法。结果表明,以实地调查数据为真值,轻小型机载LiDAR点云数据提取的林分平均高精度达83%,提取的单木树高精度达88%,LiDAR数据获取的每公顷株数与目视解译的株数相比,精度达84%。
(3)轻小型航空遥感影像能有效提取林分郁闭度、林木平均冠幅、单木冠幅等林木参数。
在充分研究轻小型高分辨率航空遥感影像的光谱、纹理特征的基础上,通过面向对象的分类方法,对轻小型航空遥感影像进行多尺度分割。该方法能有效的提取林木与非林木区域,并在此基础上估测林分郁闭度、平均冠幅、单木冠幅等林木参数。其中,轻小型高空间分辨率遥感影像的单木冠幅提取精度为72%,林分平均冠幅精度达到87%。
(4)综合轻小型机载LiDAR及高分航空数据提取森林几何参数。
综合轻小型机载LiDAR数据以及其同步的高分航空数据两者的优势,以具有空间三维坐标的LiDAR数据提取林木树高、林分密度,以空间分辨率达到0.05m的航空影像提取的林木冠幅、郁闭度,估测单木材积,并建立起林分蓄积量的估测模型。由高分辨率影像估算的单木冠幅,通过查一元航空立木材积表估算的单木材积精度为51%,再结合LiDAR数据获取的单木树高,查二元航空立木材积表得到的单木材积精度为57%。建立平均树高、样地林木株数、平均冠幅、林分郁闭度与样地蓄积量的多元线性回归模型,其相关系数为0.583,决定系数0.34。
综上所述,轻小型机载LiDAR数据和高分航空影像,提取的样地尺度的林分平均高、平均冠幅,以及单木尺度的树高和冠幅等参数时,精度能达到72%-88%,而估测单木材积时精度较低,一元、二元航空立木材积表精度分别为51%和57%。根据轻小型机载LiDAR数据及航空影像获取的样地参数建立起的林分蓄积多元线性估测模型,其相关系数不高,为0.583。由于数据的限制,该模型能在多大范围内推广应用,还需要更多的地面调查和验证试验。如何通过数据挖掘,充分利用轻小型机载LiDAR数据及其同步的高空间分辨率航空遥感影像,提高其单木尺度和样地尺度的林木参数估测精度,有待下一步研究。
As a new remote sensing platform, Combining Laser scanning rangefinder, digital camera and POS (Posture Observation System) together, light and small airborne remote sensing system can obtain high density LiDAR point cloud data and high spatial resolution aerial remote sensing image of study area with laser detection and ranging technology and digital photogrammetry technology. The purpose of this study is to model and estimate forestry parameters by building aerial stem volume table with ground survey data, processing and analyzing point cloud data and aerial image of light and small airborne remote sensing system. The main conclusions include:
(1) The DBH(diameter at breast height) estimated model of Chinese red pine (Pinus massoniana) base on filed survey data has high precision.
Base on sample plot survey data,5candidate DBH-K(Crown diameter) and10candidated DBH-H(tree height) were chosen to build the optimal DBH-K, DBH-H, and DBH-K, H model to estimated DBH from crown diameter and tree height of Chinese red pine, the highest determination coefficient was0.615. In addition, one and two variable timber volume table of Chinese red pine in Shangcheng area were referenced to build one and two variable timber volume aerial table, which will help to estimate forestry parameters base on LiDAR data and high spatial resolution aerial images from light and small airborne remote sensing.
(2) The light and small airborne LiDAR data can extract forestry parameters such as average stand high, individual tree high and stand density effectively.
With the spatial analysis function of GIS, airborne LiDAR point cloud data were used in extracting the average stand high, individual tree high and stand density, and compare with filed survey data, the estimated accuracy of average stand high and individual tree high are83%and88%respectively, and compared with visual interpretation the accuracy of tree number extracted from LiDAR data is84%.
(3) The high spatial resolution aerial image of light and small airborne remote sensing can extract forestry parameters such as crown density, stand average tree crown and individual tree crown effectively.
In the full study of spectral and texture features of high-resolution aerial remote sensing image of light and small airborne remote sensing, base on object-oriented classification method and multi-scale segmentation, forest and non-forest area can be extracted firstly, and then forestry parameters such as crown density, stand average tree crown and individual tree crown can be extracted effectively. The accuracy of individual tree crown is72%, while the average tree crown reaches87%.
(4) Estimating forest parameters by integrated application of the LiDAR data and high spatial resolution aerial images data.
Integrated the advantages of airborne LiDAR data and its synchronization high spatial resolution aerial image, tree high and forest density can be extracted from LiDAR data which contain the three-dimensional coordinates, tree crown diameter, canopy density can be extracted from aerial image with a0.05m high spatial resolution, then individual tree volume and forestry stand volume can be estimated and modeled with the parameters come from light and small airborne remote sensing system. The estimated accuracy of individual tree volume base on one variable timber volume aerial table is51%, and57%while base on two variables timber volume aerial table. A multiple linear regression model estimated forestry stand volume with average tree high, forest density, average crown and canopy density as dependent variables has a correlation coefficient of0.583, and the determination coefficient is0.34.
Above all, the accuracy of forestry stand and individual tree parameters such as average tree height, average crown diameter, individual tree high and crown diameter estimated from airborne LiDAR data and high spatical resolution aerial images are between72%-88%, the accuracy of individual stem volume are51%and57%respectively base on one and two variable timber volume aerial tables. The multiple linear regression model estimated forestry stand volume base on the forestry parameters estimated from light and small airborne remote sensing data has a correlation coefficient of0.583. As the limit of modeling data, more filed survey and verification test needed to find the application area. Further research need to be done to find out how to make full use of the light and small airborne remote sensing data, dig the inner knowledge of LiDAR data and high spatial resolution aerial images, and improve the estimated accuracy of individual tree and forestry stand parameters.
(1)根据地面调查数据建立的马尾松胸径估测模型具有较高的精度。
通过地面样地调查的数据,参考前人研究的5个主要候选的胸径-冠径关系模型、10个胸径-树高关系的模型,建立起商城地区马尾松最优的胸径-冠径、胸径-树高模型,以及胸径-冠径、树高综合模型,实现以冠径、树高估测胸径,其最高决定系数达到0.615。另外,参考了商城地区马尾松树种的一元、二元材积表,建立起马尾松的航空立木一元、二元材积表,作为后期根据轻小型机载LiDAR点云数据及高精度航空影像反演林木参数的依据。
(2)轻小型机载LiDAR数据能有效提取林分平均高、单木树高、林分密度等林木参数。
利用GIS的空间分析功能,研究轻小型机载LiDAR点云数据提取林分平均高、单木树高、每公顷株数等林木参数的方法。结果表明,以实地调查数据为真值,轻小型机载LiDAR点云数据提取的林分平均高精度达83%,提取的单木树高精度达88%,LiDAR数据获取的每公顷株数与目视解译的株数相比,精度达84%。
(3)轻小型航空遥感影像能有效提取林分郁闭度、林木平均冠幅、单木冠幅等林木参数。
在充分研究轻小型高分辨率航空遥感影像的光谱、纹理特征的基础上,通过面向对象的分类方法,对轻小型航空遥感影像进行多尺度分割。该方法能有效的提取林木与非林木区域,并在此基础上估测林分郁闭度、平均冠幅、单木冠幅等林木参数。其中,轻小型高空间分辨率遥感影像的单木冠幅提取精度为72%,林分平均冠幅精度达到87%。
(4)综合轻小型机载LiDAR及高分航空数据提取森林几何参数。
综合轻小型机载LiDAR数据以及其同步的高分航空数据两者的优势,以具有空间三维坐标的LiDAR数据提取林木树高、林分密度,以空间分辨率达到0.05m的航空影像提取的林木冠幅、郁闭度,估测单木材积,并建立起林分蓄积量的估测模型。由高分辨率影像估算的单木冠幅,通过查一元航空立木材积表估算的单木材积精度为51%,再结合LiDAR数据获取的单木树高,查二元航空立木材积表得到的单木材积精度为57%。建立平均树高、样地林木株数、平均冠幅、林分郁闭度与样地蓄积量的多元线性回归模型,其相关系数为0.583,决定系数0.34。
综上所述,轻小型机载LiDAR数据和高分航空影像,提取的样地尺度的林分平均高、平均冠幅,以及单木尺度的树高和冠幅等参数时,精度能达到72%-88%,而估测单木材积时精度较低,一元、二元航空立木材积表精度分别为51%和57%。根据轻小型机载LiDAR数据及航空影像获取的样地参数建立起的林分蓄积多元线性估测模型,其相关系数不高,为0.583。由于数据的限制,该模型能在多大范围内推广应用,还需要更多的地面调查和验证试验。如何通过数据挖掘,充分利用轻小型机载LiDAR数据及其同步的高空间分辨率航空遥感影像,提高其单木尺度和样地尺度的林木参数估测精度,有待下一步研究。
As a new remote sensing platform, Combining Laser scanning rangefinder, digital camera and POS (Posture Observation System) together, light and small airborne remote sensing system can obtain high density LiDAR point cloud data and high spatial resolution aerial remote sensing image of study area with laser detection and ranging technology and digital photogrammetry technology. The purpose of this study is to model and estimate forestry parameters by building aerial stem volume table with ground survey data, processing and analyzing point cloud data and aerial image of light and small airborne remote sensing system. The main conclusions include:
(1) The DBH(diameter at breast height) estimated model of Chinese red pine (Pinus massoniana) base on filed survey data has high precision.
Base on sample plot survey data,5candidate DBH-K(Crown diameter) and10candidated DBH-H(tree height) were chosen to build the optimal DBH-K, DBH-H, and DBH-K, H model to estimated DBH from crown diameter and tree height of Chinese red pine, the highest determination coefficient was0.615. In addition, one and two variable timber volume table of Chinese red pine in Shangcheng area were referenced to build one and two variable timber volume aerial table, which will help to estimate forestry parameters base on LiDAR data and high spatial resolution aerial images from light and small airborne remote sensing.
(2) The light and small airborne LiDAR data can extract forestry parameters such as average stand high, individual tree high and stand density effectively.
With the spatial analysis function of GIS, airborne LiDAR point cloud data were used in extracting the average stand high, individual tree high and stand density, and compare with filed survey data, the estimated accuracy of average stand high and individual tree high are83%and88%respectively, and compared with visual interpretation the accuracy of tree number extracted from LiDAR data is84%.
(3) The high spatial resolution aerial image of light and small airborne remote sensing can extract forestry parameters such as crown density, stand average tree crown and individual tree crown effectively.
In the full study of spectral and texture features of high-resolution aerial remote sensing image of light and small airborne remote sensing, base on object-oriented classification method and multi-scale segmentation, forest and non-forest area can be extracted firstly, and then forestry parameters such as crown density, stand average tree crown and individual tree crown can be extracted effectively. The accuracy of individual tree crown is72%, while the average tree crown reaches87%.
(4) Estimating forest parameters by integrated application of the LiDAR data and high spatial resolution aerial images data.
Integrated the advantages of airborne LiDAR data and its synchronization high spatial resolution aerial image, tree high and forest density can be extracted from LiDAR data which contain the three-dimensional coordinates, tree crown diameter, canopy density can be extracted from aerial image with a0.05m high spatial resolution, then individual tree volume and forestry stand volume can be estimated and modeled with the parameters come from light and small airborne remote sensing system. The estimated accuracy of individual tree volume base on one variable timber volume aerial table is51%, and57%while base on two variables timber volume aerial table. A multiple linear regression model estimated forestry stand volume with average tree high, forest density, average crown and canopy density as dependent variables has a correlation coefficient of0.583, and the determination coefficient is0.34.
Above all, the accuracy of forestry stand and individual tree parameters such as average tree height, average crown diameter, individual tree high and crown diameter estimated from airborne LiDAR data and high spatical resolution aerial images are between72%-88%, the accuracy of individual stem volume are51%and57%respectively base on one and two variable timber volume aerial tables. The multiple linear regression model estimated forestry stand volume base on the forestry parameters estimated from light and small airborne remote sensing data has a correlation coefficient of0.583. As the limit of modeling data, more filed survey and verification test needed to find the application area. Further research need to be done to find out how to make full use of the light and small airborne remote sensing data, dig the inner knowledge of LiDAR data and high spatial resolution aerial images, and improve the estimated accuracy of individual tree and forestry stand parameters.
引文
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