CN104656098B - A kind of method of remote sensing forest biomass inverting - Google Patents
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Abstract
本发明公开了一种遥感森林生物量反演的方法,该方法在对遥感数据预处理的基础上,分别从LiDAR点云(包含冠层三维空间信息)及多光谱(包含冠层上表面的光谱信息)数据中提取植被冠层的特征变量;通过相关性分析筛选以上LiDAR点云和多光谱特征变量,并结合地面实测生物量信息通过逐步回归模型反演地上和地下生物量。通过本发明构建的北亚热带森林生物量的优化反演模型可将模型“决定系数”R2提高3‑24%;并可高精度估算森林生物量,将“相对均方根误差”rRMSE降低2‑10%。可应用在林业调查、森林资源监测、森林碳储量评估及森林生态系统的研究等领域,并为森林可持续经营及森林资源的综合利用提供定量化的数据支持。
The invention discloses a method for remote sensing forest biomass inversion. On the basis of preprocessing the remote sensing data, the method respectively extracts the data from the LiDAR point cloud (including the three-dimensional spatial information of the canopy) and the multi-spectrum (including the canopy upper surface information). The characteristic variables of the vegetation canopy were extracted from the spectral information) data; the above-mentioned LiDAR point cloud and multi-spectral characteristic variables were screened through correlation analysis, and the aboveground and underground biomass were retrieved through a stepwise regression model combined with the ground-measured biomass information. The optimal inversion model of the northern subtropical forest biomass constructed by the present invention can improve the model "coefficient of determination" R by 3-24%; and can estimate the forest biomass with high precision, and reduce the "relative root mean square error" rRMSE by 2 -10%. It can be applied in the fields of forestry survey, forest resources monitoring, forest carbon stock assessment and forest ecosystem research, and provide quantitative data support for sustainable forest management and comprehensive utilization of forest resources.
Description
技术领域technical field
本发明涉及森林有害生物的监测和无公害控制技术领域,具体涉及一种遥感森林生物量反演的方法。The invention relates to the technical field of forest pest monitoring and pollution-free control, in particular to a remote sensing forest biomass inversion method.
背景技术Background technique
精确的生物量估算对于森林资源监测、森林碳储量评估及森林生态系统的研究有重要意义。同时,这些信息也能够为森林可持续经营及森林资源的综合利用提供定量化的数据支持。传统的生物量调查方法耗时费力,且只能获得有限的“点”上信息,难于在大区域上实用化推广;而遥感技术却能够准确、快速地获取各个尺度的森林参数,具有很好的实用价值和应用前景。Accurate biomass estimation is of great significance for forest resource monitoring, forest carbon stock assessment and forest ecosystem research. At the same time, these information can also provide quantitative data support for sustainable forest management and comprehensive utilization of forest resources. The traditional biomass survey method is time-consuming and laborious, and can only obtain limited "point" information, which is difficult to be practically promoted in a large area; however, remote sensing technology can accurately and quickly obtain forest parameters at various scales, and has a very good practical value and application prospects.
Landsat系列卫星可获取中等和大尺度上的森林多光谱(光学)信息。其最新的Landsat 8OLI传感器(该传感器搭载在美国航天航空局(NASA)2013年2月11日发射的Landsat 8卫星上)在波段的设置及对植被的敏感性上上相比之前的TM等传感器有较大提升。然而光学遥感仍难以穿透森林冠层获得其垂直结构信息,且在森林覆盖度高(植被生长旺盛)的区域获取森林生物量信息时易饱和。激光雷达(LiDAR,Light Detection AndRanging)是近年来迅速发展的主动遥感技术,其发射的激光脉冲可以穿透植被冠层获得其三维结构和能量信息,以往研究表明LiDAR在精确估算不同森林类型的生物物理和结构特性方面具有较大潜力。The Landsat series of satellites can acquire forest multispectral (optical) information on medium and large scales. Its latest Landsat 8OLI sensor (the sensor is carried on the Landsat 8 satellite launched by NASA on February 11, 2013) is compared with previous sensors such as TM in terms of band setting and sensitivity to vegetation. There is a big improvement. However, optical remote sensing is still difficult to penetrate the forest canopy to obtain its vertical structure information, and it is easy to be saturated when obtaining forest biomass information in areas with high forest coverage (vegetative growth). LiDAR (LiDAR, Light Detection AndRanging) is an active remote sensing technology that has developed rapidly in recent years. The laser pulses it emits can penetrate the vegetation canopy to obtain its three-dimensional structure and energy information. Previous studies have shown that LiDAR can accurately estimate the biological characteristics of different forest types. It has great potential in terms of physical and structural properties.
近年来反演森林生物量的研究有:1)Zheng等2004在《Remote Sensing ofEnvironment》第35卷上发表了“Estimating aboveground biomass using Landsat7ETM+data across a managed landscape in northern Wisconsin,USA”,该研究借助从ETM+(Landsat 7)影像上提取的NDVI等植被指数反演了美国威斯康辛州北部针叶林的地上生物量信息。2)杨存建等2004年在《植物生态学报》28卷上发表了“不同龄组的热带森林植被生物量与遥感地学数据之间的相关性分析”利用TM(Landsat5)影像原始波段的方法对云南西双版纳热带森林植被生物量进行估算。3)Ferster等2009年在《Canadian Journal ofRemote Sensing》第35卷上发表了“Aboveground Large Tree Mass Estimation in aCoastal Forest in British Columbia Using Plot-Level Metrics and IndividualTree Detection from LiDAR”,该研究借助从小光斑(直径:0.1-2m)LiDAR数据中提取的点云高度与冠层密度信息,反演了温带森林的地上生物量。4)Saatchi等2011年在《Proceedings of the National Academy of Sciences of the United States ofAmerica》第12期上发表了“Benchmark Map of Forest Carbon Stocks in TropicalRegions across Three Continents”,该研究通过从GLAS(Geoscience Laser AltimeterSystem)大光斑(直径:52-90m)LiDAR数据中提取与树高信息相关的特征变量,反演了热带雨林的生物量。然而,以上方法仅从单一的角度去挖掘传统“光学”和LiDAR数据,特异性较低,且对特征变量的挖掘深度较浅(即并未系统分组从多个角度提取和筛选特征变量),还不能精确的对生物量进行反演。In recent years, researches on inversion of forest biomass include: 1) Zheng et al published "Estimating aboveground biomass using Landsat7ETM+data across a managed landscape in northern Wisconsin, USA" in Volume 35 of "Remote Sensing of Environment" in 2004. Vegetation indices such as NDVI extracted from ETM+ (Landsat 7) images retrieved aboveground biomass information of coniferous forests in northern Wisconsin, USA. 2) Yang Cunjian et al. published "Correlation Analysis between Tropical Forest Vegetation Biomass of Different Age Groups and Remote Sensing Geoscience Data" in "Journal of Plant Ecology" Volume 28 in 2004. Using the original band method of TM (Landsat5) images to analyze the Estimation of vegetation biomass in Xishuangbanna tropical forest. 3) Ferster et al published "Aboveground Large Tree Mass Estimation in a Coastal Forest in British Columbia Using Plot-Level Metrics and IndividualTree Detection from LiDAR" in Volume 35 of "Canadian Journal of Remote Sensing" in 2009. : 0.1-2m) The point cloud height and canopy density information extracted from the LiDAR data were used to invert the aboveground biomass of temperate forests. 4) Saatchi et al. published "Benchmark Map of Forest Carbon Stocks in Tropical Regions across Three Continents" in the 12th issue of "Proceedings of the National Academy of Sciences of the United States of America" in 2011. This study was obtained from GLAS (Geoscience Laser Altimeter System ) large spot (diameter: 52-90m) LiDAR data to extract characteristic variables related to tree height information, and invert the biomass of tropical rainforest. However, the above methods only mine traditional "optical" and LiDAR data from a single angle, with low specificity, and the mining depth of feature variables is relatively shallow (that is, feature variables are not systematically extracted and screened from multiple angles), It is not yet possible to accurately invert the biomass.
发明内容Contents of the invention
发明目的:针对现有技术中存在的不足,本发明提出一种遥感森林生物量反演的方法,具有特异性强、成本低、易于推广应用等特点。Purpose of the invention: Aiming at the deficiencies in the prior art, the present invention proposes a remote sensing forest biomass inversion method, which has the characteristics of strong specificity, low cost, and easy popularization and application.
技术方案:为了实现上述发明目的,本发明采用的技术方案为:Technical solution: In order to realize the above-mentioned purpose of the invention, the technical solution adopted in the present invention is:
一种遥感森林生物量反演的方法:在对遥感数据预处理的基础上,分别从LiDAR点云(包含冠层三维空间信息)及多光谱(包含冠层上表面的光谱信息)数据中提取植被冠层的特征变量;通过相关性分析筛选以上LiDAR点云和多光谱特征变量,并结合地面实测生物量信息通过逐步回归模型反演地上和地下生物量。A remote sensing forest biomass inversion method: on the basis of preprocessing the remote sensing data, extracting The characteristic variables of the vegetation canopy; the above-mentioned LiDAR point cloud and multispectral characteristic variables were screened through correlation analysis, and combined with the measured biomass information on the ground, the aboveground and underground biomass were inverted through a stepwise regression model.
所述遥感森林生物量反演的方法,包括以下步骤:The method for inversion of forest biomass by remote sensing comprises the following steps:
1)OLI影像预处理:首先借助OLI传感器的辐射定标参数对原始影像进行辐射定标;将原始DN值转化为像元辐射亮度值;再以FLAASH模型对影像进行大气校正,从而将辐射亮度值转化为地表实际反射率;然后对影像进行几何精校正,选取同名地物点,采用二次多项式进行校正,校正误差控制在0.1个像元以内,并采用最邻近像元法进行重采样。1) OLI image preprocessing: First, radiometrically calibrate the original image with the radiometric calibration parameters of the OLI sensor; convert the original DN value into a pixel radiance value; Then the image is geometrically corrected, and the point of the same name is selected, and the quadratic polynomial is used for correction. The correction error is controlled within 0.1 pixels, and the nearest neighbor pixel method is used for resampling.
2)LiDAR数据预处理:2) LiDAR data preprocessing:
a)噪声水平估计和数据平滑:首先把原始数据转换到频率域,再将频率较高的低值部分作为噪声水平的判断标准;然后选用高斯滤波器进行平滑,这是由于高斯滤波器在有效平滑数据的同时,还可以最大限度地保持原有曲线的趋势;a) Noise level estimation and data smoothing: first convert the original data to the frequency domain, and then use the low-value part with higher frequency as the judgment standard of the noise level; then use the Gaussian filter for smoothing, because the Gaussian filter is effective in While smoothing the data, it can also maintain the trend of the original curve to the greatest extent;
b)高斯拟合(分解)及波形数据点云化:基于回波数据是多个高斯函数的累加这一假设,对波形数据采用非线性最小二乘法进行拟合;然后通过局部最大峰值检测滤波算法从处理后的波形数据上提取离散点云,每个离散点中记录了返回信号的能量和振幅信息;b) Gaussian fitting (decomposition) and point cloudization of waveform data: Based on the assumption that the echo data is the accumulation of multiple Gaussian functions, the waveform data is fitted using the nonlinear least squares method; and then filtered through local maximum peak detection The algorithm extracts discrete point clouds from the processed waveform data, and the energy and amplitude information of the returned signal is recorded in each discrete point;
c)生成数字地形:LiDAR数据高度归一化的目的是为了得到去除了地形影响的“真实”植被高度,通常采用原始LiDAR数据高度信息减去地形高度得到;因此,精确生成数字地形模型(DTM)是计算归一化植被高度的重要前提;首先对从波形数据中提取出离散点云进行分类,然后对末次回波进行Kraus滤波处理用以去除非地面点,最后使用滤波后的末次回波数据并借助自然邻近法插值生成数字地形模型;c) Generating digital terrain: The purpose of height normalization of LiDAR data is to obtain the "true" vegetation height without the influence of terrain, which is usually obtained by subtracting terrain height from the original LiDAR data height information; therefore, the accurate generation of digital terrain model (DTM ) is an important prerequisite for calculating the normalized vegetation height; firstly, classify the discrete point cloud extracted from the waveform data, then perform Kraus filtering on the last echo to remove non-ground points, and finally use the filtered last echo Data and generate a digital terrain model by means of natural proximity method interpolation;
d)再利用DEM对植被回波点的高程进行归一化处理,即使植被点的高度值转化为相对于地面的高度值,并通过左下角和右上角的坐标对每块样地进行裁切;最后通过GIS分析工具提取55个样地对应坐标位置的归一化点云数据;d) Use DEM to normalize the elevation of the vegetation echo points, even if the height value of the vegetation point is converted into a height value relative to the ground, and cut each plot by the coordinates of the lower left corner and upper right corner ;Finally, extract the normalized point cloud data corresponding to the coordinate positions of 55 sample plots through GIS analysis tools;
3)OLI特征变量提取:通过对OLI影像进行波段组合、缨帽变换、纹理信息提取、主成分分析、最小噪声分离变换以及多种植被指数变换,提取5组特征变量(详见表1),即原始单波段变量、波段组合变量、信息增强组变量、植被指数变量以及纹理信息变量;其中纹理分析针对主成分分析的第一主成分进行;3) OLI feature variable extraction: By performing band combination, tasseled cap transformation, texture information extraction, principal component analysis, minimum noise separation transformation and various vegetation index transformations on OLI images, five groups of characteristic variables were extracted (see Table 1 for details), That is, the original single-band variables, band combination variables, information-enhanced group variables, vegetation index variables, and texture information variables; where texture analysis is performed on the first principal component of principal component analysis;
4)提取的LiDAR点云特征变量:LiDAR点云特征变量是基于三维归一化LiDAR点云值计算了4组特征变量(详见表2),即高度变量、高度百分位数变量、冠层密度变量、冠层覆盖度变量;4) Extracted LiDAR point cloud feature variables: LiDAR point cloud feature variables are based on three-dimensional normalized LiDAR point cloud values to calculate four groups of feature variables (see Table 2 for details), namely height variable, height percentile variable, crown Layer density variable, canopy coverage variable;
5)特征变量筛选:将提取的LiDAR特征变量及OLI特征变量与需要预测的参数进行Pearson’s相关性分析,选取Pearson’s相关系数的绝对值高于0.2的特征变量作为建模候选变量;Pearson’s相关系数的计算方法为:5) Feature variable screening: Perform Pearson's correlation analysis on the extracted LiDAR feature variables and OLI feature variables and the parameters to be predicted, and select the feature variables whose absolute value of Pearson's correlation coefficient is higher than 0.2 as modeling candidate variables; Pearson's correlation coefficient The calculation method is:
式中,xi为地面实测的某林分特征,yi为某LiDAR特征变量,为xi的平均值,为yi的平均值;In the formula, x i is the measured characteristics of a forest stand on the ground, y i is a LiDAR feature variable, is the average value of xi , is the average value of y i ;
6)统计分析:将地面实测汇总的生物量信息作为因变量,遥感方法提取的特征变量作为自变量,建立多元回归模型。运用逐步进入法(stepwise)和检验决定系数(R2)的变化情况来选择进入模型的合适变量;如果有自变量使统计量F值过小并且T检验达不到显著水平(P值>0.1),则予以剔除;F值较大且T检验达到显著水平(P值<0.05)则得以进入;采用决定系数(R2)、均方根误差(RMSE)和相对均方根误差(rRMSE)评价回归模型的精度;6) Statistical analysis: The biomass information collected from ground measurements was used as the dependent variable, and the feature variables extracted by remote sensing methods were used as independent variables to establish a multiple regression model. Use the stepwise method (stepwise) and test the change of the coefficient of determination (R 2 ) to select the appropriate variables to enter the model; if there are independent variables that make the statistic F value too small and the T test cannot reach the significant level (P value>0.1 ), then it will be eliminated; if the F value is large and the T test reaches a significant level (P value <0.05), then it can be entered; the coefficient of determination (R 2 ), root mean square error (RMSE) and relative root mean square error (rRMSE) are used Evaluate the accuracy of the regression model;
式中,xi为地面实测的某林分特征,为xi的平均值,为模型估算的某林分特征,n为样地数量;In the formula, x i is the characteristics of a forest stand measured on the ground, is the average value of xi , is the characteristic of a forest stand estimated by the model, and n is the number of sample plots;
由(3)式可见,rRMSE为RMSE(均方根误差)与(实测值均值)的百分比。It can be seen from formula (3) that rRMSE is RMSE (root mean square error) and (measured value mean) percentage.
本发明的遥感森林生物量反演的方法,在对LiDAR和OLI数据进行预处理的基础上,分别从点云(包含冠层三维空间信息)及多光谱(包含冠层上表面的光谱信息)数据中提取植被冠层的特征变量,然后在筛选特征变量的基础上结合地面实测生物量信息通过逐步回归模型反演地上和地下生物量,其创新点和特色如下:1)从冠层三维空间信息及其上表面的光谱信息这两个维度上充分挖掘两者所包含的植被生物物理特性信息;2)借助相关性分析对以上提取出的特征变量进行筛选,并用于最终的反演模型,从而利于机理解释、方法移植;3)由于OLI数据是免费数据,故该方法也很大程度上节省了多个尺度上提升生物量反演精度的成本。The method for remote sensing forest biomass inversion of the present invention, on the basis of preprocessing LiDAR and OLI data, respectively from the point cloud (comprising three-dimensional space information of the canopy) and multispectral (comprising the spectral information of the upper surface of the canopy) Extract the characteristic variables of the vegetation canopy from the data, and then combine the measured biomass information on the ground to invert the aboveground and underground biomass through the stepwise regression model on the basis of screening the characteristic variables. information and the spectral information on the upper surface to fully exploit the vegetation biophysical characteristics information contained in the two dimensions; 2) use the correlation analysis to screen the above extracted characteristic variables and use them in the final inversion model, This is conducive to mechanism interpretation and method transplantation; 3) Since OLI data is free data, this method also greatly saves the cost of improving the accuracy of biomass inversion on multiple scales.
有益效果:与现有技术相比,本发明通过集成LiDAR点云和OLI多光谱特征变量,从冠层三维空间信息以及其上表面的光谱信息维度充分挖掘两者所包含的植被生物物理特性信息,并借助相关性分析对以上提取出的特征变量进行筛选,最终确定反演模型。该方法有利于机理解释、方法移植;且由于OLI数据是免费数据,故该方法也很大程度上节省了多个尺度上提升生物量反演精度的成本。前期实验验证结果表明,通过本发明构建的北亚热带森林生物量的优化反演模型(与基于单一多光谱数据源的方法相比)可将模型“决定系数”R2提高3-24%;并可高精度估算森林生物量(将“相对均方根误差”rRMSE降低2-10%)。可应用在林业调查、森林资源监测、森林碳储量评估及森林生态系统的研究等领域,并为森林可持续经营及森林资源的综合利用提供定量化的数据支持。Beneficial effects: Compared with the prior art, the present invention fully excavates the vegetation biophysical characteristic information contained in the three-dimensional space information of the canopy and the spectral information dimension of the upper surface by integrating LiDAR point cloud and OLI multi-spectral characteristic variables , and with the help of correlation analysis to screen the above extracted feature variables, and finally determine the inversion model. This method is conducive to mechanism interpretation and method transplantation; and because OLI data is free data, this method also greatly saves the cost of improving the accuracy of biomass inversion on multiple scales. Preliminary experimental verification results show that the optimized inversion model of the northern subtropical forest biomass constructed by the present invention (compared with the method based on a single multispectral data source) can improve the model "coefficient of determination" R 2 by 3-24%; And it can estimate the forest biomass with high precision (reduce the "relative root mean square error" rRMSE by 2-10%). It can be applied in the fields of forestry survey, forest resources monitoring, forest carbon stock assessment and forest ecosystem research, and provide quantitative data support for sustainable forest management and comprehensive utilization of forest resources.
附图说明Description of drawings
图1是研究区真彩色航空像片及55块样地分布图;Figure 1 is the true-color aerial photo of the study area and the distribution map of 55 plots;
图2是遥感森林生物量反演的方法技术路线图;Figure 2 is the technical roadmap of remote sensing forest biomass retrieval methods;
图3是各特征变量与地上生物量的Pearson’s相关系数图;Figure 3 is the Pearson's correlation coefficient diagram between each characteristic variable and aboveground biomass;
图4是各特征变量与地下生物量的Pearson’s相关系数图;Figure 4 is a graph of Pearson's correlation coefficient between each characteristic variable and underground biomass;
图5是OLI模型-地上生物量样地实测值与模型预测值的对比散点图;Figure 5 is a scatter diagram of the OLI model-comparison of the measured values of aboveground biomass plots and the predicted values of the model;
图6是OLI模型-地下生物量样地实测值与模型预测值的对比散点图;Figure 6 is a scatter diagram of the OLI model - the comparison between the measured value of the underground biomass sample plot and the predicted value of the model;
图7是LiDAR模型-地上生物量样地实测值与模型预测值的对比散点图;Figure 7 is a scatter diagram of the LiDAR model-comparison of the measured values of aboveground biomass plots and the predicted values of the model;
图8是LiDAR模型-地下生物量样地实测值与模型预测值的对比散点图;Figure 8 is a scatter diagram of the LiDAR model-comparison of the measured value of the underground biomass sample plot and the predicted value of the model;
图9是综合模型-地上生物量样地实测值与模型预测值的对比散点图;Figure 9 is a scatter diagram of the comprehensive model-comparison of the measured values of the aboveground biomass sample plots and the predicted values of the model;
图10是综合模型-地下生物量样地实测值与模型预测值的对比散点图。Figure 10 is a scatter diagram of the comprehensive model-comparison of the measured values of underground biomass plots and the predicted values of the model.
具体实施方式Detailed ways
下面结合具体实施例对本发明作进一步的说明。The present invention will be further described below in conjunction with specific examples.
实施例1Example 1
一种遥感森林生物量反演的方法,技术路线如图1所示,该方法为集成LiDAR点云和OLI多光谱特征变量的的遥感森林生物量反演的方法,在对遥感数据预处理的基础上,分别从LiDAR点云(包含冠层三维空间信息)及多光谱(包含冠层上表面的光谱信息)数据中提取植被冠层的特征变量;通过相关性分析筛选以上LiDAR点云和多光谱特征变量,并结合地面实测生物量信息通过逐步回归模型反演地上和地下生物量。具体如下:A method for remote sensing forest biomass inversion. The technical route is shown in Figure 1. This method is a method for remote sensing forest biomass inversion that integrates LiDAR point cloud and OLI multispectral feature variables. In the preprocessing of remote sensing data On this basis, the characteristic variables of the vegetation canopy were extracted from the LiDAR point cloud (including the three-dimensional spatial information of the canopy) and the multi-spectral (including the spectral information of the upper surface of the canopy) data respectively; Spectral characteristic variables, combined with ground-measured biomass information, were used to invert aboveground and belowground biomass through a stepwise regression model. details as follows:
研究区位于江苏省常熟市国营虞山林场(120°42′9.4″E,31°40′4.1″N),属亚热带季风气候,气候温和,年平均降水量1054毫米,面积约1103hm2,其海拔高度为20-261m。虞山林场属于北亚热带次生混交林,主要森林类型为针叶林,阔叶林和混交林,其中主要针叶树种有马尾松(Pinus massoniana)、杉木(Cunninghamia lanceolata)、湿地松(Pinuselliottii)和黑松(Pinus thunbergii)等;主要阔叶树种有麻栎((Quercus acutissima)、香枫(Liquidambar formosan)及部分常绿阔叶树种,如壳斗科植物(Fagaceae)、樟科植物(Lauraceae)和山茶科植物(Theaceae)。The research area is located in the state-owned Yushan Forest Farm in Changshu City, Jiangsu Province (120°42′9.4″E, 31°40′4.1″N), which belongs to the subtropical monsoon climate, with a mild climate and an average annual precipitation of 1054mm. The area is about 1103hm 2 The height is 20-261m. Yushan Forest Farm belongs to the northern subtropical secondary mixed forest. The main forest types are coniferous forest, broad-leaved forest and mixed forest. The main coniferous tree species are Pinus massoniana (Pinus massoniana), Chinese fir (Cunninghamia lanceolata), Slash pine (Pinuselliottii) and Black pine (Pinus thunbergii), etc.; the main broad-leaved tree species are Quercus acutissima (Quercus acutissima), sweet maple (Liquidambar formosan) and some evergreen broad-leaved tree species, such as Fagaceae, Lauraceae and camellia Plants of the family Theaceae.
根据三类调查历史数据(1995,2012)中的森林类型、年龄和立地指数等指标在研究区范围内设置55个方形样地(大小:30×30m,设置时间:2012年7-8月及2013年8月)。根据树种组成比例将样地划分为针叶林(n=13)、阔叶林(n=16)和混交林(n=26)三种类型。样地调查过程中,对于胸径大于5cm的树,逐一测定单木的树种、胸径(用围尺测量)、树高和枝下高(利用Vertex IV激光测高器测量)以及冠幅(即两个主方向上的投影距离,用皮尺测量),对于胸径小于5cm的和死掉的树进行计数,但不参与生物量的计算。样地西南角坐标使用差分GPS(Trimble GeoXH6000Handheld GPS units)测定,通过接收JSCORS广域差分信号定位,精度优于0.5米(图2)。55 square plots (size: 30×30m, setting time: July-August 2012 and August 2013). According to the proportion of tree species, the plots were divided into three types: coniferous forest (n=13), broad-leaved forest (n=16) and mixed forest (n=26). During the plot survey, for trees with a diameter at breast height greater than 5 cm, the tree species, diameter at breast height (measured with a girth), tree height and height under branches (measured with a Vertex IV laser altimeter) and crown width (that is, two The projection distance in the main direction, measured with a tape measure), counts the dead trees with a DBH less than 5cm, but does not participate in the calculation of biomass. The coordinates of the southwest corner of the sample plot were determined using differential GPS (Trimble GeoXH6000 Handheld GPS units), and the positioning accuracy was better than 0.5 meters by receiving JSCORS wide-area differential signals (Figure 2).
根据单木调查数据汇总样地尺度的相关森林参数,包括样地尺度上的单位面积地上、地下生物量(t·hm-2)。生物量信息通过异速生长方程(遵从就近原则)计算单木的生物量,并汇总得到每块样地的单位面积地上生物量(WA)和地下生物量(WB)。According to the individual tree survey data, the relevant forest parameters at the plot scale were summarized, including aboveground and belowground biomass per unit area (t·hm -2 ) at the plot scale. Biomass information Calculate the biomass of a single tree through the allometric growth equation (following the principle of proximity), and summarize the aboveground biomass (W A ) and belowground biomass (W B ) per unit area of each plot.
OLI影像预处理。研究采用2013年7月19日的Landsat 8OLI影像(条带号119/38)中的2-7波段,影像空间分辨率为30m,辐射分辨率为12bit,光谱范围覆盖11个波段。首先借助OLI传感器的辐射定标参数对原始影像进行辐射定标。将原始DN值转化为像元辐射亮度值。再以FLAASH模型对影像进行大气校正(大气模型:Tropical;气溶胶模型:Urban;气溶胶反演法:2-Band(K-T);初始能见度:40;波谱响应函数:ldcm_oli.sli),从而将辐射亮度值转化为地表实际反射率。然后对影像进行几何精校正,选取40个同名地物点,采用二次多项式进行校正,校正误差控制在0.1个像元以内,并采用最邻近像元法进行重采样。OLI image preprocessing. The study used the 2-7 bands in the Landsat 8OLI image (strip number 119/38) on July 19, 2013. The spatial resolution of the image is 30m, the radiation resolution is 12bit, and the spectral range covers 11 bands. First, radiometric calibration is performed on the original image with the help of the radiometric calibration parameters of the OLI sensor. Convert the original DN value to the pixel radiance value. Then use the FLAASH model to perform atmospheric correction on the image (atmospheric model: Tropical; aerosol model: Urban; aerosol inversion method: 2-Band (K-T); initial visibility: 40; spectral response function: ldcm_oli.sli), so that The radiance value is converted to the actual reflectance of the ground surface. Then the image was geometrically corrected, and 40 points of the same name were selected, and the quadratic polynomial was used for correction. The correction error was controlled within 0.1 pixel, and the nearest neighbor pixel method was used for resampling.
LiDAR数据预处理。1)噪声水平估计和数据平滑:首先把原始数据转换到频率域,再将频率较高的低值部分作为噪声水平的判断标准。然后选用高斯滤波器进行平滑,这是由于高斯滤波器在有效平滑数据的同时,还可以最大限度地保持原有曲线的趋势。2)高斯拟合(分解)及波形数据点云化:基于回波数据是多个高斯函数的累加这一假设,对波形数据采用非线性最小二乘法进行拟合。然后通过局部最大峰值检测滤波算法从处理后的波形数据上提取离散点云,每个离散点中记录了返回信号的能量和振幅信息。3)生成数字地形:LiDAR数据高度归一化的目的是为了得到去除了地形影响的“真实”植被高度,通常采用原始LiDAR数据高度信息减去地形高度得到。因此,精确生成数字地形模型(DTM)是计算归一化植被高度的重要前提。首先对从波形数据中提取出离散点云进行分类,然后对末次回波进行Kraus滤波处理用以去除非地面点,最后使用滤波后的末次回波数据并借助自然邻近法插值生成数字地形模型。再利用DEM对植被回波点的高程进行归一化处理,即使植被点的高度值转化为相对于地面的高度值,并通过左下角和右上角的坐标对每块样地进行裁切。最后通过GIS分析工具提取55个样地对应坐标位置的归一化点云数据。LiDAR data preprocessing. 1) Noise level estimation and data smoothing: first convert the original data to the frequency domain, and then use the low-value part with higher frequency as the judgment standard of the noise level. Then the Gaussian filter is selected for smoothing, because the Gaussian filter can maintain the trend of the original curve to the greatest extent while effectively smoothing the data. 2) Gaussian fitting (decomposition) and point cloudization of waveform data: Based on the assumption that the echo data is the accumulation of multiple Gaussian functions, the nonlinear least square method is used to fit the waveform data. Then the discrete point cloud is extracted from the processed waveform data through the local maximum peak detection filtering algorithm, and the energy and amplitude information of the returned signal is recorded in each discrete point. 3) Generating digital terrain: The purpose of normalizing the height of LiDAR data is to obtain the "real" vegetation height without the influence of terrain, which is usually obtained by subtracting the terrain height from the height information of the original LiDAR data. Therefore, accurate generation of digital terrain model (DTM) is an important prerequisite for calculating normalized vegetation height. Firstly, the discrete point cloud extracted from the waveform data is classified, and then Kraus filtering is performed on the last echo to remove non-ground points. Finally, the digital terrain model is generated by interpolating the filtered last echo data with the help of natural proximity method. Then use the DEM to normalize the elevation of the vegetation echo points, that is, the height value of the vegetation point is converted into a height value relative to the ground, and cut each plot by the coordinates of the lower left corner and upper right corner. Finally, the normalized point cloud data of the corresponding coordinate positions of 55 sample plots were extracted by GIS analysis tools.
通过对OLI影像进行波段组合、缨帽变换、纹理信息提取、主成分分析、最小噪声分离变换以及多种植被指数变换,提取5组(共53个)特征变量,6个原始单波段变量、10个波段组合变量、10个信息增强组变量、18个植被指数变量以及9个纹理信息变量其中纹理分析针对主成分分析的第一主成分进行,窗口大小为3×3,滞后距离为1个像元。53个变量的含义及计算公式(见表1)。LiDAR特征变量是基于三维归一化LiDAR点云值计算了4组(34个)特征变量:10个高度变量、13个高度百分位数变量、10个冠层密度变量、1个冠层覆盖度变量。34个变量的含义及计算公式(见表2)。By performing band combination, tasseled cap transformation, texture information extraction, principal component analysis, minimum noise separation transformation and multiple vegetation index transformations on OLI images, 5 groups (53 in total) of feature variables were extracted, 6 original single-band variables, 10 band combination variables, 10 information enhancement group variables, 18 vegetation index variables, and 9 texture information variables. Texture analysis is performed on the first principal component of principal component analysis, with a window size of 3×3 and a lag distance of 1 image Yuan. The meaning and calculation formula of 53 variables (see Table 1). LiDAR feature variables are based on 3D normalized LiDAR point cloud values to calculate 4 groups (34) of feature variables: 10 height variables, 13 height percentile variables, 10 canopy density variables, and 1 canopy coverage degree variable. The meaning and calculation formula of 34 variables (see Table 2).
表1 OLI特征变量及描述Table 1 OLI characteristic variables and description
将提取的共87个(34个LiDAR特征变量及53个OLI特征变量)变量与需要预测的参数进行Pearson相关性分析,选取Pearson’s相关系数的绝对值高于0.2的特征变量作为建模候选变量。Pearson’s相关系数的计算方法为:A total of 87 extracted variables (34 LiDAR feature variables and 53 OLI feature variables) were analyzed by Pearson correlation with the parameters to be predicted, and the feature variables whose absolute value of Pearson's correlation coefficient was higher than 0.2 were selected as modeling candidate variables. The calculation method of Pearson's correlation coefficient is:
式中,xi为地面实测的某林分特征,yi为某LiDAR特征变量,为xi的平均值,为yi的平均值;In the formula, x i is the measured characteristics of a forest stand on the ground, y i is a LiDAR feature variable, is the average value of xi , is the average value of y i ;
研究选出了34个候选变量,这些候选变量及其与需要预测的参数的Pearson’s相关系数的绝对值如图3和图4所示。这34个变量与生物量的Pearson’s相关系数较高且关系显著,说明它们之间有较好的线性关系。因此把这34个特征变量作为最终建模候选变量。The study selected 34 candidate variables, and the absolute values of these candidate variables and their Pearson's correlation coefficients with the parameters to be predicted are shown in Figure 3 and Figure 4. The Pearson's correlation coefficients between these 34 variables and biomass are high and significant, indicating that there is a good linear relationship between them. Therefore, these 34 characteristic variables are regarded as the final modeling candidate variables.
表2 LiDAR特征变量及描述Table 2 LiDAR characteristic variables and description
将地面实测汇总的生物量信息作为因变量,遥感方法提取的特征变量作为自变量,建立多元回归模型。运用逐步进入法(stepwise)和检验决定系数(R2)的变化情况来选择进入模型的合适变量。如果有自变量使统计量F值过小并且T检验达不到显著水平(P值>0.1),则予以剔除;F值较大且T检验达到显著水平(P值<0.05)则得以进入。采用决定系数(R2)、均方根误差(RMSE)和相对均方根误差(rRMSE)评价回归模型的精度;A multiple regression model was established by using the biomass information collected from ground measurements as dependent variables and the feature variables extracted by remote sensing methods as independent variables. Appropriate variables to enter the model were selected by using the stepwise method (stepwise) and examining the variation of the coefficient of determination (R 2 ). If there is an independent variable that makes the F value of the statistic too small and the T test cannot reach the significant level (P value>0.1), it will be eliminated; if the F value is large and the T test reaches the significant level (P value<0.05), it will be included. The accuracy of the regression model was evaluated by coefficient of determination (R 2 ), root mean square error (RMSE) and relative root mean square error (rRMSE);
式中,xi为地面实测的某林分特征,为xi的平均值,为模型估算的某林分特征,n为样地数量;In the formula, x i is the characteristics of a forest stand measured on the ground, is the average value of xi , is the characteristic of a forest stand estimated by the model, and n is the number of sample plots;
由(3)式可见,rRMSE为RMSE(均方根误差)与(实测值均值)的百分比。It can be seen from formula (3) that rRMSE is RMSE (root mean square error) and (measured value mean) percentage.
首先分别利用OLI特征变量和LiDAR特征变量构建OLI生物量估算模型(简称OLI模型)和LiDAR生物量估算模型(简称LiDAR模型),再基于这两类特征变量构建综合生物量估算模型(简称综合模型)。构建这三种模型时,均分两种情况进行分析,首先是对所有样地无区分的统计分析,再将样地按树种组成分成针叶林、阔叶林和混交林分别进行分析。针对不同森林类型的各模型精度评价如表3。基于不同森林类型地上和地下生物量样地实测值与模型(OLI模型、LiDAR模型和综合模型)预测值的对比散点见图5-10。Firstly, the OLI biomass estimation model (OLI model for short) and the LiDAR biomass estimation model (LiDAR model for short) were constructed using OLI characteristic variables and LiDAR characteristic variables respectively, and then a comprehensive biomass estimation model (comprehensive model for short) was constructed based on these two types of characteristic variables. ). When constructing these three models, the analysis was divided into two situations. Firstly, the statistical analysis of all sample plots was performed without distinction, and then the sample plots were divided into coniferous forest, broad-leaved forest and mixed forest according to the composition of tree species for analysis. The accuracy evaluation of each model for different forest types is shown in Table 3. See Figure 5-10 for the comparison scatter points between the measured values and the predicted values of the models (OLI model, LiDAR model and comprehensive model) based on aboveground and underground biomass of different forest types.
表3 针对不同森林类型的模型精度评价表Table 3 Model accuracy evaluation table for different forest types
其中,R2为模型决定系数;RMSE为均方根误差;rRMSE为相对均方根误差。Among them, R 2 is the coefficient of determination of the model; RMSE is the root mean square error; rRMSE is the relative root mean square error.
以上结果表明集成模型的反演结果要优于单一特征变量模型(即,OLI模型和LiDAR模型),其模型拟合效果提升为:R2提高3-24%;估算精度提升为:rRMSE降低2-10%。The above results show that the inversion result of the integrated model is better than that of the single characteristic variable model (ie, OLI model and LiDAR model), and the model fitting effect is improved: R 2 increased by 3-24%; the estimation accuracy is improved: rRMSE decreased by 2 -10%.
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Application publication date: 20150527 Assignee: Beijing Huamei Wanxiang Technology Co., Ltd. Assignor: Nanjing Forestry University Contract record no.: 2018320000235 Denomination of invention: Method for inverting remote sensing forest biomass Granted publication date: 20180413 License type: Common License Record date: 20181024 Application publication date: 20150527 Assignee: Nanjing city Pukou District moon farm Assignor: Nanjing Forestry University Contract record no.: 2018320000234 Denomination of invention: Method for inverting remote sensing forest biomass Granted publication date: 20180413 License type: Common License Record date: 20181024 |
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