基于无人机影像的树顶点和树高提取及其影响因素分析

对基于无人机影像生成的树冠高度模型(Canopy Height Model,CHM),采用局部最大值算法进行树顶点和树高提取的可行性进行了探讨。此外,还探讨了分辨率、窗口大小对于树顶点提取的影响。以密集的针阔混交林为样地,利用SfM(Structure from Motion)算法结合无人机影像对研究区进行三维重建,得到点云、数字表面模型(Digital Surface Model,DSM)、数字高程模型(Digital Elevation Model,DEM)等一系列三维数据并生成CHM。然后,对不同分辨率的CHM使用不同的平滑窗口大小、移动窗口大小组合进行树顶点的提取并对结果进行精度评价。当CHM分辨率为0.4m,平滑窗口大小为3×3像元,移动窗口大小为3×3像元时,树顶点的提取精度最高,F测度为77.08%。将基于该组合提取正确的37个树顶点对应的提取树高与实地测量得到的树高对比,R~2为0.966 9,RMSE为1.411 4m,rRMSE=10.69%。研究结果表明:利用无人机影像可以较好地提取复杂树林的树顶点和树高;基于局部最大值算法提取树顶点,需要根据实际情况确定CHM的分辨率、平滑窗口大小和移动窗口大小,以获得最佳提取结果。     

基于无人机可见光遥感的单木树高提取方法研究

利用目前流行的高分辨率可见光无人机遥感影像生成树木冠层高度模型,采用分水岭分割算法提取单木树高的研究具有重要理论和实践意义。以位于云南省富民县的天然云南松纯林为研究对象,通过大疆Phantom 4 Pro无人机获取低空可见光遥感影像,利用Pix4D Mapper对无人机影像进行预处理及三维重建,生成三维点云,利用LiDAR360处理三维点云,构建DSM,DEM并生成CHM;采用分水岭分割算法对不同郁闭度条件下获得的CHM进行单木分割及树高提取,对提取结果进行精度评价。结果表明:分水岭分割算法能够准确分割CHM,利用无人机可见光遥感影像进行单木树高提取是可行的;将基于无人机可见光影像提取的树高值与野外实地调查得到的树高值进行对比,R²为0.893,RMSE为1.23m,估测精度为87.58%;同时,林分郁闭度会对单木树高估测产生影响,根据不同郁闭度条件下提取的3组样木树高与实地测量树高的决定系数(R²)分别是0.857,0.939和0.921,RMSE分别为1.450,1.097,0.896m,在低郁闭度林分内树高估测的精度显著高于高郁闭度林分。     

ICESat-GLAS激光天顶角对反演森林冠层高度的影响

为了量化激光天顶角对ICESat-GLAS波形数据反演森林冠层高度的影响,以吉林省汪清林业局经营区为例,基于ICESat-GLAS波形数据及DEM数据,在Allouis模型和Nie模型基础上,分别引入激光天顶角,对光斑内坡度引起的高度距离(GroundExtent)进行修正,建立森林冠层高度估测模型,并通过模型对坡度的校正能力、天顶角引起的GroundExtent理论误差以及大气延迟增量三个方面讨论分析天顶角在反演森林冠层高度中的影响。结果表明:天顶角的引入能够提高模型的估测精度,决定系数(R2)分别提高了6.56%、4.26%,且能更好地校正地形坡度;在外部条件相同的情况下,由天顶角(1°)引起的GroundExtent理论误差在0.122~1.100m范围内;在天顶延迟约为2.3m时,天顶角(1°)对大气延迟增量的影响为0.04~3.50mm。由此可知天顶角对估测森林冠层高度存在一定的影响,引入激光天顶角的冠层估测模型能更准确地反演森林冠层高度。     

星载LiDAR与HJ-1A/HSI高光谱数据联合估测区域森林冠层高度

【目的】将ICESat-GLAS波形数据与HJ-1A/HSI高光谱数据联合,借助HSI高光谱数据提供的连续高分辨率光谱信息,实现区域森林冠层高度的估测,降低由于GLAS光斑呈离散条带状分布无法覆盖整个研究区造成的估测误差。【方法】首先,从平滑后的ICESat-GLAS波形数据中提取波形参数(波形长度W和地形坡度参数TS),基于W和TS建立GLAS森林冠层高度估测模型,并利用此模型计算研究区所有GLAS光斑内的森林冠层高度;然后,采用最小噪声分离法(MNF)对HJ-1A/HSI高光谱数据进行降维,提取前3个MNF分量(MNF1,MNF2,MNF3);最后,基于支持向量回归机(SVR)算法,利用GLAS估测的森林冠层高度和3个MNF分量建立区域森林冠层高度SVR估测模型,并估测研究区内无GLAS光斑覆盖区域的森林冠层高度,生成森林冠层高度分布图。【结果】从ICESat-GLAS波形数据中提取的地形坡度参数TS与野外实测地形坡度具有显著线性关系(R2=0.78);基于W和TS建立的GLAS森林冠层高度估测模型的R~2=0.78,RMSE=2.51 m,模型验证的R~2=0.85,RMSE=1.67 m;基于支持向量回归机算法建立的SVR模型建模的R2=0.70,RMSE=3.62 m,模型验证的R2=0.67,RMSE=4.42 m。采用野外数据对最终得到的森林冠层高度分布图的估测误差进行分析,结果估测误差最大值为7.10 m,最小值为0.07 m,平均值为1.78 m,估测误差的标准差为1.49 m,Q1为0.75 m,Q3为2.31 m。【结论】从ICESat-GLAS波形数据中提取的地形坡度参数TS能够很好地反映地形坡度的变化,本研究建立的线性关系模型可克服对数关系模型在平坦地区解释困难的问题。基于支持向量回归机算法,将ICESat-GLAS波形数据与HJ-1A/HSI高光谱数据联合,可克服ICESat-GLAS由于光斑呈离散条带状分布无法实现区域森林冠层高度估测的不足,实现对区域森林冠层高度的高精度估测。     

基于无人机可见光影像的华山松人工林计测参数提取

针对无人机在森林资源监测中的便携性特点,利用无人机RGB三波段影像进行森林计测参数(株数、树高及蓄积量)的提取及精度验证。以华山松人工林为研究对象,以无人机RGB影像为主要信息源,在前期进行5块0.08hm~2华山松人工林标准地单木定位的基础上,采用冠层高度模型(CHM)最大值法和点云分割方法,提取华山松人工林计测参数,建立无人机RGB影像的华山松人工林单木二元材积模型。研究结果表明:1)采用CHM最大值分割法较点云分割方法精度高,单木株数分割精度分别为87.17%和80.79%;提取得到的树高与其地面实测所得树高的R~2相比较,使用CHM方法,R~2为0.71;而使用点云算法,R~2为0.69。2)基于CHM最大值法提取的单株冠幅和树高所建立的二元材积模型,其决定系数(R~2)为0.94,均方根误差(RMSE)为0.033 8m~3;与基于云南省华山松人工林二元材积表的标准地实测蓄积量调查结果相比,基于无人机RGB数据的5块标准地蓄积量监测精度分别为79.72%,81.64%,83.57%,82.49%,80.28%,平均精度达81.54%。基于无人机RGB影像的华山松人工林在森林计测参数提取中,CHM最大值分割法优于点云分割,所建立的树高和冠幅二元材积模型,可为华山松单层人工林无人机遥感监测提供参考。     

基于无人机航测数据的森林郁闭度和蓄积量估测

蓄积量是评价森林资源质量或状况的重要指标,为了解决实测郁闭度和蓄积量费时费力以及无法充分利用航测原始数据生成各项数据的问题,以无人机航测数据的点云数据和正射影像为研究数据,利用冠层高度模型提取高程,通过一元线性回归分析估测平均树高和平均胸径模型;使用改进形态学分水岭方法提取树冠个数;通过主成分回归建立郁闭度模型;结合提取与估测的GIS因子,用偏最小二乘法建立蓄积量模型。结果表明:平均树高模型精度为97.34%、平均胸径模型精度为91.27%,改进分水岭提取树冠精度为80.03%,郁闭度模型精度为83.18%,蓄积量模型精度可达88.43%。蓄积量模型的所有特征因子均是通过遥感方法从无人机原始航测数据中提取而来,充分利用了无人机航测数据。实验建立的树高、胸径和郁闭度模型可以有效地估测森林平均树高、胸径及郁闭度,改进后的分水岭算法减少了过分割,蓄积量模型能够有效估测蓄积量,提高了蓄积量提取效率,节省了大量的人力物力。     

基于六株木法的杉木蓄积量估测研究

蓄积量是森林资源调查和监测的重要指标。本研究以湖南省攸县黄丰桥林场杉木树种为研究对象,采用随机抽样方法布点,共布设样地110块,其中杉木有效观测样地97块。样地内调查以距离样地中心位置最近的6株木为对象,观测每株杉木的胸径、树高及最远杉木到样地中心的距离,并计算这6株杉木样圆的覆盖面积,来估测样地蓄积量。结果表明：在95%的可靠性下,平均胸径估测精度为90%,平均树高估测精度为88%,蓄积量估测精度达89%,说明利用六株木法估测杉木蓄积量具有较好的效果。     

六株木测树法估测人工杉木林蓄积量的研究

以湖南省攸县黄丰桥林场杉木树种为研究对象,采用随机抽样方法布设样地110块,其中杉木有效观测样地97块.以距样地中心位置最近的6株木为对象,观测每株杉木的胸径、树高及最远杉木到样地中心的距离,以计算这6株杉木样圆的覆盖面积来估测样地蓄积量,并将估测结果与角规测树法进行对比分析.结果表明,在95%的可靠性下,平均胸径估测精度为90%,平均树高估测精度为88%,蓄积量估测精度达89%,估测结果与角规测树法所得结果十分接近,表明利用六株木法估测杉木蓄积量具有较好的效果.     

手杖式测树仪立木树高量测研究

【目的】树高是森林经营决策中最重要的一个参量,常用于估计森林生长、年龄、材积、生物量和碳储量等立木参数,其精度对立木质量的评价及森林生长的预测分析影响重大。为解决传统的树高量测仪器移动不便,测量周期长,人力耗损大等问题。【方法】以近景摄影测量为基础,构建了一种以登山杖绑定安卓智能手机为测量平台的便携、快捷的树高测量装备;针对立木生长环境有无坡度,拍摄是否产生倾角等情况建立了树高量测模型。在手机环境下,研制了立木树高测量软件APP。APP采用上下分屏技术,利用手机成像系统以及坐标系转换,使屏幕与待测立木建立关联,从而自动解算立木的深度信息;结合手机内部方向传感器的强大性能,单站作业可实时获取树高估计值。在北京市平谷区红石门村选取不同坡度的307株立木作为研究对象,使用该装备分别在上坡位和下坡位对其进行量测,将测定结果与全站仪多次量测求得的平均值进行对比分析。【结果】结果表明,树高估计值平均绝对误差为0.21 m,平均相对误差为2.11%,整体精度达到97.89%。当处于下坡位观测时,树高估计值平均绝对误差为0.11 m,平均相对误差为1.16%,树高精度高达98.84%。当处于上坡位观测时,树高估计值平均绝对误差为0.32 m,平均相对误差为3.07%,树高精度达到96.93%。智能手机倾斜角较大时,精度达到95.19%,智能手机倾斜角较小时,精度高达99.03%,说明手杖式测树仪观测时所产生倾角越小,精度越高。量测同一株立木时,下坡位观测的精度优于上坡位。【结论】此装备的研发满足国家森林资源连续清查中的测量精度要求,且装备成本低、灵活性强、不依赖其他设备获取深度信息、携带方便、具有较高利用价值,未来可作为森林资源调查树高测量装备。     

联合资源三号与机载LiDAR的林分平均树高估测

提出一种将资源三号(ZY-3)立体影像的空间连续测量特性与LiDAR数据的高精度定位测高优势相结合的林分平均树高估测方法。首先从LiDAR离散点云提取地面点并内插生成分辨率为1m的林区DEM,同时根据点云强度提取与DEM同源且分辨率为1m的正射影像,分别作为ZY-3数据定向处理的高程控制基准和平面控制基准。通过ZY-3多类像对组合提取研究区DSM,其中三视DSM较二视DSM高程精度最佳。基于三视DSM,林区DEM,ZY-3多光谱数据提取的植被指数和野外实测树高数据,利用回归分析方法及高程误差修正方法分别建立了四个树高估测模型,实验表明,经高程误差修正后的改进树高估测模型精度最高,模型Adj R~2=0.913,其精度达到93.29%,是最佳树高估测模型。     

基于低密度机载LiDAR和CCD数据的林分平均高提取

本文基于低密度的机载激光雷达(L iDAR)数据生成林区树冠高度模型(CHM),结合高分辨率CCD数码相机影像勾绘林分多边形,由改进的树冠识别算法提取林分平均树高。结果表明:全部有效数据林分总体精度达74.86%,刺槐精度达75.62%,油松精度达74.74%,结果受点云密度影响,使得阔叶树种的精度稍高于针叶树种,因此,低密度激光雷达数据结合高分辨率CCD可以快速、准确地提取林分平均高。     

高精度DEM支持下的多时期航片杉木人工林树高生长监测

【目的】集成多时期航片数据和由机载激光雷达数据获取的密集林区数字高程模型,估测多时期杉木人工林冠层高度,并对其生长情况进行定量监测,为多时期航片监测森林生长趋势和评价林地生产力提供可能。【方法】首先基于分类后的激光雷达点云数据获得林下高精度数字高程模型和森林数字表面模型,利用航片数据构建立体像对,通过自动立体匹配算法生成森林冠层的摄影测量数字表面模型,然后借助数字高程模型将2种数字表面模型进行高度归一化,提取研究区多时期森林冠层高度。利用1996、2004年历史航片和2014年数字航片以及激光雷达数据,构建18年内皖南杉木人工林3期森林冠层高度,并对其精度进行分析。【结果】1)由2014年数字航片和激光雷达数据获取的森林冠层高度的R^2为0. 52,RMSE为1. 79 m; 2)由2014年数字航片处理得到的森林冠层高度与对应样地实测上层木的平均高验证精度较高,平均绝对误差1. 59 m,平均相对误差15%,最大绝对误差3. 45 m,最大相对误差30. 80%,测量精度85. 00%; 3)由1996、2004、2014年航片得到3期杉木人工林冠层高度,其增长趋势与树高生长曲线预测趋势一致。【结论】在多山复杂地形条件下,利用航片可准确定量反映山脊向阳面的森林冠层高度变化,但对于山谷阴影处,则会出现冠层高度被低估情况,利用多期航片结合高精度DEM数据可定量反映上层木的冠层高度变化。     

Mapping of snow-damaged trees based on bitemporal airborne LiDAR data

The use of multitemporal LiDAR data in forest-monitoring applications has been so far largely unexplored. In this work, we aimed to develop and test a simple method for the detection of snow-induced canopy changes by employing bitemporal LiDAR data acquired in 2006–2010. Our study area was located in southern Finland (62°N, 24°E), where snow-induced damage occurred in 10 permanent Scots pine (Pinus sylvestris)-dominated plots in winter 2009–2010. For the detection of snow-damaged crowns, we developed a ?CHM method by contrasting bitemporal LiDAR canopy height models (CHMs) and analyzing the resulting difference image, using binary image operations to extract the damaged crowns. Furthermore, we examined the structural and spatial factors that could explain snow damage at the individual tree level. The ?CHM method developed is based on two threshold parameters, i.e., the required height difference in the contrasted CHMs and the minimum plausible area of damage. When testing the performance of ?CHM method, we found that the plot-level omission error rates were 19–75%, while the commission error rates were 0–21%. Furthermore, the relative estimation accuracy of the damaged crown projection area (DCPA) ranged from ?16.4 to 5.4%. The observed damage could be explained at tree level by stem tapering, relative tree size, and local stand density. To conclude, ?CHM method developed constitutes a potential tool for the monitoring of structural canopy changes in the dominant tree layer if dense multitemporal LiDAR data are available.     

无人机RGB影像中人工林单木位置的提取

从无人机RGB影像中提取单木位置时,由于树冠与非树冠植被的颜色相似,以及树冠之间存在粘连的问题,导致单木位置提取精度不高.针对这些问题,提出一种结合冠层高度模型(CHM)和形态学细化算法的人工林单木位置提取方法.首先根据无人机RGB影像生成数字正射影像(DOM)、数字高程模型(DEM)、数字表面模型(DSM),利用可见...     

Small area estimation of forest attributes in the Norwegian National Forest Inventory

The Norwegian National Forest Inventory (NNFI) provides estimates of forest parameters on national and regional scales by means of a systematic network of permanent sample plots. One of the biggest challenges for the NNFI is the interest in forest attribute information for small sub-populations such as municipalities or protected areas. Frequently, too few sampled observations are available for such small areas to allow estimates with acceptable precision. However, if an auxiliary variable exists that is correlated with the variable of interest, small area estimation (SAE) techniques may provide means to improve the precision of estimates. The study aimed at estimating the mean above-ground forest biomass for small areas with high precision and accuracy, using SAE techniques. For this purpose, the simple random sampling (SRS) estimator, the generalized regression (GREG) estimator, and the unit-level empirical best linear unbiased prediction (EBLUP) estimator were compared. Mean canopy height obtained from a photogrammetric canopy height model (CHM) was the auxiliary variable available for every population element. The small areas were 14 municipalities within a 2,184 km² study area for which an estimate of the mean forest biomass was sought. The municipalities were between 31 and 527 km² and contained 1–35 NNFI sample plots located within forest. The mean canopy height obtained from the CHM was found to have a strong linear correlation with forest biomass. Both the SRS estimator and the GREG estimator result in unstable estimates if they are based on too few observations. Although this is not the case for the EBLUP estimator, the estimators were only compared for municipalities with more than five sample plots. The SRS resulted in the highest standard errors in all municipalities. Whereas the GREG and EBLUP standard errors were similar for small areas with many sample plots, the EBLUP standard error was usually smaller than the GREG standard error. The difference between the EBLUP and GREG standard error increased with a decreasing number of sample plots within the small area. The EBLUP estimates of mean forest biomass within the municipalities ranged between 95.01 and 153.76 Mg ha^?1, with standard errors between 8.20 and 12.84 Mg ha^?1.     

基于高分辨率航空遥感影像的林分因子智能识别技术研究

森林资源监测的数字化和智能化是未来发展的主要趋势。基于高分辨率航空、多光谱遥感数据和数字地表模型(DSM)等数据,利用计算机深度学习方法,研究乔木林小班的郁闭度、平均树高、总株数3项主要林分调查因子的数字化智能提取方法。结果表明,郁闭度判读的平均准确率可达到98.6%;平均树高判读的平均准确率可达到90%;株数判读的平均准确率可达到82.36%。     

基于TM影像数据的林分冠层郁闭度反演技术研究

本文以崂山林场为研究区域,利用森林资源二类调查数据和TM影像数据,分析了林分郁闭度与遥感因子之间的定量关系,在此基础上利用多元回归分析法结合实测数据构建郁闭度估测模型,并对模型精度进行检验,结果表明,预估精度达到81.6%,估测效果较好。利用该模型,反演了研究区的林分冠层郁闭度,将崂山林场的林分冠层郁闭度分为四个等级,即非林地区,低郁闭度区,中郁闭度区和高郁闭度区,研究区的森林郁闭度分布呈现西北部和东南部较低,而中部和南部相对较高。     

Testing the usability of truncated angle count sample plots as ground truth in airborne laser scanning-based forest inventories

Airborne laser scanner (ALS)-based forest inventory method usuallyadopt a laser canopy height distribution approach in which forestcharacteristics are predicted using measures such as percentilesof the distribution of laser canopy heights across a fixed area.The method requires a ground-truth sample of accurately measuredfield plots. One possibility for reducing the costs lies inthe use of existing field plots for ground-truth purposes. Themost obvious alternative in Finland would be to use truncatedangle count sample plots of the National Forest Inventory ormore locally data of checking of inventory by compartments.Due to the lack of suitable angle count ground-truth data andcorresponding laser data, we tested this possibility using dataon fixed-area sample plots, in which tree locations were simulated.The trees for a truncated angle count sample plot were thenchosen and the resulting data together with the characteristicsof an ALS-based canopy height distribution were used to constructregression models to predict stem volume, basal area, stem number,basal area median diameter and height. The accuracy of the standattributes was found to be almost as good as in the case ofmodels of fixed-area plots.     

Determination of Mean Tree Height of Forest Stands by Digital Photogrammetry

The mean tree height of 73 forest stands in a 1000 ha forest area was determined from canopy heights generated by automatic image matching using a digital photogrammetric workstation and digitized panchromatic aerial photographs with a scale of 1:15 000. First, the mean height of each stand was computed as the arithmetic mean of the quantile corresponding to the 75th percentile of the distribution of the canopy heights from the image matching within square grid cells with cell sizes of 236-400 m². The mean heights from the image matching underestimated the true heights by 5.42 m. Secondly, field-measured mean tree heights of 165 georeferenced sample plots distributed systematically throughout the 1000 ha forest area were regressed against the mean heights derived from the image matching. The regression equations were used to predict the mean heights of the 73 stands. In very young forest stands, the predicted mean heights overestimated the true heights by 0.4 m and the precision was 0.9-1.0 m. In young and mature stands, the average difference between predicted height and ground-truth ranged between -1.6 and 0.5 m, and the precision ranged from 1.1 to 2.1 m.