Multiple attribute decision making for individual tree detection using high-resolution laser scanning

A canopy height model (CHM) is a standard LiDAR-derived product for deriving relevant forest inventory information, including individual tree positions, crown boundaries and plant density. Several image-processing techniques for individual tree detection from LiDAR data have been extensively described in literature. Such methods show significant performance variability depending on the vegetation characteristics of the monitored forest. Moreover, over regions of high vegetation density, existing algorithms for individual tree detection do not perform well for overlapping crowns and multi-layered forests. This study presents a new time and cost-efficient procedure to automatically detect the best combination of the morphological analysis for reproducing the monitored forest by estimating tree positions, crown boundaries and plant density from LiDAR data. The method needs an initial calibration phase based on multi attribute decision making-simple additive weighting (MADM-SAW). The model is tested over three different vegetation patterns: two riparian ecosystems and a small watershed with sparse vegetation. The proposed approach allows exploring the dependences between CHM filtering and segmentation procedures and vegetation patterns. The MADM architecture is able to self calibrate, automatically finding the most accurate de-noising and segmentation processes over any forest type. The results show that the model performances are strongly related to the vegetation characteristics. Good results are achieved over areas with a ratio between the average plant spacing and the average crown diameter (TCI) greater than 0.59, and plant spacing larger than the remote sensing data spatial resolution. The proposed algorithm is thus shown a cost effective tool for forest monitoring using LiDAR data that is able to detect canopy parameters in complex broadleaves forests with high vegetation density and overlapping crowns and with consequent significant reduction of the field surveys, limiting them over only the calibration site.     

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。     

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...     

Site quality assessment of a Pinus radiata plantation in Victoria,Australia, using LiDAR technology

The aim of site quality assessment of Pinus radiata plantations is to determine the quality and productivity of the growing stock at different sites. It provides a useful indication of the site productivity to assist in the allocation of optimum thinning and fertiliser regimes and the scheduling of silvicultural operations. The predominant stand height (PDH) at a specific reference age, also known as site index (SI), is often used for site quality assessment of Pinus radiata plantations in Australia, as it is closely correlated with site productivity. However, measuring PDH in the field can be a time- and resource-consuming task. This paper proposes the use of light detection and ranging (LiDAR) data to estimate PDH for assessing the site quality of Pinus radiata. LiDAR provides highly accurate digital elevation and surface data that can be used to build a canopy height model (CHM). In this study, the state-of-the-art image segmentation technique, marker-controlled watershed segmentation, was employed for identifying locations of individual trees and estimating their heights from a CHM. Using an empirically derived SI equation, PDHs with reference age 11 years (SI₁₁) were estimated from the tallest trees identified in each forest stand, and were then used to determine the site quality class for each stand. The comparison of LiDAR-derived tree heights with field measurements produced an RMSE value of 0.42 m. The maximum horizontal distance between the field-measured locations of individual trees and the LiDAR-detected locations of their treetops was 1.87 m. Site quality classification was conducted in terms of 0.05 ha gridded plots, which revealed more detailed spatial variations of site quality across the study area than classification based on management plots. The study demonstrated that LiDAR provides an effective and accurate method for site quality classification of Pinus radiata.     

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。     

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。     

激光雷达在森林垂直结构参数估算中的应用

激光雷达是近年来迅速发展的主动遥感技术, 激光脉冲对森林具有很强的穿透能力, 在森林垂直结构参数估测中具有巨大的潜力与优势。文中分别总结了小光斑和大光斑激光雷达在获取树高、生物量等森林参数中的应用及其优缺点, 同时分析比较了小光斑和大光斑激光雷达在估测森林参数上的不同; 最后重点介绍了目前唯一的星载大光斑激光雷达ICESat/GLAS系统, 总结分析了其在大面积森林空间结构参数估算中的应用现状, 并对激光雷达前景及其应用中存在的问题进行了探讨和展望。     

基于ICESat-GLAS波形估测平均树高的研究

以吉林省汪清林业局经营区域为例,基于星载激光雷达ICESat-GLAS回波参数,构建了平均树高回归模型,预估精度为84.05%;利用反距离加权法,对ICESat-GLAS光斑平均树高估测值进行差值运算,得到初始CHM(Canopy Height Model),实现了平均树高空间连续分布制图;再利用坡度校正和3×3移动窗口差分滤波平滑初始CHM,得到研究区平均树高修正CHM,预估精度达到91.52%。研究结果表明,坡度校正和移动窗口差分滤波方法能有效削弱坡度影响,剔除异常点,提高平均树高估测精度。     

小光斑激光雷达数据估测森林树高研究进展

小光斑激光雷达可以同时获得森林的垂直及水平结构参数,因光斑直径较小,可以做到森林单木结构参数的准确估计,进而推广到样方甚至更大区域森林结构参数的估计,近年来在林业中得到广泛应用。文中主要从树高估计方面对小光斑激光雷达在林业中的应用进行研究,通过对先前类似文献进行归纳总结发现,在小光斑激光雷达估测森林树高方面仍存在着一些问题,从而限制了森林树高估测精度的提高,如点云分类算法、点云密度、森林郁闭度、单木的准确分割等,还对小光斑激光雷达估计森林树高中所存在的问题进行了概括,并提出了改进建议。     

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.     

Algorithm for improving the co-registration of LiDAR-derived digital canopy height models and field data

Improving trees location under LiDAR-derived digital canopy height models (DCMs) is of great interest as discrepancies between both dataset influence the accuracy of the estimations of forest attributes. A method is proposed for the co-registration of LiDAR-derived DCMs with local field positional measurements under a dense tree canopy. This approach consists of two main stages: (1) the assessment of the match between the LiDAR-derived digital terrain model and topographic surveying measurements when shifting the coordinates around a measured position; and (2) a comparison between the field height of selected trees and the LiDAR-derived DCM. Satisfactory results were obtained from geo-referencing field data and LiDAR models for characterizing the forest structure in heterogeneous Pinus sylvestris stands. Closure error of topographic surveying was 17.7 cm, and GPS accuracy to 95 % probability was below 10 cm, thus considerably lower than the resolution of the LiDAR models (1 m-pixel). The best co-location for field trees and LiDAR models provided a coefficient of determination of 0.56 between field-measured tree heights and LiDAR-derived DCM values.     

Mapping site index and age by linking a time series of canopy height models with growth curves

Site index (SI) is one of the main measures of forest productivity in North America. For monospecific even-age stands, it is defined as the height of dominant trees at a given reference age or presented as an age–height curve. SI normally reflects the overall effect of all the environmental parameters that determine height growth locally. However, measuring SI can only be achieved though field observations and is, for this reason, limited to sample plots. In this study, we propose a new method for quantifying and mapping SI and age based on known age–height curves and time series of canopy height models (CHMs) produced using digital photogrammetry and lidar. Digital surface models (DSMs) are created by applying an automated stereo-matching algorithm to scanned aerial photographs. The canopy height is obtained by subtracting the lidar ground elevations from the DSM. Using aerial photographs covering the 1945–2003 interval and a recent lidar coverage, CHMs could be reconstructed retrospectively for a period of over 58 years. Regionally calibrated age–height curves were fitted to observations that were extracted cell-wise from the historical CHMs to estimate SI and age values for all undisturbed locations. Results demonstrate that SI and age of jack pine (Pinus banksiana [Lamb.]) stands can be quantified respectively with an average bias of 0.76 m (2.41 m root mean squared error, RMSE) and 1.86 years (7 years RMSE). The method can be used to produce quasi-continuous maps of SI and age and to estimate productivity in a spatially explicit way.     

机载LiDAR和高光谱融合实现普洱山区树种分类

[目的]通过机载遥感影像对普洱山区进行植被分类研究,为山区森林经营规划与可持续经营方案的制图提供高效应用途径。[方法]将2014年4月航拍的机载AISA Eagle II高光谱和Li DAR同步数据融合,利用点云数据提取的数字冠层高度模型(CHM)得到树种的垂直结构信息,结合经过主成分分析(PCA)的高光谱降维影像,选用支持向量机(SVM)分类器进行分类。[结果]普洱市万掌山实验区主要树种分为思茅松、西南桦、刺栲、木荷等。融合影像数据分类的总体精度和Kappa系数分别为80.54%、0.78,比单一高光谱影像数据分类精度分别提高6.55%、0.08,其中主要经营树种思茅松的制图精度达到了90.24%。[结论]该方法对山区主要树种的识别是有效的,将机载Li DAR与高光谱影像融合可以有效改善分类精度。     

A semi-variogram approach for estimating stems per hectare in Eucalyptus grandis plantations using discrete-return lidar height data

Estimating stems per hectare (SPHA) for a given forest area from high spatial resolution remotely sensed data usually follows the identification of individual trees. A common method of tree identification is through local maxima filtering, which in the context of a lidar canopy height model (CHM), seeks to locate the highest value within a specified neighbourhood of pixels. Hence, specifying an appropriate window size is a critical consideration. This study investigated the potential of the semi-variogram range towards defining an average window size for a given plot within Eucalyptus species plantations. The analysis also included comparisons of CHMs with three pixel sizes (spatial resolutions) (0.2 m, 0.5 m, and 1 m) at lidar point density of 5 points/m² and three lidar point densities (1 point/m², 3 points/m², and 5 points/m²). These variations were introduced to study the effect of interpolated height surface resolution and lidar point density, respectively, on the identification of trees. Semi-variogram analysis yielded range values that varied distinctly with spatial resolution and point density. Computation of SPHA based on the semi-variogram range values resulted in overall accuracies of 73%, 56%, and 41% for 0.2 m, 0.5 m, and 1 m resolutions, respectively. A comparative approach, that defines window size based on pre-determined tree spacing, yielded corresponding accuracies of 82%, 82%, and 68% at the respective CHM resolutions. Point density comparisons based on interpolated CHM of 0.2 m resolution and the semi-variogram approach resulted in similar results between 5 points/m² (73%) and 3 points/m² (70%), whereas 1 point/m² returned the lowest accuracy (56%). Similar trends with superior accuracies were observed using the pre-determined tree spacing approach from the same resolution CHM: 82% (5 points/m²), 80% (3 points/m²), and 74% (1 point/m²). While all estimates were negatively biased, the CHM with a 0.2 m spatial resolution at a point density of 3 points/m² resulted in a reasonable level of accuracy, negating the need for high density (>3 points/m²) lidar surveys for this purpose. It was concluded that the semi-variogram approach showed promise for estimation of SPHA, particularly due to its independence from a priori knowledge regarding the tree stocking of the plantation.     

Forest stand age classification using time series of photogrammetrically derived digital surface models

In this research, we developed and tested a remote sensing-based approach for stand age estimation. The approach is based on changes in the forest canopy height measured from a time series of photo-based digital surface models that were normalized to canopy height models using an airborne laser scanning derived digital terrain model (DTM). Representing the Karelian countryside, Finland, CHMs from 1944, 1959, 1965, 1977, 1983, 1991, 2003, and 2012 were generated and allow for characterization of forest structure over a 68-year period. To validate our method, we measured stand age from 90 plots (1256?m²) in 2014, whereby producer's accuracy ranged from 25.0% to 100.0% and user's accuracy from 16.7% to 100.0%. The wide range of accuracy found is largely attributable to the quality and characteristics of archival images and intrastand variation in stand age. The lowest classification accuracies were obtained for the images representing the earliest dates. For forest managers and agencies that have access to long-term photo archives and a detailed DTM, the estimation of stand age can be performed, improving the quality and completeness of forest inventory databases.     

Leaf distribution in large trees and stands of the floodplain forest in southern Moravia

Vertical distributions of leaf dry mass (M(d)) and leaf area (A(f)) were related to relative irradiance (I(r); I(r) above the stand = 1) in closed-canopy, old-growth stands of the floodplain forest in southern Moravia composed largely of Quercus, Fraxinus and Tilia species. Foliage area and mass at any given canopy height were converted to solar equivalent leaf area (A(s)) and mass (M(s)) by multiplying actual values at a given level in the canopy by the relative irradiance at that position. Stand leaf area index (LAI) was 5 (7 including shrub and herb layer), and solar equivalent parameters reached about 25% of that amount. In all species, vertical profiles of both relative irradiance and leaf dry mass to area ratio (LMA) were sigmoidal and the two variables were linearly related. The dominant, upper canopy species had a larger proportion of solar equivalent foliage than suppressed understory species. For individual trees of all species, the upper canopy had a larger proportion of solar equivalent foliage than the lower canopy. Light compensation points at both the leaf and whole-tree level were defined according to leaf or tree position, size and structure. I conclude that optimization of A(s) for forest stands may be used as a basis for determining thinning schedules and evaluating tree survival after damage to tree crowns by various factors.     

Airborne laser scanning in forest management: Individual tree identification and laser pulse penetration in a stand with different levels of thinning

This study examined the ability of an airborne laser scanner to identify individual trees in the canopy of a Chamaecyparis obtusa stand and investigated the relationship between the penetration rate of the laser pulses and stand attributes under different canopy conditions caused by different levels of thinning. Individual tree crowns were identified from a digital canopy model (DCM) derived from airborne laser scanner data by the watershed segmentation method. The identification rate of individual trees in blocks with heavy thinning (ratio of the basal area of the felled trees to the total basal area, hereinafter thinning ratio of the basal area, 38.0%), moderate thinning (30.4%), and no thinning was 95.3%, 89.2%, and 60.0%, respectively. Individual tree heights were estimated from the DCM values by local maximum filtering within identified individual crowns. Tree height in the three blocks was estimated with a root-mean-square error of 0.95, 0.65, and 0.68 m, respectively. Tree heights determined in a field survey were regressed against those estimated from the DCM, yielding coefficients of determination (r²) of 0.71, 0.87, and 0.85, respectively, for the blocks with heavy thinning, moderate thinning, and no thinning, respectively, and 0.86 overall. The respective penetration rates of the laser pulses through the canopy to the ground were 50.6%, 43.1%, and 9.2%. Regression of the laser pulse penetration rate against the thinning ratio of the basal area and against the total basal area of the remaining trees in 25 quadrats established in the blocks, yielded r² values of 0.89 and 0.74, respectively.     

Use of LiDAR and supplemental data to estimate forest maturity in Charles County,MD, USA

This study evaluated the utility of remotely sensed data to estimate forest maturity within Charles County, MD. We calculated tree canopy height using airborne scanning LiDAR (light detection and ranging) data over the entire county, and compared this to crown top height, stand age, and other data collected from randomly selected plots on the ground. Canopy height was a strong predictor of forest age, and we improved predictive power by including other variables such as land cover, slope, stream proximity, wetlands, and floodplains. These comparisons allowed us to construct a spatial model classifying forest in the study area into three age categories: ≤30 years old, 30–70 years old, and >70 years old, corresponding to young, intermediate, and mature. This spatial model was used to help characterize ecosystem condition and wildlife habitat, and help prioritize conservation decisions in the study area.     

机载激光雷达和航空数码影像单木树高提取

用激光雷达(LiDAR)数据和航空数码影像相结合进行单木水平树高反演.对研究区的LiDAR点云数据进行滤波和分类,根据地形特点、地表植被状况以及其他地类的分布,采用Tin Filter滤波算法提取地面回波点和植被回波点.用面向对象的方法对高空间分辨率(25 cm)的航空数码影像进行单株木检测.通过多尺度、树冠模式的分割创建影像对象和类层次,用最邻近距离和成员函数法进行影像对象的分类,并基于分类结果进行再分割.对分割后的树冠多边形进行边缘优化,以准确识别单株木.将植被回波点和影像分割后得到的树冠多边形进行叠加,计算多边形内的LiDAR数据最大高程差值,与实测树高进行相关分析,建立单木树高估测回归方程,平均估测精度为74.89%.     

结合自适应阈值与峰值的LiDAR林分平均高反演方法研究

随着激光雷达获取的点云密度不断增加,提取样地尺度的林分平均高成为可能。但样地尺度林分平均高的提取精度与树种之间的关系尚不明确,急需一种能适应各种树种的林分平均高提取方法。以广西国有高峰林场为例,采用机载LiDAR点云数据生成的冠层高度模型(Canopy height model,CHM),结合地面实测的201个样地数据,提出了一种结合自适应阈值与峰值的林分平均高提取算法,并分析了树种对提取精度的影响。结果表明:1)不同树种的林分平均高提取精度存在差异,杉木精度最高,而桉树和其他阔叶树种精度次之;2)自适应阈值结合峰值的算法能够较好提取林分平均高(R²=0.75,RMSE=3.11m,rRMSE=22.07%),并且对于不同的树种都有较强的稳健性;3)阔叶树种和针叶树种对不同的提取方法存在敏感性差异。研究提取的林分平均高可为森林蓄积量与生物量反演研究提供依据和参考。