Spatial modelling of the fraction of photosynthetically active radiation absorbed by a boreal mixedwood forest using a lidar–hyperspectral approach

The spatial variability of the fraction of photosynthetically active radiation absorbed by the canopy (fPAR) was characterized for a heterogeneous boreal mixedwood forest site located in northern Ontario, Canada, based on relationships found between fPAR and light detection and ranging (lidar) data over different canopy architectures. Estimates of fPAR were derived from radiation measurements made above the canopy at a flux tower and below-canopy radiation was measured across a range of species compositions and canopy architectures. Airborne lidar data were used to characterize spatial variability of canopy structure around the flux tower and a map of mean canopy chlorophyll concentration was derived from airborne hyperspectral imagery. Different volumes of lidar points for the locations directly above each photosynthetically active radiation (PAR) sensor were examined to determine if there is an optimal method of relating lidar returns to estimated fPAR values.The strongest correlations between mean lidar height and fPAR occurred when using points that fell within a theoretical cone which originated at the PAR sensor having a solid angle α = 55°. For diffuse conditions, the correlation (r) between mean lidar height versus fPAR × chlorophyll was stronger than between mean lidar height versus fPAR by 8% for mean daily fPAR and from 10 to 20% for diurnal fPAR, depending on solar zenith angle. For direct light conditions, the relationship was improved by 12% for mean daily fPAR and 12–41% for diurnal relationships.Linear regression models of mean daily fPAR × chlorophyll versus mean lidar height were used in conjunction with gridded lidar data and the canopy chlorophyll map to generate maps of mean daily fPAR for direct and diffuse sunlight conditions. Site average fPAR calculated from these maps was 0.79 for direct light conditions and 0.78 for diffuse conditions. When compared to point estimates of mean daily fPAR calculated on the tower, the average fPAR was significantly lower than the point estimate. Subtracting the direct sunlight fPAR map from the diffuse sunlight fPAR map revealed a distinct spatial pattern showing that areas with open canopies and relatively low chlorophyll (e.g., black spruce patches) have a higher fPAR under direct sunlight conditions, while closed canopies with higher chlorophyll (e.g., deciduous species) absorb more PAR under diffuse conditions. These findings have implications for scaling from point measurements at flux towers to larger resolution satellite imagery and addressing local scale heterogeneity in flux tower footprints.     

最小二乘法与SVM组合的林果行间自主导航方法

为了提高作业装备在果园与树林行间的自主导航性能,该研究提出一种基于最小二乘法与支持向量机(Support Vector Machine,SVM)融合的树行识别与导航方法。研究采用履带式小型喷雾机为作业平台,通过低成本的单线激光雷达获取果园或树林环境点云数据,融合姿态传感器进行数据校正,利用最小二乘法拟合识别树行,结合SVM算法,预测果园行间中心线,作为作业平台的参考导航线。在桃园、柑橘园、松树林3种不同的行间环境对导航算法进行了测试验证,并以松树林导航为例进行分析。试验结果表明:该导航算法最大横向偏差为107.7 mm,横向偏差绝对平均值不超过17.8 mm,结合作业平台的行驶轨迹,说明该导航算法能够保证作业平台沿树行行间中心线自主导航行驶,能够满足作业装备在果园与树林行间自主导航作业的需求。     

Object-based semi-automatic approach for forest structure characterization using lidar data in heterogeneous Pinus sylvestris stands

In this paper, we present a two-stage approach for characterizing the structure of Pinus sylvestris L. stands in forests of central Spain. The first stage was to delimit forest stands using eCognition and a digital canopy height model (DCHM) derived from lidar data. The polygons were then clustered (k-means algorithm) into forest structure types based on the DCHM data within forest stands. Hypsographs of each polygon and field data validated the separability of structure types. In the study area, 112 polygons of Pinus sylvestris were segmented and classified into five forest structure types, ranging from high dense forest canopy (850 trees ha⁻¹ and Loreýs height of 17.4 m) to scarce tree coverage (60 tree ha⁻¹ and Loreýs height of 9.7 m). Our results indicate that the best variables for the definition and characterization of forest structure in these forests are the median and standard deviation (S.D.), both derived from lidar data. In these forest types, lidar median height and standard deviation (S.D.) varied from 15.8 m (S.D. of 5.6 m) to 2.6 m (S.D. of 4.5 m). The present approach could have an operational application in the inventory procedure and forest management plans.     

近红外激光云高仪与微脉冲激光雷达比对分析

云在气象领域扮演着重要的角色,因此准确测量云高有助于分析天气系统未来的变化,为气象等领域提供依据。为了比较CL51激光云高仪和微脉冲激光雷达(MPL)这2个云高仪的性能,对位于南京信息工程大学的2台仪器2012年9—11月这3个月的数据进行分析,得出如下结论:CL51和MPL第一层云底高度的相关性很高,相关系数为0.89;CL51的所有数据平均高度为3.879 km,MPL的所有数据平均高度为3.587 km,说明这2台仪器的一致性很高。     

激光雷达数据在森林参数获取中的应用

由于激光雷达技术可直接获取地物的三维空间坐标信息, 使得激光雷达数据在森林参数估测方面具有独特的优势。文中首先介绍了激光雷达的一般工作原理, 然后总结了国外近年来激光雷达数据在森林参数(树高、冠幅、生物量、碳储量)获取方面的应用, 以及国内林业工作者在激光雷达数据应用方面所做的尝试, 最后对激光雷达数据在森林参数获取方面的前景进行了展望。     

基于无人机机载激光雷达估算黄河三角洲孤岛刺槐林地上生物量

研究旨在利用无人机机载激光雷达数据来计算黄河三角洲孤岛林场刺槐地上生物量。以黄河三角洲孤岛林场刺槐林为研究对象,利用无人机机载激光雷达数据,采用分水岭分割算法从单木尺度提取孤岛林场刺槐的树高和冠幅;然后利用背包移动雷达数据提取8 个样方单株刺槐胸径;再结合异速生长方程,从单木尺度计算8 个样方的刺槐地上生物量。为了验证结果,用野外实测树高和胸径验证雷达数据中提取的单木结构参数;最后利用无人机机载激光雷达提取的树高和冠幅及两者的对数与乘积的对数形式,构建估算刺槐地上生物量模型,对模型估算过程中存在不确定性进行讨论,获得孤岛林场生物量分布图,并且结合前人研究的孤岛刺槐健康状况进行分析。结果表明：（1）基于分水岭分割算法能较精确地从无人机机载激光雷达数据中提取刺槐的单木结构参数。（2）树高与冠幅乘积的对数模型估算效果（R2=0.82,RMSE=3.66 kg/株）优于非对数模型（R2=0.58,RMSE=6.73 kg/株）,也优于两者对数的模型（R2=0.76,MSE=4.52 kg/株）。（3）模型构建过程中有一定的不确定性,这种不确定性主要来自单木识别过程。（4）刺槐地上生物量高低与其健康状况有很强的相关性。     

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.     

激光雷达沙尘参数提取技术研究

对激光雷达沙尘天气监测技术进行了研究,详细分析了Mie散射激光雷达气溶胶和沙尘参数计算方法.分析、设计并用C#言开发了用于处理和分析激光雷达回波信号的大气和沙尘参数反演软件系统.利用后向散射激光雷达(LB-D200)对北京市2006年春季沙尘天气进行监测试验,通过软件计算气溶胶参数,计算结果与其他监测结果相符.结果表明:通过退偏振系数可以计算出大气气溶胶中沙尘含量,后向散射激光雷达在沙尘天气沙尘的垂直分布探测中具有优势.     

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.     

Combining ALS and NFI training data for forest management planning: a case study in Kuortane,Western Finland

Forest inventories based on airborne laser scanning (ALS) have already become common practice in the Nordic countries. One possibility for improving their cost effectiveness is to use existing field data sets as training data. One alternative in Finland would be the use of National Forest Inventory (NFI) sample plots, which are truncated angle count (relascope) plots. This possibility is tested here by using a training data set based on measurements similar to the Finnish NFI. Tree species-specific stand attributes were predicted by the non-parametric k most similar neighbour (k-MSN) approach, utilising both ALS and aerial photograph data. The stand attributes considered were volume, basal area, stem number, mean age of the tree stock, diameter and height of the basal area median tree, determined separately for Scots pine, Norway spruce and deciduous trees. The results obtained were compared with those obtained when using training data based on observations from fixed area plots with the same centre point location as the NFI plots. The results indicated that the accuracy of the estimates of stand attributes derived by using NFI training data was close to that of the fixed area plot training data but that the NFI sampling scheme and the georeferencing of the plots can cause problems in practical applications.