Using multi-source data to map and model the predisposition of forests to wind disturbance

Natural disturbances such as wind are known to cause threats to ecosystem services as well as sustainable forest ecosystem management. The objective of this research was to better understand and quantify drivers of predisposition to wind disturbance, and to model and map the probability of wind-induced forest disturbances (P_DIS) in order to support forest management planning. To accomplish this, we used open-access airborne light detection and ranging (LiDAR) data as well as multi-source National Forest Inventory (NFI) data to model P_DIS in southern Finland. A strong winter storm occurred in the study area in December 2011. High spatial resolution aerial images, acquired after the disturbance event, were used as reference data. Potential drivers associated with P_DIS were examined using a multivariate logistic regression model. The model based on LiDAR provided good agreement with detected areas susceptible to wind disturbance (73%); however, when LiDAR was combined with multi-source NFI data, the results were more promising: prediction accuracy increased to 81%. The strongest predictors in the model were mean canopy height, mean elevation, and stem volume of the main tree species (Norway spruce and Scots pine). Our results indicate that open-access LiDAR data can be used to model and map the probability of predisposition to wind disturbance, providing spatially detailed, valuable information for planning and mitigation purposes.     

Estimation of shrub biomass by airborne LiDAR data in small forest stands

The presence of shrub vegetation is very significant in Mediterranean ecosystems. However, the difficulty involved in shrub management and the lack of information about behavior of this vegetation means that these areas are often left out of spatial planning projects. Airborne LiDAR (Light Detection And Ranging) has been used successfully in forestry to estimate dendrometric and dasometric variables that allow to characterize forest structure. In contrast, little research has focused on shrub vegetation. The objective of this study was to estimate dry biomass of shrub vegetation in 83 stands of radius 0.5 m using variables derived from LiDAR data. Dominant species was Quercus coccifera, one of the most characteristic species of the Mediterranean forests. Density of LiDAR data in the analyzed stands varied from 2 points/m² to 16 points/m², being the average 8 points/m² and the standard deviation 4.5 points/m². Under these conditions, predictions of biomass were performed calculating the mean height, the maximum height and the percentile values 80th, 90th, and 95th derived from LiDAR in concentric areas whose radius varied from 0.50 m to 3.5 m from the center of the stand. The maximum R² and the minimum RMSE for dry biomass estimations were obtained when the percentile 95th of LiDAR data was calculated in an area of radius 1.5 m, being 0.48 and 1.45 kg, respectively. For this radius, it was found that for the stands (n = 39) where the DTM is calculated with high accuracy (RMSE lower than 0.20 m) and with a high density of LiDAR data (more than 8 points/m²) the R² value was 0.73. These results show the possibility of estimating shrub biomass in small areas when the density of LiDAR data is high and errors associated to the DTM are low. These results would allow us to improve the knowledge about shrub behavior avoiding the cost of field measurements and clear cutting actions.     

Detection of individual trees and estimation of tree height using LiDAR data

For estimation of tree parameters at the single-tree level using light detection and ranging (LiDAR), detection and delineation of individual trees is an important starting point. This paper presents an approach for delineating individual trees and estimating tree heights using LiDAR in coniferous (Pinus koraiensis, Larix leptolepis) and deciduous (Quercus spp.) forests in South Korea. To detect tree tops, the extended maxima transformation of morphological image-analysis methods was applied to the digital canopy model (DCM). In order to monitor spurious local maxima in the DCM, which cause false tree tops, different h values in the extended maxima transformation were explored. For delineation of individual trees, watershed segmentation was applied to the distance-transformed image from the detected tree tops. The tree heights were extracted using the maximum value within the segmented crown boundary. Thereafter, individual tree data estimated by LiDAR were compared to the field measurement data under five categories (correct delineation, satisfied delineation, merged tree, split tree, and not found). In our study, P. koraiensis, L. leptolepis, and Quercus spp. had the best detection accuracies of 68.1% at h = 0.18, 86.7% at h = 0.12, and 67.4% at h = 0.02, respectively. The coefficients of determination for tree height estimation were 0.77, 0.80, and 0.74 for P. koraiensis, L. leptolepis, and Quercus spp., respectively.     

基于树高和树冠因子的立木材积模型研究

[目的]由于激光雷达技术已经能准确测定立木树高及相关树冠因子,应用该技术建立基于树高和树冠因子的立木材积模型,为激光技术在森林蓄积估计中提供技术支撑.[方法]利用云杉、冷杉、栎树、桦树4个树种组的3 010株实测样木数据,分析了立木材积与胸径、树高、树冠因子之间的相关关系;并通过对数回归方法构建了基于树高和树冠因子的立木材积模型,用确定系数R²和平均预估误差MPE等6项指标对模型进行评价.[结果]表明,立木材积与单一因子之间的相关,以胸径最为紧密,其次是树高,再次是冠长和冠幅.基于树高和树冠因子的立木材积模型中,以树高和冠幅作为解释变量的二元模型效果较好,再增加冠长因子的三元模型改进不大.云杉、冷杉、栎树、桦树4个树种组基于树高冠幅的立木材积模型,其R²分别为0.81、0.80、0.76和0.77,MPE分别为4.7%、5.3%、5.4%和5.3%,模型预估精度均能达到95%左右.[结论]本文对材积与林木因子之间相关关系的定量分析,建立了云杉、冷杉、栎树、桦树4个树种的立木材积模型,模型预估精度高.为激光雷达技术定量估测森林参数提供了依据.     

Aboveground biomass quantification and tree-level prediction models for the Brazilian subtropical Atlantic Forest

Forests are the largest biological reservoir of biomass and carbon on the planet. This fact turns them into the main tool to neutralise the CO₂ emitted by human activities. Despite such importance, the uncertainties associated with biomass estimates in forests, especially in (sub)tropical forests, are enormous. Facing this scenario, the objectives of this study were (1) to quantify through destructive sampling the aboveground biomass (AGB) of 105 trees of 47 species occurring in a secondary subtropical evergreen rainforest in Brazil; (2) to investigate the AGB distribution in different tree compartments; and (3) to fit tree-level models to improve biomass estimates for the referred forest type. The results revealed that most of the AGB was stored in the compartments stem and large branches (diameter ≥ 5 cm). There was an increase in the proportion of biomass – in relation to the total tree AGB (kg) – allocated in the large branches as tree diameter at breast height (DBH) increased; this pattern was not observed for the compartments stem, thin branches (diameter < 5 cm), and leaves. The compartments thin branches and leaves represented between 5.4% and 17.0%, and 1.3% and 2.9% of the total tree AGB, respectively. From the 10 fitted biomass models, the linearised power models yielded the smallest errors. The best performance model, which returned a mean bias of 1.7%, may be written as AGB = exp(?8.9807 + 2.1642·ln[DBH] + 0.5072·ln[h] + 0.9999·ln[ρ_bas]); Baskerville’s factor = 1.0175. If there are no (reliable) data on tree total height (h; m), the following model, which embedded the DBH and wood basic specific gravity (ρ_bas; kg m^?3), may be employed: AGB = exp(?9.0086 + 2.4606·ln[DBH] + 1.0895·ln[ρ_bas]); Baskerville’s factor = 1.0206.     

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.     

Measuring heights to crown base and crown median with LiDAR in a mature,even-aged loblolly pine stand

This study evaluated the possibility of measuring the height to the base of the live crown and the height to the median of canopy elements with airborne scanning LiDAR (Light Detection And Ranging) in a simple, even-aged stand of loblolly pine. The first step in determining these heights was fitting truncated Weibull functions to the vertical distribution of elevations where discrete laser pulses were reflected from the dominant canopy strata. The height to the canopy median was defined as height at the median of the distribution. The height to the base of the live crown was defined as the height where the upper tail the distribution asymptotes to zero returns. Ground-based and LiDAR-based estimates of the canopy median differed by 0.3 m and were not significantly different (P = 0.23). Ground- and LiDAR-based estimates of the base of the live crown differed by 0.6 m and were significantly different (P = 0.03). LiDAR-based estimates of the canopy median exhibited positive bias over most of the range of field-measured values. Analyses of the LiDAR data resulted in overestimating the height to the canopy base over most of the range in field-measured values; however, the difference between ground and LiDAR-based estimates were negatively correlated with ground-based measurements. Average tree diameter was calculated with LiDAR-generated heights to the canopy median and to the base of the live crown. The overall average diameter was not statistically different from the overall quadratic mean diameter measured on the ground, demonstrating the possible utility of these canopy variables to forest managers working with simple stands such as plantations.     

Integration of LiDAR and QuickBird imagery for mapping riparian biophysical parameters and land cover types in Australian tropical savannas

Riparian zones are exposed to increasing pressures because of disturbance from agricultural and urban expansion and overgrazing. Accurate and cost-effective mapping of riparian environments is important for baseline inventories and monitoring and managing their functions associated with water quality, biodiversity, and wildlife habitats. In this study, we integrate remotely sensed light detection and ranging (LiDAR) data and high spatial resolution satellite imagery (QuickBird-2) to estimate riparian biophysical parameters and land cover types in the Fitzroy catchment in Queensland, Australia. An object based image analysis (OBIA) was adopted for the study. A digital terrain model (DTM), a tree canopy model (TCM) and a plant projective cover (PPC) map were first derived from the LiDAR data. A map of the streambed was then produced using the DTM information. Finally, all the LiDAR-derived biophysical map products and the QuickBird image bands were combined in an OBIA to (1) map the following land cover types: riparian vegetation, streambed, bare ground, woodlands and rangelands; (2) determine the distribution of overhang vegetation within the streambed; and (3) measure the width of both the riparian zone and the streambed. The combined use of both datasets allowed accurate land cover mapping, with an overall accuracy of 85.6%. The estimated widths of the riparian zone and the streambed showed strong correlation with the actual field measurements (r = 0.82 and 0.98 respectively). Our results show that the combined use of LiDAR and high spatial resolution imagery can potentially be used for the assessment of the riparian condition in a tropical savanna woodland riparian environment. This work also shows the capacity of OBIA to assist in the assessment of the composition of the riparian environment from multiple image datasets.     

Using canopy heights from digital aerial photogrammetry to enable spatial transfer of forest attribute models: a case study in central Europe

This paper describes a workflow utilizing detailed canopy height information derived from digital airphotos combined with ground inventory information gathered in state-owned forests and regression modelling techniques to quantify forest-growing stocks in private woodlands, for which little information is generally available. Random forest models were trained to predict three different variables at the plot level: quadratic mean diameter of the 100 largest trees (d₁₀₀), basal area weighted mean height of the 100 largest trees (h₁₀₀), and gross volume (V). Two separate models were created – one for a spruce- and one for a beech-dominated test site. We examined the spatial portability of the models by using them to predict the aforementioned variables at actual inventory plots in nearby forests, in which simultaneous ground sampling took place. When data from the full set of available plots were used for training, the predictions for d₁₀₀, h₁₀₀, and V achieved out-of-bag model accuracies (scaled RMSEs) of 15.1%, 10.1%, and 35.3% for the spruce- and 15.9%, 9.7%, and 32.1% for the beech-dominated forest, respectively. The corresponding independent RMSEs for the nearby forests were 15.2%, 10.5%, and 33.6% for the spruce- and 15.5%, 8.9%, and 33.7% for the beech-dominated test site, respectively.     

Allometric model of the height–diameter curve for even-aged pure stands of Japanese cedar (<Emphasis Type="Italic">Cryptomeria japonica</Emphasis>)

We derived an allometric model of the height–diameter curve for even-aged pure stands, which was a modification of the earlier model proposed by Inoue (2000a). An individual-dependent allometric equation was used as the height–diameter curve. Using the discriminant analysis method, all trees composed of a stand were stratified into upper and lower trees. It was assumed that both relationships between mean tree height H _m and upper tree height H _u and between mean DBH D _m and mean DBH of upper trees D _u could be described by the time-dependent allometric power equations. The height–diameter curve showed an average relationship between tree height and DBH of a given stand at a given time, and hence it could be assumed that the height–diameter curve contained two points (D _m , H _m ) and (D _u , H _u ). With these assumptions, we derived an allometirc model of height–diameter curve, which allowed the coefficients of the curve to be estimated from mean tree height and mean DBH. The proposed model was fitted to Japanese cedar (Cryptomeria japonica D. Don) data. The error ratio of the allometric model ranged from 2.254% to 13.412% (mean = 6.785%), which was significantly smaller than that of the earlier model. When the error of mean tree height was ±1.0 m or less, the effect of the error of mean tree height on the error ratio was comparatively small. This suggested that the error of ±1.0 m in mean tree height could be accepted in the estimation of height–diameter curve using the allometric model. These features enable us to combine the allometric model with Hirata’s vertical angle-count sampling or growth models. In conclusion, the allometric model would be one of the most practical and convenient approaches for estimating the height–diameter relationship of even-aged pure stands.     

Combining field and control team assessments to obtain error estimates for surveys of crown condition

Three field teams undertook a survey of crown transparency in Switzerland in 1993. A fourth team (control) also visited a subset of the trees that had already been visited by the field teams and reported estimates for the same. The problem of estimating the proportion (P_c) of trees with total crown transparency at least equal to a (fixed) level c is addressed. The method assumes that the control team estimate is an unbiased estimate of P_c and therefore if the expected difference between a field team estimate and the control team estimate is non‐zero then the field team estimate is biased. This assumption is most likely to be met in those countries or areas where the control team consists of the instructors. When this assumption cannot be met, as long as the bias of the control team remains the same over the years, the estimates of P_c from different years can still be used to study, for example, whether there is any temporal trend in P_c . A weighted average of the bias‐corrected field team estimates is used to determine the confidence limits. The method is applied to the Swiss national forest damage inventory data from the year 1993. The same approach can also be taken for estimating the proportion of cases where a random variable X, qualitative (such as the social position of a tree) or quantitative (such as the diameter of a tree at breast height), assumes values in a particular set.     

The inventory of carbon stock in New Zealand’s post-1989 planted forest for reporting under the Kyoto protocol

The United Nations Framework Convention on Climate Change (UNFCCC) requires reporting net carbon stock changes and anthropogenic greenhouse gas emissions, including those related to forests. This paper describes the design and implementation of a nation-wide forest inventory of New Zealand’s planted post-1989 forests that arose from Land Use, Land-Use Change and Forestry activities (LULUCF) under Article 3.3 of the Kyoto Protocol. The majority of these forests are planted with Pinus radiata, with the remainder made up of other species exotic to New Zealand. At the start of the project there was no on-going national forest inventory that could be used as a basis for calculating carbon stocks and meet Good Practice Guidelines.A network of ground-based permanent sample plots was installed with airborne LiDAR (Light Detection and Ranging) for double sampling using regression estimators to predict carbon in each of the four carbon pools of above- and below-ground live biomass, dead wood and litter. Measurement, data acquisition and quality assurance/control protocols were developed specifically for the inventory, carried out in 2007 and 2008. Plots were located at the intersection of a forest with a 4 km square grid, coincident with an equivalent 8 km square grid established over the indigenous forest and “grassland with woody biomass” (Other Wooded Land). Planted tree carbon within a ground plot was calculated by an integrated system of growth, wood density and compartment allocation models utilising the data from measurements of trees and shrubs on the plots. This system, called the Forest Carbon Predictor, predicts past and future carbon in a stand and is conditioned so that the calculated basal area and mean top height equals that obtained by conventional mensuration methods at the time of the plot measurement. Mean per hectare carbon stocks were then multiplied by an estimate of the total area of post 1989 forests obtained from wall to wall mapping using a combination of satellite imagery and ortho-photography.The network of permanent samples plots and LiDAR double sampling methodology was designed to be simple and robust to change over time. In the future, using LiDAR should achieve sampling efficiencies over using ground plots alone and reduces any problems regarding restricted access on the ground. The network is to be remeasured at the end of commitment period 1, 2012, and the carbon stocks re-estimated in order to calculate change.     

Scaling-up from tree to stand transpiration for a warm-temperate multi-specific broadleaved forest with a wide variation in stem diameter

Previous studies have demonstrated a clear relationship between diameter at breast height (DBH) and tree transpiration (Q _T) in multi-specific broadleaved forests. However, these studies were conducted with a limited range of tree sizes and species, and thus many multi-specific broadleaved forests fall outside these conditions. Therefore, this study examined the relationship between DBH and Q _T in a warm-temperate multi-specific broadleaved forest (n = 12 species) with a wide range of tree sizes (5.0–70.0 cm DBH) using the Granier-type heat dissipation method. The results showed that, although sap flow density varied between individual trees and species, there was a significant relationship between log Q _T and log DBH (r ² = 0.66, P < 0.001) because of the strong dependence of sapwood area on DBH. This study confirmed the applicability of the relationship for the stand transpiration (E _C) estimates even in a multi-specific broadleaved forest with a wide variation in DBH. Our results also revealed that selecting the sample trees in descending order of DBH effectively reduced potential errors in E _C estimates for a specific sample size, as larger trees contribute more to E _C. This information should be useful for future studies investigating the transpiration of multi-specific broadleaved forests, reducing errors during the scaling-up procedure.     

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。     

滇西南巨尾桉人工林木根径、胸径与树高模型研究

为研究巨尾桉人工林林木根径、胸径与树高数学模型,在滇西南的双江县选取138株（其中建模样本103株,检验样本35株）树干健全、通直圆满、无分杈的巨尾桉人工林样木,采集其根径与树高、胸径与树高成对数据。利用SPSS 21.0回归分析,分别建立线性方程、对数曲线、指数曲线和Logistic曲线等11个数学模型,以决定系数（R²）、估计值的标准误（SEE）、均方差（RMSE）、平均绝对偏差(MAD)、相对误差（RS）、模型预估精度（P）、显著性(Sig)P值作为模型评价指标,对比11个模型的拟合效果。结果表明,11个根径—树高模型显著性（Sig）P<0.001,其中有9个模型的预估精度大于97%,回归方程达到极显著水平,拟合效果较好;11个胸径—树高模型显著性（Sig）P<0.001,其中9个模型的预估精度大于97%,回归方程达到极显著水平,拟合效果也较好。通过对各项评价指标进行比较分析,确定三次项曲线H=a₀+a₁DR+a₂DR²+a₃DR³是拟合效果最好的根径—树高数学模型,代入拟合参数后的根径—树高数学模型表达式为H=11.069901-0.729389DR+0.096782DR²-0.001984DR³;线性方程H=a₀+a₁D是拟合效果最好的胸径—树高数学模型,代入拟合参数的胸径—树高数学模型表达式为H=4.818702+0.891217D。     

Estimation of whole-stem respiration,incorporating vertical and seasonal variations in stem CO<Subscript>2</Subscript> efflux rate,of <Emphasis Type="Italic">Chamaecyparis obtusa</Emphasis> trees

We examined vertical and seasonal variations in stem respiration rates in a 50-year-old plantation of Japanese cypress, Chamaecyparis obtusa (Sieb. et Zucc.) Endl., in central Japan, and discuss a practical and precise method to scale a point-measured stem CO₂ efflux rate up to whole-stem respiration. For five selected trees, stem CO₂ efflux rates were measured at breast height (1.3 m) and at five or six points above breast height (at approximately 2 m intervals) every 1 or 2 months over two consecutive years. Daily total stem respiration rate (surface area basis) was greater inside the crown than below the crown, especially during the growing season. By incorporating the vertical profile of the respiration rate, annual whole-stem respiration was estimated for each sample tree (R _y). We then compared this estimate (R _y) with another estimate of annual whole-stem respiration (R′_y) obtained using a conventional method; it is assumed that the area-based respiration rate at breast height is constant throughout the stem. The ratio of these two estimates (R′_y/R _y) was usually less than 1, indicating that the assumptions used to calculate R′_y underestimate annual whole-stem respiration. We found that R′_y/R _y was negatively correlated with the ratio of crown length to tree height (crown ratio). These results suggest that annual whole-stem respiration in this C. obtusa plantation is substantially affected by the relative proportion of within-crown stem with higher respiratory activity. Methodologically, our results imply that incorporating the crown ratio into the conventional method would improve the accuracy of annual whole-stem respiration estimates.     

Adapting a growth equation to model tree regeneration in mountain forests

Management and risk analysis of protection forests depend on a reliable estimation of regeneration processes and tree growth under different site conditions. While the growth of forest stands and thus the average growth of larger trees is well studied and published in yield tables as well as embodied in numerous simulation models, there is still a lack of information about the crucial initial stages of tree growth. Thus, we evaluated juvenile tree growth for different site conditions in the Swiss Alps and developed an approach to model both the early and later stages of growth based on the Bertalanffy equation. This equation is physiologically well founded and requires only two parameter estimates: a maximum tree height and a growth parameter. Data for the parameter estimation were available from studies of tree regeneration at a range of sites in Switzerland: growth patterns of larch (Larix decidua) were available from a high-elevation afforestation experiment. For spruce (Picea abies), data were obtained from a blowdown area in the Alps. The growth equation was fitted to the observed data and we found a good correlation of the fitted curves with the observed data. The parameter estimates were validated with independent data sets. The extrapolated growth curves, calculated with the estimated growth rates, correspond well to the validation data. Thus, it is possible to use the Bertalanffy equation to model both the early and later stages of growth. With this approach, we provide a basis for modelling the growth of juvenile and mature trees of different tree species in mountain forests of the European Alps.     

Estimating individual tree heights of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR

Recently, it was shown that individual tree heights could be accurately estimated using small-footprint airborne light detection and ranging (LiDAR) remote sensing. Because most of the areas studied previously were limited to flat terrain, we investigated the accuracy of LiDAR-derived individual tree height estimates for different types of topographical features in mountainous forests with a steeper and more complex topography. Several middle-aged (40–50 years old) sugi (Cryptomeria japonica D. Don) plantations are found in the mountainous regions in Japan; hence, we chose 48-year-old sugi plantations to investigate the accuracy of these estimates. The surveyed area was divided into three types of topographical features; steep slope (mean slope ± SD; 37.6° ± 5.8°), gentle slope (15.6° ± 3.7°), and gentle yet rough terrain (16.8° ± 7.8°). Before estimating tree heights, the number of detected trees within each topographical feature was researched. In each of these terrains, the percentage of trees detected correctly was 74%, 86%, and 92%; the average error between LiDAR-derived and field-measured tree heights was 0.227m, –0.473m, and –0.183m; and the accuracy of the LiDAR-derived tree height estimates, given as root mean square error (RMSE), was 0.901m, 0.846m, and 0.576m, respectively. Consequently, the procedure presented in this study could detect most canopy trees and estimate individual tree heights with an accuracy better than 1m, even in a forest with a mean slope angle of approximately 38°; thus, indicating that small-footprint airborne LiDAR will be a useful tool for accurately estimating the heights of individual canopy trees in sugi plantations in mountainous areas.     

山苍子家系幼林生长性状遗传变异及稳定性分析

[目的]对山苍子不同家系幼林生长性状进行遗传变异及稳定性分析,为山苍子优良家系选择及推广提供参考。[方法]以13个半同胞山苍子家系为材料,采用随机区组设计,分别在重庆万州、湖北京山和福建清流3个试验点营建家系试验林。对23年生山苍子幼林的树高和地径性状进行测定和多点联合分析,并采用AMMI模型对山苍子各家系的生长稳定性进行评价。[结果](1)山苍子家系的树高和地径在家系和地点效应上均达到显著(0. 01 P0. 05)或极显著(P 0. 01)水平,地径的家系和地点交互作用达到显著水平(0. 01 P 0. 05); 3个试验点树高和地径的表型变异系数为20. 98%～29. 27%,遗传变异系数为0. 62%～20. 35%,其中,地径的表型和遗传变异系数普遍大于树高;树高的家系遗传力为0. 003～0. 578,地径的家系遗传力为0. 455～ 0. 806。(2)综合双标图和稳定性参数得出:家系JY2和GX4地径表现较好且稳定性较高。[结论]山苍子家系的树高和地径在家系和地点间的差异均达到极显著水平(P 0. 01),存在丰富的遗传变异。在家系水平上,树高受低等到中等强度的遗传控制,地径受中等到高等强度的遗传控制。各家系在福建清流试点生长表现最好,湖北京山试点居次,重庆万州试点最差,其中家系JY2和GX4在3个试验点地径均表现较好且稳定性较高。     

Allometric relationship for estimating above-ground biomass of Aegialitis rotundifolia Roxb. of Sundarbans mangrove forest, in Bangladesh

Tree biomass plays a key role in sustainable management by providing different aspects of ecosystem. Estimation of above ground biomass by non-destructive means requires the development of allometric equations. Most researchers used DBH (diameter at breast height) and TH (total height) to develop allometric equation for a tree. Very few species-specific allometric equations are currently available for shrubs to estimate of biomass from measured plant attributes. Therefore, we used some of readily measurable variables to develop allometric equations such as girth at collar-height (GCH) and height of girth measuring point (GMH) with total height (TH) for A. rotundifolia, a mangrove species of Sundarbans of Bangladesh, as it is too dwarf to take DBH and too irregular in base to take Girth at a fixed height. Linear, non-linear and logarithmic regression techniques were tried to determine the best regression model to estimate the above-ground biomass of stem, branch and leaf. A total of 186 regression equations were generated from the combination of independent variables. Best fit regression equations were determined by examining co-efficient of determination (R2), co-efficient of variation (CV), mean-square of the error (MSerror), residual mean error (Rsme), and F-value. Multiple linear regression models showed more efficient over other types of regression equation. The performance of regression equations was increased by inclusion of GMH as an independent variable along with total height and GCH.