Mapping and Characterizing Selected Canopy Tree Species at the Angkor World Heritage Site in Cambodia Using Aerial Data

At present, there is very limited information on the ecology, distribution, and structure of Cambodia’s tree species to warrant suitable conservation measures. The aim of this study was to assess various methods of analysis of aerial imagery for characterization of the forest mensuration variables (i.e., tree height and crown width) of selected tree species found in the forested region around the temples of Angkor Thom, Cambodia. Object-based image analysis (OBIA) was used (using multiresolution segmentation) to delineate individual tree crowns from very-high-resolution (VHR) aerial imagery and light detection and ranging (LiDAR) data. Crown width and tree height values that were extracted using multiresolution segmentation showed a high level of congruence with field-measured values of the trees (Spearman’s rho 0.782 and 0.589, respectively). Individual tree crowns that were delineated from aerial imagery using multiresolution segmentation had a high level of segmentation accuracy (69.22%), whereas tree crowns delineated using watershed segmentation underestimated the field-measured tree crown widths. Both spectral angle mapper (SAM) and maximum likelihood (ML) classifications were applied to the aerial imagery for mapping of selected tree species. The latter was found to be more suitable for tree species classification. Individual tree species were identified with high accuracy. Inclusion of textural information further improved species identification, albeit marginally. Our findings suggest that VHR aerial imagery, in conjunction with OBIA-based segmentation methods (such as multiresolution segmentation) and supervised classification techniques are useful for tree species mapping and for studies of the forest mensuration variables.     

Estimating Stem Volume Using QuickBird Imagery and Allometric Relationships for Open Populus xiaohei Plantations

There has been a great deal of interest in studying the crown of trees using remote sensing data.In this study,crownwidth was extracted from high-resolution QuickBird images for open Populus xiaohei plantations.Regression modelsfor predicting the individual stem volumes of Populus xiaohei were established using extracted crown width,as well asestimated tree parameters(i.e.diameter at breast height[DBH]and tree height)as predictors.Our results indicated thatcrown width could be accurately extracted from QuickBird images using a multi-scale segmentation approach with a meanrelative error of 5.74%,especially for wide-spacing stands.Using either extracted crown width alone or with estimatedDBH and tree height can successfully estimate individual stem volume of Populus xiaohei with the R~2 value ranging from0.87 to 0.92 depending on different model forms.In particular,the two second-order polynomial models(model2 andmodel 6),based on QuickBird image-derived crown widths and estimated DBH and tree heights,respectively,were the bestat describing the relationship between stem volume and tree characteristics.     

Estimating Stem Volume Using QuickBird Imagery and Allometric Relationships for Open Populus xiaohei Plantations

There has been a great deal of interest in studying the crown of trees using remote sensing data. In this study, crown width was extracted from high-resolution QuickBird images for open Populus xiaohei plantations. Regression models for predicting the individual stem volumes of Populus xiaohei were established using extracted crown width, as well as estimated tree parameters （i.e. diameter at breast height [DBH] and tree height） as predictors. Our results indicated that crown width could be accurately extracted from QuickBird images using a multi-scale segmentation approach with a mean relative error of 5.74%, especially for wide-spacing stands. Using either extracted crown width alone or with estimated DBH and tree height can successfully estimate individual stem volume of Populus xiaohei with the R2 value ranging from 0.87 to 0.92 depending on different model forms. In particular, the two second-order polynomial models （model 2 and model 6）, based on QuickBird image-derived crown widths and estimated DBH and tree heights, respectively, were the best at describing the relationship between stem volume and tree characteristics.     

Estimating Canopy Structure in an Amazon Forest from Laser Range Finder and IKONOS Satellite Observations1

Canopy structural data can be used for biomass estimation and studies of carbon cycling, disturbance, energy balance, and hydrological processes in tropical forest ecosystems. Scarce information on canopy dimensions reflects the difficulties associated with measuring crown height, width, depth, and area in tall, humid tropical forests. New field and spaceborne observations provide an opportunity to acquire these measurements, but the accuracy and reliability of the methods are unknown. We used a handheld laser range finder to estimate tree crown height, diameter, and depth in a lowland tropical forest in the eastern Amazon, Brazil, for a sampling of 300 trees stratified by diameter at breast height (DBH). We found significant relationships between DBH and both tree height and crown diameter derived from the laser measurements. We also quantified changes in crown shape between tree height classes, finding a significant but weak positive trend between crown depth and width. We then compared the field‐based measurements of crown diameter and area to estimates derived manually from panchromatic 0.8 m spatial resolution IKONOS satellite imagery. Median crown diameter derived from satellite observations was 78 percent greater than that derived from field‐based laser measurements. The statistical distribution of crown diameters from IKONOS was biased toward larger trees, probably due to merging of smaller tree crowns, underestimation of understory trees, and overestimation of individual crown dimensions. The median crown area derived from IKONOS was 65 percent higher than the value modeled from field‐based measurements. We conclude that manual interpretation of IKONOS satellite data did not accurately estimate distributions of tree crown dimensions in a tall tropical forest of eastern Amazonia. Other methods will be needed to more accurately estimate crown dimensions from high spatial resolution satellite imagery.     

Estimating the heights and diameters at breast height of trees in an urban park and along a street using mobile LiDAR

Because trees can positively influence local environments in urban ecosystems, it is important to measure their morphological characteristics, such as height and diameter at breast height (DBH). However, measuring these data for each individual tree is a time-consuming process that requires a great deal of manpower. In this study, we investigated the feasibility of using mobile LiDAR to estimate tree height and DBH along urban streets and in urban parks. We compared measurements from a mobile LiDAR unit with field measurements of tree height and DBH in urban parks and streets. The height-above-ground and Pratt circle fit methods were applied to calculate tree height and DBH, respectively. The LiDAR-estimated tree heights were highly accurate albeit slightly underestimated, with a root mean square error of 0.359 m for the street trees and 0.462 m for the park trees. On the other hand, the estimated DBHs were moderately accurate and overestimated, with a root mean square error of 3.77 cm for the street trees and 8.95 cm for the park trees. Densely planted trees in the park and obstacles in urban areas result in “shadows” (areas with no data), reducing accuracy. Irregular trunk shapes and scanned data that did not include full data point coverage of every trunk were the reasons for the errors. Despite these errors, this study highlights the potential of tree measurements obtained with mobile terrestrial LiDAR platforms to be scaled up from point-based locations to neighborhood-scale and city-scale inventories.

     

中亚热带森林单木地上生物量的机载激光雷达估测

以雪峰山武冈林场为研究对象,利用遥感数据和地面实测样地数据,研究机载激光雷达（LiDAR）估测中亚热带森林乔木层单木地上生物量的能力.利用条件随机场和最优化方法实现LiDAR点云的单木分割,以单木尺度为对象提取的植被点云空间结构、回波特征以及地形特征等作为遥感变量,采用回归模型估测乔木层地上生物量.结果表明: 针叶林、阔叶林和针阔混交林的单木识别率分别为93%、86%和60%;多元逐步回归模型的调整决定系数分别为0.83、0.81和0.74,均方根误差分别为28.22、29.79和32.31 t·hm^-2;以冠层体积、树高百分位值、坡度和回波强度值构成的模型精度明显高于以树高为因子的传统回归模型精度.以单木为对象从LiDAR点云中提取的遥感变量有助于提高森林生物量估测精度.
     

Estimating stem volume and biomass of Pinus koraiensis using LiDAR data

The objective of this study was to estimate the stem volume and biomass of individual trees using the crown geometric volume (CGV), which was extracted from small-footprint light detection and ranging (LiDAR) data. Attempts were made to analyze the stem volume and biomass of Korean Pine stands (Pinus koraiensis Sieb. et Zucc.) for three classes of tree density: low (240 N/ha), medium (370 N/ha), and high (1,340 N/ha). To delineate individual trees, extended maxima transformation and watershed segmentation of image processing methods were applied, as in one of our previous studies. As the next step, the crown base height (CBH) of individual trees has to be determined; information for this was found in the LiDAR point cloud data using k-means clustering. The LiDAR-derived CGV and stem volume can be estimated on the basis of the proportional relationship between the CGV and stem volume. As a result, low tree-density plots had the best performance for LiDAR-derived CBH, CGV, and stem volume (R ² = 0.67, 0.57, and 0.68, respectively) and accuracy was lowest for high tree-density plots (R ² = 0.48, 0.36, and 0.44, respectively). In the case of medium tree-density plots accuracy was R ² = 0.51, 0.52, and 0.62, respectively. The LiDAR-derived stem biomass can be predicted from the stem volume using the wood basic density of coniferous trees (0.48 g/cm³), and the LiDAR-derived above-ground biomass can then be estimated from the stem volume using the biomass conversion and expansion factors (BCEF, 1.29) proposed by the Korea Forest Research Institute (KFRI).     

Amazon Forest Structure from IKONOS Satellite Data and the Automated Characterization of Forest Canopy Properties

We developed an automated tree crown analysis algorithm using 1-m panchromatic IKONOS satellite images to examine forest canopy structure in the Brazilian Amazon. The algorithm was calibrated on the landscape level with tree geometry and forest stand data at the Fazenda Cauaxi (3.75° S, 48.37° W) in the eastern Amazon, and then compared with forest stand data at Tapajos National Forest (3.08° S, 54.94° W) in the central Amazon. The average remotely sensed crown width (mean ± SE) was 12.7 ± 0.1 m (range: 2.0–34.0 m) and frequency of trees was 76.6 trees/ha at Cauaxi. At Tapajos, remotely sensed crown width was 13.1 ± 0.1 m (range: 2.0–38.0 m) and frequency of trees was 76.4 trees/ha. At both Cauaxi and Tapajos, the remotely sensed average crown widths were within 3 percent of the crown widths derived from field measurements, although crown distributions showed significant differences between field-measured and automated methods. We used the remote sensing algorithm to estimate crown dimensions and forest structural properties in 51 forest stands (1 km²) throughout the Brazilian Amazon. The estimated crown widths, tree diameters (dbh), and stem frequencies differed widely among sites, while estimated biomass was similar among most sites. Sources of observed errors included an inability to detect understory crowns and to separate adjacent, intermingled crowns. Nonetheless, our technique can serve to provide information about structural characteristics of large areas of unsurveyed forest throughout Amazonia.     

Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data

We evaluated the effectiveness of integrating discrete return light detection and ranging (LiDAR) data with high spatial resolution near-infrared digital imagery for object-based classification of land cover types and dominant tree species. In particular we adopted LiDAR ratio features based on pulse attributes that have not been used in past studies. Object-based classifications were performed first on land cover types, and subsequently on dominant tree species within the area classified as trees. In each classification stage, two different data combinations were examined: LiDAR data integrated with digital imagery or digital imagery only. We created basic image objects and calculated a number of spectral, textural, and LiDAR-based features for each image object. Decision tree analysis was performed and important features were investigated in each classification. In the land cover classification, the overall accuracy was improved to 0.975 when using the object-based method and integrating LiDAR data. The mean height value derived from the LiDAR data was effective in separating “trees” and “lawn” objects having different height. As for the tree species classification, the overall accuracy was also improved by object-based classification with LiDAR data although it remained up to 0.484 because spectral and textural signatures were similar among tree species. We revealed that the LiDAR ratio features associated with laser penetration proportion were important in the object-based classification as they can distinguish tree species having different canopy density. We concluded that integrating LiDAR data was effective in the object-based classifications of land cover and dominant tree species.     

The Grove of Giants: Tasmania's most carbon-dense forest

The Grove of Giants in the Huon Valley of Tasmania, Australia is renowned for its large trees. A team of tree climbers and citizen scientists undertook a carbon assessment of a 2 hectare plot within the Grove of Giants. The largest 16 trees in the plot (>2.5 m DBH) were measured by tree climbers allowing for accurate estimation of tree volume. Understory trees, coarse woody debris, root biomass and soil carbon were also estimated, making this study the most comprehensive assessment of forest carbon in Tasmania. Total forest carbon was estimated to be 1312 tonnes per hectare. Large trees had the highest carbon stocks, accounting for 44% of the total store. Coarse woody debris represented 19% of the forest's carbon, root biomass was 14%, while the understory trees accounted for 12% and soil carbon for 11%. This is the highest carbon stock recorded in Tasmania and is above the average estimates for temperate forest ecosystems in other parts of the world. Protecting Tasmania's forests, especially mature wet Eucalypt forests, is important to avoid potential greenhouse gas emissions and ensure safe storage of the carbon in the land sector.     

基于不同预测变量的天然椴树可加性地上生物量模型构建

森林生物量是森林生态系统的最基本数量特征,生物量数据是研究许多林业问题和生态问题的基础,因此,准确测定生物量对于计算碳储量以及研究气候变化、森林健康、森林生产力、养分循环等十分重要.目前,测算森林生物量常用的方法为生物量模型估算法.本研究基于小兴安岭地区和张广才岭地区97株实测生物量数据,建立了3个天然椴树立木可加性生物量模型系统(基于胸径的一元可加性生物量模型系统、基于胸径和树高的二元可加性生物量模型系统、基于最优变量的最优可加性生物量模型系统),采用非线性似乎不相关回归法进行参数估计,用加权方法解决模型的异方差问题,并采用“刀切法”进行模型检验.结果表明: 3种可加性生物量模型系统均能较好地对椴树各部分生物量进行拟合和预测(调整后确定系数R_a²>0.84,平均预测误差百分比MPE<8.5%,平均绝对误差MAE<16.3 kg,平均百分标准误差MPSE<28.5%),其中,树干和地上生物量的拟合效果优于树叶、树枝和树冠;在引入树高和树冠因子后,提高了模型的拟合效果和预测能力(R_a²提高0.01～0.04,MAE降低0.01～4.55 kg),缩小了预测值置信区间的范围,树干、树叶和地上生物量提高较多,树枝和树冠提高较少.总体来看,最优生物量模型系统效果最好,其次为二元生物量模型系统,再次是一元生物量模型系统,添加树高和树冠因子进行生物量模型的构建十分必要.     

Terrestrial laser scanner based 3D reconstruction of trees and retrieval of leaf area index in a forest environment

Three-dimensional reconstruction of trees and the estimation of biophysical parameters is significant for the management of forest resources, ecological studies carbon cycle and biodiversity. Terrestrial LiDAR data provides detailed, objective and three-dimensional measurement of forest structure and exact metrics of the tree canopies. Several methods for tree detection including canopy height models and raster interpolation models are based on commercial software and huge data processing. The objective of the given study is the three-dimensional reconstruction of trees by implementing segmentation algorithms and thereby estimating the Leaf Area Index of individual tree segments by terrestrial laser scanned data in the Mudumalai forests of Western Ghats, India. The hierarchical minimum cut segmentation method is used for the three-dimensional reconstruction of the individual trees by tracking cylinders along individual branches and trees in a hierarchical order. Super voxel clustering method is also implemented in the study for tree reconstruction and estimating the tree parameters. Leaf area index is calculated by applying a multivariate regression technique for the heights and the diameter obtained from both the segmentation methods. Results obtained indicated a strong correlation with the in-situ measurements which are obtained from the instruments. The approach addresses the applicability of segmentation algorithms which can be run fully automatically. The approach successfully reconstructed a high precision and realistic model of trees in the Western Ghats region which failed in the case of traditional tree modeling methods which requires multiple instruments operating simultaneously for extracting each parameter. The method proved that using TLS; multiple forest parameters can be estimated simultaneously.     

Monitoring individual tree‐based change with airborne lidar

Understanding the carbon flux of forests is critical for constraining the global carbon cycle and managing forests to mitigate climate change. Monitoring forest growth and mortality rates is critical to this effort, but has been limited in the past, with estimates relying primarily on field surveys. Advances in remote sensing enable the potential to monitor tree growth and mortality across landscapes. This work presents an approach to measure tree growth and loss using multidate lidar campaigns in a high‐biomass forest in California, USA. Individual tree crowns were delineated in 2008 and again in 2013 using a 3D crown segmentation algorithm, with derived heights and crown radii extracted and used to estimate individual tree aboveground biomass. Tree growth, loss, and aboveground biomass were analyzed with respect to tree height and crown radius. Both tree growth and loss rates decrease with increasing tree height, following the expectation that trees slow in growth rate as they age. Additionally, our aboveground biomass analysis suggests that, while the system is a net source of aboveground carbon, these carbon dynamics are governed by size class with the largest sources coming from the loss of a relatively small number of large individuals. This study demonstrates that monitoring individual tree‐based growth and loss can be conducted with multidate airborne lidar, but these methods remain relatively immature. Disparities between lidar acquisitions were particularly difficult to overcome and decreased the sample of trees analyzed for growth rate in this study to 21% of the full number of delineated crowns. However, this study illuminates the potential of airborne remote sensing for ecologically meaningful forest monitoring at an individual tree level. As methods continue to improve, airborne multidate lidar will enable a richer understanding of the drivers of tree growth, loss, and aboveground carbon flux.     

Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City,Northwest China

Crop biomass is an important ecological indicator of growth, light use efficiency, and carbon stocks in agro-ecosystems. Light detection and ranging (LiDAR) or laser scanning has been widely used to estimate forest structural parameters and biomass. However, LiDAR is rarely used to estimate crop parameters because the short, dense canopies of crops limit the accuracy of the results. The objective of this study is to explore the potential of airborne LiDAR data in estimating biomass components of maize, namely aboveground biomass (AGB) and belowground biomass (BGB). Five biomass-related factors were measured during the entire growing season of maize. The field-measured canopy height and leaf area index (LAI) were identified as the factors that most directly affect biomass components through Pearson's correlation analysis and structural equation modeling (SEM). Field-based estimation models were proposed to estimate maize biomass components during the tasseling stage. Subsequently, the maize height and LAI over the entire study area were derived from LiDAR data and were used as input for the estimation models to map the spatial pattern of the biomass components. The results showed that the LiDAR-estimated biomass was comparable to the field-measured biomass, with root mean squared errors (RMSE) of 288.51 g/m² (AGB), and 75.81 g/m² (BGB). In conclusion, airborne LiDAR has great potential for estimating canopy height, LAI, and biomass components of maize during the peak growing season.     

基于边际回归的沈阳市绿化树种人工油松冠形模拟

构建城市典型绿化树种树冠外部轮廓预估模型,可以为城市绿化树种的空间配置优化奠定基础。本研究以辽宁省沈阳市典型绿化树种人工油松为对象,基于Crown Window装置获取60株样木树冠形状,利用幂函数、分段抛物线方程和修正Kozak方程选取基础模型,通过再参数化引入树冠结构因子(最大树冠半径)和相邻木竞争变量(相邻木平均树高、平均胸径、平均冠幅、相邻木株数,以及样木与其相邻木平均树冠接触高),构建耦合相邻木竞争及树冠结构因子的油松树冠外部轮廓预估模型。结果表明: 修正Kozak方程R_a²最大、均方根误差(RMSE)最小,模型稳定性良好,选取其为构建油松冠形模型的基础模型。通过再参数化在基础模型中引入最大树冠半径和相邻木平均胸径后,模型的R_a²提高0.0693,MSER为14.4%。分析最大树冠半径和相邻木竞争对油松树冠形状的影响发现,最大树冠半径对树冠形状影响较大,树冠半径随最大树冠半径增大而增大;相邻木平均胸径对树冠形状影响相比最大树冠半径较弱,树冠上半部分随相邻木竞争增强而增大,树冠下半部分随相邻木竞争增强而变小。本研究构建的耦合相邻木竞争及最大树冠半径的油松树冠外部轮廓边际回归模型具有良好的拟合优度,能够合理地模拟及预测沈阳市典型绿化树种人工油松的树冠形状。     

3D Tree Dimensionality Assessment Using Photogrammetry and Small Unmanned Aerial Vehicles

Detailed, precise, three-dimensional (3D) representations of individual trees are a prerequisite for an accurate assessment of tree competition, growth, and morphological plasticity. Until recently, our ability to measure the dimensionality, spatial arrangement, shape of trees, and shape of tree components with precision has been constrained by technological and logistical limitations and cost. Traditional methods of forest biometrics provide only partial measurements and are labor intensive. Active remote technologies such as LiDAR operated from airborne platforms provide only partial crown reconstructions. The use of terrestrial LiDAR is laborious, has portability limitations and high cost. In this work we capitalized on recent improvements in the capabilities and availability of small unmanned aerial vehicles (UAVs), light and inexpensive cameras, and developed an affordable method for obtaining precise and comprehensive 3D models of trees and small groups of trees. The method employs slow-moving UAVs that acquire images along predefined trajectories near and around targeted trees, and computer vision-based approaches that process the images to obtain detailed tree reconstructions. After we confirmed the potential of the methodology via simulation we evaluated several UAV platforms, strategies for image acquisition, and image processing algorithms. We present an original, step-by-step workflow which utilizes open source programs and original software. We anticipate that future development and applications of our method will improve our understanding of forest self-organization emerging from the competition among trees, and will lead to a refined generation of individual-tree-based forest models.     

不同林分起源的相容性生物量模型构建

目前为止已有不同方法构建生物量相容性模型,但不同林分起源的生物量相容性模型很少报道。针对此问题,以150株南方马尾松(Pinus masson iana)地上生物量数据为例,利用比例平差法和非线性联立方程组法建立不同起源地上生物量以及干材、干皮、树枝和树叶各分项生物量相容的通用性模型。根据分配层次不同,两种方法又各自考虑总量直接控制和分级联合控制两种方案。从直径、树高、地径、枝下高和冠幅5个林分变量中选取不同的变量构建一元、二元和三元生物量模型,并利用加权最小二乘回归法消除生物量模型中存在的异方差性。结果为:比例平差法和非线性联立方程组法都能有效保证各分项生物量总和等于总生物量,模型预测精度满足要求。总体而言,非线性联立方程组方法比比例平差方法精度高,同时两种方法中总量直接控制法比分级联合控制法预测效果好;各分项生物量模型本身作为权函数能有效消除异方差;各分项对应的三元生物量模型预测精度最高,其次是二元生物量模型,最低是一元生物量模型,但这些差异不是很大。总之,为权衡考虑模型预测精度和调查成本,建议把直径和树高作为协变量利用总量直接控制非线性联立方程组法对不同起源生物量建模。     

基于地面LiDAR数据的塔里木河下游胡杨林结构参数反演

以塔里木河下游天然胡杨林为研究对象,利用Riegl VZ-1000型地面激光扫描仪(Terrestrial Laser Scanning, TLS)获取离河道不同距离的8个样方内513棵胡杨的三维点云数据,通过建立冠层高度模型、Hough变换等方法获取单木株数和结构参数,并与传统的每木检尺实测数据和无人机(Unmanned Aerial Vehicle, UAV)低空影像进行对比,验证激光雷达方法的测树精度;对TLS获取的胡杨树形参数进行相关性分析,并建立关系模型;探讨不同水胁迫条件(不同离河道距离,不同地下水埋深)对胡杨单木结构参数的影响;最后按不同径级划分胡杨的年龄,得出各龄级胡杨所占比例。结果表明:(1)TLS能够高精度获取不同密度和长势的胡杨单木株数和结构参数,单株准确分割比率为94%—100%,相对于UAV低空影像更为准确;(2)TLS获取的胡杨树高(Tree height,TH)、胸径(Diameter at breast height, DBH)、冠幅直径(Crown diameter,CD)和冠幅面积(Crown area,CA)与传统实测值拟合度R~2较高,分别为0.95、0.97、0.77和0.84,表明实测数据和TLS获取数据无明显差异;(3)胡杨CD、CA分别与TH呈显著正相关,其相关性系数为0.73、0.67;基于此构建了胡杨TH与CD的关系模型,即TH=2.6274×CD~(0.706),R~2为0.64;(4)根据径级划分胡杨年龄段可知,DBH为15—30 cm的近熟林比例最大,占8个样方内监测胡杨总株数的47%,表明胡杨种群年龄结构相对稳定并总体态势良好,呈现了生态输水对塔河下游胡杨种群恢复有明显的促进作用。总之,激光雷达技术能够客观反映胡杨树形结构参数,可替代耗力、耗费、耗时的传统实测方法,为时时掌握胡杨林生长发育、长势动态以及多尺度、多时相生态耗水研究提供高精度信息,为干旱区荒漠河岸林的有效保护与可持续管理提供科学依据。     

Competition-dependent modelling of foliage biomass in forest stands

Currently, foliage biomass is estimated based on stem diameter or basal area. However, it is questionable whether the relations between foliage and stem observed from plantations of a single tree species can be applied to stands of different structure or species composition. In this paper, a procedure is presented to simulate foliage and branch biomass of tree crowns relative to crown size and light competition. Crowns are divided into layers and segments and each segment is divided into a foliated and an unfoliated fraction. Depending on the competitive status of the segment, leaf area density, specific leaf area and foliated branch fraction are determined. Based on this information, foliage biomass is calculated. The procedure requires a crown shape function and a measure to characterise competition for light and space of each individual segment within the canopy. Simple solutions are suggested for both requirements to enable an application with data that can be measured non-destructively in the field; these were stem position, tree height, crown base height, crown radii and some general crown shape information. The model was parameterised from single trees of Norway spruce and European beech and partly evaluated with independent data close to the investigation plot. Evaluations showed that the model can attribute the ecology of the different crown forms. Modelled foliage distribution for beech and spruce as well as total needle biomass of spruce agreed well with measurements but foliage biomass of beech was underestimated. The results are discussed in the context of a general model application in structured forests.     

城市土地利用是否会降低区域碳吸收能力?——台州市案例研究

城市土地利用显著改变了原有生态系统的结构和功能,特别是建成区植被的碳吸收和碳储存能力。该研究通过实地调查和测量,估算城市建成区内乔木、灌木、草坪的生物量和净初级生产力(net primary productivity,NPP),该方法考虑了园林管理(如修剪或割草)对建成区碳吸收和碳储存的影响。结果表明,台州城市树木个体生物量年增量是野外森林中同类树木的近2倍;乔木修剪量占乔木NPP的1/3。目前台州市建成区的植被碳吸收能力为2.1×103kgC.hm–2.a–1(其中乔木的贡献为64%,灌木为9%,草坪为27%),低于本地野外森林同面积的碳吸收能力;通过与野外常绿阔叶林比较发现,增加台州建成区的绿化覆盖率(从23%提高到46%)即可补偿因城市扩张引起的植被碳吸收能力的损失。