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.     

Identification of boreal forest stands with high herbaceous plant diversity using airborne laser scanning

Boreal forest stands with high herbaceous plant species diversity have been found to be one of the main habitats for many endangered species, but the locations and sizes of these herb-rich forest stands are not well known in many areas. Better identification of the stands could improve both their conservation and management. A new approach is proposed here for locating the mature herb-rich forest stands using airborne laser scanner (ALS) data and logistic regression, or the k-NN classifier. We show that ALS technology is capable of distinguishing the ecologically important herb-rich forests from those growing on less fertile site types, mainly on the basis of unique but quantifiable crown structure and vertical profile that characterise forests on high fertility sites. The study site, Koli National Park, is located on the border of the southern and middle boreal vegetation zones in Finland, and includes 63 herb-rich forest stands of varying sizes. The model and test data comprised 274 forest stands belonging to five forest site types varying from very fertile to poor. The best overall classification accuracy achieved with the k-NN method was 88.9%, the herb-rich forests being classified correctly in 65.0% of cases and the other forest site types in 95.7%. The best overall classification accuracy achieved with logistic regression was 85.6%, being 55.0% for the herb-rich forests and 94.3% for the other forest site types. Both methods demonstrated promising potential for separating herb-rich forests from other forest site types, although slightly better results were obtained with the non-parametric k-NN method, which was capable of utilising a higher number of explanatory variables. It is concluded that ALS-based data analysis techniques are applicable to the detection of mature boreal herb-rich forests in large-scale forest inventories.     

Mapping dead wood distribution in a temperate hardwood forest using high resolution airborne imagery

Dead wood, in the form of coarse woody debris and standing dead wood, or snags, is an essential structural component of forest ecosystems. It plays a key role in nutrient cycling, ecosystem functions and provision of habitat for a wide variety of species. In order to manage dead wood in a temperate hardwood forest, an understanding of its availability and spatial distribution is important. This research evaluates airborne digital camera remote sensing for mapping temperate forest dead wood across an area within Gatineau Park, Canada. Two approaches were evaluated: (1) direct detection and mapping of canopy dead wood (dead branches and tall snags) through the combination of three techniques in a hybrid classification: ISODATA clustering, object-based classification, and spectral unmixing, and (2) indirect modelling of coarse woody debris and snags using spectral and spatial predictor variables extracted from the imagery. Indirect modelling did not provide useful results while direct detection was successful with field validation showing 94% accuracy for detected canopy level dead wood objects (i.e. 94% of validation sites with canopy dead wood were detected correctly) and 90% accuracy for control sites (i.e. 90% of validation sites with no canopy level dead wood were identified correctly). The procedures presented in this paper are repeatable and could be used to monitor dead wood over time, potentially contributing to applications in forest carbon budget estimation, biodiversity management, and forest inventory.     

Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project

This research reports the major results from an evaluation of the first Nordic operational stand-based forest inventory using airborne laser scanner data. Laser data from a forest area of 250 km² were used to predict six biophysical stand variables used in forest planning. The predictions were based on regression equations estimated from 250 m² field training plots distributed systematically throughout the forest area. Test plots with an approximate size of 0.1–0.4 ha were used for validation. The testing revealed standard deviations between ground-truth values and predicted values of 0.36–1.37 m (1.9–7.6%) for mean height, 0.70–1.55 m (3.0–7.6%) for dominant height, 2.38–4.88 m² ha^?1 (7.8–14.2%) for basal area and 13.9–45.9 m³ ha^?1 (6.5–13.4%) for stand volume. No serious bias was detected.     

A method for axenic sampling of wood from standing trees

A method is described to collect wood samples axenically from standing trees by the use of a Pressler borer in conjunction with protective sterile jackets.     

Developing an airborne laser scanning dominant height model from a countrywide scanning survey and national forest inventory data

Abstract

An airborne laser scanning (ALS) dominant height model was developed based on data from a national scanning survey with the aim of developing a digital terrain model (DTM) for Denmark. Data obtained in the ongoing Danish national forest inventory (NFI) were used as reference data. The data comprised a total of 2072 measurements of dominant height on NFI sample plots inventoried in 2006–2007 and their corresponding ALS data. The dominant height model included four variables derived from the ALS point cloud distribution. The variables were related to canopy height, canopy density and species composition on individual plots. The RMSE of the final model was 2.25 m and the model explained 93.9% of the variation (R ²). The model was successful in predicting dominant height across a wide range of forest tree species, stand heights, stand densities, canopy cover and growing conditions. The study demonstrated how low-density ALS data obtained in a survey not specifically aimed at forest applications may be used for obtaining biophysical forest properties such as dominant height, thereby reducing the overall forest inventory costs.     

Automatic recognition and measurement of single trees based on data from airborne laser scanning over the richly structured natural forests of the Bavarian Forest National Park

The purpose of this study was to test a method for delineating individual tree crowns based on a fully automated recognition methodology. The study material included small-footprint time-of-flight laser scanner data acquired in the spring and summer of 2002. The data were collected with a Toposys II airborne laser system flown over the Norway spruce (Picea abies) and European beech (Fagus sylvatica) dominated forests of the Bavarian Forest National Park, Germany. The applied algorithm, which earlier had been validated for Swedish forest conditions, is a watershed algorithm that is based on the use of laser scanning data. 2584 trees in a total of 28 representative reference stands, each 0.1–0.25 ha in area, were included in the investigation. With the algorithm, 76.9% of the trees in the upper layer could be recognised. This corresponds to 85.2% of the timber volume determined by ground measurements. The results for conifers were more accurate in this respect than for deciduous trees. A negative aspect was the number of falsely identified trees, the percentage of which was 5.4%.     

Comparison of terrestrial laser scanning and X-ray scanning in measuring Scots pine (Pinus sylvestris L.) branch structure

While X-ray scanning is increasingly used to measure the interior quality of logs, terrestrial laser scanning (TLS) could be used to collect information on external tree characteristics. As branches are one key indicator of wood quality, we compared TLS and X-ray scanning data in deriving whorl locations and each whorl’s maximum branch and knot diameters for 162 Scots pine (Pinus sylvestris L.) log sections. The mean number of identified whorls per tree was 37.25 and 22.93 using X-ray and TLS data, respectively. The lowest TLS-derived whorl in each sample tree was an average 5.56?m higher than that of the X-ray data. Whorl-to-whorl mean distances and the means of the maximum branch and knot diameters in a whorl measured for each sample tree using TLS and X-ray data had mean differences of ?0.12?m and ?6.5?mm, respectively. One of the most utilized wood quality indicators, tree-specific maximum knot diameter measured by X-ray, had no statistically significant difference to the tree-specific maximum branch diameter measured from the TLS point cloud. It appears challenging to directly derive comparative branch structure information using TLS and X-ray. However, some features that are extractable from TLS point clouds are potential wood quality indicators.     

Predicting dynamic modulus of elasticity of Norway spruce structural timber by forest inventory,airborne laser scanning and harvester-derived data

Norway spruce structural timber is one of the most important products of the Norwegian sawmilling industry, and a high grade-yield of structural timber is therefore important for the economic yield. Presorting of logs suited for production of structural timber might be one option to increase the grade yield. In this study, dynamic modulus of elasticity (E_dyn) of structural timber was predicted based on forest inventory data at site level and single-tree data from airborne laser scanning (ALS) and harvester. The models were based on 611 boards from 4 sites in southeastern Norway. Important variables at site level were elevation, site index (SI), and mean stand age. However, when combining data from all information sources, mean stand age and site index were the only significant variables at site level. Tree height and variables describing the crown, like crown length and crown volume, were important vaiables extracted from ALS data. Stem diameter measures and tapering were important variables measured by the harvester. The combined model with variables from all three information sources reduced the variance the most, especially when using individual tree age instead of average stand age. However, combining all these data requires accurate positioning of the trees by the harvester.     

Demarcation of juvenile and mature woods of planted Chinese fir and its wood quality prediction

lntroductionChinesefir(Cunntwh8mi8IanceoIat8(Lamb.)Hook)isaveryimportantspeciesofwoodresourcesinSouthernChina.TheheredityandameliorationonChinesefirareaIwayspaidgreatattention.However,inthepast,thefocaIpointOftheworkonheredityandamelioF8tionwashowtoCuItivatetheChinesefirwithcharaCtersoff8stgrowth,goodfigure,wideadaPt-abilityandStrongdiseaseresistance.LittleworkwasdoneonameIior8tionofwoodquaIity.AlongwiththecompositionchangeoftheforeStresourcesinChina,Chinesewoodindustryismoredependen…     

Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

A conceptual model describing why laser height metrics derived from airborne discrete return laser scanner data are highly correlated with above ground biomass is proposed. Following from this conceptual model, the concept of canopy-based quantile estimators of above ground forest biomass is introduced and applied to an uneven-aged, mature to overmature, tolerant hardwood forest. Results from using the 0th, 25th, 50th, 75th and 100th percentiles of the distributions of laser canopy heights to estimate above ground biomass are reported. A comparison of the five models for each dependent variable group did not reveal any overt differences between models with respect to their predictive capabilities. The coefficient of determination (r ² ) for each model is greater than 0.80 and any two models may differ at most by up to 9%. Differences in root-mean-square error (RMSE) between models for above ground total, stem wood, stem bark, live branch and foliage biomass were 8.1, 5.1, 2.9, 2.1 and 1.1 Mg ha^?1, respectively.     

木／塑纤维复合材料力学性能平衡性初探

用于轿车内衬件的木／塑纤维复合材料,既要求有一定的力学强度又要求有一定的弹性和韧性,本文就木／塑纤维复合材料力学性能的平衡性（主要指静曲强度与弹性模量的平衡）进行了研究。试验表明：不同的胶粘剂对木／塑纤维复合材料的力学性能的平衡性具有很显著的影响。     

Evaluation of surface smoothness by laser displacement sensor 1: effect of wood species

The best methods for determining surface roughness in an industrial environment are of the noncontact variety, with reproduction of the profile. The objective of this work was to compare the roughness profile obtained by a contact stylus with a commercial laser displacement sensor (LDS). Measurements were done using 15 wood species with different densities and colors, based on which special triangle profiles were prepared. The accuracy of the laser sensor was examined by statistical analysis of roughness parameters measured from the profiles. Experimental results show that LDS profiles were imitated correctly. However, LDS accuracy depends on the scanned wood properties (density and color), installation position of the sensor, and profile shape. It was found that evaluation of dark and high-density wooden surfaces was imperfect.Part of this work was presented at the 50th Annual Meeting of the Japan Wood Research Society, Kyoto, April 2000; and at the 51st Annual Meeting of the Japan Wood Research Society, Tokyo, April 2001     

Estimation of stem attributes using a combination of terrestrial and airborne laser scanning

Properties of individual trees can be estimated from airborne laser scanning (ALS) data provided that the scanning is dense enough and the positions of field-measured trees are available as training data. However, such detailed manual field measurements are laborious. This paper presents new methods to use terrestrial laser scanning (TLS) for automatic measurements of tree stems and to further link these ground measurements to ALS data analyzed at the single tree level. The methods have been validated in six 80 × 80 m field plots in spruce-dominated forest (lat. 58°N, long. 13°E). In a first step, individual tree stems were automatically detected from TLS data. The root mean square error (RMSE) for DBH was 38.0 mm (13.1 %), and the bias was 1.6 mm (0.5 %). In a second step, trees detected from the TLS data were automatically co-registered and linked with the corresponding trees detected from the ALS data. In a third step, tree level regression models were created for stem attributes derived from the TLS data using independent variables derived from trees detected from the ALS data. Leave-one-out cross-validation for one field plot at a time provided an RMSE for tree level ALS estimates trained with TLS data of 46.0 mm (15.4 %) for DBH, 9.4 dm (3.7 %) for tree height, and 197.4 dm³ (34.0 %) for stem volume, which was nearly as accurate as when data from manual field inventory were used for training.     

Z-Directional distribution of fiber orientation of Japanese and western papers determined by confocal laser scanning microscopy

Confocal laser scanning microscopy was applied to visualize inside fibers stained with a fluorescent dye, and the fiber orientation distribution in the thickness direction was determined by image analysis from optically sliced images. Fiber orientation angle and anisotropy were determined by the fast Fourier transform method, which holds advantages over the conventionally applied Hough transform with regard to evaluation of fiber width-weighted contribution and intrafiber segmental contribution. An orthogonally layered Japanese paper handmade by the flow-sheet forming method resulted in a clear change of fiber orientation angle from 0° to 90°. Machine-made wood-containing printing paper showed the highest anisotropy, while copier paper showed a low anisotropy. Thick papers like the wood-free paperboard and recycled packaging board showed that the maximum measured thickness was about 150 μm. The depth limit to detect fluorescence was considered to depend on the apparent density of the paper and the light absorption character of the fibers. Part of this study was presented at the 54th Annual Meeting of the Japan Wood Research Society, Sapporo, Japan, August 2004     

不同地理种源的人工林长白落叶松木材材性的研究

参照国家木材物理力学试验方法标准对采自东北林业大学帽儿山实验林场的15年生人工林长白落叶松不同地理种源的试材,测定了气干密度、年轮宽度、晚材率、硬度、管胞长度、管胞长宽比等参数;并通过综合评估分析处理试验数据。鉴于落叶松主要作工程用材和制浆用材,从木材材性角度评价地理种源的优劣,旨在为速生人工林木的定向培育提供科学依据。     

Application of high-resolution airborne data using individual tree crowns in Japanese conifer plantations

We investigated conifer plantation management in Japan using high-resolution airborne data based on an individual tree crown (ITC) approach. This study is the first to apply this technique to Japanese forests. We found that forest resources can be measured at the level of a single tree. We also produced a tree-crown map for a test site with Chamaecyparis obtusa, Pinus densiflora, Larix kaempferi, Cryptomeria japonica, other conifers, and broadleaved trees, with a classification accuracy of 78%. Forest-stand polygons with tree-cover types were generated from this map, a tree-density map, and a crown-occupied-area map. Forest information for the stand polygons was extracted automatically and compared with detailed field-survey data. The error between our ITC estimates and the field-survey data ranged from 0.3 to 30.2%, depending on tree crown size, density, and other factors. Errors were highest for high-density stands with mixed compositions and tree crown diameters ≤5.0 m. However, the error for stands with crown diameters ≥6.2 m was 11.6% or less. Therefore, this technique is best suited to pure Japanese conifer plantations without multiple layers or high-density stands.     

Comparison of estimators and feature selection procedures in forest inventory based on airborne laser scanning and digital aerial imagery

Digital maps of forest resources are a crucial factor in successful forestry applications. Since manual measurement of this data on large areas is infeasible, maps must be constructed using a sample field data set and a prediction model constructed from remote sensing materials, of which airborne laser scanning (ALS) data and aerial images are currently widely used in management planning inventories. ALS data is suitable for the prediction of variables related to the size and volume of trees, whereas optical imagery helps in improving distinction between tree species. We studied the prediction of forest attributes using field data from National Forest Inventory complemented with ad hoc field plots in combination with ALS and aerial imagery data in Aland province, Finland. We applied feature selection with genetic algorithm and greedy forward selection and compared multiple linear and nonlinear estimators. Maximally around 40 features from a total of 154 were required to achieve the best prediction performances. Tree height was predicted with normalized root mean squared error value of 0.1 and tree volume with a value around 0.25. Predicting the volumes of spruce and broadleaved trees was the most challenging due to small proportions of these tree species in the study area.     

Assessment of wood surface roughness: comparison of tactile roughness and three-dimensional parameters derived using a robust Gaussian regression filter

Japanese oak and Japanese beech were sanded by hand with abrasive papers of varying grit number. Two three-dimensional parameters selected to characterize their surface roughness – one parameter for the distribution of roughness-profile peaks and the other for the relative area of the roughness-profile peaks above the threshold height – were compared against tactile roughness. The parameters were obtained from roughness profiles as determined by a robust Gaussian regression filter (RGRF) using seven cutoffs. The RGRF filtering process was adjusted specifically for the evaluation of wood surface roughness. Except for a cutoff wavelength of 0.25mm, the RGRF lent itself well to the determination of roughness profiles. No distortion of roughness profiles occurred around deep valleys, and there was a good correlation between the parameters and tactile roughness.Part of this study was presented at the 2002 Kansai Branch Office lecture meeting of the Japan Society for Precision Engineering, Kyotanabe, August 2002