Assessing 3D point clouds from aerial photographs for species-specific forest inventories

In this study we assessed the potential of using photogrammetric data for species-specific forest inventories. The method is based on a combination of Dirichlet and ordinary linear regression models. This approach was used to predict species proportions, main tree species, total, and species-specific volume. Structural and spectral variables were used as predictors. The models were validated using 63 independent validation stands. The results from airborne laser scanning (ALS) data combined with spectral data and photogrammetric data obtained using aerial imagery with different forward overlaps of 80% and 60% were compared. The best photogrammetry-based models predicted species proportions with a relative root mean square error (RMSE) of 21.4%, classified dominant species with 79% accuracy, predicted total volume with relative RMSE of 13.4%, and predicted species-specific volume with relative RMSE of 36.6%, 46.5%, and 84.9% for spruce, pine, and deciduous species, respectively. The results were similar for the three point cloud datasets obtained from aerial imagery and ALS and the accuracies of the predictions were comparable to methods used in operational FMI. The study highlights the effectiveness of forest inventories carried out using photogrammetric data, which – differently from ALS, can include species-specific information without relying on multiple data sources.     

Predicting species-specific basal areas in urban forests using airborne laser scanning and existing stand register data

The aim of this work was to examine how well species-specific stand attributes can be predicted using a combination of airborne laser scanning (ALS) and existing stand register data in urban forests. In this context, the ability of three data combinations: ALS data and stand register data, ALS data and digital aerial images and all of these combined, was tested in the prediction of species-specific basal areas. We divided tree species into seven and three different tree species strata and applied two prediction methods: (1) regression method, in which the predicted total basal area was divided into tree species based on tree species proportions from stand register data, and (2) the nearest neighbour (NN) method, in which tree species proportions were used as predictor variables for species-specific basal areas. Prediction models were built based on training data of 205 field plots, and the accuracy of the models was tested based on validation data of 52 forests stands. Our results showed that species-specific predictions of seven tree species were more accurate when tree species proportions from stand register data were used in the prediction. Both the regression and the NN method provided reasonable accuracy. This study showed that tree species information from existing stand register data could be used as an alternative for aerial images in ALS-based forests inventories. The use of ALS data together with stand register data and small field data could also be economically beneficial in an inventory of urban forests.     

Characterizing forest species composition using multiple remote sensing data sources and inventory approaches

Abstract

The purpose of the study was to evaluate tree species composition estimated using combinations of different remotely sensed data with different inventory approaches for a forested area in Norway. Basal area species composition was estimated as both species proportions and main species by using data from airborne laser scanning (ALS) and airborne (multispectral and hyperspectral) imagery as auxiliary information in combination with three different inventory approaches: individual tree crown (ITC) approach; semi-individual tree crown (SITC) approach; and area-based approach (ABA). The main tree species classification obtained an overall accuracy higher than 86% for all ABA alternatives and for the two other inventory approaches (ITC and SITC) when combining ALS and hyperspectral imagery. The correlation between estimated species proportions and species proportions measured in the field was higher for coniferous species than for deciduous species and increased with the spectral resolution used. Especially, the ITC approach provided more accurate information regarding the proportion of deciduous species that occurred only in small proportions in the study area. Furthermore, the species proportion estimates of 83% of the plots deviated from field measured species proportions by two-tenths or less. Thus, species composition could be accurately estimated using the different approaches and the highest levels of accuracy were attained when ALS was used in combination with hyperspectral imagery. The accuracies obtained using the ABA in combination with only ALS data were encouraging for implementation in operational forest inventories.     

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.     

Economic utility of 3D remote sensing data for estimation of site index in Nordic commercial forest inventories: a comparison of airborne laser scanning,digital aerial photogrammetry and conventional practices

ABSTRACT

Forest productivity is a crucial variable in forest planning, usually expressed as site index (SI). In Nordic commercial forest inventories, SI is commonly estimated by a combination of aerial image interpretation, field assessment and information obtained from previous inventories. Airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) data can alternatively be used for SI estimation, however the economic utilities of the inventory methods have not been compared. We compared seven methods of SI estimation in a cost-plus-loss analysis, by which we added the expected economic losses due to sub-optimal treatment decisions to the inventory costs. The methods comprised direct and indirect estimation from combinations of ALS, DAP and stand register data, and manual interpretation from aerial imagery supported by field assessment and information from previous inventories (conventional practices). The choice of method had great impact on both the accuracy and the economic value of the produced estimates. Direct methods using bitemporal ALS and DAP data gave the best accuracy and the smallest total cost. DAP was a suitable and low-cost data source for SI estimation. Estimation from single-date ALS and DAP data and age obtained from the stand register provided practical alternatives when applied to even-aged stands.     

Spatial partitioning of biomass and diversity in a lowland Bolivian forest: Linking field and remote sensing measurements

Large-scale inventories of forest biomass and structure are necessary for both understanding carbon dynamics and conserving biodiversity. High-resolution satellite imagery is starting to enable structural analysis of tropical forests over large areas, but we lack an understanding of how tropical forest biomass links to remote sensing. We quantified the spatial distribution of biomass and tree species diversity over 4 ha in a Bolivian lowland moist tropical forest, and then linked our field measurements to high-resolution Quickbird satellite imagery. Our field measurements showed that emergent and canopy dominant trees, being those directly visible from nadir remote sensors, comprised the highest diversity of tree species, represented 86% of all tree species found in our study plots, and contained the majority of forest biomass. Emergent trees obscured 1–15 trees with trunk diameters (at 1.3 m, diameter at breast height (DBH)) ≥20 cm, thus hiding 30–50% of forest biomass from nadir viewing. Allometric equations were developed to link remotely visible crown features to stand parameters, showing that the maximum tree crown length explains 50–70% of the individual tree biomass. We then developed correction equations to derive aboveground forest biomass, basal area, and tree density from tree crowns visible to nadir satellites. We applied an automated tree crown delineation procedure to a high-resolution panchromatic Quickbird image of our study area, which showed promise for identification of forest biomass at community scales, but which also highlighted the difficulties of remotely sensing forest structure at the individual tree level.     

Airborne laser scanning-based decision support for wood procurement planning

We present a decision support tool for guiding the selection of marked stands based on airborne laser scanning (ALS) data. We describe three stages, namely (1) wall-to-wall mapping of the stands matured for cutting using low-density ALS data; (2) tree-level inventory of these stands using high-density ALS data and (3) theoretical bucking of the imputed tree stems to produce detailed information on their characteristics. We tested them in a Scots pine dominated boreal forest area in Eastern Finland, where 79 sample plots were measured in the field. The detection of the stands matured for cutting had a success rate of 95% and our results demonstrated a further potential to limit the result towards stands dominated by certain species by means of intensity values derived from the low-density ALS data. The applied single-tree detection and estimation chain produced detailed tree-level information and realistic diameter distributions, yet the detection was highly emphasised on the dominant tree layer. The error levels in the estimates were generally less than standard deviations of the field attributes. Finally, plot-level accumulations of saw-log volumes were found rather similar, whether the input was based on the imputed tree data or trees measured in the field. The results are considered useful for ranking the stands based on their properties, whether the aim in the wood procurement is to focus on certain species or to select stands suitable for production needs.     

Aboveground tree volume and phytomass prediction equations for forest species in Italy

In this article, we present equations derived for the prediction of the aboveground tree volume and phytomass for twenty-five of the most important forest species growing in Italy. These equations result from ongoing research aiming to fill a gap in the models available at the national scale. With regard to volume, the results are particularly important for thirteen species or groups of species that were once scaled with models, conventionally assumed as reference models, available for other species. In Italy, phytomass models had never been constructed at the national level before. For any single tree, specific equations allow estimations of the following tree components to be made: stem and large branches (for either volume or phytomass), small branches (phytomass), stump (phytomass) and the whole tree phytomass. The models have been constructed on the basis of nearly 1,300 sampling units (sample trees). Although these equations must be considered intermediate results of the ongoing research because only half the scheduled number of samples has been collected, they have already been used in the practice, for example in the estimates reported in the recently published second national forest inventory.     

Cultural knowledge of forests and allied tree system management around Mabira Forest Reserve,Uganda

The cultural universe is sometimes confusing,surprising and murky, so many cultural maps get drawn,discussed and envisioned. A study was undertaken around Mabira Forest Reserve in central Uganda to identify the trees and shrubs culturally managed on-farm, assess the cultural practices of forest and tree system management and determine the relationship between farmer gender and forest and tree system management. We engaged 203 farmers in focus group discussions and semi-structuredinterviews to collect data. Qualitative data were jointly evaluated with farmers; quantitative data were analyzed in SPSS 20.0. The results showed a high likelihood for involvement of local people in tree or forest management for economic gain, as timber and fast-growing species were highly ranked. Food and medicinal species were also regarded as important, suggesting high prospects of integrating them into the local farming system or protecting them in the forest. Numerous cultural practices(including rituals, trenching, bark slashing, ring barking, spot weeding and use of organic manure and pesticides) of forest and tree system management were acknowledged. However, their knowledge was mixed and unclear about distinct cultural and supportive arrangements for natural forest and tree restoration. While gender was not a significant cultural attribute for knowledge of the forest and allied tree system management, age substantially affected farmer propensity for various timber products. Also farmer's family size influenced the collection of tree wildings and fodder. We encourage considering gender disparities and livelihood needs including income, during selection of cultural practices for forest and tree restoration.     

Laser scanning of forest resources: the nordic experience

This article reviews the research and application of airborne laser scanning for forest inventory in Finland, Norway and Sweden. The first experiments with scanning lasers for forest inventory were conducted in 1991 using the FLASH system, a full-waveform experimental laser developed by the Swedish Defence Research Institute. In Finland at the same time, the HUTSCAT profiling radar provided experiences that inspired the following laser scanning research. Since 1995, data from commercially operated time-of-flight scanning lasers (e.g. TopEye, Optech ALTM and TopoSys) have been used. Especially in Norway, the main objective has been to develop methods that are directly suited for practical forest inventory at the stand level. Mean tree height, stand volume and basal area have been the most important forest mensurational parameters of interest. Laser data have been related to field training plot measurements using regression techniques, and these relationships have been used to predict corresponding properties in all forest stands in an area. Experiences from Finland, Norway and Sweden show that retrieval of stem volume and mean tree height on a stand level from laser scanner data performs as well as, or better than, photogrammetric methods, and better than other remote sensing methods. Laser scanning is, therefore, now beginning to be used operationally in large-area forest inventories. In Finland and Sweden, research has also been done into the identification of single trees and estimation of single-tree properties, such as tree position, tree height, crown width, stem diameter and tree species. In coniferous stands, up to 90% of the trees represented by stem volume have been correctly identified from canopy height models, and the tree height has been estimated with a root mean square error of around 0.6 m. It is significantly more difficult to identify suppressed trees than dominant trees. Spruce and pine have been discriminated on a single-tree level with 95% accuracy. The application of densely sampled laser scanner data to change detection, such as growth and cutting, has also been demonstrated.     

采伐迹地森林恢复树种选择造林效果分析

基于永新县七溪岭林场采伐迹地森林恢复的森林调查,从树高生长量和蓄积生长量两个方面分析选择不同树种造林的林地生产能力,总结同一树种连栽的生产能力低下和更换树种造林的效果显著,指出采伐迹地森林恢复树种选择对国有林场大面积经营森林的重要性以及树种选择的方法。     

尾叶桉、巨桉优树选择标准的建立

尾叶桉、巨桉是华南地区引种成功及重点改良的2种桉树,以往选优基本上是围绕短周期纸浆纤维材改良目标而进行,选优林分的林龄小于15 a,多为6 a以下。本文以林龄16 a以上的尾叶桉、巨桉林分进行选优,以期为中大径材桉树培育提供改良用的优树材料。采用三株优势木对比法和配对T检验法确定优树生长量临界值,并兼顾干形质量指标进行选优。通过对25株候选优树和75株优势木生长量、分枝与干形分值的分析,建立了尾叶桉、巨桉优树选择标准：优树的单株材积≥3株优势木平均单株材积的1.35倍,胸径≥3株优势木平均胸径的1.15倍或树高≥3株优势木平均树高的1.03倍,分枝＋干形得分≥7。在候选的25株优树中有17株符合标准被评为优树,入选率为68%。     

样木解析法及其在材积表编制中的应用研究

提出了一种材积表编制样本资料收集的新方法———样木解析法。即在标准地中采用随机或机械抽样方法选取样木,进行树干解析,获得样木生长过程;通过建立树皮系数与树皮率回归模型,将样木解析后的各龄阶去皮直径和去皮材积转化为带皮直径和带皮材积,一株样木可获得龄阶数个编表样本。经四川农业大学实习林场楠木一元材积表、二元材积表编制实践,有减少编表样本收集工作量、消耗林木少的优点,且能达到精度要求,同时,也为珍稀树种材积表编制样本资料收集提供了新的方法。     

基于激光雷达数据的东北林区航空林分材积表编制

利用东北林区云冷杉林、落叶松林、樟子松林、红松林、栎树林、桦树林、杨树林、榆树林、椴树林和水胡黄林10种森林类型的1947个样地的激光雷达数据和地面实测蓄积量数据,首先通过多元线性回归和非线性回归方法,分别建立基于机载激光雷达数据的森林蓄积量回归估计模型,并通过对比分析,确定统一形式的基础回归模型;然后利用哑变量建模方法,建立基于不同森林类型参数和相同激光雷达变量的蓄积量模型。结果表明,研究建立的10种森林类型的线性蓄积量回归模型的解释变量个数在2~7之间,确定系数在0.460~0.858之间;非线性蓄积量回归模型的解释变量个数在2~4之间,确定系数在0.461~0.846之间。基于点云平均高度和平均强度建立的10种森林类型的二元蓄积量模型(研究称之为标准模型),其确定系数在0.440~0.815之间,平均预估误差在2.88%~4.42%之间,平均百分标准误差在16.76%~25.52%之间,预估精度基本达到森林资源规划设计调查技术规定要求。依据研究建立的10种森林类型的蓄积量模型,可以编制基于激光雷达数据的航空林分材积表,在森林资源调查实践中推广应用。     

Applied 3D texture features in ALS-based forest inventory

Airborne laser scanning (ALS) data are not usually considered to be very informative with respect to tree species, and this information is often obtained by combining such data with spectral image material. The aim was to test the ability of height, density, intensity and applied 2D and 3D texture variables derived solely from a very high-density ALS point cloud to describe the crown shape and structure characteristics required for tree species discrimination. Linear discriminant analysis was used to find optimal combinations of variables within the predictor groups, and classifications based on variables from different groups were compared. The third power of the tree diameter was used as a stem volume approximate, and rather than examining species alone, the classification was evaluated with respect to the volume approximates assigned to the predicted species. The sensitivity of pulse density to the methodology presented here was determined by simulating thinned data sets by reducing the initial pulse density. The reliability of the estimates was analysed both with functions generated using the original data and with new functions for each thinning level. Alpha shape metrics developed for describing tree crowns constructed from the 3D point clouds proved capable of discriminating between all three species groups evaluated, and several height distribution and textural variables were found to discriminate between the coniferous tree species. The results demonstrate the importance of species interpretation in forest inventories based on allometric modelling, but then indicate that species-specific estimation could be carried out using ALS-derived variables alone.     

Total tree,merchantable stem and branch volume models for miombo woodlands of Malawi

The objective of this study was to develop general (multispecies) models for prediction of total tree, merchantable stem and branch volume including options with diameter at breast height (dbh) only, and with both dbh and total tree height (ht), as independent variables. The modelling data set was based on destructively sampled trees and comprised 74 trees from 33 tree species, collected from four forest reserves located in different ecological zones of Malawi. The dbh and ht ranges for the data set were 5.3–111.2?cm and 3.0–25.0?m, respectively. A number of alternative model forms were tested and the final model selection was based on root mean square error (RMSE) values calculated using a leave-one-out cross-validation procedure. The model performances and the evaluations of the finally selected models (R? 2 range 0.72 to 0.92; RMSE range 38% to 71%; mean prediction errors range ?1.4% to 1.3%) suggest that all models can be used over a wide range of geographical and ecological conditions in Malawi with an appropriate accuracy in predictions. The appropriateness of the developed models was also supported by the fact that the mean prediction errors of these models were much lower than the mean prediction errors (range ?23.6% to 48.9%) of some previously developed models tested on our data.     

基于无人机影像的森林信息获取研究进展

随着无人机技术的不断发展,其在林业中的应用越来越广泛。无人机航空摄影作为高分影像的重要获取方式,具有成本低、效率高、时效性强等特点,己逐渐成为森林资源调查与监测的新途径。研究者已不再满足于无人机影像在森林资源分类和空间分析等方面的应用研究,而是开始关注单木树高、胸径、冠幅等具体树木信息的获取以及森林信息量化等方面的研究。文中综述近几年无人机在森林结构参数、生物量、蓄积量、立木材积表、林区规划、树木空间分布以及三维建模等方面的研究进展,并对无人机在森林信息获取中的研究和应用提出建议。     

福建省薪炭林研究

为缓解福建省能源紧张的局面,筛选出可供福建省利用的薪炭林树种,采用实地调查和资料收集相结合的方法,对福建省薪炭林树种的种类、生态学特性、分布状况、薪材特性等进行调查,比较不同树种的生产力及热值特性。在此基础上,采用层次分析方法分别从薪材产量、生长量、轮伐期及热值4个方面对福建省主要薪炭林树种进行综合评价,筛选出窿缘桉、铁刀木、黑荆树、大叶相思、尾叶桉、银合欢、马占相思等15种值得福建省推广的薪炭林树种。     

Measuring loblolly pine crowns with drone imagery through deep learning

In modeling forest stand growth and yield,crown width,a measure for stand density,is among the parameters that allows for estimating stand timber volumes.However,accurately measuring tree crown size in the field,in particu-lar for mature trees,is challenging.This study demonstrated a novel method of applying machine learning algorithms to aerial imagery acquired by an unmanned aerial vehi-cle (UAV) to identify tree crowns and their widths in two loblolly pine plantations in eastern Texas,USA.An ortho mosaic image derived from UAV-captured aerial photos was acquired for each plantation (a young stand before canopy closure,a mature stand with a closed canopy).For each site,the images were split into two subsets:one for training and one for validation purposes.Three widely used object detection methods in deep learning,the Faster region-based convolutional neural network (Faster R-CNN),You Only Look Once version 3 (YOLOv3),and single shot detection(SSD),were applied to the training data,respectively.Each was used to train the model for performing crown recogni-tion and crown extraction.Each model output was evaluated using an independent test data set.All three models were successful in detecting tree crowns with an accuracy greater than 93％,except the Faster R-CNN model that failed on the mature site.On the young site,the SSD model performed the best for crown extraction with a coefficient of determination(R2) of 0.92,followed by Faster R-CNN (0.88) and YOLOv3(0.62).As to the mature site,the SSD model achieved a R2 as high as 0.94,follow by YOLOv3 (0.69).These deep leaning algorithms,in particular the SSD model,proved to be successfully in identifying tree crowns and estimat-ing crown widths with satisfactory accuracy.For the pur-pose of forest inventory on loblolly pine plantations,using UAV-captured imagery paired with the SSD object deten-tion application is a cost-effective alternative to traditional ground measurement.     

松树分子标记辅助育种研究进展

松树是世界上森林生态系统和人工林的重要树种,松树的遗传改良开展早,进展快,成效大.分子标记技术为缩短育种周期,提高育种效率提供了有力的工具.本文回顾了世界上松树分子标记遗传图谱构建、比较遗传作图、数量性状位点定位和标记辅助选择的研究进展.已经构建遗传连锁图的林木有13个属,近40个树种,其中松树占40%,而大多数松树遗传图谱仍然是不完整的,不能覆盖全基因组;比较遗传作图显示松属树种具有高度的遗传保守性;数量性状位点(QTL)定位表明,大多数性状的遗传基础存在着主效基因,为开展分子标记辅助选择提供了良好的基础.杂种松部分重要性状的遗传控制中存在着树种效应,对标记辅助选择和育种策略的制订具有指导意义.