Discrimination of caatinga species based on bark using near infrared spectroscopy

The measurements of tree attributes required for forest monitoring and management planning, e.g., National Forest Inventories, are derived by rather time-consuming field measurements on sample plots, using calipers and measurement tapes. Therefore, forest managers and researchers are looking for alternative methods. Currently, terrestrial laser scanning (TLS) is the remote sensing method that provides the most accurate point clouds at the plot-level to derive these attributes from. However, the demand for even more efficient and effective solutions triggers further developments to lower the acquisition time, costs, and the expertise needed to acquire and process 3D point clouds, while maintaining the quality of extracted tree parameters. In this context, photogrammetry is considered a potential solution. Despite a variety of studies, much uncertainty still exists about the quality of photogrammetry-based methods for deriving plot-level forest attributes in natural forests. Therefore, the overall goal of this study is to evaluate the competitiveness of terrestrial photogrammetry based on structure from motion (SfM) and dense image matching for deriving tree positions, diameters at breast height (DBHs), and stem curves of forest plots by means of a consumer grade camera. We define an image capture method and we assess the accuracy of the photogrammetric results on four forest plots located in Austria and Slovakia, two in each country, selected to cover a wide range of conditions such as terrain slope, undergrowth vegetation, and tree density, age, and species. For each forest plot, the reference data of the forest parameters were obtained by conducting field surveys and TLS measurements almost simultaneously with the photogrammetric acquisitions. The TLS data were also used to estimate the accuracy of the photogrammetric ground height, which is a necessary product to derive DBHs and tree heights. For each plot, we automatically derived tree counts, tree positions, DBHs, and part of the stem curve from both TLS and SfM using a software developed at TU Wien (Forest Analysis and Inventory Tool, FAIT), and the results were compared. The images were oriented with errors of a few millimetres only, according to checkpoint residuals. The automatic tree detection rate for the SfM reconstruction ranges between 65% and 98%, where the missing trees have average DBHs of less than 12 cm. For each plot, the mean error of SfM and TLS DBH estimates is −1.13 cm and −0.77 cm with respect to the caliper measurements. The resulting stem curves show that the mean differences between SfM and TLS stem diameters is at maximum −2.45 cm up to 3 m above ground, which increases to almost +4 cm for higher elevations. This study shows that with the adopted image capture method, terrestrial SfM photogrammetry, is an accurate solution to support forest inventory for estimating the number of trees and their location, the DBHs and stem curve up to 3 m above ground.

Machine learning techniques (ML) have gained attention in precision agriculture practices since they efficiently address multiple applications, like estimating the growth and yield of trees in forest plantations. The combination between ML algorithms and spectral vegetation indices (VIs) from high-spatial-resolution line measurement, segment: 0.079024 m multispectral imagery, could optimize the prediction of these biometric variables. In this paper, we investigate the performance of ML techniques and VIs acquired with an unnamed aerial vehicle (UAV) to predict the diameter at breast height (DBH) and total height (Ht) of eucalyptus trees. An experimental site with six eucalyptus species was selected, and the Parrot Sequoia sensor was used. Several ML techniques were evaluated, like random forest (RF), REPTree (DT), alternating model tree (AT,) k-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), linear regression (LR), and radial basis function (RBF). Each algorithm performance was verified using the correlation coefficient (r) and the mean absolute error (MAE). We used, as input, 34 VIs as numeric variables to predict DHB and Ht. We also added to the model a categorical variable as input identifying the different eucalyptus trees species. The RF technique obtained an overall superior estimation for all the tested configurations. Still, the RBF also showed a higher performance for predicting DHB, numerically surpassing the RF both in r and MAE, in some cases. For Ht variable, the technique that obtained the smallest MAE was SVM, though in a particular test. In this regard, we conclude that a combination of ML and VIs extracted from UAV-based imagery is suitable to estimate DBH and Ht in eucalyptus species. The approach presented constitutes an interesting contribution to the inventory and management of planted forests.

Accurate forest parameters are crucial for ecological protection, forest resource management and sustainable development. The rapid development of remote sensing can retrieve parameters such as the leaf area index, cluster index, diameter at breast height (DBH) and tree height at different scales (e.g., plots and stands). Although some LiDAR satellites such as GEDI and ICESAT-2 can measure the average tree height in a certain area, there is still a lack of effective means for obtaining individual tree parameters using high-resolution satellite data, especially DBH. The objective of this study is to explore the capability of 2D image-based features (texture and spectrum) in estimating the DBH of individual tree. Firstly, we acquired unmanned aerial vehicle (UAV) LiDAR point cloud data and UAV RGB imagery, from which digital aerial photography (DAP) point cloud data were generated using the structure-from-motion (SfM) method. Next, we performed individual tree segmentation and extracted the individual tree crown boundaries using the DAP and LiDAR point cloud data, respectively. Subsequently, the eight 2D image-based textural and spectral metrics and 3D point-cloud-based metrics (tree height and crown diameters) were extracted from the tree crown boundaries of each tree. Then, the correlation coefficients between each metric and the reference DBH were calculated. Finally, the capabilities of these metrics and different models, including multiple linear regression (MLR), random forest (RF) and support vector machine (SVM), in the DBH estimation were quantitatively evaluated and compared. The results showed that: (1) The 2D image-based textural metrics had the strongest correlation with the DBH. Among them, the highest correlation coefficient of −0.582 was observed between dissimilarity, variance and DBH. When using textural metrics alone, the estimated DBH accuracy was the highest, with a RMSE of only 0.032 and RMSE% of 16.879% using the MLR model; (2) Simply feeding multi-features, such as textural, spectral and structural metrics, into the machine learning models could not have led to optimal results in individual tree DBH estimations; on the contrary, it could even reduce the accuracy. In general, this study indicated that the 2D image-based textural metrics have great potential in individual tree DBH estimations, which could help improve the capability to efficiently and meticulously monitor and manage forests on a large scale.

The forest growth and yield models, which are used as important decision-support tools in forest management, are commonly based on the individual tree characteristics, such as diameter at breast height (DBH), crown ratio, and height to crown base (HCB). Taking direct measurements for DBH and HCB through the ground-based methods is cumbersome and costly. The indirect method of getting such information is possible from remote sensing databases, which can be used to build DBH and HCB prediction models. The DBH and HCB of the same trees are significantly correlated, and so their inherent correlations need to be appropriately accounted for in the DBH and HCB models. However, all the existing DBH and HCB models, including models based on light detection and ranging (LiDAR) have ignored such correlations and thus failed to account for the compatibility of DBH and HCB estimates, in addition to disregarding measurement errors. To address these problems, we developed a compatible simultaneous equation system of DBH and HCB error-in-variable (EIV) models using LiDAR-derived data and ground-measurements for 510 Picea crassifolia Kom trees in northwest China. Four versatile algorithms, such as nonlinear seemingly unrelated regression (NSUR), two-stage least square (2SLS) regression, three-stage least square (3SLS) regression, and full information maximum likelihood (FIML) were evaluated for their estimating efficiencies and precisions for a simultaneous equation system of DBH and HCB EIV models. In addition, two other model structures, namely, nonlinear least squares with HCB estimation not based on the DBH (NLS and NBD) and nonlinear least squares with HCB estimation based on the DBH (NLS and BD) were also developed, and their fitting precisions with a simultaneous equation system compared. The leave-one-out cross-validation method was applied to evaluate all estimating algorithms and their resulting models. We found that only the simultaneous equation system could illustrate the effect of errors associated with the regressors on the response variables (DBH and HCB) and guaranteed the compatibility between the DBH and HCB models at an individual level. In addition, such an established system also effectively accounted for the inherent correlations between DBH with HCB. However, both the NLS and BD model and the NLS and NBD model did not show these properties. The precision of a simultaneous equation system developed using NSUR appeared the best among all the evaluated algorithms. Our equation system does not require the stand-level information as input, but it does require the information of tree height, crown width, and crown projection area, all of which can be readily derived from LiDAR imagery using the delineation algorithms and ground-based DBH measurements. Our results indicate that NSUR is a more reliable and quicker algorithm for developing DBH and HCB models using large scale LiDAR-based datasets. The novelty of this study is that the compatibility problem of the DBH model and the HCB EIV model was properly addressed, and the potential algorithms were compared to choose the most suitable one (NSUR). The presented method and algorithm will be useful for establishing similar compatible equation systems of tree DBH and HCB EIV models for other tree species.

In forest management, determining characteristics of trees is essential for assessing tree volume and providing other mathematical equations. In this study, the characteristics of 256 trees including stump diameter, diameter at breast height, total height, crown height, height of trunk with and without branch were measured in an even-aged Fraxinus stand at Chelir part of Kheyroud Forest in north of Iran. Results showed there are significant statistical correlations among various characteristics of Fraxinus excelsior trees, and mathematic-statistical equations were established among these characteristics. We found the incremental power relation between stump diameter (dst) and diameter at breast height (d), which can be used for estimating diameter at breast height of cut trees. Also, another equation was established between diameter at breast height and total height (ht) which is used for estimating total height of those trees for which diameter at breast height is available. An incremental logarithmic relation was found between diameter at breast height and crown height (hc) and an inverse one between diameter at breast height and trunk height (trh) for this species. Also, a sigmoid relation was found between stump diameter and total height. Results implied that trunk height decreases and crown height increases as total height increases. Trees with higher diameters have high height of trunk without branch. The relationship between Slenderness coefficient (h/d) and diameter at breast height of the trees was found to be a power type.

Aim of study: The objective of this work was to evaluate the potential of NIR spectroscopy to differentiate Fabaceae species native to Araucaria forest fragments.Area of study; Trees of the evaluated species were collected from an Araucaria forest stand in the state of Santa Catarina, southern Brazil, in the region to be flooded by the São Roque hydroelectric project.Material and methods: Discs of three species (Inga vera, Machaerium paraguariense and Muellera campestris) were collected at 1.30 meters from the ground. They were sectioned to cover radial variation of the wood (regions near bark, intermediate and near pith). After wood analysis, the same samples were carbonized. Six spectra were obtained from each specimen of wood and charcoal. The original and second derivative spectra, principal component statistics and classification models (Artificial Neural Network: ANN, Support Vector Machines with kernel radial basis function: SVM and k-Nearest Neighbors: k-NN) were investigated.Main results: Visual analysis of spectra was not efficient for species differentiation, so three NIR classification models for species discrimination were tested. The best results were obtained with the use of k-NN for both wood and charcoal and ANN for wood analysis. In all situations, second derivative NIR spectra produced better results.Research highlights: Correct discrimination of wood and charcoal species for control of illegal logging was achieved. Fabaceae species in an Araucaria forest stand were correctly identified.Keywords: Araucaria forest; identification of species; classification models.Abbreviations used: Near infrared: NIR, Lages Herbarium of Santa Catarina State University: LUSC, Principal component analysis: PCA, artificial neural network: ANN, support vector machines with kernel radial basis function: SVM, k-nearest neighbors: k-NN.

Accurately measuring tree diameter at breast height (DBH) and estimating tree positions in a sample plot are important in tree mensuration. The main aims of this paper include (1) developing a new, integrated device that can identify trees using the quick response (QR) code technique to record tree identifications, measure DBH, and estimate tree positions concurrently; (2) designing an innovative algorithm to measure DBH using only two angle sensors, which is simple and can reduce the impact of eccentric stems on DBH measures; and (3) designing an algorithm to estimate the position of the tree by combining ultra-wide band (UWB) technology and altitude sensors, which is based on the received signal strength indication (RSSI) algorithm and quadrilateral localization algorithm. This novel device was applied to measure ten 10 × 10 m square plots of diversified environments and various tree species to test its accuracy. Before measuring a plot, a coded sticker was fixed at a height of 1.3 m on each individual tree stem, and four UWB module anchors were set up at the four corners of the plot. All individual trees’ DBHs and positions within the plot were then measured. Tree DBH, measured using a tree caliper, and the values of tree positions, measured using tape, angle ruler, and inclinometer, were used as the respective reference values for comparison. Across the plots, the decode rate of QR codes was 100%, with an average response time less than two seconds. The DBH values had a bias of 1.89 mm (1.88% in relative terms) and a root mean square error (RMSE) of 5.38 mm (4.53% in relative terms). The tree positions were accurately estimated; the biases on the x-axis and the y-axis of the tree position were −8.55–14.88 cm and −12.07–24.49 cm, respectively, and the corresponding RMSEs were 12.94–33.96 cm and 17.78–28.43 cm. The average error between the estimated and reference distances was 30.06 cm, with a standard deviation of 13.53 cm. The device is cheap and friendly to use in addition to its high accuracy. Although further studies are needed, our method provides a great alternative to conventional tools for improving the efficiency and accuracy of tree mensuration.

PDF HTML阅读 XML下载 导出引用 引用提醒 天目山近自然毛竹林空间结构与胸径的关系 DOI: 10.5846/stxb201308252154 作者: 作者单位: 浙江农林大学环境与资源学院 临安,浙江农林大学环境与资源学院 临安,浙江农林大学环境与资源学院 临安,浙江农林大学环境与资源学院 临安,天目山国家级自然保护区管理局 临安 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(31170595); 国家"十二五"科技支撑计划(2012BAD22B0503); 浙江省重点科技创新团队(2010R50030); 教育部留学回国人员科研启动金项目(20101561); 国家林业局造林司委托项目(SFA2130218-2) The relationship between spatial structure and DBH of close-to-nature Phyllostachys edulis stands in Tianmu Mountain Author: Affiliation: School of Environment and Resource,Zhejiang A F University,School of Environment and Resource,Zhejiang A F University,School of Environment and Resource,Zhejiang A F University,School of Environment and Resource,Zhejiang A F University,Management Office,National Nature Reserve of Tianmu Mountain,Linan Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:以浙江省天目山国家级自然保护区内的近自然毛竹林为研究对象,设置1块100m×100m的固定标准地,采用相邻网格调查法划分为100个调查单元,通过全站仪精确定位毛竹基部的三维坐标(X, Y, Z),利用角尺度、大小比数和年龄隔离度3个林分空间结构指数,并按毛竹胸径大小划分为Ⅰ(DBH < 7cm) 、Ⅱ(7cm ≤ DBH<13cm)、Ⅲ(DBH ≥ 13cm)3个径级,分析近自然毛竹林空间结构与胸径的关系。结果表明:毛竹林直径结构呈右偏近似正态分布,与乔木同龄林直径结构较接近;Ⅰ、Ⅱ、Ⅲ径级毛竹以及全林分的空间分布格局分别为聚集、随机、随机和随机,毛竹林角尺度随径阶的增加有减小的趋势,且服从幂函数关系,决定系数R2=0.7793,各径阶角尺度无显著性差异(P > 0.05);毛竹林整体处于中庸状态,胸径大小分化不明显,各径级毛竹优势度排序为Ⅲ > Ⅱ > Ⅰ,毛竹林大小比数随径阶的增加而减小,与胸径呈线性关系,决定系数R2=0.9233,各径阶大小比数差异极显著(P < 0.01);毛竹林的平均年龄隔离度为0.8178,属强度至极强度异龄,各径级毛竹年龄隔离程度大小排序为Ⅲ > Ⅱ > Ⅰ,毛竹林年龄隔离度随径阶的增大呈现逐渐递增的趋势,且服从幂函数关系,决定系数R2=0.6774,各径阶年龄隔离度差异极显著(P < 0.01)。 Abstract:Tree diameter structure is an important and basic role in forest structure. For the reason that the diameter at breast height (DBH) can be easily and accurately measured. Because it has a close relationship with variables such as stand density, tree age, tree height, canopy, biodiversity and so on, diameter structure is often used as a direct-response stand structure characteristic. DBH belongs to a set of non-spatial forest structure factors, and is frequently used to analyze the relationship between stand structure and growth, e.g. Recently, studies of forest spatial structure have expanded, partly due to the fact that the DBH is an important factor in explaining the growth of trees, and in many cases because the diameter structure has been used to analyze the relationship between spatial structure and DBH. Similarly, DBH is also an important factor in reflecting the growth of moso bamboo (Phyllpstachys edulis) forests. The DBH of moso bamboo can directly influence leaf area and root area volume, which affects the growth of moso bamboo. Others have performed a number of research studies on the relationship between DBH and other bamboo forest structure factors, such as bamboo height, age composition, canopy structure, and so on. However, these factors are generally measures of non-spatial structure. Recently, research suggests that a distance-dependent spatial index can accurately describe the moso bamboo stand structure, and therefore it is important to analyze and control the relationship between moso bamboo stand structure and function. Previous studies rarely reported the relationship between moso bamboo stand spatial structure and diameter. Therefore, three spatial structure parameters, uniform angle index, neighborhood comparison and age mingling degree were used to analyze the relationship between moso bamboo spatial structure and diameter, and thus provide a theoretical basis for sustainable moso bamboo forest management. The study was established in a close-to nature moso bamboo stand in Tianmu Mountain National Nature Reserve, Zhejiang province. The study design involved a fixed plot of 1hm2 (100 m×100 m), which was divided into 100 units by adjacent grid inventory. Each moso bamboo was located in terms of x-, y-, and z-coordinates using a Total Station. Three spatial structure parameters, including neighborhood comparison, uniform angle index, age mingling degree were evaluated. The DBH was recorded into one of three classes: Ⅰclass (DBH < 7 cm), Ⅱ class (7 cm ≤ DBH < 13 cm), Ⅲ class (DBH ≥ 13 cm). These classes were used to analyzed the relationship between spatial structure and DBH of the close-to-nature moso bamboo stand. The results showed that the frequency distribution of DBH had a right-skewed normal distribution, which is the similar to even-aged arbor stands. The spatial pattern of class Ⅱand class Ⅲ was of random distribution, similar to the distribution of the whole stand, but the pattern of class Ⅰshowed an aggregation distribution pattern. The uniform angle indexes decreased with increasing diameter classes, and results showed that the uniform index had a powerful relationship with DBH, with the determination coefficient between DBH and uniform index being 0.7793. The uniform angle of different diameter classes showed no obvious significant difference (P > 0.05). The neighborhood comparison showed that for stands in an intermediate status, the DBH differentiation was not significant. The neighborhood comparison values showed that the ranking of the dominant degree was: Ⅲ > Ⅱ > Ⅰ. And the neighborhood comparisons decreased with increasing diameter classes, as well as had a significantly linear correlation with DBH (the determination coefficient was 0.9233). The neighborhood comparison of different diameter classes showed significant differences (P < 0.01). The average age mingling of the stand was 0.8178, suggesting the age mingling intensity was intensive. The age mingling values showed the ranking of age segregation was: Ⅲ > Ⅱ > Ⅰ. And age mingling increased with increasing diameter classes, as well as had a strong relationship with DBH, where the determination coefficient was 0.6774. The age mingling of different diameter classes was also significanly different (P < 0.01). 参考文献 相似文献 引证文献

Accurate estimation of tree position, diameter at breast height (DBH), and tree height measurements is an important task in forest inventory. Mobile Laser Scanning (MLS) is an important solution. However, the poor global navigation satellite system (GNSS) coverage under the canopy makes the MLS system unable to provide globally-consistent point cloud data, and thus, it cannot accurately estimate the forest attributes. SLAM could be an alternative for solutions dependent on GNSS. In this paper, a mobile phone with RGB-D SLAM was used to estimate tree position, DBH, and tree height in real-time. The main aims of this paper include (1) designing an algorithm to estimate the DBH and position of the tree using the point cloud from the time-of-flight (TOF) camera and camera pose; (2) designing an algorithm to measure tree height using the perspective projection principle of a camera and the camera pose; and (3) showing the measurement results to the observer using augmented reality (AR) technology to allow the observer to intuitively judge the accuracy of the measurement results and re-estimate the measurement results if needed. The device was tested in nine square plots with 12 m sides. The tree position estimations were unbiased and had a root mean square error (RMSE) of 0.12 m in both the x-axis and y-axis directions; the DBH estimations had a 0.33 cm (1.78%) BIAS and a 1.26 cm (6.39%) root mean square error (RMSE); the tree height estimations had a 0.15 m (1.08%) BIAS and a 1.11 m (7.43%) RMSE. The results showed that the mobile phone with RGB-D SLAM is a potential tool for obtaining accurate measurements of tree position, DBH, and tree height.

Forest resource inventory is a significant part of the sustainable management of forest ecosystems. Finding methods to accurately estimate the diameter at breast height (DBH), tree height and tree position is a significant part of forest resource inventory. The traditional methods of forest resource inventory are expensive, difficult, laborious and time-consuming; the existing systems are not convenient to carry, resulting in low working efficiency. In addition, it is usually necessary to rely on a forest compass, DBH taper and RTK or handheld GPS to set up the plot. These instruments each have a single function and cannot achieve accurate positioning under the forest canopy. Therefore, it is necessary to update the existing equipment and technology. This study aimed to design. a multi-functional, high-precision, real-time. positioning intelligent tree-measuring instrument that integrates plot the set-up, DBH measurement, tree height measurement and tree position measurement. The instrument is based on the ultra-wideband positioning principle, sensor technology, image processing technology, trigonometric functions, tree surveying and other related theories and realizes the functions of plot set-up, tree position measurement, DBH measurement, tree height measurement and other functions. The device was tested in four square plots. The results showed that the root mean squared. error (RMSE). of the tree position estimates ranged from 0.07 m to 0.16 m, while the relative root mean squared error (rRMSE) of the DBH estimates of individual trees ranged from 3.01 to 6.43%, which is acceptable for practical applications in traditional forest inventory. The rRMSE of the tree height estimates ranged from 3.47 to 5.21%. Furthermore, the cost of this instrument is only about one-third that of traditional forestry survey tools, while the work efficiency is three times that of the traditional measurement methods. Overall, the results confirmed that the tree measuring instrument is a practical tool for obtaining. accurate measurements of the tree position, DBH and tree height for forest inventories.

&amp;lt;p&amp;gt;The area of the Mediterranean basin is expected to be threatened by more severe and prolonged droughts and heat waves. Therefore, a more exhaustive knowledge about growth-climate responses in forest trees is necessary in order to adopt mitigation and adaptation strategies in forest management and planning. Climate change will cause shifts of the climate envelope for tree species, potentially leading to migration of species distribution. Under these circumstances, investigations on growth-climate relationships in trees of different provenances of the same species are important for the success of climate-smart forestry. Provenance trials are useful for understanding the response of this species to drought stress. We studied growth-climate relationships in 40-year-old trees of maritime pine (&amp;lt;em&amp;gt;Pinus pinaster&amp;lt;/em&amp;gt; Ait.) from five provenances (Corsica, Portugal, Tuscany, and two native ones: Telti and Limbara) grown on four different sites in Sardinia island (Montes, Montarbu, Uatzo and Usinav&amp;amp;#224;), Italy. In details, for all trees in each site, measurements of stem diameter at breast height (DBH) and plant height (H) were collected. For each site-provenance combination, two incremental cores were collected for each tree; successively, samples were cross-dated and standardized. Weather data (temperature and precipitation) were collected from CRU data online (http://www.cru.uea.ac.uk/). Differences in DBH and H were found among sites. In particular, the highest values for DBH and H were found in Montes and Uatzo, respectively. Instead, Montarbu showed the lowest mean values for both parameters. Differences among provenances were also observed. Specifically, in Montarbu, the greatest H were found for Tuscany and the lowest for Corsica (p&amp;lt;0.0001). The same pattern was also found for DBH, but without statistical significance (p&amp;gt;0.5). In Uatzo, Corsica showed the highest mean values for both H and DBH, while the lowest DBH was observed for Tuscany (p=0.0008), and the lowest H was found for Limbara (p&amp;lt;0.0001) provenance, respectively. No significant differences were found for both H and DBH in Montes. Finally, in Usinav&amp;amp;#224;, Limbara showed significant higher values, for both H and DBH, compared to the other provenances (p&amp;lt;0.001). Temperature had a greater influence on growth traits in Montarbu, especially for spring-summer period, with Telti and Tuscany having the most significant correlation. Precipitation, instead, mostly affected Usinav&amp;amp;#224;. On the other hand, in Montes and Uatzo, no significant correlations between climate and growth were observed. However, different climate-growth relationships were observed among provenances. In conclusion, our results suggest that, after 40 years of growth, greater H and DBH were found in the sites with lower temperature and higher precipitation (Uatzo and Montes). Interestingly, in Uatzo, Corsica showed the highest values of both DBH and H, while Limbara presented the lowest growth. Noteworthy, Limbara showed greater H in Usinav&amp;amp;#224; (the warmest and driest site), whereas in the previous survey, Limbara had the lowest H. These results represent a further step in identifying potential genetic variation in tree growth and drought tolerance of maritime pine in Mediterranean conditions. Data collected in long-term experimental plots and repeated measurements are confirmed of fundamental importance to estimate the resilience of forest species to climatic changes.&amp;lt;/p&amp;gt;

Diameter at Breast Height (DBH) plays an important role in forest management. New technologies such as airborne and terrestrial Light Detection and Ranging (LiDAR) are expensive and time-consuming. We explored the iPad LiDAR sensor to measure DBH considering affordability and availability. The study was carried out at a research forest plantation near Thunder Bay, Ontario, Canada. Five plots were selected for differences in stem spacing, average DBH and species, and made 360° scans separately. A manual circle fitting method was used to produce circular features for the estimation of DBH. The coefficient of determination (R 2) between estimated DBH and field measurements was 0.52, indicating a moderate accuracy. The Root Mean Square Error (RMSE) of DBH estimation ranged from 2.82 cm to 8.24 cm. The accuracy of smaller red pine trees was found to be relatively higher than that of larger red pine and white spruce trees. These accuracies did not change with the distance from the scanning location to trees. Therefore, results showed the potential use of an iPad LiDAR Scanner for DBH estimation. We recommend developing an application combining the iPad’s location and LiDAR sensors and incorporating a precise positioning method for fieldwork, and further broad field testing.

PDF HTML阅读 XML下载 导出引用 引用提醒 天然次生林蒙古栎种群空间格局 DOI: 10.5846/stxb201707101243 作者: 作者单位: 中国林业科学研究院资源信息研究所,中国林业科学研究院资源信息研究所,中国林业科学研究院资源信息研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家"十三五"重点研发计划项目（2017YFC0504101） Spatial pattern of Quercus mongolica in natural secondary forest Author: Affiliation: Research Institute of Forest Resources Information Techniques,Chinese Academy of Forestry,Research Institute of Forest Resources Information Techniques,Chinese Academy of Forestry,Research Institute of Forest Resources Information Techniques,Chinese Academy of Forestry Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:以蒙古栎天然次生林中的蒙古栎（Quercus mongolica）种群为研究对象，在吉林省汪清林业局塔子沟林场设置了2块1hm2群落组成和结构存在差异的样地（样地A、样地B）。采用相邻网格调查法将每块样地划分为100个10m×10m的调查单元，精确定位单元内每株林木的空间坐标（X，Y），调查所有胸径（DBH）≥1cm的林木基本信息。采用径阶替代年龄的方法，将蒙古栎划分为4个不同的生长阶段：Ⅰ龄级（1cm≤DBH < 10cm），Ⅱ龄级（10cm≤DBH < 20cm），Ⅲ龄级（20cm≤DBH < 30cm），Ⅳ龄级（DBH≥30cm）。运用单变量、双变量成对相关函数、标记相关函数、标记变异函数，分析了不同群落中蒙古栎种群在不同空间尺度上的分布格局。结果显示：（1）两块样地中蒙古栎在大尺度上均呈随机分布，聚集分布主要集中在中小尺度，这主要是由Ⅰ、Ⅱ龄级的蒙古栎在中小尺度上的强度聚集所致，两块样地均未出现均匀分布的格局；样地A中蒙古栎聚集的尺度和强度均明显大于样地B中蒙古栎的聚集；（2）样地A中Ⅰ、Ⅱ、Ⅲ龄级的蒙古栎之间在中小尺度上呈正关联，高龄级与低龄级之间则呈负关联，尤其是Ⅳ龄级与Ⅰ、Ⅱ龄级之间；而样地B中几乎未出现负关联的格局，各龄级之间以无关联为主，伴随以小尺度和低强度的正关联；（3）样地A中蒙古栎的空间自相关性较强，尤其体现在林木胸径方面；相比之下，样地B中胸径和树高的空间自相关得以减弱。上述结果表明，研究的空间尺度大小、物种的生长阶段、群落的发育程度均会给蒙古栎种群的空间分布格局造成影响。该研究有助于深入了解蒙古栎及蒙古栎次生林的现状、生长特性和发展趋势，可为东北林区大面积的蒙古栎天然次生林的可持续经营提供参考。 Abstract:Spatial signatures often reveal ecological processes, and spatial pattern analysis is an important method for studying population characteristics, interspecies relationships, and the relationships between population and the environment. Due to the distinctive characteristics and the current situation of Quercus mongolica in China, it is necessary to investigate the ecological processes in Q. mongolica stands. To realize the status and development tendency of the Q. mongolica population in the Q. mongolica natural secondary forest, its spatial patterns were studied based on two permanent sample plots(plot A and plot B) with different compositions and structures(i.e.,different coenotypes) over an area of 1hm2(100m×100m) in Tazigou Forest Farm of Wangqing Forestry Bureau, Jilin province. The essential features, which include species identity, diameter at breast height(DBH), tree height, clear bole height, crown breadth, and coordinate information of each tree(DBH ≥ 1), of the two plots were surveyed by the adjacent grid method(10m×10m). Using the method of diameter class(instead of the age), Q. mongolica was divided into four different growth stages:stage I(1cm ≤ DBH < 10cm), stage Ⅱ(10cm ≤ DBH < 20cm), stage Ⅲ(20cm ≤ DBH < 30cm), stage IV(DBH ≥ 30cm). In accordance with the spatial point pattern theory, univariate pair-correlation function g1(r), bivariate pair-correlation function g12(r), mark correlation function kmm(r), and mark variogram function γ(r) were adopted to evaluate the spatial patterns of Q. mongolica in different communities at various scales. The results showed that(1) All Q. mongolica populations in the two plots showed random spatial distribution at large scales, and the aggregated distribution was mainly concentrated at medium and small scales. The aggregated distribution at stages I and Ⅱ at medium and small scales was the primary cause for the above phenomenon. Uniform distribution was not observed in the two plots at all scales. The aggregated distribution of Q. mongolica in plot A is stronger than that in plot B.(2) The spatial associations of Q. mongolica among the stages I, Ⅱ, and Ⅲ in plot A are positive at medium and small scales. The spatial associations between older age and younger age classes were negative, especially between the stages IV, I, and Ⅱ. In plot B, however, negative spatial association was scarcely observed at all scales. The relationships between different stages were mostly uncorrelated with positive spatial association at small scale and low intensity.(3) The spatial autocorrelation of plot A is stronger than that of plot B, and the conspicuous expression mainly appears at DBH. The spatial autocorrelation of plot B for DBH and height is weakened to some extent compared to that of plot A. These results demonstrated that the spatial patterns of Q. mongolica are affected by the space scale of the sample plot, the growth stage of species, and the developmental stage of the community. This helps us to understand the current situation, growth characteristics, and developmental tendency of Q. mongolica population and its natural secondary forest, and can provide an important reference for sustainable forest management and ecosystem conservation in the Q. mongolica natural secondary forest area of northeast China. 参考文献 相似文献 引证文献

Evaluation of different classification methods for the diagnosis of schizophrenia based on functional near-infrared spectroscopy.

Background and Objectives: Teak (Tectona grandis) is one of Thailand's most valuable economic tree species, prized for its exceptional qualities such as durability, aesthetic appeal, and resistance to pests and diseases. Its popularity spans both domestic and international markets, making accurate estimation of merchantable teak volume essential for forest management and the timber industry. Precise volume estimation facilitates accurate economic valuation and minimizes resource losses associated with traditional destructive tree-felling methods. Recent advancements in technology, light detection and ranging or LiDAR, have provided opportunities to enhance the accuracy and efficiency of forest resource data collection. Among these tools, the Terrestrial Laser Scanner (TLS) stands out for its ability to generate high-resolution three-dimensional (3D) models of individual trees and forest stands in a non-destructive method. Unlike traditional methods, TLS enables data collection without damaging trees, offering a sustainable and efficient alternative for forestry applications. This study aimed to develop a methodology for utilizing TLS to construct 3D models of teak trees to estimate merchantable wood volume. The results were compared with the traditional felling method, which is considered the most accurate but destructive. In addition, the study attempts to establish predictive equations and volume tables for estimating merchantable teak volume. These outputs aimed to support sustainable forest management practices, reduce resource wastes, and facilitate the economic valuations of teak for the timber industry. Methods: The study was conducted in the Mae Chang forest plantation, located in Mae Mo district, Lampang province, Thailand. The stratified random sampling was employed to establish 12 sample plots, each sizing 40 m × 40 m. The plots were divided into two age groups based on plantation year: 18 years old (planted in 2006) and 39 years old (planted in 1985), with six plots in each group. Data collection involved using a Faro Focus S150 TLS to scan individual trees in each plot. The TLS data were processed to create detailed 3D tree models for further analysis. Merchantable timber volume was calculated using two methods: the stem curve method and quantitative structure models (QSM), both implemented through the 3D forest software. The TLS-derived volume estimates were validated by felling selected sample trees. The diameters and merchantable lengths of these felled trees were measured, and their volumes were calculated using Huber’s formula. Statistical analysis was performed to compare TLS-derived volumes with traditional felling-based volumes using paired t-tests. The accuracy of TLS-derived estimates was evaluated using root mean square error (RMSE). Additionally, predictive equations for merchantable volume estimation were developed using regression analysis. Two types of equations were created: one using diameter at breast height (DBH) as the sole variable and the other incorporating both DBH and merchantable height (HM). A volume table was constructed based on these equations, enabling non-destructive volume estimation for teak trees of varying DBH and HM ranges. Results: The 3D models generated using TLS provided merchantable volume estimates that closely aligned with volumes derived from felled trees. Statistical analysis revealed no significant differences between TLS-derived volumes and traditional felling-based volumes (p &gt; 0.05). The RMSE values for TLS-derived estimates were 0.02 and 0.03 cubic meters when using the stem curve and QSM methods, respectively, indicating high accuracy. Two predictive equations for merchantable volume estimation were developed. The first equation utilized DBH as a single predictor variable and demonstrated a coefficient of determination (R²) of 0.966. The second equation incorporated both DBH and HM, achieving a higher R² value of 0.988. Validation of these equations using data from felled trees showed mean absolute percentage errors (MAPE) of 17.81% and 11.01%, respectively, confirming their suitability for practical applications. The most accurate equation was subsequently used to construct a merchantable volume table, which estimating based on DBH and HM ranges without requiring tree felling. This table provides a practical tool for forest managers and timber industry stakeholders to assess teak volume efficiently and sustainably. Conclusion: The results of this study demonstrate that TLS is a reliable and effective tool for non-destructive estimation of merchantable teak volume. The 3D models generated using TLS data enable precise volume estimation through methods such as stem curve and QSM, producing results that are statistically comparable to traditional destructive felling methods. The acceptable error margins and high accuracy of TLS-derived estimates highlight its potential as a sustainable alternative to conventional volume estimation techniques. By minimizing the need for destructive sampling, TLS-based methods contribute to sustainable forest management practices, preserving valuable tree resources while maintaining economic productivity. Furthermore, the development of predictive equations and merchantable volume tables offers practical solutions for forest managers and timber industry professionals, streamlining the assessment of merchantable volume and economic valuation. Overall, this study underscores the importance of integrating advanced technologies such as TLS into forestry practices. By improving the accuracy and sustainability of resource assessments, these innovations can support long-term forest conservation and enhance the economic potential of Thailand's teak plantations.