Tree crown detection and delineation in satellite images using probabilistic voting

Crop yield forecasting in a region has become an important research area due to global warming and related climate changes. Although this can be performed by available statistical information, obtaining recent and up to date data to extract reliable statistical information is not easy. Very high resolution satellite images can be used for this purpose. However, manually processing these images acquired from large regions is neither feasible nor reliable. Therefore, automated methods are needed for this purpose. In this study, we propose a novel method to help forecasting the crop yield in an orchard. The number of trees in an orchard with the size and type of each tree crown gives an approximate crop that can be harvested. Therefore, we focus on both tree crown detection and delineation for this purpose. The proposed method for tree crown detection is based on probabilistic voting. For tree crown delineation, we propose a watershed segmentation based ellipse fitting method. We tested the proposed method on 17 satellite images containing 13,476 trees. We compared the method with the classical local maxima/minima filtering and a recent method in literature using three more test images. These tests indicate the strengths and weaknesses of the proposed method.     

Automated tree crown detection and size estimation using multi-scale analysis of high-resolution satellite imagery

We tested an automated multi-scale approach for detecting individual trees and estimating tree crown geometry using high spatial resolution satellite imagery. Individual tree crowns are identified as local extrema points in the Laplacian-of-Gaussian scale-space pyramid that is constructed based on linear scale-space theory. The approach simultaneously detects tree crown centres and estimates tree crown sizes (radiuses). We evaluated our method using two 0.6-m resolution QuickBird images of a forest site that underwent a large shift in tree density between image captures due to drought-associated mortality. The automated multi-scale approach produced tree count estimates with an accuracy of 54% and 73% corresponding to the dense and sparse forests, respectively. Estimated crown diameters were linearly correlated with field-measured crown diameters (r = 0.73–0.86). Tree count accuracies and size estimates were comparable with alternative methods. Future use of the presented approach is merited based on the results of our study, but requires further investigation in a broader range of forest types.     

A refined method for estimating capsule crops in individual jarrah (Eucalyptus marginata) crowns

An accurate measure of the number of capsules in the crowns of jarrah (Eucalyptus marginata) trees is needed to assess the potential for seedling regeneration prior to silvicultural treatment in jarrah forests. The current method of estimating capsule crops on jarrah trees uses stem diameter and estimates of capsule density in the crown, but has not been fully validated. In this study, we sought to develop an accurate and practical method of assessing capsule crops in the crowns of individual jarrah trees. We did this by measuring a number of tree characteristics prior to felling them. A total of 24 trees were selected, spanning a range of sizes and crown conditions, and the total number of capsules was counted for each tree. Multiple linear regression was used to model capsule number against various combinations of eight different tree/crown variables, with model fit compared using an adjusted coefficient of determination (adjR²). The final model recommended for field use included three easily measured variables (stem diameter, subjective assessment of capsule density, and subjective assessment of capsule clump distribution in the crown) and had a high degree of predictability (adjR² = 0.83), which was the same as that of the full model. This method substantially improved estimates of crown capsule numbers compared with the method currently used (adjR² increased from 0.29 to 0.83), which tended to underestimate canopy capsule numbers.     

Identifying crown areas in an undulating area planted with eucalyptus using unmanned aerial vehicle near-infrared imagery

In this letter, we propose an identification method of tree crown areas for imagery captured by a near-infrared camera on board an unmanned aerial vehicle platform over an undulating Eucalyptus planting area in Guangdong Province, China. The method extracts crown areas by applying mathematical morphology, unsupervised segmentation based on J-value segmentation, local spatial statistics, and Iterative Self-Organizing Data Analysis Technique Algorithm. Two morphology filters and four segmentation scales were compared between densely and sparsely planted plots as well as sunlit and shaded plots. The opening operation by the window size of 9×9 pixel and segmentation by the seed area sized 65×65 pixel achieved the best performance with overall accuracy of 91%, 93%, 89% and 91% in densely sunlit, sparsely sunlit, densely shaded and sparsely shaded plots.     

Evaluating wood volume estimates derived from Quickbird imagery with GEOBIA for Pinus nigra trees in the Pentalofo forest,northern Greece

The objective of this research was to evaluate wood volume estimates of Pinus nigra trees in forest stands, which were derived utilizing Geographic Object-Based Image Analysis. Information on forest parameters such as wood volume and number of trees is useful for forest management facilitating forest sustainability. Most of the existing approaches used to estimate wood volume of forest trees require field measurements, which are laboursome. In this study, the collected field data were utilized only in order to investigate the results. Wood volume was estimated based on an individual tree crown approach and using monoscopic satellite images in combination with allometric data. The study area is the Pentalofo forest, which is located in Kozani prefecture in western Macedonia, Northern Greece. About 1 plot surface of 0.1143 ha was utilized. During the preprocessing, a pansharpened image was produced from two Quickbird satellite images (one multispectral image of 2.4 m spatial resolution and one panchromatic image of 0.6 m spatial resolution). Bands of this image were utilized single or in combination in order to delineate the tree crowns individually. The allometric equation served in order to calculate the tree Diameter at Breast Height (DBH) utilizing the detected tree crowns. The evaluation was conducted on three levels: (i) number of trees, (ii) DBH class distribution and (iii) wood volume. On the third level, the evaluation procedure was conducted twice; once using field height and once without. The difference between the results and the field data for the wood volume reached a maximum of approximately 30%. The total number of trees was exactly the same as counted in the field and the DBH distribution showed a tendency for the trees to move to a higher DBH class, resulting in an overestimation of the wood volume.     

Estimation of forest stand diameter class using airborne lidar and field data

In this study, a method for estimating the stand diameter at breast height (DBH) classes in a South Korea forest using airborne lidar and field data was proposed. First, a digital surface model (DSM) and digital terrain model (DTM) were generated from the lidar data that have a point density of 4.3 points/m², then a tree canopy model (TCM) was created by subtracting the DTM from the DSM. The tree height and crown diameter were estimated from the rasterized TCM using local maximum points, minimum points and a circle fitting algorithm. Individual tree heights and crown diameters were converted into DBH using the allometric equations obtained from the field survey data. We calculated the proportion of the total number of individual trees belonging to each DBH class in each stand to determine the stand DBH class according to the standard guidelines. More than 60% of the stand DBH classes were correctly estimated by the proposed method, and their area occupied over 80% of the total forest area. The proposed method generated more accurate results compared to the digital forest type map provided by the government.     

Development of an automated individual tree detection model using point cloud LiDAR data for accurate tree counts in a Pinus radiata plantation

We develop and evaluate a new individual tree detection (ITD) algorithm to automatically locate and estimate the number of individual trees within a Pinus radiata plantation from relatively sparse airborne LiDAR point cloud data. The area of interest comprised stands covering a range of age classes and stocking levels. Our approach is based on local maxima (LM) filtering that tackles the issue of selecting the optimal search radius from the LiDAR point cloud for every potential LM using metrics derived from local neighbourhood data points; thus, it adapts to the local conditions, irrespective of canopy variability. This was achieved through two steps: (i) logistic regression model development using simulated stands composed of individual trees derived from real LiDAR point cloud data and (ii) application testing of the model using real plantation LiDAR point cloud data and geolocated, tree-level reference crowns that were manually identified in the LiDAR imagery. Our ITD algorithm performed well compared with previous studies, producing RMSE of 5.7% and a bias of only ?2.4%. Finally, we suggest that the ITD algorithm can be used for accurately estimating stocking and tree mapping, which in turn could be used to derive the plot-level metrics for an area-based approach for enhancing estimates of stand-level inventory attributes based on plot imputation.     

Deep learning-based tree classification using mobile LiDAR data

Our work addresses the problem of extracting and classifying tree species from mobile LiDAR data. The work includes tree preprocessing and tree classification. In tree preprocessing, voxel-based upward-growing filtering is proposed to remove ground points from the mobile LiDAR data, followed by a tree segmentation that extracts individual trees via Euclidean distance clustering and voxel-based normalized cut segmentation. In tree classification, first, a waveform representation is developed to model geometric structures of trees. Then, deep learning techniques are used to generate high-level feature abstractions of the trees’ waveform representations. Quantitative analysis shows that our algorithm achieves an overall accuracy of 86.1% and a kappa coefficient of 0.8 in classifying urban tree species using mobile LiDAR data. Comparative experiments demonstrate that the uses of waveform representation and deep Boltzmann machines contribute to the improvement of classification accuracies of tree species.     

Canopy surface reconstruction from a LiDAR point cloud using Hough transform

Tree height and canopy volume are critical forestry parameters that are used to derive estimates of growth, carbon sequestration, standing timber volume, and biomass. Through the use of light detection and ranging, these attributes can be estimated rapidly over large areas. At the stand level, estimates of these attributes have been derived successfully from canopy height models. However, a number of challenges identified in using canopy height models remain, such as correcting for height underestimation and canopy surface irregularities, such as data pits and holes that may result from acquisition and/or post-processing, and consistent delineation of tree crowns – all of which can limit the accurate retrieval of individual tree and crown attributes. In this letter, a novel canopy model is proposed in which individual tree crowns are represented as objects for which delineations can be derived through geometric operations. The technique is based on fitting simple geometric shapes to the raw light detection and ranging point cloud and thereby compensates for this underestimation, reduces data size, and allows effective and efficient modelling at the individual tree level.     

Effects of wind sway on stem form and crown development of Scots pine (Pinus sylvestris L.)

The distribution of growth was examined in 45-year-old Scots pine (Pinus sylvestris L.) trees stayed with wires attached at 30% and at 30% and 60% of the tree height, and in trees with a metal sail attached in the top of the crown. The treatments commenced in 1983 and 16 trees per treatment, including the control, were sampled in 1988. Increments of stem diameter, height, and volume in treated and control trees were estimated from stem and branch mensuration. Staying the trees promoted radial growth by about 10% above the point of attachment. Increasing the wind sway with sails promoted radial growth below the crown. Changes in the distribution of growth within the crown were not as obvious as the changes along the stem below the crown.     

Bell miner associated dieback: nutrient cycling and herbivore crown damage in Eucalyptus propinqua

Various interactions appear to be involved in bell miner associated dieback (BMAD) of eucalypts. Native bell miner birds (Manorina melanophrys) defend the psyllids from predation by other birds while a dense understorey (notably Lantana camara) appears to favour the bell miners. The understory is likely to influence soil nutrient availability to the trees by changing the carbon to nitrogen ratio. We investigated the link between soil and leaf nutrient status and crown health as measured by crown index (CI) for Eucalyptus propinqua. Study sites included Bald Knob State Forest (SF), Donaldson SF, Mt Lindesay SF around Woodenbong and two locations on a Toonumbar private property in north-eastern New South Wales. Comparison of E. propinqua leaf macro- and micronutrient status using the ‘paired data sign test’ detected leaf nutrient differences between lightly and severely BMAD-affected tree crowns. Single and multivariate analysis investigated nutrient association with CI. Sign test results across all sites indicated that leaf iron content in trees with low CI (less healthy trees) was significantly higher (P = 0.01) than in healthy trees. In the three SFs the affected crowns also had significantly higher nitrogen to potassium ratios (P = 0.02). Other elements correlated with low CI were low boron (P = 0.06) across all sites and high zinc (P = 0.09) and low sodium values (P = 0.09) for Toonumbar sites. The correlation between soil and leaf nutrients was not significant and we found no significant correlations between soil nutrients and CIs. We did not detect any clear association between understorey (L. camara) and soil nutrients. During leaf sampling we found few psyllids but many concealer moth caterpillars. The scarcity of psyllids during sampling, inconclusive nutrient cycling results and nutrient correlations with CI were likely to be due to drought. Resampling under normal weather conditions with more typical organic matter decomposition rates is recommended.     

冠修复与直接生物材料填充牙齿生存率的Meta分析

背景:临床研究表明,根管治疗后冠修复能有效防止牙齿折裂。目的:应用系统评价的方法分析冠修复是否提高不同材料填充根管治疗后牙齿的长期成功率。方法:通过Medline数据库和手动检索1980/2010有关根管治疗后冠修复的相关文献,根据纳入和排除标准,分两步选择适用文献12篇进行质量评估。对"失败"作了统一的定义,作为统一的研究结果。对文献进行均衡性检测,将各文献数据合并计算出根管治疗后冠修复的总体平均成功率。结果与结论:所有文献按0～1标准的平均加权质量分为0.607,将各篇文献的质量分分别作为其合并样本量的权数。根管治疗后冠修复的5篇文献10年生存率为80%。根管治疗后生物材料直接充填的文献8篇,10年生存率仅为65%(直接充填的材料包括复合树脂和银汞合金)。说明根管治疗后冠修复的牙齿10年成功率较高,而直接生物材料填充仅能在短期内获得较满意的效果。     

Forest stand delineation from lidar point-clouds using local maxima of the crown height model and region merging of the corresponding Voronoi cells

In this study, a novel automatic forest stand segmentation method based on Voronoi cells and Airborne Laser Scanner data was developed and validated using a systematic grid of field plots. The automatic method produce results comparable to manual stand delineation.     

冠修复与直接生物材料填充牙齿生存率的Meta分析

背景：临床研究表明,根管治疗后冠修复能有效防止牙齿折裂。目的：应用系统评价的方法分析冠修复是否提高不同材料填充根管治疗后牙齿的长期成功率。方法：通过Medline数据库和手动检索1980/2010有关根管治疗后冠修复的相关文献,根据纳入和排除标准,分两步选择适用文献12篇进行质量评估。对＂失败＂作了统一的定义,作为统一的研究结果。对文献进行均衡性检测,将各文献数据合并计算出根管治疗后冠修复的总体平均成功率。结果与结论：所有文献按0～1标准的平均加权质量分为0.607,将各篇文献的质量分分别作为其合并样本量的权数。根管治疗后冠修复的5篇文献10年生存率为80%。根管治疗后生物材料直接充填的文献8篇,10年生存率仅为65%（直接充填的材料包括复合树脂和银汞合金）。说明根管治疗后冠修复的牙齿10年成功率较高,而直接生物材料填充仅能在短期内获得较满意的效果。     

釉锆全瓷冠在低（牙合）龈距离Bicon种植修复中的应用探讨

目的 探讨数字化釉锆全瓷冠在低（牙合）龈距离Bicon种植修复中的应用。 方法 42例（牙合）龈距离不足的牙列缺损的患者植入55颗Bicon种植体,根据所选基台进行数字化设计制作釉锆全瓷冠,体外进行基台牙冠的粘接,形成一体化基台冠,完成固定式种植义齿修复,并对修复体进行1~30月的回访。 结果 55颗种植体存活率为98.18%,修复体返工率为0%,最长达30个月的观察期内未发生崩瓷及冠脱落。 结论 数字化釉锆冠能够解决咬合空间不足所引起的Bicon种植后期修复问题,同时其强度大,密合度高,具有较好的功能及美学效果,为（牙合）龈距离不足的种植患者提供新的修复方法。       

二氧化锆全瓷冠与金合金烤瓷冠在上前牙牙体缺损修复中的效果及安全性比较

目的 研究分析二氧化锆全瓷冠与金合金烤瓷冠在上前牙牙体缺损修复中的效果及安全性.方法 前瞻性选取2017年7月至2019年5月在聊城市人民医院口腔科进行牙体缺损修复患者124例作为研究对象,按随机数字表法分为对照组和观察组,各62例.对照组给予金合金烤瓷冠进行修复,观察组则给予二氧化锆全瓷冠修复.采用改良美国公共卫生署...     

Sensitivity of LiDAR-derived three-dimensional shape signatures for individual tree crowns: a simulation study

Following previous research that demonstrates the effectiveness of three-dimensional (3D) shape signatures for characterizing individual tree crowns derived from Light Detection and Ranging (LiDAR) data, this letter presents a simulation study on the sensitivity of 3D shape signatures. Based on the observation of LiDAR point clouds for tree crowns, a mathematical model is designed to generate simulation data. Factors affecting LiDAR-derived 3D shape signatures of individual tree crowns are then tested, including the number of points, the size and location of sampling circles and the influence of neighbouring crowns. The results suggest that it is possible to obtain 3D shape signatures of individual crowns based on automated treetop detection, and that a combination of multiple sample circles may provide more reliable results than single sample circles in characterizing 3D crown shapes.     

Change detection of mountain birch using multi-temporal ALS point clouds

The use of multi-temporal laser scanner data is potentially an efficient method for monitoring of vegetation changes, for example, at the alpine treeline. Methods for relative calibration of multi-temporal airborne laser scanning (ALS) data sets and detection of experimental changes of tree cover in the forest–tundra ecotone was tested in northern Sweden (68° 20′ N, 19° 01′ E). Trees were either partly or totally removed on 6 m radius sample plots to simulate two classes of biomass change. Histogram matching was successfully used to calibrate the laser metrics from the two data sets and sample plots were then classified into three change classes. The proportion of vegetation returns from the canopy was the most important explanatory variable, which provided an overall accuracy of 88%. The classification accuracy was clearly dependent on the density of the forest.     

Segmentation of elastographic images using a coarse-to-fine active contour model

Delineation of radiofrequency-ablation-induced coagulation (thermal lesion) boundaries is an important clinical problem that is not well addressed by conventional imaging modalities. Elastography, which produces images of the local strain after small, externally applied compressions, can be used for visualization of thermal coagulations. This paper presents an automated segmentation approach for thermal coagulations on 3-D elastographic data to obtain both area and volume information rapidly. The approach consists of a coarse-to-fine method for active contour initialization and a gradient vector flow, active contour model for deformable contour optimization with the help of prior knowledge of the geometry of general thermal coagulations. The performance of the algorithm has been shown to be comparable to manual delineation of coagulations on elastograms by medical physicists (r = 0.99 for volumes of 36 radiofrequency-induced coagulations). Furthermore, the automatic algorithm applied to elastograms yielded results that agreed with manual delineation of coagulations on pathology images (r = 0.96 for the same 36 lesions). This algorithm has also been successfully applied on in vivo elastograms.     

基于卷积神经网络检测肺结节

Objective Major challenges in the current automatic detection of lung nodules from chest CT images are to improve the sensitivity and to reduce the false positive rate. A new scheme based on convolutional neural network was proposed in this study. Methods The method applied an automatic anatomy recognition (AAR) methodology based on fuzzy modeling ideas and an iterative relative fuzzy connectedness (IRFC) delineation algorithm for the segmentation of lung parenchyma in CT images. The segmented lung image was inputted into the conventional neural networks for feature extraction of pulmonary nodules. The network adopted position-sensitive score maps to express the location information of lung nodules. Results This method could obtain accurate segmentation of the lung parenchyma in the data set of Tianchi Medical AI Contest, and the accuracy, sensitivity, specificity and false-positive rate of lung nodules detected was 95.60%, 95.24%, 95.97% and 4.03%, respectively. Conclusion Detection of pulmonary nodules based on convolutional neural networks has high accuracy and efficiency, and good robustness.