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.     

Using tree hollow data to define large tree size for use in habitat assessment

Habitat assessments often require observers to estimate tree hollows in situ, which can be costly, destructive and prone to bias. An alternative is to count the number of trees above a specific size. The size at which a tree develops hollows differs substantially among tree species. To assist with setting standards for habitat assessment we defined a large tree as the size at which a species has a 50% probability of supporting a 2-cm diameter hollow. We estimated this size for 68 species using a meta-analysis based on 18 data sources. We found that large tree size ranged from 21 to 106 cm diameter at breast height (DBH). Each species was attributed to vegetation types (formations and classes) to explore variation in large tree sizes. Despite considerable variation within vegetation classes and formations, our results suggest that a large tree size of approximately 50 cm DBH may be appropriate for most vegetation types, with lower estimates in semi-arid vegetation (~30 cm) and higher estimates in wet sclerophyll forests (~80 cm). Our estimates provide empirical support for defining large trees at species vegetation class and formation levels within New South Wales, and highlights the need for more empirical data.     

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.     

Lightning injuries: prevention and on-site treatment in mountains and remote areas. Official guidelines of the International Commission for Mountain Emergency Medicine and the Medical Commission of the International Mountaineering and Climbing Federation (ICAR and UIAA MEDCOM)

Lightning is a hazard during outdoor activities, especially for hikers and mountaineers. Specific preventive measures include staying off ridges and summits, and away from single trees. If possible, stay close to a wall but keeping a distance of at least 1m away from the wall. All metal objects (carabiners, crampons, ice-axe, ski poles, etc.) should be removed and stored away safely. Lightning currents can follow wet ropes. To prevent blunt trauma the helmet should not be removed. Move as quickly as possible away from wire ropes and iron ladders. The crouch position should be adopted immediately if there is a sensation of hair "standing on end". Crackling noises or a visible glow indicate an imminent lightning strike. Rescue of lightning victims may be hazardous. Airborne helicopters can be struck by lightning with disastrous effects. It is prudent to wait until the danger of further strikes has passed. Treatment of lightning victims is based upon the ABCs - (Assessment) airway, breathing and circulation. Victims who are not breathing can often be resuscitated and should be helped first. Respiratory arrest may be prolonged, but the prognosis can be excellent if breathing is supported. Standard Advanced Life Support (ALS), if necessary, should be given at the scene.     

Stem biomass estimation based on stem reconstruction from terrestrial laser scanning point clouds

Forest biomass is often difficult to quantify because field measurements are time consuming and require destructive sampling. This study explores the retrieval of stem biomass of individual trees by terrestrial laser scanning (TLS). Destructive sampling was done to collect biomass data from sample trees and used as a dependent variable in a regression analysis. Two biomass estimation models were investigated: one based on diameter at breast height (DBH) and another based on the sum of the stem section volume. Both the DBH and the stem section volume were determined from automatic reconstruction of the stem curves. Two tree species (Scots pine and Norway spruce) were considered together. The quality of the performance of the models was evaluated via a leave-one-out cross-validation strategy using accurate field measurements for 30 trees. The correlation coefficient (r) and root mean square errors (RMSEs) between the predicted and measured stem biomass were used as measures of goodness of model fitting. The model with DBH as the predictor produced an r-value of 0.93 and an RMSE of 21.5%. For the model using the reconstructed stem and correspondingly derived stem volume as the predictor, an r-value of 0.98 and an RMSE of 12.5% were achieved. The results indicated that TLS measurements are capable of assessing stem biomass with high automation and accuracy by reconstructing the stem from TLS point clouds.     

Walking speed and distance in different environments of subjects in the later stage post-stroke

The purpose of this study is to assess short- and long-distance walking performance in indoor and outdoor environments of slow and fast walkers' subjects living in the community in the later stage post-stroke. Thirty-six subjects with at least 6 months post-stroke were included and divided into two groups based on their walking speed in the clinical setting. Thirty-meter walk tests (30?mWT) at self-selected and maximum speeds were assessed in three environments: (1) clinical setting; (2) basement setting; and (3) outdoor setting. Six-minute walk test (6?MWT) distance was assessed in the clinical and outdoor settings. The differences between the 30?mWT and the 6?MWT, as measured by the actual distance obtained in the 6?MWT and the predicted distance calculated for the 30?mWT, were also investigated. There was no difference in walking speed when subjects performed short-distance walking in different environments. However, a difference was found in performance of long-distance walking. Subjects who walked 0.8?m/s or faster also walked further in the outdoor setting. The findings of our study demonstrate that in those who scored below 0.8?m/s, performance of short- and long-distance walking evaluated in an indoor environment reflects the results obtained in an outdoor environment. However, for subjects post-stroke who score 0.8?m/s or faster, distance was increased in the outdoor environments during long-distance walking. Walking speed obtained over a short distance seemed to overestimate long-distance walking capacity for the slow walkers, despite the environment.     

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.     

Estimating the volume of Australian hardwoods using centroid sampling

Centroid sampling was tested on 18 sample trees of Flindersia brayleyana and 124 sample trees of five species of Eucalyptus all of which were measured in detail. Volumes of the main bole of each tree and of logs (length approx. 6 m) derived from each bole were estimated using both Huber's formula and centroid sampling. These estimates were compared with ‘true’ volume obtained by summing the volumes derived using Smalian's formula of all the measured short sections comprising the main bole and each log respectively.

The mean error of the centroid estimate of volume of the main bole was not significant for Eucalyptus sieberi and E. diversicolor and was less than that derived from Huber's formula for all species except E. regnans. When all species were combined, the centroid estimate was clearly more accurate, its mean error (-2%) being just significant (p=0.046). Centroid sampling also gave more accurate estimates of the volume of the component logs.

Results of this study and earlier work suggest that centroid sampling is applicable to tree species of both excurrent and deliquescent growth habit.     

The effect of TLS point cloud sampling on tree detection and diameter measurement accuracy

Terrestrial laser scanning (TLS) is a vital technique for collecting data with millimetre-level detail from the surrounding forest or individual trees. The downside of the technique is the vast amount of data it can collect, which requires efficient data processing methods. An increasing number of manual and automatic methods have been developed in recent years and most of them use point cloud sampling to fasten the processing. The challenge in the sampling procedures is the point density of TLS, which decreases rapidly as a function of distance from the scanner location. Therefore, traditional sampling procedures are not suitable for processing TLS point clouds. The present study focuses on evaluating two sampling procedures (presented in Puttonen et al. 2013) that aim to reduce the point density without losing the characteristics of the full point cloud. The study goal was to assess the effect of these two sampling procedures in tree detection and diameter at breast height (1.3 m, DBH) measurement accuracies in two sample cases. The results demonstrated that the point cloud sampling could be effectively used without losing accuracy in tree detection. However, for measurement of single-tree attributes, the use of full point cloud is recommended. Further evaluation of the methods is required with more diverse data set and due to the manual processing applied here. The automatic approach is mandatory if the approach is to be considered for more operational use.     

Authors and fields of research

Growth and morphological differences between 12 provenances of Grevillea robusta (from New South Wales and Queensland, Australia) grown at two locations on the tropical Atherton Tablelands of northern Australia were investigated at age 40 months. The following measurements were taken: tree survival; height; diameter at breast height (DBH); number of branches and forks per tree; branch diameter and angle; crown diameter and tree form.

Significant differences between the provenances were found in most of the characteristics measured. Only DBH showed a site x provenance interaction. Two provenances (Duck Creek and Tyalgum) performed best. Provenance similarities in their morphological responses were examined using hierarchical cluster analysis.     

Thinning Pinus radiata plantation by operator selection: 1. Effects on the residual forest

Field trials were undertaken in Pinus radiata plantation, in order to compare thinning where the harvesting machine operator selects the trees to be thinned without prior tree marking, with conventional tree-marked thinning, in terms of stand and tree parameters such as residual stocking and spacing, basal area, mean tree diameter, and frequency of damaged trees. These trials included first and second thinning on high and low site quality and two trials at operational scale. On the whole, operators thinned to the same stand density and tree diameter distribution as tree-marked thinning in comparable stands. The removal of undesirable trees was generally as good as in tree-marked thinning. In two trials operator selected thinning resulted in a better quality residual stand than tree-marked thinning. Trials at the operational level also indicate practicalities of two types of guidelines for operator selection thinning: a diameter limit; and spacing of residual trees.     

Accuracy of Kinect’s skeleton tracking for upper body rehabilitation applications

Abstract

Games and their use in rehabilitation have formed a new and rapidly growing area of research. A critical hardware component of rehabilitation programs is the input device that measures the patients’ movements. After Microsoft released Kinect, extensive research has been initiated on its applications as an input device for rehabilitation. However, since most of the works in this area rely on a qualitative determination of the joints’ movements rather than an accurate quantitative one, detailed analysis of patients’ movements is hindered. The aim of this article is to determine the accuracy of the Kinect’s joint tracking. To fulfill this task, a model of upper body was fabricated. The displacements of the joint centers were estimated by Kinect at different positions and were then compared with the actual ones from measurement. Moreover, the dependency of Kinect’s error on distance and joint type was measured and analyzed.

• Implications for Rehabilitation

• It measures and reports the accuracy of a sensor that can be directly used for monitoring physical therapy exercises.

• Using this sensor facilitates remote rehabilitation.

     

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.     

Polynomial taper model for Queensland plantation hoop pine

A stem taper model, involving a high order polynomial developed using plantation hoop pine (Araucaria cunninghamii Ait. ex D. Don) trees grown in Queensland, is presented. The stem profile for each tree was initially modelled using functions of diameter under bark and height, as the dependent and independent variables respectively. Parameter estimates from these individual tree regressions were subsequently modelled, using total height and diameter at breast height under bark (DBHub) as the independent variables. Finally, total height and DBHub were modelled in terms of predominant height' and diameter at breast height over bark (DBHob).

The largest mean error in the estimates of stem diameter predicted using the final model, with predominant height and DBHob as the independent variables, was 0.12 cm. The overall mean relative error in the estimate of volume, based on coefficients modelled using predominant height and DBHob, was 0.20 per cent. Apart from its accuracy, the main advantages of the model include its simplicity and its ability to model the whole tree using a single function.     

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.     

Mapping tree stress associated with urban pollution using the WorldView-2 Red Edge band

A small urban park in Yerevan, Armenia, was studied using biogechemical analysis of the tree canopy, field spectral reflectance measurements of tree leaves, simulated WorldView-2 multispectral data generated from the leaf spectra, and two summer images of real WorldView-2 data. The tree canopy of the park is dominated by two trees, Robinia pseudoacacia L. (locust) and Fraxinus excelsior L. (ash). The Highest values of lead, nickel, molybdenum, copper and zinc were found in leaves harvested from trees adjacent to the streets, whereas most of the lowest values for those metals were found in the interior of the park. A t-test of the field spectral measurements indicated that the green and red edge spectral reflectance of leaves from trees near the streets was significantly higher than that of leaves of trees in the interior (p < 0.05). However, in simulated WorldView-2 multispectral data, the street and interior leaves were only statistically separable in band 6 (Red Edge) raw data and hyperspherical direction cosine (HSDC) normalized band 6 data. HSDC-normalized band 6 digital numbers from real WorldView-2 data of 16 June and 9 August 2011 from trees adjacent to the streets were statistically higher than the interior locations for both dates. Maps of anomalously high HSDC-normalized band 6 values show a concentration on the park edges, suggesting vehicle pollution may indeed be the cause of the observed patterns.     

Continuous fields of land cover for the conterminous United States using Landsat data: first results from the Web-Enabled Landsat Data (WELD) project

Vegetation Continuous Field (VCF) layers of 30 m percent tree cover, bare ground, other vegetation and probability of water were derived for the conterminous United States (CONUS) using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data sets from the Web-Enabled Landsat Data (WELD) project. Turnkey approaches to land cover characterization were enabled due to the systematic WELD Landsat processing, including conversion of digital numbers to calibrated top of atmosphere reflectance and brightness temperature, cloud masking, reprojection into a continental map projection and temporal compositing. Annual, seasonal and monthly WELD composites for 2008 were used as spectral inputs to a bagged regression and classification tree procedure using a large training data set derived from very high spatial resolution imagery and available ancillary data. The results illustrate the ability to perform Landsat land cover characterizations at continental scales that are internally consistent while retaining local spatial and thematic detail.     

Tree crown segmentation based on a tree crown density model derived from Airborne Laser Scanning

ABSTRACT

This letter describes a new algorithm for automatic tree crown delineation based on a model of tree crown density, and its validation. The tree crown density model was first used to create a correlation surface, which was then input to a standard watershed segmentation algorithm for delineation of tree crowns. The use of a model in an early step of the algorithm neatly solves the problem of scale selection. In earlier studies, correlation surfaces have been used for tree crown segmentation, involving modelling tree crowns as solid geometric shapes. The new algorithm applies a density model of tree crowns, which improves the model’s suitability for segmentation of Airborne Laser Scanning (ALS) data because laser returns are located inside tree crowns. The algorithm was validated using data acquired for 36 circular (40 m radius) field plots in southern Sweden. The algorithm detected high proportions of field-measured trees (40–97% of live trees in the 36 field plots: 85% on average). The average proportion of detected basal area (cross-sectional area of tree stems, 1.3 m above ground) was 93% (range: 84–99%). The algorithm was used with discrete return ALS point data, but the computation principle also allows delineation of tree crowns in ALS waveform data.     

Evidence for the involvement of cerebroside sulfate in opiate receptor binding: Studies with Azure A and jimpy mutant mice.

The role of cerebroside sulfate in opiate action and binding was studied by examining the effect of decreasing the availability of the glycolipid in vivo on morphine analgetic activity and receptor affinity. Available cerebroside sites were decreased either by injecting Azure A at a dose with selective high affinity for sulfo-lipids, or by using "jimpy" mice, a genetic leukodystropic mutant mouse with a deficiency in brain sulfatides. Injections of Azure A (4.4 MG/KG) intracerebroventricularly produced a 2-fold increase in the AD50 of morphine (tail-flick test) within 2 hr. The genetic mutation also resulted in a decreased sensitivity to morphine; the morphine AD50 was 6- to 11-fold higher in jimpy mice than in their normal littermates. Azure A produced a dose-dependent inhibition of opiate binding to synaptosomal plasma membranes. There was 85% inhibition of [3H]morphine binding with 0.2 micrometer Azure A, and 40% inhibition of [3H]naloxone binding with 5 micrometer Azure A. The inhibition of morphine binding was competitive and was demonstrated to be different from that elicited by Na+. Synaptic membranes from jimpy mice exhibited a decrease in number of binding sites for morphine. Binding of Azure A to cerebroside sulfate, associated with or in the proximity of the receptor sites, was suggested by the fact that in the presence of 0.2 micrometer Azure A, no inhibition of [3H]morphine binding to synaptosomal plasma membranes of jimpy mice could be demonstrated whereas 31% inh-bition was observed in membranes from control littermates. Based on the findings, it is concluded that cerebroside sulfate strategically located may have a role in binding morphine and mediating its effects.     

Characterizing tree canopy loss using multi-source GIS data in Central Massachusetts,USA

Despite numerous ecosystem services provided by urban trees, they are continually threatened by combined natural disturbances, invasive species, development and negligent management practices. This research characterizes the amount and cause of tree loss in Worcester, Massachusetts, in the northeast United States, and neighbouring towns between 2008 and 2010 using multi-source remotely sensed imagery and historical land cover maps (1976–2009). Historical land-change analysis reveals that proportional forest cover loss in the Worcester County study area exceeds that of the state by 0.26% per year, 67% of which was driven by the expansion of low-density residential land use. Between 2008 and 2010, 2% of Worcester County’s tree canopy was lost to high- and low-density urban development (47% of the total loss), United States Department of Agriculture (USDA) tree removal for Asian longhorned beetle (ALB) eradication (25%), timber harvest (15%) and ice storm damage (6%). The use of multi-source geographic information system (GIS) data to characterize tree canopy loss makes it a flexible and replicable method to monitor urban tree health.