Mean Volume Estimation Research Articles

Comparison of stem volume estimates from terrestrial point clouds for mature Douglas-fir (Pseudotsuga menziessi (Mirb.) Franco)

As a complement to traditional estimates of stem dimensions from numerical models, terrestrial light detection and ranging (Lidar) provides direct stem diameter and volume values using cylindrical models constructed from point clouds. This study used two approaches to estimate total stem volume using Lidar and compared them with two empirical equations, one used by the Forest Inventory Analysis in the Pacific Northwest (FIA-PNW) and one based on a taper equation. We fitted point clouds of 10 Douglas-fir with three sets of cylinder models that are distinguished by their segment length (i.e. 0.5 m, 1 m, and 2 m), then developed three taper equations based on the point-cloud-based diameter estimated previously. We estimated the total stem volume of the tree with eight models: six-point cloud-based (i.e. three taper and three cylinders) and two empirical. Finally, we used simulations to extrapolate the volume estimations of various methods for different diameters at breast height (DBH) classes. We found that all the point-cloud-based taper equations were similar in their performance (R2=0.94, RMSE = 4.6 cm) and produced mean volume estimates greater than mean estimates of the existing models. The cylinder models produced 11–16% greater mean volume estimates than the FIA-PNW estimate, with the 0.5 m segment length producing the greatest values, followed by the 1 m and 2 m segment length. The simulated data suggested that the mean volume estimates of a given DBH class are different when using different computation methods. ANOVA revealed a combined effect of the computation methods and the DBH class on the mean volume estimates. We conclude that the point-cloud-based taper equations, after being symmetrically calibrated, would be consistent with the regional stem volume estimates, whereas the cylinder models would be better in estimating individual stem volume. When constructing Lidar-based cylinder models in future applications, cylinder segment length would need to be adjusted to the length and DBH of the stem, as well as to the objectives of the research.

Cascaded Regression Neural Nets for Kidney Localization and Segmentation-free Volume Estimation.

Kidney volume is an essential biomarker for a number of kidney disease diagnoses, for example, chronic kidney disease. Existing total kidney volume estimation methods often rely on an intermediate kidney segmentation step. On the other hand, automatic kidney localization in volumetric medical images is a critical step that often precedes subsequent data processing and analysis. Most current approaches perform kidney localization via an intermediate classification or regression step. This paper proposes an integrated deep learning approach for (i) kidney localization in computed tomography scans and (ii) segmentation-free renal volume estimation. Our localization method uses a selection-convolutional neural network that approximates the kidney inferior-superior span along the axial direction. Cross-sectional (2D) slices from the estimated span are subsequently used in a combined sagittal-axial Mask-RCNN that detects the organ bounding boxes on the axial and sagittal slices, the combination of which produces a final 3D organ bounding box. Furthermore, we use a fully convolutional network to estimate the kidney volume that skips the segmentation procedure. We also present a mathematical expression to approximate the 'volume error' metric from the 'Sørensen-Dice coefficient.' We accessed 100 patients' CT scans from the Vancouver General Hospital records and obtained 210 patients' CT scans from the 2019 Kidney Tumor Segmentation Challenge database to validate our method. Our method produces a kidney boundary wall localization error of ~2.4mm and a mean volume estimation error of ~5%.

MRI based volumetric measurements of vestibular schwannomas in patients with neurofibromatosis type 2: comparison of three different software tools

Neurofibromatosis type 2 is a neurogenetic disorder with an incidence of about 1:33.000. Hallmarks are bilateral benign vestibular schwannomas, which can lead to deafness or brainstem compression. Volumetric tumor measurements are essential to assess the efficacy of new therapies. We present a statistical and methodical comparison of three volumetric image analysis tools. We performed volumetric measurements on phantoms with predefined volumes (0.1 to 8.0 ml) and tumors seen on 32 head MRI scans from eight NF2 patients with BrainLab, ITK-Snap, or OsiriX. The software was compared with regard to accuracy and reproducibility of the measurements and time required for analysis. The mean volume estimated by all three software programs differed significantly from the true volume of the phantoms, but OsiriX and BrainLab gave estimates that were not significantly different from each other. For the actual tumors, the estimated volumes with all three software tools showed a low coefficient of variability, but the mean volume estimates differed among the tools. OsiriX showed the shortest analysis time. Volumetric assessment of MRI images is associated to an intrinsic risk of miscalculation. For precise volumes it is mandatory to use the same volumetric tools for all measurements.

Size-class structure of the forests of Finland during 1921\u20132013: a recovery from centuries of exploitation, guided by forest policies

Frequency distributions of tree diameters are a powerful tool for analyzing changes of tree populations in large areas. We analyzed the densities and mean volume estimates of trees in different size classes for the Finnish forests over the time-span of the National Forest Inventories (1921–2013). The results display a general increase in trees in all size classes, species group and geographical area, mainly after the 1970s. The densities of medium- and large-sized conifers showed large increases in the southern boreal subzone, spruces even more than pines. Small- to medium-sized pines have increased in the middle and northern boreal subzones. The shifts in growing stock are related to changing land use, resulting from the development of the society. The low quantities of both growing stock and large trees during the 1920s reflect a poor initial state of forests. Several land use forms of the former agriculture-based society were detrimental to forests, including slash and burn agriculture, cattle grazing and tar production. The pressure from alternative land use forms was stronger in southern Finland, where the population density (people per km2) is much higher. Between 1971 and 2013, the changes in size-class structure can be attributed mainly to intensified silviculture boosted by actions of the Finnish governments, including both legislation and financial support for management activities. Not only the development of growing stock has exceeded expectations, but the increase has also concentrated in the economically valuable trees in the large size classes.

Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic Forest.

The majority of Atlantic Forest fragments in Southern Brazil are second-growth forests dominated by fast-growing species with considerable market-value timber. Nevertheless, volume prediction models are scarce, especially to estimate tree total volume (i.e., stem plus branches). This study approached the issue through the following aims: to fit and select stem and total volume models (generic and species-specific) using data from 288 harvested trees in a management operation, and to fit generic and species-specific bark factors. The power model embedding diameter at breast height (D) and tree stem or total height (H) presented the greatest prediction strength for both stem and total tree volume. Models including only D to predict total tree volume were similar to double-entry models regarding goodness-of-fit. Therefore, they may be useful in the context of subtropical closed-canopy forests, where the difficulty and uncertainty in H measurements are not trivial. Species-specific models fitted for Miconia cinnamomifolia (DC) Naudin. and Hyeronima alchorneoides Allemão surpassed generic models only for the former species. Nevertheless, the prediction improvement should offset the eventual extra efforts implied in the collection of reliable samples of these species. Finally, bark factors stood as a satisfactory tool for inside bark mean volume estimation.

Estimation of tephra volumes from sparse and incompletely observed deposit thicknesses

We present a Bayesian statistical approach to estimate volumes for a series of eruptions from an assemblage of sparse proximal and distal tephra (volcanic ash) deposits. Most volume estimates are of widespread tephra deposits from large events using isopach maps constructed from observations at exposed locations. Instead, we incorporate raw thickness measurements, focussing on tephra thickness data from cores extracted from lake sediments and through swamp deposits. This facilitates investigation into the dispersal pattern and volume of tephra from much smaller eruption events. Given the general scarcity of data and the physical phenomena governing tephra thickness attenuation, a hybrid Bayesian-empirical tephra attenuation model is required. Point thickness observations are modeled as a function of the distance and angular direction of each location. The dispersal of tephra from larger well-estimated eruptions are used as leverage for understanding the smaller unknown events, and uncertainty in thickness measurements can be properly accounted for. The model estimates the wind and site-specific effects on the tephra deposits in addition to volumes. Our technique is exemplified on a series of tephra deposits from Mt Taranaki (New Zealand). The resulting estimates provide a comprehensive record suitable for supporting hazard models. Posterior mean volume estimates range from 0.02 to 0.26 km 3. Preliminary examination of the results suggests a size-predictable relationship.

Estimation of Bladder Volume From Afferent Neural Activity

Refractive urinary dysfunction in individuals suffering from neurogenic bladder syndrome can be treated with implanted neurostimulators that restore, to some degree, the control of the urinary bladder. A sensor capable of relaying feedback from bladder activity to the implanted neurostimulator is required to implement a closed-loop system to improve overall implant efficacy and minimize deleterious effects to neural tissue caused by continuous electrical stimulation. In this paper, we present a method that allows real-time estimation of bladder volume from the primary afferent activity of bladder mechanoreceptors. Our method was validated with data acquired from anesthetized rats in acute experiments. It was possible to qualitatively estimate three states of bladder fullness in 100% of trials when the recorded afferent activity exhibited a Spearman's correlation coefficient of 0.6 or better. Furthermore, we could quantitatively estimate bladder volume, and also its pressure, using timeframes of properly chosen duration. The mean volume estimation error was 5.8 ±3.1%. Our results also demonstrate that it is possible to quantify both phasic and tonic bladder responses during slow filling and isovolumetric measurements, respectively.

The effect of methodology on volumetric uncertainty estimation in static reservoir models

The importance of reservoir uncertainties for the prediction of petroleum recovered volumes and fluid flow performance is well known. Large uncertainties in reservoir gross volume estimation derive not only from the measurement errors, but also from the way that the static reservoir model is constructed. The aim of this article is to explore the uncertainty induced by the combination of the different steps in construction of the reservoir model. These steps can be split into three main areas: geometry, internal heterogeneities, and petrophysical properties. The first task consists of building the architecture through the delineation of units. It must deal with the sparseness of control data (layer intercepts measured at few wells, sometimes deviated) and the complexity of the stratigraphic description (several heterogeneous layers, presence of faults) and account for the seismic horizons observed. Each layer is then populated with lithofacies reflecting the geological environment. This environment is characterized by the depositional sequence as well as the number of lithotypes (facies with similar properties), their proportions, spatial trends, and arrangement (transition between facies). Interpreted seismic attributes can bring additional constraints on the lithotype proportions. Each lithotype is then assigned petrophysical properties. Finally, the hydrocarbon volumes in place are calculated above an oil-water contact. Uncertainties are evaluated within a realistic layering model, using relevant stochastic techniques (categorical and multigaussian simulations) and choosing the adequate parameters. The quantification is performed on global volumes as well as on more local criteria.The results show that the choice of modeling workflow has an impact on the hydrocarbon volumetric assessment, affecting both the estimate of mean volume and the range of uncertainty about the mean. This needs to be taken into account when undertaking reservoir volumetric assessments.

Validation of acoustic rhinometry by using the Cavalieri principle to estimate nasal cavity volume in cadavers.

The ability of acoustic rhinometry to provide valid estimates of nasal cavity volume has been tested using cadaveric heads. Values obtained for the cavities in five intact heads were compared with unbiased estimates of the volumes of the same specimens. An unbiased estimate is one which does not depart in a systematic manner from the true value. Here, unbiased estimates were made by applying the Cavalieri principle to uniform random samples of 4-7 macroscopic slices per specimen. Using acoustic rhinometry, the mean volume (left and right sides combined) amounted to 31.3 ml with a co-efficient of variation of 77%. On the same specimens, the Cavalieri estimate of mean volume (both cavities plus the adjacent nasopharynx) was 31.1 ml (66%). There was a significant and positive correlation between the two data sets (correlation co-efficient 0.97). Repeated acoustic measurements on the same cavities gave a measuring error of about 4%. We conclude that acoustic rhinometry can provide valid and efficient estimates of the volume of the nasal cavity plus nasopharynx.

Smoothed Versus Unsmoothed LiDAR in a Double-Sample Forest Inventory

Abstract Light detection and ranging (LiDAR) data at 0.5- and 1-m postings were used in a double-sample forest inventory on Louisiana State University's Lee Experimental Forest, Louisiana. Phase 2 plots were established with differential global positioning system (DGPS). Tree dbh (>4.5in.) and two sample heights were measured on every 10th plot of the Phase 1 sample. Volume was computed for natural and planted pine and mixed hardwood species. LiDAR trees were selected with focal filter procedures from smoothed and unsmoothed LiDAR canopy surfaces. Dbh-height and ground-LiDARheight models were used to predict dbh from LiDAR height and compute Phase 2 estimates of ft2 basal area and ft3 volume. Phase 1 LiDAR estimates were computed by randomly assigning heights to species classes using the probability distribution from ground plots in eachinventory strata. Phase 2 LiDAR estimates were computed by randomly assigning heights to species-product groups using a Monte Carlo simulation for each ground plot. Regression coefficients for Phase 2 estimates of ft2 and ft3 from the smoothed versus unsmoothed surfacesof high- and low-density LiDAR were computed by species group. Regression estimates for combined volume were partitioned by species-product distribution of Phase 2 volume. There was no statistical difference (α = 0.05) between smoothed versus unsmoothed for high- and low-density LiDAR on adjusted mean volume estimates (sampling errors of 9.52 versus 8.46% for high-density and 9.25 versus 7.65% for low-density LiDAR). South. J. Appl. For. 29(1):40–47.

High- Versus Low-Density LiDAR in a Double-Sample Forest Inventory

Abstract Light detection and ranging (LiDAR) data at 0.5- and 1-m postings were used in a double-sample forest inventory on Louisiana State University's Lee Experimental Forest, Louisiana. Phase 2 plots were established with DGPS. Tree dbh (>4.5 in.) and two sample heights (minimum and maximum dbh) were measured on every 10th plot of the phase 1 sample. Volume was computed for natural and planted pine and mixed hardwood species. LiDAR trees were selected with focal filter procedures and heights computed as the height difference between interpolated canopy and DEM surfaces. Dbh-height and ground-LiDAR height models were used to predict dbh from adjusted LiDAR height and compute ground and LiDAR estimates of ft2 basal area and ft3 volume. Phase 1 LiDAR estimates were computed by randomly assigning heights to species classes using the probability distribution from ground plots in each inventory strata. Phase 2 LiDAR estimates were computed by randomly assigning heights to species-product groups using a Monte Carlo simulation for each ground plot. There was no statistical difference between high-versus low-density LiDAR estimates on adjusted mean volume estimates (sampling errors of 8.16 versus 7.60% without height adjustment and 8.98 versus 8.63% with height adjustment). Low-density LiDAR surfaces without height adjustment produced the lowest sampling errors for stratified and nonstratified, double-sample volume estimates. South. J. Appl. For. 28(4):205–210.

Nonparametric estimation of stand volume using spectral and spatial features of aerial photographs and old inventory data

The forest management planning inventories for private forests in Finland are currently carried out in stand-level field inventories. To decrease the amount of fieldwork, aerial photographs and old inventory data could be utilized. The main objectives were to test the accuracy of a method based on these data sources and the effect of stand shape on the accuracy. Median pixel values, semivariances, and old inventory data were extracted for each of the 577 stands in the study. These data were applied as indicator attributes in k-nearest-neighbor estimation of stand volume. Stand-level estimates were computed as weighted means of k most similar neighbors. When all the stands were used, a root mean square error of 29.9% was obtained. Old inventory data proved to be valuable auxiliary information. It was also found that exclusion of stands with tortuous boundaries and small area decreased the error. The accuracy of mean volume estimation just met the requirements for stand-level inventory, but the method still needs further research before the final conclusion of the applicability for management planning.

An interactive image analysis system for mean particle volume estimation using stereological principles

SUMMARYA semi‐automatic image analysis system based on the stereological approach of point‐sampled intercepts for mean volume estimation is described. The method used requires no assumptions about particle geometries and is thus general in its application. To ease implementation in this application, the assumption of particle convexity is made, making the measurement of intercepts easier for the user.The system was tested with computer‐generated sample sections to determine the accuracy and precision of the estimates obtained. The results were found to be within 10% of expected values in all cases. When tested with ‘real’ samples a typical result gave a coefficient of variation of 9% from eight independent estimates of one sample.