Area-based Estimates Research Articles

Tensile behavior of polycarbonate: Key aspects for accurate constitutive modelling and simulation

Polycarbonate (PC) is a thermoplastic polymer used in many engineering applications such as safety devices and aerospace components. However, the unique behavior of PC under tensile load and its effects on the estimation of its constitutive curve are often overlooked in the literature, neglecting to consider crucial aspects of the characterization process. This work carries out a comprehensive analysis of the mechanical behavior of PC to understand the key points for accurate constitutive modeling and simulation of its static tensile performance, including its unconventional deformation mechanisms. The work starts from the accurate analysis of a representative experimental static tensile test on a rectangular section PC specimen and the evaluation of its true stress-strain curve. This analysis, carried out considering the classic length-based approach and the more accurate area-based approach, makes it possible to evaluate in detail the peculiar tensile behavior of this material. The mathematical form of the PC true stress-strain curve is then justified and the coincidence of the obtained length-based and area-based estimates of the same is demonstrated. Then, based on the in-depth understanding of the dynamics underlying the mechanical behavior of PC and making use of FEM simulations, the key points for obtaining its constitutive curve are defined. It is demonstrated that the constitutive curve is able to completely determine the behavior of PC, including its peculiar deformation mechanism. It is also highlighted which specific characteristics of the constitutive curve are critical in affecting various aspects of the material's behavior. The final constitutive curve of PC at hand is then obtained with an inverse approach, capable of accurately simulating all aspects of its tensile behavior. The validity of the proposed modelling key points is then confirmed, effectively explaining the underlying phenomena controlling the tensile behavior of PC and massively reducing uncertainty in the estimation of its constitutive curve starting from its area-based true curve.

Augmented reality for measuring diameter at breast height using the iPhone measure app: outcomes on tree- and stand-level estimates of basal area in a Carpathian mixed forest

Augmented reality for measuring diameter at breast height using the iPhone measure app: outcomes on tree- and stand-level estimates of basal area in a Carpathian mixed forest

Estimating the amount of British Columbia’s “big-treed” old growth: Navigating messy indicators

British Columbia’s (BC) diverse forest ecosystems include highly productive old growth with global importance for carbon storage and biodiversity. Current estimates of the remaining amount of “big-treed” old growth vary 10-fold, creating uncertainty that challenges provincial attempts to shift management policy toward ecological integrity. This uncertainty arises from using different remotely sensed indicators and definitions of tree size. No ideal indicator exists. We attempt to improve clarity by evaluating the reliability of candidate indicators, calibrating selected indicators to improve consistency, and generating multiple estimates of the amount of big-treed old growth using calibrated indicators. To evaluate reliability, we compared inventory estimates of tree size and site productivity with measured tree size in 1,945 ground plots. To assess the amount of big-treed old growth, we determined an equivalent “big” size threshold for each indicator and calculated the area of old growth above the size threshold. Stand volume, tree density, basal area, and diameter estimates performed poorly; we selected tree height and two measures of site productivity for further analysis. Estimated tree height best indicated the current old growth size, followed by inventory-based site index and finally ecosystem-based site index. The calibrated indicators agreed that very little remaining old growth supports large trees (1.5–3.3% for the biggest trees; 6–13% including medium-sized trees that represent the largest growing trees in lower productivity interior ecosystems). Tree height cannot be used to compare the remaining area of big-treed old growth to the area expected naturally, an important input for ecological risk assessment and conservation planning because height data are lost from the inventory after harvest. The two calibrated site productivity indicators agreed that the amount remaining is less than 30% of the expected historical amount, posing a high risk to biodiversity and resilience. We recommend using estimated height to identify the biggest remaining old-growth stands for regional planning and calibrated inventory-based site index for risk assessment until a detailed ecosystem mapping has been verified to represent old-growth variability. To reduce uncertainty, we suggest that planning groups compare several indicators and analysis approaches, adjusted to ensure equivalence, and use precaution to avoid any unknowingly increasing risks.

Diversity, Biomass and Carbon Storage Potential of Some Tree Species in a Nigerian Natural Forest

This study was carried out to assess diversity, biomass and carbon storage potential of some tree species in a Nigerian forest. All trees with Dbh >10cm were enumerated. Tree growth variables, namely the Diameter at the base (Db), Diameter at breast height (Dbh), Diameter at the middle (Dm), Diameter at the top (Dt) and height, were measured for basal area and volume estimation and their frequency of occurrence was ascertained for tree diversity assessment. Fifty-six (56) trees distributed among 21 species and 11 families were enumerated in this study area. Some of these species were Acacia ataxacantha, Blighia sapida, Alstonia bonnie, Ceiba pentandra, Celtis zenkeri, Khaya ivorensis, etc. Funtumia elastica had the highest frequency of occurrence (11 stems) with a Relative Density of 19.64%. Therefore, it could be regarded as the most abundant tree species in the forest. Shannon Wiener index of 2.62 was recorded for this study with an evenness value of 0.86. Khaya senegalensis stored the highest carbon of 4.86 tonnes, and total Above Ground Biomass (ABG) of 53.64 g/m2, equivalent to 26.82 tonnes of Carbon was obtained for all the tree species. The results from this study showed that there is high level of forest degradation in the study area. Though, the forest could only store small amount of carbon but it has been able to reduce the amount of carbon escaping into the atmosphere. Conservative measures must be put in place to protect the forest from further degradation and this will go a long way in mitigating climate change by serving as carbon sinks.

Bias in product availability estimates from contraceptive outlet surveys: Evidence from the Consumer's Market for Family Planning (CM4FP) study.

Area-based sampling approaches designed to capture pharmacies, drug shops, and other non-facility service delivery outlets are critical for accurately measuring the contraceptive service environment in contexts of increasing de-medicalization of contraceptive commodities and services. Evidence from other disciplines has demonstrated area-based estimates may be biased if there is spatial heterogeneity in product distribution, but this bias has not yet been assessed in the context of contraceptive supply estimates. The Consumer's Marker for Family Planning (CM4FP) study conducted censuses and product audits of contraceptive outlets across 12 study sites and 2-3 rounds of quarterly data collection in Kenya, Nigeria, and Uganda. We assessed bias in estimates of contraceptive product availability by comparing estimates from simulations of area-based sampling approaches with census counts among all audited facilities for each study site and round of data collection. We found evidence of bias in estimates of contraceptive availability generated from simulated area-based sampling. Within specific study sites and rounds, we observed biased sampling estimates for several but not all contraceptive method types, with bias more likely to occur in sites with heterogeneity in both spatial distribution of outlets and product availability within outlets. In simulations varying size of enumeration areas (EA) and number of outlets sampled per EA, we demonstrated that the likelihood of substantial bias decreases as EA size decreases and as the number of outlets sampled per EA increases. Straightforward approaches such as increasing sample size per EA or applying statistical weights may be used to reduce area-based sampling bias, indicating a pragmatic way forward to improve estimates where design-based sampling is infeasible. Such approaches should be considered in development of improved methods for area-based estimates of contraceptive supply-side environments.

Using Airborne Lidar, Multispectral Imagery, and Field Inventory Data to Estimate Basal Area, Volume, and Aboveground Biomass in Heterogeneous Mixed Species Forests: A Case Study in Southern Alabama

Airborne light detection and ranging (lidar) has proven to be a useful data source for estimating forest inventory metrics such as basal area (BA), volume, and aboveground biomass (AGB) and for producing wall-to-wall maps for validation of satellite-derived estimates of forest measures. However, some studies have shown that in mixed forests, estimates of forest inventory derived from lidar can be less accurate due to the high variability of growth patterns in multispecies forests. The goal of this study is to produce more accurate wall-to-wall reference maps in mixed forest stands by introducing variables from multispectral imagery into lidar models. Both parametric (multiple linear regression) and non-parametric (Random Forests) modeling techniques were used to estimate BA, volume, and AGB in mixed-species forests in Southern Alabama. Models from Random Forests and linear regression were competitive with one another; neither approach produced substantially better models. Of the best models produced from linear regression, all included a variable for multispectral imagery, though models with only lidar variables were nearly as sufficient for estimating BA, volume, and AGB. In Random Forests modeling, the most important variables were those derived from lidar. The following accuracy was achieved for linear regression model estimates: BA R2 = 0.36, %RMSE = 31.26, volume R2 = 0.45, %RMSE = 35.30, and AGB R2 = 0.41, %RMSE = 31.31. The results of this study show that the addition of multispectral imagery is not substantially beneficial for improving estimates of BA, volume, and AGB in mixed forests and suggests that the investigation of other variables to explain forest variability is necessary.

Accuracy and Precision of Stem Cross-Section Modeling in 3D Point Clouds from TLS and Caliper Measurements for Basal Area Estimation

The utilization of terrestrial laser scanning (TLS) data for forest inventory purposes has increasingly gained recognition in the past two decades. Volume estimates from TLS data are usually derived from the integral of cross-section area estimates along the stem axis. The purpose of this study was to compare the performance of circle, ellipse, and spline fits applied to cross-section area modeling, and to evaluate the influence of different modeling parameters on the cross-section area estimation. For this purpose, 20 trees were scanned with FARO Focus3D X330 and afterward felled to collect stem disks at different heights. The contours of the disks were digitized under in vitro laboratory conditions to provide reference data for the evaluation of the in situ TLS-based cross-section modeling. The results showed that the spline model fit achieved the most precise and accurate estimate of the cross-section area when compared to the reference cross-section area (RMSD (Root Mean Square Deviation) and bias of only 3.66% and 0.17%, respectively) and was able to exactly represent the shape of the stem disk (ratio between intersection and union of modeled and reference cross-section area of 88.69%). In comparison, contour fits with ellipses and circles yielded higher RMSD (5.28% and 10.08%, respectively) and bias (1.96% and 3.27%, respectively). The circle fit proved to be especially robust with respect to varying parameter settings, but provided exact estimates only for regular-shaped stem disks, such as those from the upper parts of the stem. Spline-based models of the cross-section at breast height were further used to examine the influence of caliper orientation on the volume estimation. Simulated caliper measures of the DBH showed an RMSD of 3.99% and a bias of 1.73% when compared to the reference DBH, which was calculated via the reference cross-section area, resulting in biased estimates of basal area and volume. DBH estimates obtained by simulated cross-calipering showed statistically significant deviations from the reference. The findings cast doubt on the customary utilization of manually calipered diameters as reference data when evaluating the accuracy of TLS data, as TLS-based estimates have reached an accuracy level surpassing traditional caliper measures.

High-Density UAV-LiDAR in an Integrated Crop-Livestock-Forest System: Sampling Forest Inventory or Forest Inventory Based on Individual Tree Detection (ITD)

Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m2), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m2 and 2300 m2) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and the heights measured in the LiDAR point clouds. We performed a comparison of the variables number of trees, basal area, and volume per hectare. The variables by scenarios were submitted to analysis of variance to verify if the averages are considered different or equivalent. The RMSE (%) were calculated to explain the deviation between the measured volume (filed) and estimated volume (LiDAR) values of these variables. Additionally, we calculated rRMSE, Standard error, AIC, R2, Bias, and residual charts. The basal area values ranged from 7.40 m2 ha−1 (C1380) to 8.14 m2 ha−1 281 (C2300), about −5.9% less than the real value (8.65 m2 ha−1). The C2300 scenario was the only one whose confidence interval (CI) limits included the basal area real. For the total stand volume, the ITD scenario was the one that presented the closer values (689.29 m3) to the real total value (683.88 m3) with the real value positioned in the CI. Our findings indicate that for the stand conditions under study, the SFI approach (C2300) that considers an area of 2300 m2 is adequate to generate estimates at the same level as the ITD approach. Thus, our study should be able to assist in the selection of an optimal plot size to generate estimates with minimized errors and gain in processing time.

Addressing critical influences on L-band radar backscatter for improved estimates of basal area and change

L-band synthetic aperture radar (SAR) backscatter intensity is sensitive to land cover and can be used to estimate vegetation measures such as basal area (BA) and biomass. However, the estimation of BA, and especially change in BA, can be hampered by the influences upon backscatter of external factors such as imaging geometry, terrain topology, prevailing moisture conditions and even SAR sensor characteristics. This paper describes a method of reducing the adverse effects of such extraneous influences on vegetation and change estimates derived from single-channel SAR data. Empirical corrections for terrain slope and cross-track tendencies were applied and linear least squares difference minimization used to normalize the backscatter differences between scenes. The method was applied to state-wide coverage of L-band, fine-mode, HV polarization Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) data over New South Wales (NSW), Australia. The data were acquired with different sensors over two “observational epochs”: ALOS PALSAR in 2009 and ALOS-2 PALSAR-2 in 2016/17. The SAR datasets presented significant variations in backscatter intensity beyond those attributable to changes in vegetation cover. The corrective procedures resulted in improved uniformity of observed backscatter dependence on vegetation. Variations in backscattering coefficient between swaths were reduced by as much as 1.75 dB and 25% of the standard deviation in mean backscattering coefficients in common areas and at near- and far-range. This corresponded to a correction in BA estimate of 4.4 m2 ha−1. The method was observed to reduce ambiguities in regrowth estimates at swath boundaries and correct estimates of BA change by as much as 30% over large areas. The resulting estimates of 7-year change in BA provide spatially explicit forest structural information that is assisting in monitoring changes in woody vegetation across NSW.

Comparison of density and basal area estimation of mountain natural forests based on distance-based sampling methods in Zhejiang, China

ObjectiveThe sustainable development of forest ecology and forest management practices is inseparable from the support of forest surveys. Different sampling methods have an unavoidable impact on the collection of natural community characteristic information. An appropriate method reduces the cost of the investigation to the maximum degree under the premise of ensuring accuracy. Distance-based sampling methods are widely used because of their excellent performance in estimating forest population characteristics. The purpose of this study is to compare and find an efficient sampling method of natural broad-leaved forests in mountainous areas of Zhejiang, China, which is of great significance to large-scale field survey practice in similar areas. MethodOur study used census survey data from fixed monitoring sample plots of natural broad-leaved forest in Wuyanling National Nature Reserve, Zhejiang, China as an example and simulated different distance-based sampling methods, including n-tree distance (NTD), point-centered quarter (PCQ), and T-square (Ts), combined with several estimators to estimate the stand density and basal area. The results were compared with the actual mean values of the 100% survey. ResultWe found that different sampling methods and estimators significantly influenced the results. NTD1 overestimated both the stand density and basal area, while NTD2 performed the best, with the lowest RMSE. Secondary performance was obtained with Ts3, Ts5, and Ts6, with small RMSEs of density and basal area. The RMSEs of the PCQ and Ts sampling methods based on a single distance were all large. ConclusionThe NTD sampling method with the NTD2 estimator is recommended to estimate the stand density and basal area for field investigation of natural forests in the Zhejiang mountainous area.

Developing a forest inventory approach using airborne single photon lidar data: from ground plot selection to forest attribute prediction

Abstract An increasing number of jurisdictions are integrating airborne laser scanning (ALS) into forest inventory programs to produce spatially explicit and accurate inventories of forest resources. However, wall-to-wall ALS coverage relative to the total area of managed forest remains limited in large forest nations such as Canada, wherein logistics, cost and acquisition capacity can be limiting factors. Technologies such as single photon light detection and ranging (SPL) have emerged commercially, which have the capacity to provide efficient ALS acquisitions over large areas and with a greater point density than conventional linear-mode ALS. However, the large-scale operational application of SPL in a forest inventory still needs to be effectively demonstrated. In this study, we used wall-to-wall SPL data (collected with a Leica SPL100) across a 630 000 ha boreal forest in Ontario, Canada to develop a forest inventory. Specifically, we used a structurally guided sampling approach enabled via a principal component analysis of the SPL100 data to establish a network of 250 ground plots. Random forest models were then used to produce area-based estimates of forest attributes of interest. Results demonstrated that the sampling approach enabled the optimization and enhancement of the existing plot network by extending the range of sampled structural types and reducing the number of plots in oversampled forest types. Moreover, Lorey’s height, basal area, quadratic mean diameter at breast height, stem density, gross and merchantable volume and above-ground biomass were estimated with a relative root mean square error of 8.5, 19.76, 13.97, 30.82, 21.53, 23.79 and 22.87 per cent, respectively, and relative bias <1 per cent. Model accuracies achieved using the SPL100 were comparable with those obtained using linear-mode ALS in a previous forest inventory. This study demonstrates the utility of the SPL100 for the complete development of a forest inventory over large forest areas, from ground plot establishment through to the production of forest attribute estimates.

Investigating the Effects of k and Area Size on Variance Estimation of Multiple Pixel Areas Using a k-NN Technique for Forest Parameters

Combining auxiliary variables and field inventory data of forest parameters using the model-based approach is frequently used to produce synthetic estimates for small areas. These small areas arise when it may not be financially feasible to take ground measurements or when such areas are inaccessible. Until recently, these estimates have been calculated without providing a measure of the variance when aggregating multiple pixel areas. This paper uses a Random Forest algorithm to produce estimates of quadratic mean diameter at breast height (QMDBH) (cm), basal area (m2 ha−1), stem density (n/ha−1), and volume (m3 ha−1), and subsequently estimates the variance of multiple pixel areas using a k-NN technique. The area of interest (AOI) is the state owned commercial forests in the Slieve Bloom mountains in the Republic of Ireland, where the main species are Sitka spruce (Picea sitchensis (Bong.) Carr.) and Lodgepole pine (Pinus contorta Dougl.). Field plots were measured in summer 2018 during which a lidar campaign was flown and Sentinel 2 satellite imagery captured, both of which were used as auxiliary variables. Root mean squared error (RMSE%) and R2 values for the modelled estimates of QMDBH, basal area, stem density, and volume were 19% (0.70), 22% (0.67), 28% (0.62), and 26% (0.77), respectively. An independent dataset of pre-harvest forest stands was used to validate the modelled estimates. A comparison of measured values versus modelled estimates was carried out for a range of area sizes with results showing that estimated values in areas less than 10–15 ha in size exhibit greater uncertainty. However, as the size of the area increased, the estimated values became increasingly analogous to the measured values for all parameters. The results of the variance estimation highlighted: (i) a greater value of k was needed for small areas compared to larger areas in order to obtain a similar relative standard deviation (RSD) and (ii) as the area increased in size, the RSD decreased, albeit not indefinitely. These results will allow forest managers to better understand how aspects of this variance estimation technique affect the accuracy of the uncertainty associated with parameter estimates. Utilising this information can provide forest managers with inventories of greater accuracy, therefore ensuring a more informed management decision. These results also add further weight to the applicability of the k-NN variance estimation technique in a range of forests landscapes.

Estimating Primary Forest Attributes and Rare Community Characteristics Using Unmanned Aerial Systems (UAS): An Enrichment of Conventional Forest Inventories

The techniques for conducting forest inventories have been established over centuries of land management and conservation. In recent decades, however, compelling new tools and methodologies in remote sensing, computer vision, and data science have offered innovative pathways for enhancing the effectiveness and comprehension of these sampling designs. Now with the aid of Unmanned Aerial Systems (UAS) and advanced image processing techniques, we have never been closer to mapping forests at field-based inventory scales. Our research, conducted in New Hampshire on complex mixed-species forests, used natural color UAS imagery for estimating individual tree diameters (diameter at breast height (dbh)) as well as stand level estimates of Basal Area per Hectare (BA/ha), Quadratic Mean Diameter (QMD), Trees per Hectare (TPH), and a Stand Density Index (SDI) using digital photogrammetry. To strengthen our understanding of these forests, we also assessed the proficiency of the UAS to map the presence of large trees (i.e., >40 cm in diameter). We assessed the proficiency of UAS digital photogrammetry for identifying large trees in two ways: (1) using the UAS estimated dbh and the 40 cm size threshold and (2) using a random forest supervised classification and a combination of spectral, textural, and geometric features. Our UAS-based estimates of tree diameter reported an average error of 19.7% to 33.7%. At the stand level, BA/ha and QMD were overestimated by 42.18% and 62.09%, respectively, while TPH and SDI were underestimated by 45.58% and 3.34%. When considering only stands larger than 9 ha however, the overestimation of BA/ha at the stand level dropped to 14.629%. The overall classification of large trees, using the random forest supervised classification achieved an overall accuracy of 85%. The efficiency and effectiveness of these methods offer local land managers the opportunity to better understand their forested ecosystems. Future research into individual tree crown detection and delineation, especially for co-dominant or suppressed trees, will further support these efforts.

Addressing management practices of private forests by remote sensing and open data: A tentative procedure

The qualitative and quantitative knowledge of forestry stands is a fundamental requirement for their management and exploitation planning. Traditional field measurements, based on plots, are certainly accurate, but time-consuming and expensive; moreover, it cannot provide a wall-to-wall estimate of measures over large areas. Even though they are essential for calibrate and validate models and respectively results, remote sensing could be a useful support for forest planners when trying to describe wide areas. Optical images from medium-high resolution satellite missions are widely employed due to their accessibility, affordability and readiness to use. Within this framework, in this study an approach based on open-data from the Copernicus Sentinel-2 mission and Regional low density LiDAR data was developed in order to assess private forest wood resource in the Susa Valley (Piemonte Region, NW - Italy). The proposed methodology aims to support private forest management by the local forest consortium for basal area and wood volume estimation of forest stands. Specifically, the process was based on the jointly use of Multi-layer Perceptron (MLP) artificial neural network (ANN), trained and validated with respect to ground data from 285 surveyed plot. Furtherly, to refined wood volume estimates correcting factors accounting for slope and local stand fertility were applied. Estimates of volume stands were generated separately for conifers and broadleaves. Results prove that the adoption of correcting factors improved volume estimates by the MLP and ANN about 2% (relative mean absolute error) and 13% in conifer and broadleaf stands respectively.

325-OR: Genetic and Socioeconomic Factors Are Independently Associated with Type 2 Diabetes

Genetics and socioeconomic status (SES) are key drivers of increased type 2 diabetes (T2D) risk, but the extent to which they impact disease in an additive or non-additive manner is unknown. We examined T2D prevalence using cross-sectional electronic health record (EHR) data from 27,771 participants of European ancestry in the Mass General Brigham Biobank. A polygenic score for T2D was derived using a previously published globally expanded score (gePS). Multiple area-based socioeconomic risk estimates were evaluated (including deprivation indices and a machine learning model incorporating hundreds of census variables) with similar impact on T2D risk; here, an individual’s estimated SES risk was defined as the percent of residents in their census tract with less than high school education. T2D status was derived using a machine-learning algorithm validated by expert chart review to have 90% positive predictive value. The age and sex-adjusted odds of T2D was 1.72 (95% CI 1.63-1.81, p<0.001) per SD increase of T2D gePS and 1.36 (95% CI 1.31-1.42, p<0.001) per SD increase in socioeconomic risk. In a combined model of gePS and socioeconomic risk, these associations persisted, with no evidence of interaction. Persons in the highest quintile of both genetic and socioeconomic risk had an almost 3-fold risk of T2D compared to those of middle quintile risk for both (OR 2.67, 95% CI 2.08-3.44, p<0.001). Notably, the T2D risk of individuals in the highest quintile of genetic risk but lowest quintile of socioeconomic risk was indistinguishable from that of those in the middle quintile for both risk factors (OR 1.13, 95% CI 0.85-1.51). These data provide evidence for the independent associations of genetic risk and SES with T2D and suggest that living in a community with high SES is associated with reduced T2D risk, even among those at increased genetic risk. Our findings raise the hypothesis that interventions targeted at specific elements of SES, such as education, may reduce risk of T2D in those with high genetic risk. Disclosure S. Cromer: Employee; Spouse/Partner; Johnson & Johnson Medical Devices Companies. C. M. Lakhani: Consultant; Self; xy. ai. J. M. Mercader: None. T. Majarian: None. J. C. Florez: Consultant; Self; Goldfinch Bio, Inc., Other Relationship; Self; Novo Nordisk. A. Manning: Employee; Spouse/Partner; Emulate, Inc, Stock/Shareholder; Spouse/Partner; Invitae. J. Merino: None. M. Udler: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (T32DK007028)

An approximate point-based alternative for the estimation of variance under big BAF sampling

BackgroundA new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes Bitterlich sampling (point sampling) with two BAF sizes, a small BAF for tree counts and a larger BAF on which tree measurements are made usually including DBHs and heights needed for volume estimation.MethodsThe new estimator is derived using the Delta method from an existing formulation of the big BAF estimator as consisting of three sample means. The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce’s formula.ResultsSeveral computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature. In simulations the new estimator performed well and comparably to existing variance formulas.ConclusionsA possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees, an assumption required by all previous big BAF variance estimation formulas. Although this correlation was negligible on the simulation stands used in this study, it is conceivable that the correlation could be significant in some forest types, such as those in which the DBH-height relationship can be affected substantially by density perhaps through competition. We derived a formula that can be used to estimate the covariance between estimates of mean basal area and the ratio of estimates of mean volume and mean basal area. We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of frac {1}{n} where n is the number of sample points.

Mapping structural attributes of tropical dry forests by combining Synthetic Aperture Radar and high‐resolution satellite imagery data

AbstractAimOptical satellite imagery has been used for mapping the spatial distribution of vegetation structure attributes; however, obtaining accurate estimates with optical imagery can be difficult in tropical forests due to their dense canopy and multi‐layered vegetation. Synthetic aperture radar imagery can be more suitable in this case, as the radar signal can penetrate the forest canopy and interact with stems, providing a better estimation of the vegetation structure. This study compared the accuracy of forest species richness, tree diameter, height, and basal area estimates obtained using Sentinel‐2 and Advanced Land Observing Satellite ‐1 (ALOS) Phased Array type L‐band Synthetic Aperture Radar (PALSAR) data, either combined or separately.LocationThe Yucatan Peninsula, Mexico.MethodsField data were collected in three 3600‐km2‐window areas with three different types of tropical dry forest. Three random forest regression models were fitted: one using explanatory variables derived from Sentinel‐2 data, a second using predictor variables derived from ALOS PALSAR, and the third using a combination of explanatory variables from both sensors. A variance partitioning analysis was carried out to examine the percent variability of each vegetation attribute that was explained by the models combining the explanatory variables of the two sensors (ALOS PALSAR and Sentinel‐2).ResultsVegetation attribute estimation errors ranged from 13% to 38.5% when using ALOS PALSAR variables and from 11% to 33% when using Sentinel‐2 variables. Combining variables from both sensors provided more accurate estimates of vegetation attributes. A 5% reduction of the estimated error, and an increase from 0.50 to 0.63 of the percentage of variation explained by the models (R2) were achieved.ConclusionsOur results suggest that both ALOS PALSAR and Sentinel‐2 data provide accurate estimates of vegetation structure and species richness in tropical dry forests. However, combining explanatory variables from the two sensors improved the estimation accuracy of vegetation attributes.

Geometric analysis of cone crusher liner shape: Geometric measures, methods for their calculation and linkage to crusher behaviour

This paper presents a set of related methods for performing a computational geometry analysis of a cone crusher shape that provides geometric information for use in understanding cone crusher flows and breakage and their variation with key crusher geometric parameters. Algorithms that are able to calculate geometric measures such as the variation of the cross-sectional area available for flow at each height in the crusher, open and closed side areas, geometry based reduction ratios, eccentric throw as well as the open and closed side setting are described. These are demonstrated in a worked example of a specific short head cone crusher showing how systematic variation in the cone vertical displacement and eccentric angle affects geometric measures. Both the OSS and CSS are found to vary almost linearly with cone height and eccentric angle. The eccentric throw is found to be nearly invariant for changes in cone height but to vary strongly with eccentric angle. There is close to a linear relationship between variation in choke area (representing throughput) and the CSS (representing maximum product size) when the cone height is changed. In contrast, the choke area is almost independent of the CSS when the eccentric angle is varied meaning that throughput and breakage top size are independently controllable. Linear and area-based estimates of the reduction ratio can be used to bound the likely breakage performance. This analysis can be used in conjunction with DEM models to help to guide the evolution of liner configurations.

Estimates of Tree Canopy Closure and Basal Area as Proxies for Tree Crown Volume at a Stand Scale

Research Highlights: Studies on tree canopy dwelling species often require simple proxies of tree canopy volume estimated at a stand level. These include allometrically related tree crown parameters such as crown area and basal area, and canopy cover. Background and Objectives: In monoculture Scot’s pine and mixed pine/Norway spruce forest, we aimed to test the relationships between tree diameter at breast height (DBH) and tree crown volume at a tree level and between densitometer canopy closure estimates and tree crown volume at a stand level. Materials and Methods: The study was carried out in eastern Latvia (hemiboreal zone) in monoculture pine and mixed coniferous stands. On a subset of trees in 22 forest stands (88 100 m2 plots), we determined the best regression model that described the relationship between tree DBH and crown volume for spruce and pine. Tree crown volume at a stand level was determined from the individual tree volume estimates calculated from these regression models. On a stand level, we also calculated regression models for densitometer closure estimates versus total crown volume for pine and mixed stands. Results: Linear mixed effects models showed significant relationships between DBH and crown volume for pine (R2 = 0.63) and spruce (R2 = 0.40), indicating that basal area could be used as a predictor of crown volume at a stand level. Variance explained by a regression model of canopy closure versus tree crown volume at a stand level was R2 = 0.52. Conclusions: Tree basal area and crown closure can be used as proxies of tree crown volume at a stand scale in monoculture stands. In mixed stands estimates of crown volume based on basal area need to be calculated separately for each tree species, while canopy closure will provide an estimate of total crown volume.

Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

The estimation and mapping of forest stand characteristics are vital because this information is necessary for sustainable forest management. The present study considers the use of a Bayesian additive regression trees (BART) algorithm as a non-parametric classifier using Sentinel-2A data and topographic variables to estimate the forest stand characteristics, namely the basal area (m2/ha), stem volume (m3/ha), and stem density (number/ha). These results were compared with those of three other popular machine learning (ML) algorithms, such as generalised linear model (GLM), K-nearest neighbours (KNN), and support vector machine (SVM). A feature selection was done on 28 variables including the multi-spectral bands on Sentinel-2 satellite, related vegetation indices, and ancillary data (elevation, slope, and topographic solar-radiation index derived from digital elevation model (DEM)) and then the most insignificant variables were removed from the datasets by recursive feature elimination (RFE). The study area was a mountainous forest with high biodiversity and an elevation gradient from 26 to 1636 m. An inventory dataset of 1200 sample plots was provided for training and testing the algorithms, and the predictors were fed into the ML models to compute and predict the forest stand characteristics. The accuracies and certainties of the ML models were assessed by their root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) values. The results demonstrated that BART generated the best basal area and stem volume predictions, followed by GLM, SVM, and KNN. The best RMSE values for both basal area (8.12 m2/ha) and stem volume (29.28 m3/ha) estimation were obtained by BART. Thus, the ability of the BART model for forestry application was established. On the other hand, KNN exhibited the highest RMSE values for all stand variable predictions, thereby exhibiting the least accuracy for this specific application. Moreover, the effectiveness of the narrow Sentinel-2 bands around the red edge and elevation was highlighted for predicting the forest stand characteristics. Therefore, we concluded that the combination of the Sentinel-2 products and topographic variables derived from the PALSAR data used in this study improved the estimation of the forest attributes in temperate forests.