Estimating Bulk Density Research Articles

Filling the gaps in soil data: A multi-model framework for addressing data gaps using pedotransfer functions and machine-learning with uncertainty estimates to estimate bulk density

Legacy soil datasets are a valuable resource and should be used to the greatest extent possible. However, such datasets may be incomplete, and lack observations for every attribute, as the dataset may be compiled from multiple studies. To use these datasets in soil mapping and modeling work, it is useful to fill the gaps in the dataset with estimated values. Machine learning is an approach that can provide estimates with high accuracy. In this study, the machine learner Random Forest (RF) was used to estimate bulk density values in an existing dataset from the province of British Columbia (BC), Canada, which was used as a case study dataset. As the dataset had missing observations across all attributes, multiple models needed to be generated and tested to determine the accuracy of the estimated values produced. A total of 512 models were tested using RF, which were then ranked based on the concordance correlation coefficient (CCC) of their estimates; the CCC of all models ranged from 0.51 to 0.92. The estimates of 27 of these models were then used to fill the missing observations in the dataset; the accuracy of these models ranged from a CCC of 0.63 to 0.92. Further, uncertainty estimates for the predictions were generated using quantile regression, which was coupled with the RF approach. Each model tested therefore had an accuracy measurement and an uncertainty estimate. This approach, of using multiple models developed in RF, can be applied to other legacy soil datasets with inconsistently missing values to produce estimates which can fill the missing observations, and produce uncertainty estimates for those estimates.

Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

Abstract. Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data are available to evaluate model estimates of density in mountainous terrain. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne-lidar snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer-picking method that leverages the GPR reflection coherence and the distributed lidar–GPR-retrieved bulk density with machine learning. The root-mean-square error between the distributed estimates and in situ observations is 11 cm for depth, 27 kg m−3 for density, and 46 mm for SWE. The median relative uncertainty in distributed SWE is 13 %. Interactions between wind, terrain, and vegetation display corroborated controls on bulk density that show model and observation agreement. Knowledge of the spatial patterns and predictors of density is critical for the accurate assessment of SWE and essential snow research applications. The spatially continuous snow density and SWE estimated over approximately 16 km2 may serve as necessary calibration and validation for stepping prospective remote-sensing techniques toward broad-scale SWE retrieval.

Composition and Basic Physical Properties of the Phobos Surface: A Comprehensive Review

The surface of Phobos is an intriguing subject of research for many scientists. This is associated, among other things, with the fact that it is perceived as a potential launch site for future human Mars exploration. Additionally, measurements conducted on its surface would not only deepen our knowledge about Phobos but also provide insights into geochemical processes occurring on similar small bodies in the Solar System. Therefore, understanding the physical–mechanical properties of regolith is a crucial aspect of planetary exploration. These properties are key factors needed for both planning safe landings and establishing future bases on celestial bodies. In this paper, information is compiled regarding hypotheses about its origin, the probable composition of Phobos’ surface (spectral properties and HiRISE data), as well as its morphology. The article also presents the process of regolith formation covering Phobos’ surface and its presumed physical properties. It has been established that the estimated bulk density of Phobos, compared to the densities of other asteroids and meteorites, is most similar to C-type asteroids. It was also found that C-type asteroids, in terms of total porosity, best reflect Phobos. However, determining the surface composition of Phobos and its detailed physical properties requires additional information, which could be obtained through in situ studies or sample return missions.

Enhancing consistency of microphysical properties of precipitation across the melting layer in dual-frequency precipitation radar data

Abstract. Stratiform rain and the overlying ice play crucial roles in Earth's climate system. From a microphysics standpoint, water mass flux primarily depends on two variables: particles' concentration and their mass. The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement mission core satellite is a spaceborne instrument capable of estimating these two quantities through dual-wavelength measurements. In this study, we evaluate bulk statistics on the ice particle properties derived from dual-wavelength radar data in relation to the properties of rain underneath. Specifically, we focus on DPR observations over stratiform precipitation, characterized by columns exhibiting a prominent bright band, where the melting layer can be easily detected. Our analysis reveals a large increase in the retrieved mass flux as we transition from the ice to the rain phase in the official DPR product. This observation is in disagreement with our expectation that mass flux should remain relatively stable across the bright band in cold-rain conditions. To address these discrepancies, we propose an alternative retrieval algorithm that ensures a gradual transition of Dm (mean mass-weighted particle melted-equivalent diameter) and the precipitation rate across the melting zone. This approach also helps in estimating bulk ice density above the melting level. These findings demonstrate that DPR observations can not only quantify ice particle content and their size above stratiform rain regions but also estimate bulk density, provided uniform conditions that minimize uncertainties related to partial beam filling.

Conversion of Marginalized Tea Lands into Timber Plantations and its Impact on Soil Organic Carbon Content: A Case Study in Central Highlands of Sri Lanka

Tea (Camellia sinensis) plantations can be in production for about 100 years under well-managed conditions. However, soil degradation makes the tea lands marginalized and uneconomical. Land use conversion is an option for marginalized tea lands that could gain numerous environmental and economic benefits. This study estimated and compared the soil organic carbon stocks (SOC) in the surface soil layer (0–30 cm) of marginalized tea lands against those converted to timber plantations in the Badulla and Nuwaraeliya districts. Disturbed and undisturbed soil samples were collected from the top layer (0-30 cm) of the selected sites of marginalized tea lands and those converted to timber plantations for analysis. Organic matter percentage in the site-level dry soil samples was analysed using the loss on ignition method. The final carbon stocks (t C ha-1) for samples from both land use types were then estimated using the SOC percentages and bulk density estimates. The carbon stocks for the soil samples collected at marginalized tea lands and the marginalized tea lands converted to timber plantations were then statistically analysed (descriptive statistics, normality tests and two-sample t-tests) using Minitab 21.2 software. The pH of marginal tea lands (5.34±0.14) and the marginal tea lands converted to timber plantations (5.20±0.13) was slightly acidic in nature. The bulk density estimates of marginalized tea lands and marginalized tea lands converted to timber plantations were very close and estimated as 1.15±0.02. The statistical analysis showed that overall mean SOC was significantly higher under the marginalized tea lands converted to timber plantations (155.3±10.0 t C ha-1) compared to the marginalized tea lands (126.7±5.8 t C ha-1). Land use conversion has increased the SOC stocks gradually after forest establishment. The increased carbon stocks could be attributed to consistent addition of litter layer on the soil, less soil disturbance, and retarded soil erosion contributing to the buildup of soil organic matter and SOC. On the other hand, low carbon stocks in the marginalized tea land could be attributed to unsustainable agronomical practices. Our findings suggest that converting marginalized tea lands in central highlands into timber plantations could enhance the sequestration of atmospheric CO2, significantly contributing to climate change mitigation.
 Keywords: Soil organic carbon, Land use change, Climate change, Tea plantations

Non-destructive multi-sensor core logging allows for rapid imaging and estimation of frozen bulk density and volumetric ice content in permafrost cores

Abstract. Permafrost cores provide physical samples that can be used to measure the characteristics of frozen ground. Measurements of core physical properties, however, are typically destructive and time intensive. In this study, multi-sensor core logging (MSCL) is used to provide a rapid (∼2–3 cm core depth per minute), high-resolution, non-destructive method to image and collect the physical properties of permafrost cores, allowing for the visualization of cryostructures and estimation of frozen bulk density, magnetic susceptibility, and volumetric ice content. Six permafrost cores with differing properties were analyzed using MSCL and compared with established destructive analyses to assess the potential of this instrument both in terms of accuracy and relative rate of data acquisition. A calibration procedure is presented for gamma ray attenuation from a 137Cs source that is specific to frozen-core materials. This accurately estimates frozen bulk density over the wide range of material densities found in permafrost. MSCL frozen bulk density data show agreement with destructive analyses based on discrete-sample measurements, with an RMSE of 0.067 g cm−3. Frozen bulk density data from the gamma attenuation, along with soil dry bulk density measurements for different sediment types, are used to estimate volumetric ice content. This approach does require an estimation of the soil dry bulk density and assumption of air content. However, the averaged results for this method show good agreement with an RMSE of 6.7 %, illustrating MSCL can provide non-destructive estimates of volumetric ice contents and a digital archive of permafrost cores for future applications.

Detection of Over Normal Pore Pressure Intervals by Using Well Logs

Pore pressure is very important parameter that impacting on drilling, production planning and operations. Drilling and production processes cannot be beginning if pore pressure is not estimated. There is a limit of difference between hydrostatic pressure of mud column and pore pressure during drilling to ensure that the layers are preserved from fracturing, as well as that kicking does not occur inside the well. Difference between pore pressure and bottom hole flowing pressure is a key for production process. Over (Abnormal) pressure intervals are causing many problems during drilling. In present study, pore pressure is estimated firstly as a hydrostatic pressure, and secondly, after determination of shale flag, two methods of Eaton slowness and Bowers original are used for detecting of over (Abnormal) pressure shale intervals. Compressional and density logs of three wells (X3, X4, and XD) located at Y oil field and producing from Asmari formation are used to perform the present study. Density log is linearly extrapolated to estimate bulk density from zero depth to last depth point at reservoir. Vertical stress is predicted for these three wells. The vertical stress gradients were 1.03, 0.99, and 0.93 psi/ft for XD, X3, and X4 wells respectively. Results are reveals that Bowers original better than Eaton slowness in detection of over (abnormal) pressure intervals where the last did not cut all shale intervals by compressional slowness shale base line that equal to 80 us/ft so, Eaton slowness method provided either very high over pressure in some shale intervals or subnormal pressure to other shale intervals and that inaccurate while Bowers original method approximately provided all shale intervals as over pressure in reasonable values. Modular dynamic tester measurements for pore pressure of these three wells are used for calibration. Maximum percent error between predicted and measured pore pressure of wells (X3, X4, and XD) are 1.2%, 0.89% and 3% respectively where these percent are very acceptable. Maximum over pressure values in Asmari formation zones at wells are as follows: 6328 psi at depth 3225 m in well X3, 7538 psi at depth 3080 m in well X4, and 6731 psi at true vertical depth 3067 m in well XD.

Combining ground penetrating radar methodologies enables large‐scale mapping of soil horizon thickness and bulk density in boreal forests

AbstractForest soil properties must be observed with the appropriate resolution by depth and landscape area to understand biogeomorphological controls on soil carbon (C). These observations, particularly in boreal forests, have been limited because of the poor resolution and unavailability of physical soil sampling results, especially for soil bulk density measurements. Ground penetrating radar (GPR) has been demonstrated to non‐destructively and continuously estimate forest soil properties required in Cstock estimates, such as soil horizon thickness and soil bulk density, across small spatial scales and shallow depths. Yet, successful small‐scale forest GPR approaches represent a potential opportunity to obtain soil property estimates at relevant resolution and depth across forest landscapes, enabling improvement to much needed soil mapping and stock estimates. This review discusses the existing soil property studies that utilize ground penetrating radar (GPR) and explores how the adaptation of GPR methodology can contribute to investigating soils in forest landscapes. We have identified common GPR surveying practices, data processing steps and interpretation methods employed in multiple studies. These approaches have proven effective in obtaining higher‐resolution estimates of important soil properties, such as bulk density and horizon thickness, within small‐scale forest plots. By applying relevant findings in this review to our own boreal forest investigation across an 80 m hillslope transect, we provide recommendations on how to tailor GPR methodology for landscape‐scale estimates of soil horizon thickness and bulk density to examine forest soil property distribution. These findings should enable the future collection of soil datasets informing the distribution of soil C stocks and their relationship to landscape features, and thus their controls and responses to climate and environmental change.

A new approach to estimate soil organic carbon content targets in European croplands topsoils

Adopting land management practices that increase the stock of soil organic carbon (SOC) in croplands is widely promoted as a win-win strategy to enhance soil health and mitigate climate change. In this context, the definition of reference SOC content and stock values is needed to provide reliable targets to farmers, policymakers, and stakeholders. In this study, we used the LUCAS dataset to compare different methods for evaluating reference SOC content and stock values in European croplands topsoils (0–20 cm depth). Methods gave generally similar estimates although being built on very different assumptions. In the absence of an objective criterion to establish which approach is the most suitable to determine SOC reference values, we propose an ensemble modelling approach that consists in extracting the estimates using different relevant methods and retaining the median value among them. Interestingly, this approach led us to select values from the three different approaches with similar frequencies. Using estimated bulk density values, we obtained a first rough estimate of 3.5 Gt C of SOC storage potential in the cropland topsoils that we interpret as a long-term aspirational target that would be reachable only under extreme changes in agricultural practices. The use of additional methods in the ensemble modelling approach and more valid statistical spatial estimates may further refine our approach designed for the estimation of SOC reference values for croplands.

A novel method incorporating large rock fragments for improved soil bulk density and carbon stock estimation

AbstractSoil bulk density (BD) is a principal component in estimating the density of soil nutrients and elements including carbon (C). Current literature states that in soils with rock fragment (RF) content ≥3% of the total sample volume, substantial differences in estimated soil organic carbon density (SOCD) are found, depending on the soil BD calculation method chosen, potentially affecting the accuracy of soil nutrient and C inventories. In many soil surveys, soil BD is not measured directly, or the core method is used as the sole determinant of soil BD, potentially neglecting the soil volume dilution effect of RFs larger than the diameter of the cores used. This study uses the core and quantitative pit methods at 10 forest sites in Ireland to determine the BD and RF mass and volume to a depth of 40 cm. The authors examine how large RFs impact BD and subsequently affect the estimated SOCD values by comparing against reference values from established soil sampling and BD calculation methods. The analysis reveals significant variations in the estimated SOCD values when the RF volume in the soil sample exceeds 8% of the total sample volume. A novel method, hereafter named “core‐scaling,” combines core and pit sampling methods to account for large RF mass and volume in BD calculations. This study suggests that using the core‐scaling method provides results that are strongly correlated with the pit method, thus offering an alternative that can also provide accurate SOCD estimates in soils with a high RF content.

Estimation of snow bulk density and snow water equivalent on the Tibetan Plateau using snow cover duration and snow depth

Study regionTibetan Plateau (TP), China. Study focusSnow water equivalent (SWE) measurements is few compared with snow depth (SD) but combining with snow bulk density (SBD) can convert SD to SWE. No robust model has been established to estimate SBD and SWE for the TP. In this study, we aims to develop a model capable of obtaining SBD and SWE on the TP based on a significant dataset of simultaneously measured SD and SWE over nearly four decades. New hydrological insights for the regionWe found unique seasonal characteristics and elevation differences in the SBD on the TP. The newly introduced factor snow cover duration (SCD) can reflect the beginning and status of each snowfall-snowmelt cycle in this type of discontinuous snow region. The developed artificial neural network (ANN) model based on two-factor of SCD and SD can capture these unique characteristics, with minimal errors and the best performance in simulating SBD and then estimating SWE on the TP. Our method improves the underestimation of the widely used regional average density during the heavy snow period, enables a reasonable conversion of the large amount of historical and growing SDs into critical SWEs, and thus improves our ability to assess snow water resources on the TP.

Denitrifying Bioreactor In Situ Woodchip Bulk Density

Highlights The bulk density of woodchips in denitrifying bioreactors in the field is unknown. In situ bulk density estimation methods were developed for use during construction or excavation. Dry bulk densities of aged woodchips at bioreactor bottoms were lower than previous literature values. Moisture and particle size and density explained some, but not all, of the variation in in situ bulk densities. Abstract. Woodchip bulk density in a denitrifying bioreactor governs system hydraulics, but this prime physical attribute has never been estimated in situ. The objectives were twofold: (1) to establish estimates of in situ woodchip bulk density at bioreactors in the field, and (2) evaluate causal factors for and resulting impacts of these estimates. Proof-of-concept bulk density methods were developed at a pilot-scale bioreactor using three ways to estimate volume: surveying the excavated area, pumping the excavation full through a flow meter, and using iPhone Light Detection and Ranging (LiDAR). These methods were then further tested at two new and three old full-size bioreactors. Additional ex situ (off-site) testing with the associated woodchips included analysis of bulk density along a moisture gradient and particle size, particle density, wood composition, and hydraulic property testing. In situ dry bulk densities based on the entire volume of the new bioreactors (206-224 kg/m3) were similar to values from previous lab-scale studies. In situ estimates for woodchips at the bottom of aged bioreactors (22-mo. to 6-y) were unexpectedly low (120-166 kg/m3), given that these woodchips would presumably be the most compacted. These low moisture-content corrected dry bulk densities were influenced by high moisture contents in situ (>70% wet basis). The impacts of particle size and particle density on bulk density were somewhat mixed across the dataset, but in general, smaller woodchips had higher dry bulk densities than larger, and several woodchips sourced from the bottom of bioreactors had low particle densities. Although dry bulk densities in the zone of flow in bioreactors in the field were shown to be relatively low, the resulting permeability coefficients under those packing conditions did not differ from those of the original woodchips. The LiDAR-based volume estimation method was the most practical for large-scale, full-size evaluations and allowed high precision with small features (e.g., vertical reactor edges, drainage fittings). Keywords: Compaction, Cone penetrometer, Drainable porosity, LiDAR, Moisture content, Survey.

An astrometric mass estimate for asteroid (223) Rosa

Context. Outer main belt asteroid (223) Rosa is a possible flyby target of opportunity for the European Space Agency (ESA) JUpiter ICy moons Explorer (JUICE) mission when it passes the asteroid belt on the way to Jupiter. The very low albedo and the featureless red spectra indicate a P-type asteroid in the Tholen taxonomy, though the yet known bulk density does not appear to match this classification. Aims. The aim of this work is to derive new estimates for the mass and bulk density of (223) Rosa. Methods. We derived the mass of Rosa by analyzing the gravitational deflection of small "test" asteroids that had a close encounter with Rosa in the past. To find such events suitable for the mass determination, we performed an encounter search with about 900 000 asteroids over the time span 1980–2030. Results. Three encounters were identified from which two independent mass estimates for Rosa were derived: M = (5.32 ± 2.17) × 1017 kg and M = (3.15 ± 1.14) × 1017 kg, respectively. The weighted mean is M = (3.62 ± 1.25) × 1017 kg. This yields to a bulk density of ρ = 1.2 ± 0.5 g cm−3, when adopting an effective diameter of D = 83 ± 8 km. This bulk density estimate is consistent with typical densities for Tholen taxonomy P-type asteroids.

Precision of mangrove sediment blue carbon estimates and the role of coring and data analysis methods.

Carbon accumulation in coastal wetlands is normally assessed by extracting a sediment core and estimating its carbon content and bulk density. Because carbon content and bulk density are functionally related, the latter can be estimated gravimetrically from a section of the core or, alternatively, from the carbon content in the sample using the mixing model equation from soil science. Using sediment samples from La Paz Bay, Mexico, we analyzed the effect that the choice of corer and the method used to estimate bulk density could have on the final estimates of carbon storage in the sediments. We validated the results using a larger dataset of tropical mangroves, and then by Monte Carlo simulation. The choice of corer did not have sizable influence on the final estimates of carbon density. The main factor in selecting a corer is the operational difficulties that each corer may have in different types of sediments. Because of the multiplication of errors in a product of two variables subject to random sampling error, when using gravimetric estimates of bulk density, the dispersion of the data points in the estimation of total carbon density rises rapidly as the amount of carbon in the sediment increases. In contrast, the estimation of total carbon density using only the carbon fraction as a predictor is very precise, especially in sediments rich in organic matter. This method, however, depends critically on the accurate estimation of the two parameters of the mixing model: the bulk density of pure peat and the bulk density of pure mineral sediment. The estimation of carbon densities in peaty sediments can be very imprecise when using gravimetric bulk densities. Estimating carbon density in peaty sediments using only the estimate of organic fraction can be much more precise, provided the model parameters are estimated with accuracy. These results open the door for simplified and precise estimates of carbon dynamics in mangroves and coastal wetlands.

Transferability of Airborne LiDAR Data for Canopy Fuel Mapping: Effect of Pulse Density and Model Formulation

Canopy fuel characterization is critical to assess fire hazard and potential severity in forest stands. Simulation tools provide useful information for fire prevention planning to reduce wildfire impacts, provided that reliable fuel maps exist at adequate spatial resolution. Free airborne LiDAR data are becoming available in many countries providing an opportunity to improve fuel monitoring at large scales. In this study, models were fitted to estimate canopy base height (CBH), fuel load (CFL) and bulk density (CBD) from airborne LiDAR in a pine stand area where four point-cloud datasets were acquired at different pulse densities. Best models for CBH, CFL and CBD fitted with LiDAR metrics from the 1 p/m2 dataset resulted in an adjusted R2 of 0.88, 0.68 and 0.58, respectively, with RMSE (MAPE) of 1.85 m (18%), 0.16 kg/m2 (14%) and 0.03 kg/m3 (20%). Transferability assessment of fitted models indicated different level of accuracy depending on LiDAR pulse density (both higher and lower than the calibration dataset) and model formulation (linear, power and exponential). Best results were found for exponential models and similar pulse density (1.7 p/m2) compared to lower (0.5 p/m2) or higher return density (4 p/m2). Differences were also observed regarding the canopy fuel attributes.

Pedotransference functions for prediction of density in soils of Piauí, Brazil / Funções de pedotransferência para predição da densidade em solos do Piauí, Brasil

The determination of the density of the horizons of a soil profile allows to evaluate certain properties, such as: porosity, hydraulic conductivity and water storage. However, measured data is not always available or easy to obtain. Thus, it was objective with the work to build and evaluate models of pedotransference function based on multiparameters of the soil as alternatives for the estimation of soil density in areas with agricultural potential of the state of Piauí. Thus, soil samples were collected from 42 soil profiles in the state of Piauí at depths of 0.0-0.20; 0.20-0.40; and 0.40-0.60 m e, characterized in terms of chemical and physical attributes. Through multiple regression analysis, two pedotransfer function models were generated: i) including all determined attributes and, ii) only the particle size analysis and total organic carbon. For both models, all classes (generalized character) were considered, as well as each individual soil class Latossolo (Ferralsol), Argissolo (Acrisol), Plintossolo (Plinthosol) and Neossolo (Arenosol). The generated pedotransference functions are alternative to estimate bulk density of soil, obtaining "excellent" performance in model I for the "Latossolos" (Ferralsols) and "Argissolos" (Acrisols), which included a greater number of variables in the construction of the predictive model of bulk density.

Pre-COVID 19 Pandemic Solid Waste Management and Characterization in Caraga State University, Ampayon, Butuan City, Philippines

Improper utilization of resources and inadequate solid waste management could lead to the increasing growth of waste and seriously affect the environment. This study aimed to assess its Solid Waste Management (SWM) using the Solid Waste Analysis (SWA) Tool and evaluate its implementation of the RA 9003 at Caraga State University before the COVID-19 pandemic. Results show that CSU was responsive to the national and local government policies on SWM in issuing memos and orders and providing facilities and manpower. Observations of reduction at source and vermicomposting were also being practiced. However, despite the provisions of color-coded and categorically labeled metal bins in specific collection points, wastes were still unsegregated, probably due to public indifference where people don’t care or lack knowledge of the possible effects of improper SWM. The personnel working in collecting and transferring waste, while utilizing a single track in hauling all types of waste, also lacked proper training and adequate Personal Protective Equipment (PPE) in handling different kinds of waste. Waste Analysis and Characterization Study (WACS) revealed that CSU could generate 31.22 tons of solid waste per year with an estimated bulk density of 470 x 103 kg/m3, hence, an average waste generation per capita of 0.018kg/day. The results imply that CSU is generating a small amount of waste compared to the estimated average per capita of 0.40 kg/day in the Philippines. Provisions of adequate facilities and implementing more reduction at source, reusing, and recycling projects in the university would be beneficial in decreasing waste generation.

Downhole density estimation using multielement geochemistry and machine learning

Machine learning (ML) was used to estimate bulk density from multielement geochemistry. At the Wheeler River site, host to the Phoenix and Gryphon uranium deposits, multielement geochemistry data were acquired for 829 exploration holes. Of those holes, 41 were logged with a downhole dual-spaced density probe during several mobilizations between 2009 and 2019. Density measurements were collected to provide constraints for inversions of airborne gravity data. To improve the density model's spatial resolution, ML models were trained to estimate bulk density from collocated multielement geochemistry data. Two geochemical laboratory methods were used (251 holes for the old method and 578 holes for the new method); therefore, two separate models were trained. Leave-one-hole-out cross-validation mean absolute error (MAE) results from the old and new geochemistry models showed similar scores of 0.027 g/cm3 and 0.025 g/cm3, respectively. Eight test holes were removed from the training data and used for final evaluation once the model was trained. Test hole results showed MAE scores of 0.026 g/cm3 for the old geochemistry model and 0.043 g/cm3 for the new geochemistry model. A unique aspect of this data set was the presence of repeat logs for multiple boreholes over a decade-long logging campaign. This provided the opportunity to assess the measurement uncertainty across time, density probes, operators, and boreholes conditions. The process of estimating downhole density from multielement geochemistry data could be used for many exploration projects to help generate better starting density models for use in geophysical inversions and other applications.

Bulk Density of Shrub Types and Tree Crowns to Use with Forest Inventories in the Iberian Peninsula

Bulk density for shrubs and tree crowns is an important variable, useful for many purposes, namely estimations for biomass and carbon sequestration and potential fire behavior prediction. In the latter case, bulk density is required to predict the rate of spread and intensity of crown fires. However, bulk density information is scarce. The estimation of bulk density is crucial to help choosing proper pyrosilviculture options to decrease fire susceptibility. Due to the similar environmental conditions and fuel characteristics in Portugal and Spain, we modelled bulk density for the most common woody species in all the Iberian Peninsula. We used 10 different shrub type formations and a set of tree species or groups common to both countries. Equations for bulk density, in both forest canopy and understory layers, were fitted as a function of biometric variables commonly used in forest inventories for the selected species. Standardized estimates of bulk density can be associated with data from the National Forest Inventories from Portugal and Spain, to estimate biomass of the forest ecosystems and to evaluate potential fire behavior involving tree canopies and shrubs.

Exploring golden eagle habitat preference using lidar-based canopy bulk density

ABSTRACT A lidar-derived canopy height profile (CHP), generated from canopy bulk density (CBD) estimates for a sequence of 1-m increments through the canopy, provides a physical measure of forest structure. Measurement of physical properties are intuitively understandable and thus facilitate the use of lidar for investigating hypotheses regarding avian resource use. We illustrate the use of lidar-derived physical measures to explore the hypothesis that golden eagles (Aquila chrysaetos) prefer an open understory, which potentially aids visual identification of prey. Two golden eagles fitted with GPS tracking devices overwintered in the New Jersey Pinelands National Preserve. We generated CHPs from discrete-return lidar data for an area occupied by the birds. We compared the CHPs of sites the birds occupied to the surrounding available habitat and found that the occupied sites were significantly lower in CBD from the ground up to 5 m for perched/stationary birds, and from the ground up to 8 m for birds in flight. These results could be used by forest resource managers for promoting golden eagle habitat through prescribed fire. In addition, these results demonstrate the power of lidar to generate physically and intuitively meaningful measures of forest structure.