Quantification Of Errors Research Articles

Quantitative Validation of MRI-Based Motion Estimation for Brain Impact Biomechanics.

Head impact can cause traumatic brain injury (TBI) through axonal overstretch or subsequent inflammation and understanding the biomechanics of the impact event is useful for TBI prevention research. Tagged magnetic resonance imaging (MRI) acquired during a mild-acceleration impact has enabled measurement and visualization of brain deformation in vivo. However, measurements using MRI are subject to error, and having independent validation while imaging in vivo is very difficult. Thus, characterizing the accuracy of these measurements needs to be done in a separate experiment using a phantom where a gold standard is available. This study describes a method for error quantification using a calibration phantom compatible with MRI and high-speed video (the gold standard). During linear acceleration, the maximum shear strain (MSS) in the phantom ranged from 0 to 12%, which is similar to in vivo brain deformation at a similar acceleration. The mean displacement error against video was 0.3±0.3 mm, and the MSS error was 1.4±0.3%. To match resolutions, video data was filtered temporally using an averaging filter. Compared to the unfiltered results, resolution matching improved the agreement between MRI and video results by 15%. In conclusion, tagged MRI analysis compares well to video data provided that resolutions are matched-a finding that is also applicable when using MRI to validate simulations.

Quantification of Measurement Errors in the Lengths of Metal-Loss Corrosion Defects Reported by Inline Inspection Tools

Abstract This paper proposes a framework to quantify the measurement error associated with lengths of corrosion defects on oil and gas pipelines reported by inline inspection (ILI) tools based on a relatively large set of ILI-reported and field-measured defect data collected from different in-service pipelines in Canada. A log-logistic model is proposed to quantify the likelihood of a given ILI-reported defect being a type I defect (without clustering error) or a type II defect (with clustering error). The measurement error associated with the ILI-reported length of the defect is quantified as the average of those associated with the types I and II defects, weighted by the corresponding probabilities obtained from the log-logistic model. The implications of the proposed framework for the reliability analysis of corroded pipelines given the ILI information are investigated using a realistic pipeline example.

Towards a Harmonized Long‐Term Spaceborne Record of Far‐Red Solar‐Induced Fluorescence

AbstractFar‐red solar‐induced chlorophyll fluorescence (SIF) has been retrieved from multiple satellites with nearly continuous global coverage since 1996. Multiple official and research‐grade retrievals provide a means for cross validation across sensors and algorithms, but produces substantial variation across products due to differences in instrument characteristics and retrieval algorithm. The lack of a consistent, calibrated SIF data set hampers scientific interpretation of planetary photosynthesis. NASA's Orbiting Carbon Observatory 2 (OCO‐2) offers small sampling footprints, high data acquisition, and repeating spatially resolved targets at bioclimatically diverse locations, providing a unique benchmark for spaceborne sensors traceable to ground data. We leverage overlap between the longer running Global Ozone Monitoring Instrument version 2 (GOME‐2) SIF time series, and more recent state‐of‐the‐art OCO‐2 and TROPOspheric Monitoring Instrument (TROPOMI) data, in a first attempt to reconcile inconsistencies in the long‐term record. After screening and correcting for key instrument differences (time of day, wavelength, Sun‐sensor geometry, cloud effects, footprint area), we find that Global Ozone Monitoring Instrument version 2 and TROPOspheric Monitoring Instrument perform exceedingly well in capturing spatial, seasonal, and interannual variability across OCO‐2 targets. However, Global Ozone Monitoring Instrument version 2 retrieval methods differ by up to a factor of 2 in signal‐to‐noise and magnitude. Magnitude differences are largely attributed to retrieval window choice, with wider windows producing higher magnitudes. The assumed SIF spectral shape has negligible effect. Substantial research is needed to understand remaining sensitivities to atmospheric absorption and reflectance. We conclude that OCO‐2 and TROPOspheric Monitoring Instrument have opened up the possibility to produce a multidecadal SIF record with well‐characterized uncertainty and error quantification for overlapping instruments, enabling back‐calibration of previous instruments and production of a consistent, research‐grade, harmonized time series.

An assessment of micromechanical models to predict the elastic constants of unidirectional polymer composites

The accuracy of existent closed-form micromechanical models to predict the five independent elastic constants of unidirectional fiber reinforced composites is assessed. A finite element model is developed to serve as a reference, properly benchmarked against other numerical models and published experimental data, and then used as a baseline for comparison. The detailed numerical analysis of the existent micromechanical models using a formal statistical metric for error quantification allows a quantitative assessment of these models, providing recommendations for the most suited model(s) for each material constant.

Predicting the response of turbulent channel flow to varying-phase opposition control: Resolvent analysis as a tool for flow control design

The present study evaluates the capabilities of a low-order flow model based on the resolvent analysis of McKeon and Sharma [B. J. McKeon and A. S. Sharma, J. Fluid Mech. 658, 336 (2010)] for the purpose of controller design for drag reduction in wall-bounded turbulent flows. To this end, we first show that the model is able to approximate the change in mean wall shear stress, which is commonly used as measure for drag reduction. We also derive an analytical expression that decomposes the drag reduction in internal flows into terms that can be predicted directly by the model and terms that allow for quantification of model error if high-fidelity data are available. We then show by example of varying-phase opposition control in a low-Reynolds-number turbulent channel flow that the drag reduction predicted by the resolvent model captures the trend observed in direct numerical simulation (DNS) over a wide range of controller parameters. The DNS results confirm the resolvent model prediction that the attainable drag reduction strongly depends on the relative phase between sensor measurement and actuator response, which raises interesting flow physics questions for future studies. The good agreement between the resolvent model and DNS further reveals that resolvent analysis, which at its heart is a linear technique, is able to approximate the response of the full nonlinear system to control. We also show that in order to make accurate predictions the model only needs to resolve a small subset of the DNS wave numbers and that the controlled resolvent modes obey the Reynolds-number scaling laws of the uncontrolled resolvent operator derived by Moarref et al. [R. Moarref et al., J. Fluid Mech. 734, 275 (2013)]. As a consequence, our results suggest that resolvent analysis can provide a suitable flow model to design feedback flow control schemes for the purpose of drag reduction in incompressible wall-bounded turbulent flows even at technologically relevant Reynolds numbers.

ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging

In electrocardiographic imaging (ECGI), one solves the inverse problem of electrocardiography (ECG) to reconstruct equivalent cardiac sources based on the body surface potential measurements and a mathematical model of the torso. Due to attenuation and spatial smoothing within the torso, this inverse problem is ill-posed. Among many regularization approaches used in the ECG literature to overcome this ill-posedness, statistical techniques have received great attention because of their flexibility to represent the data, and ability to provide performance evaluation tools for quantification of uncertainties and errors in the model. However, despite their potential to accurately reconstruct the equivalent cardiac sources, one major challenge in these methods is how to best utilize the prior information available in terms of training data. In this paper, we address the question of how to define the prior probability distributions (pdf) of the sources and the error terms so that we can obtain more accurate and robust inverse solutions. We employ two methods, maximum likelihood (ML) and maximum a posteriori (MAP), for estimating the model parameters such as the prior pdfs, error pdfs, and the state-transition matrix, based on the same training data. These model parameters are then used for the state-space representation and estimation of the epicardial potentials, which constitute the equivalent cardiac sources in this study. The performances of ML- and MAP-based model parameter estimation methods are evaluated qualitatively and quantitatively at various noise levels and geometric disturbances using two different simulated datasets. Bayesian MAP estimation, which is also a well-known statistical inversion technique, and Tikhonov regularization, which can be formulated as a special and simplified version of Bayesian MAP estimation, have been included here for comparison with the Kalman filtering method. Our results show that the state-space approach outperforms Bayesian MAP estimation in all cases; ML yields accurate results when the test and training beats come from the same physiological model, but MAP is superior to ML, especially if the test and training beats are from different physiological models. Graphical Abstract ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging.

Systematic Detection of Amino Acid Substitutions in Proteomes Reveals Mechanistic Basis of Ribosome Errors and Selection for Translation Fidelity.

The translation machinery and the genes it decodes co-evolved to achieve production throughput and accuracy. Nonetheless, translation errors are frequent, and they affect physiology and protein evolution. Mapping translation errors in proteomes and understanding their causes is hindered by lack of a proteome-wide experimental methodology. We present the first methodology for systematic detection and quantification of errors in entire proteomes. Following proteome mass spectrometry, we identify, in E.coli and yeast, peptides whose mass indicates specific amino acid substitutions. Most substitutions result from codon-anticodon mispairing. Errors occur at sites that evolve rapidly and that minimally affect energetic stability, indicating selection for high translation fidelity. Ribosome density data show that errors occur at sites where ribosome velocity is higher, demonstrating a trade-off between speed and accuracy. Treating bacteria with an aminoglycoside antibiotic or deprivation of specific amino acids resulted in particular patterns of errors. These results reveal a mechanistic and evolutionary basis for translation fidelity.

RADdesigner: a workflow to select the optimal sequencing methodology in genotyping experiments on woody plant species

Polymorphism detection in woody plant species is important in phylogenetics and an essential stage in plant breeding programs. Genotyping has benefitted from the development of next-generation sequencing (NGS) techniques. Methodologies based upon single and double-digested DNA sequencing (RADseq and ddRADseq) allow the detection of polymorphisms on thousands of loci in non-model organisms for which a reference genome is not available, such as many woody plant species. However, experiments using these methodologies are not safe from technical errors, and require careful designs in order to reduce library preparation and sequencing costs at the same time unbiased results are obtained. In order to avoid undesired effects of technical errors on genotyping, it is advisable to incorporate error quantification through pilot sequencing of technical replicates, along with filtering strategies and parameter tuning, so the technical conditions of the whole experiment can be properly defined a priori. However, these kinds of approaches to account for technical errors have been frequently disregarded in the literature. Here, we present RADdesigner, a workflow that combines pilot sequencing of replicate samples with a bioinformatic pipeline to select the most suitable methodology by quantifying genotyping errors, to optimize polymorphism detection parameters, and to perform post-filtering of the results produced by the commonly used RADseq bioinformatic pipeline IpyRAD. The workflow was validated using a pilot study that analyzed a subset of replicate samples of Quercus suber, Q. ilex, and their hybrids.

Analytical Propagation of Runoff Uncertainty Into Discharge Uncertainty Through a Large River Network

AbstractThe transport of freshwater from continents to oceans through rivers has traditionally been estimated by routing runoff from land surface models within river models to obtain discharge. This paradigm imposes that errors are transferred from runoff to discharge, yet the analytical propagation of uncertainty from runoff to discharge has never been derived. Here we apply statistics to the continuity equation within a river network to derive two equations that propagate the mean and variance/covariance of runoff errors independently. We validate these equations in a case study of the rivers in the western United States and, for the first time, invert observed discharge errors for spatially distributed runoff errors. Our results suggest that the largest discharge error source is the joint variability of runoff errors across space, not the mean or amplitude of individual errors. Our findings significantly advance the science of error quantification in model‐based estimates of river discharge.

A moving boundary model for food isothermal drying and shrinkage: One‐dimensional versus two‐dimensional approaches

AbstractWe investigate and quantify the error in the estimate of water diffusivity resulting from the adoption of one‐dimensional (1‐d) models to describe the drying process of thin discoidal or long‐thin cylindrical samples. Numerical results obtained with the 1‐d and the 2‐d moving boundary models recently proposed by Adrover, Brasiello, and Ponso (2019a, 2019b) are compared for different sample aspect ratios. The overestimate error of 1‐d model predictions of diffusivity falls below 5% for an aspect ratio radius‐over‐thickness R0/L0 > 10 for discoidal samples while, for cylindrical samples, the aspect ratio height‐to‐radius L0/R0 must be greater than 15 to get a similar level of accuracy. To overcome this issue we propose a modified 1‐d model able to take into account water flux from lateral surfaces (usually neglected in a 1‐d approach) thus reducing the overestimate error of water diffusivity below 2% even for low‐intermediate values of the aspect ratio. The 1‐d modified model is successfully applied to experimental data of convective drying of eggplant and chayote discoidal samples.Practical applicationsEnd consumer is increasingly demanding for ever more standardized and high quality food products. To meet these requirements, food industries are challenged to refine process design and control tools in order to be able to take into account the variability of both food matrices and operating conditions. This objective cannot be reached without the help of mathematical models based on first principles as the one here discussed and applied to food drying. The comparison between one‐ and two–dimensional approaches and the resulting quantification of errors in the adoption of simpler one‐dimensional models have a great practical interest for industrial applications as they provide reliable criteria to obtain good predictions even when the whole set of the needed experimental data are not available or too expensive. The developed method can be easily adopted to predict final food quality (moisture content, sample volume reduction, and deformation) and therefore to implement more refined control systems and to optimize process‐operating conditions, thus saving experimental and processing costs.

Current state of the art in the use of augmented reality in dentistry: a systematic review of the literature

BackgroundThe aim of the present systematic review was to screen the literature and to describe current applications of augmented reality.Materials and methodsThe protocol design was structured according to PRISMA-P guidelines and registered in PROSPERO. A review of the following databases was carried out: Medline, Ovid, Embase, Cochrane Library, Google Scholar and the Gray literature. Data was extracted, summarized and collected for qualitative analysis and evaluated for individual risk of bias (R.O.B.) assessment, by two independent examiners. Collected data included: year of publishing, journal with reviewing system and impact factor, study design, sample size, target of the study, hardware(s) and software(s) used or custom developed, primary outcomes, field of interest and quantification of the displacement error and timing measurements, when available. Qualitative evidence synthesis refers to SPIDER.ResultsFrom a primary research of 17,652 articles, 33 were considered in the review for qualitative synthesis. 16 among selected articles were eligible for quantitative synthesis of heterogenous data, 12 out of 13 judged the precision at least as acceptable, while 3 out of 6 described an increase in operation timing of about 1 h. 60% (n = 20) of selected studies refers to a camera-display augmented reality system while 21% (n = 7) refers to a head-mounted system. The software proposed in the articles were self-developed by 7 authors while the majority proposed commercially available ones. The applications proposed for augmented reality are: Oral and maxillo-facial surgery (OMS) in 21 studies, restorative dentistry in 5 studies, educational purposes in 4 studies and orthodontics in 1 study. The majority of the studies were carried on phantoms (51%) and those on patients were 11 (33%).ConclusionsOn the base of literature the current development is still insufficient for full validation process, however independent sources of customized software for augmented reality seems promising to help routinely procedures, complicate or specific interventions, education and learning. Oral and maxillofacial area is predominant, the results in precision are promising, while timing is still very controversial since some authors describe longer preparation time when using augmented reality up to 60 min while others describe a reduced operating time of 50/100%.Trial registrationThe following systematic review was registered in PROSPERO with RN: CRD42019120058.

Quantification of Uncertainty and Best Practice in Computing Interfacial Curvature from Complex Pore Space Images.

Recent advances in high-resolution three-dimensional X-ray CT imaging have made it possible to visualize fluid configurations during multiphase displacement at the pore-scale. However, there is an inherited difficulty in image-based curvature measurements: the use of voxelized image data may introduce significant error, which has not—to date—been quantified. To find the best method to compute curvature from micro-CT images and quantify the likely error, we performed drainage and imbibition direct numerical simulations for an oil/water system on a bead pack and a Bentheimer sandstone. From the simulations, local fluid configurations and fluid pressures were obtained. We then investigated methods to compute curvature on the oil/water interface. The interface was defined in two ways; in one case the simulated interface with a sub-resolution smoothness was used, while the other was a smoothed interface extracted from synthetic segmented data based on the simulated phase distribution. The curvature computed on these surfaces was compared with that obtained from the simulated capillary pressure, which does not depend on the explicit consideration of the shape of the interface. As distinguished from previous studies which compared an average or peak curvature with the value derived from the measured macroscopic capillary pressure, our approach can also be used to study the pore-by-pore variation. This paper suggests the best method to compute curvature on images with a quantification of likely errors: local capillary pressures for each pore can be estimated to within 30% if the average radius of curvature is more than 6 times the image resolution, while the average capillary pressure can also be estimated to within 11% if the average radius of curvature is more than 10 times the image resolution.

Dosimetric impact of placement errors in optically stimulated luminescent in vivo dosimetry in radiotherapy.

IntroductionStudies have suggested that optically stimulated luminescent dosimeters (OSLDs) can be used for in vivo dosimetry of intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT). Clinical uncertainties such as placement error have not been thoroughly investigated. The purpose of this work was to measure OSLD placement error in a clinical sample and analyze its dosimetric impact.Methods and materialsThe analysis consisted of three parts: first, quantification of placement error in a clinical sample of 128 patients yielding 293 cone-beam CT (CBCT) with visible OSLDs registered to the treatment plan; Second, correlation of placement error and clinical OSLD measurements; third, simulation of dosimeter placement in the treatment plan and correlation of recalculated dose with placement error.ResultsIn the first analysis, average placement error was 9.7 ± 9.5 mm. In the second analysis, placement error and measured-to-planned dose agreement yielded no correlation (R2 = 0.02) for a subsample of 77 CBCTs of 55 head-and-neck patients. Average placement error was 7.0 ± 6.0 mm. Several factors, including image-guided shifts, introduced uncharacterized uncertainty to the measured-to-planned dose agreement. The third analysis isolated placement error from these other effects. Average dosimetric error was −2.4 ± 19.3%. Simulated dosimetric impact was weakly correlated with placement error (R2 = 0.39). Removing outliers reduced the average dosimetric error to −2.1 ± 10.9%, marginally improving the correlation (R2 = 0.44).ConclusionPlacement error can substantially impact measured-to-planned dose agreement of OSLDs in high gradient regions, demonstrating the criticality of accurate dosimeter placement for IMRT and VMAT treatments.

Error quantification of 3D homogeneous and isotropic turbulence measurements using 2D PIV

Particle image velocimetry (PIV) has become a popular non-intrusive tool for measuring various types of flows. However, when measuring three dimensional flows with 2D PIV, there is inherent measurement error due to out-of-plane motion. Errors in the measured velocity field propagate to turbulence statistics. Since this can distort the overall flow characteristics, it is important to understand the effect of this out-of-plane error. In this study, the effect of out-of-plane motion on turbulence statistics is quantified. Using forced isotropic turbulence direct numerical simulation (DNS) flow field data provided by the Johns Hopkins turbulence database (JHTDB), synthetic image tests are performed. Turbulence statistics such as turbulence kinetic energy, dissipation rate, Taylor microscale, Kolmogorov scale, and velocity correlations are calculated. Various test cases were simulated while controlling three main parameters which affect the out-of-plane motion: PIV interrogation window size, camera inter-frame time, and laser sheet thickness. The amount of out-of-plane motion was first quantified, and then the error variation according to these parameters was examined. This information can be useful when examining fully three dimensional flows such as homogeneous and isotropic turbulence via 2D PIV.

Non-probabilistic kinematic reliability analysis of planar mechanisms with non-uniform revolute clearance joints

Owing to the inevitable phenomena in which the wear of a revolute joint is non-uniform and severely affects the output motion accuracy, a conservative and efficient motion accuracy evaluation of mechanisms considering non-uniform wear is required by designers. Hence, this paper presents an integrated non-probabilistic kinematic analysis of precision mechanisms with non-uniform wear in joint by combining multi-body dynamic analysis, wear prediction and kinematic reliability analysis. The presented accuracy measurement and corresponding solution strategy can be used as the objective or constraint in dimensional synthesis to realise a longer service life. The non-uniform clearance is quantified by unknown but bounded variables, the boundary of which is expressed by the B-spline function. Furthermore, the kinematic reliability is calculated based on the non-probabilistic interval interference model. Two numerical examples consisting of wear prediction, motion error quantification, and reliability evaluation are presented to demonstrate the validity and applicability of the developed methodology.

Application of population balance equations and interaction models in CFD simulation of the bubble distribution in dissolved air flotation

This study concerns the application of computational fluid dynamics (CFD) and population balance equations (PBEs) in biphasic (water-air) simulations of the dissolved air flotation (DAF) process. The incorporation of population balance equations for simulation of the microbubble diameter distribution was based on the literature. The gas phase was simulated as being composed of two different phases: air1 (d < 50 μm) and air2 (d ≥ 50 μm). A non-homogeneous discrete model was applied and specific models enabled the prediction of phase change by breakage or coalescence. The models Turbulent and Luo were used to represent coalescence. The grid convergence index (GCI) method was applied for quantification of the discretization error of the tested computational grids. The grid selected for the simulations showed a discretization error of 5.17%. The results indicated that the velocity vectors of the aqueous phase presented stratified horizontal flow characteristics close to the surface and vertical plug flow inside the tank. The PBE results for the air volumetric fraction demonstrated that the coalescence model had a major impact in terms of the behavior of the gas phase in the white zone. Simulations indicated that the Luo model reduced in about 60% the air1 concentration in the positions evaluated when compared with Turbulent model.

Dispersion analysis of finite difference and discontinuous Galerkin schemes for Maxwell's equations in linear Lorentz media

In this paper, we consider Maxwell's equations in linear dispersive media described by a single-pole Lorentz model for electronic polarization. We study two classes of commonly used spatial discretizations: finite difference methods (FD) with arbitrary even order accuracy in space and high spatial order discontinuous Galerkin (DG) finite element methods. Both types of spatial discretizations are coupled with second order semi-implicit leap-frog and implicit trapezoidal temporal schemes. By performing detailed dispersion analysis for the semi-discrete and fully discrete schemes, we obtain rigorous quantification of the dispersion error for Lorentz dispersive dielectrics. In particular, comparisons of dispersion error can be made taking into account the model parameters, and mesh sizes in the design of the two types of schemes. This work is a continuation of our previous research on energy-stable numerical schemes for nonlinear dispersive optical media [6,7]. The results for the numerical dispersion analysis of the reduced linear model, considered in the present paper, can guide us in the optimal choice of discretization parameters for the more complicated and nonlinear models. The numerical dispersion analysis of the fully discrete FD and DG schemes, for the dispersive Maxwell model considered in this paper, clearly indicate the dependence of the numerical dispersion errors on spatial and temporal discretizations, their order of accuracy, mesh discretization parameters and model parameters. The results obtained here cannot be arrived at by considering discretizations of Maxwell's equations in free space. In particular, our results contrast the advantages and disadvantages of using high order FD or DG schemes and leap-frog or trapezoidal time integrators over different frequency ranges using a variety of measures of numerical dispersion errors. Finally, we highlight the limitations of the second order accurate temporal discretizations considered.

Evaluation of terrestrial pan-Arctic carbon cycling using a data-assimilation system

Abstract. There is a significant knowledge gap in the current state of the terrestrial carbon (C) budget. Recent studies have highlighted a poor understanding particularly of C pool transit times and of whether productivity or biomass dominate these biases. The Arctic, accounting for approximately 50 % of the global soil organic C stocks, has an important role in the global C cycle. Here, we use the CARbon DAta MOdel (CARDAMOM) data-assimilation system to produce pan-Arctic terrestrial C cycle analyses for 2000–2015. This approach avoids using traditional plant functional type or steady-state assumptions. We integrate a range of data (soil organic C, leaf area index, biomass, and climate) to determine the most likely state of the high-latitude C cycle at a 1∘ × 1∘ resolution and also to provide general guidance about the controlling biases in transit times. On average, CARDAMOM estimates regional mean rates of photosynthesis of 565 g C m−2 yr−1 (90 % confidence interval between the 5th and 95th percentiles: 428, 741), autotrophic respiration of 270 g C m−2 yr−1 (182, 397) and heterotrophic respiration of 219 g C m−2 yr−1 (31, 1458), suggesting a pan-Arctic sink of −67 (−287, 1160) g Cm−2 yr−1, weaker in tundra and stronger in taiga. However, our confidence intervals remain large (and so the region could be a source of C), reflecting uncertainty assigned to the regional data products. We show a clear spatial and temporal agreement between CARDAMOM analyses and different sources of assimilated and independent data at both pan-Arctic and local scales but also identify consistent biases between CARDAMOM and validation data. The assimilation process requires clearer error quantification for leaf area index (LAI) and biomass products to resolve these biases. Mapping of vegetation C stocks and change over time and soil C ages linked to soil C stocks is required for better analytical constraint. Comparing CARDAMOM analyses to global vegetation models (GVMs) for the same period, we conclude that transit times of vegetation C are inconsistently simulated in GVMs due to a combination of uncertainties from productivity and biomass calculations. Our findings highlight that GVMs need to focus on constraining both current vegetation C stocks and net primary production to improve a process-based understanding of C cycle dynamics in the Arctic.

Comparison of TanDEM-X DEM with LiDAR Data for Accuracy Assessment in a Coastal Urban Area

The TanDEM-X (TDX) mission launched by the German Aerospace Center delivers unprecedented global coverage of a high-quality digital elevation model (DEM) with a pixel spacing of 12 m. To examine the relationships of terrain, vegetation, and building elevations with hydrologic, geologic, geomorphologic, or ecologic factors, quantification of TDX DEM errors at a local scale is necessary. We estimated the errors of TDX data for open ground, forested, and built areas in a coastal urban environment by comparing the TDX DEM with LiDAR data for the same areas, using a series of error measures including root mean square error (RMSE) and absolute deviation at the 90% quantile (LE90). RMSE and LE90 values were 0.49 m and 0.79 m, respectively, for open ground. These values, which are much lower than the 10 m LE90 specified for the TDX DEM, highlight the promise of TDX DEM data for mapping hydrologic and geomorphic features in coastal areas. The RMSE/LE90 values for mangrove forest, tropical hardwood hammock forest, pine forest, dense residential, sparse residential, and downtown areas were 1.15/1.75, 2.28/3.37, 3.16/5.00, 1.89/2.90, 2.62/4.29 and 35.70/51.67 m, respectively. Regression analysis indicated that variation in canopy height of densely forested mangrove and hardwood hammock was well represented by the TDX DEM. Thus, TDX DEM data can be used to estimate tree height in densely vegetated forest on nearly flat topography next to the shoreline. TDX DEM errors for pine forest and residential areas were larger because of multiple reflection and shadow effects. Furthermore, the TDX DEM failed to capture the many high-rise buildings in downtown, resulting in the lowest accuracy among the different land cover types. Therefore, caution should be exercised in using TDX DEM data to reconstruct building models in a highly developed metropolitan area with many tall buildings separated by narrow open spaces.

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

Forests in Southeast Asia are experiencing some of the highest rates of deforestation and degradation in the world, with natural forest species being replaced by cropland and plantation monoculture. In this work, we have developed an innovative method to accurately map rubber and palm oil plantations using fusion of Landsat-8, Sentinel 1 and 2. We applied cloud and shadow masking, bidirectional reflectance distribution function (BRDF), atmospheric and topographic corrections to the optical imagery and a speckle filter and harmonics for Synthetic Aperture Radar (SAR) data. In this workflow, we created yearly composites for all sensors and combined the data into a single composite. A series of covariates were calculated from optical bands and sampled using reference data of the land cover classes including surface water, forest, urban and built-up, cropland, rubber, palm oil and mangrove. This training dataset was used to create biophysical probability layers (primitives) for each class. These primitives were then used to create land cover and probability maps in a decision tree logic and Monte-Carlo simulations. Validation showed good overall accuracy (84%) for the years 2017 and 2018. Filtering for validation points with high error estimates improved the accuracy up to 91%. We demonstrated and concluded that error quantification is an essential step in land cover classification and land cover change detection. Our overall analysis supports and presents a path for improving present assessments for sustainable supply chain analyses and associated recommendations.