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Horvitz–Thompson-like stand density estimation is a method for estimating the stand density from tree crown objects extracted from airborne laser scanning data through individual tree detection. The estimator is based on stochastic geometry and mathematical morphology of the (planar) set formed by the detected tree crowns. This set is used to approximate the detection probabilities of trees. These probabilities are then used to calculate the estimate. The method includes a tuning parameter, which needs to be known to apply the method. We present a refinement of the method to allow more general detection conditions than those of previous papers. We also present and discuss the methods for estimating the tuning parameter of the estimator using a functional k-nearest neighbors method. We test the model fitting and prediction in two spatially separate data sets and examine the plot-level accuracy of estimation. The estimator produced a 13% lower RMSE (root-mean-square error) than the benchmark method in an external validation data set. We also analyze the effects of similarity and dissimilarity of training and validation data on the results.

Funding Information: This study was financially supported by Co-operation agreement with the Finnish concrete industry (project number 700721) and the Academy of Finland (Finnish Centre of Excellence in Inverse Modelling and Imaging, project number 336795). Publisher Copyright: © 2023 The Authors Segregation is one of the major phenomena decreasing the durability of concrete and increasing its risk of failure. This paper investigates the feasibility of AC Impedance Spectroscopy (ACIS) to serve as a monitoring technique for the concrete segregation during compaction. The impedances of fresh concrete were determined between compaction intervals. Changes in the calculated bulk resistivities were compared to hardened densities of the drilled cores, as well as to aggregate segregation obtained via digital image analysis of the hardened concrete sections. During the compaction process, a significant difference in the bulk resistivity was observed between the top and bottom parts of segregated specimens in comparison to unsegregated specimens. By inspecting the relationship between the measurement methods, it was found that ACIS provides a reliable approach (r=-0.948) for estimating the segregation level in cases where the resistivity is mostly affected by the movement of aggregates.

In electrical impedance tomography, we aim to solve the conductivity within a target body through electrical measurements made on the surface of the target. This inverse conductivity problem is severely ill-posed, especially in real applications with only partial boundary data available. Thus regularization has to be introduced. Conventionally regularization promoting smooth features is used, however, the Mumford--Shah regularizer familiar for image segmentation is more appropriate for targets consisting of several distinct objects or materials. It is, however, numerically challenging. We show theoretically through $\Gamma$-convergence that a modification of the Ambrosio--Tortorelli approximation of the Mumford--Shah regularizer is applicable to electrical impedance tomography, in particular the complete electrode model of boundary measurements. With numerical and experimental studies, we confirm that this functional works in practice and produces higher quality results than typical regularizations employed in electrical impedance tomography when the conductivity of the target consists of distinct smoothly-varying regions., Comment: 28 pages, 7 figures

Characterization of two-phase flows is a common problem in the process industry. Due to complexity of flows, having accurate measurements of velocity fields, phase fractions, and the volumetric flow rates is challenging. Even though several approaches have been developed, accurate measurements of the flow quantities remain a challenge. Metering volumetric flow rate requires information on the local phase fraction and velocity field in the pipe cross section to be imaged. These data are commonly obtained using two individual measurement systems, the so-called dual-modality system. This article considers a dual-modality consisting of electromagnetic flow tomography (EMFT) and electrical tomography (ET) imaging, which provide information on the velocity field and electrical conductivity distribution, respectively; the combination of EMFT and ET reconstruction can be further used for inferring the volumetric flow rate. The aim of this article is to improve the accuracy of the EMFT and ET reconstructions—and the resulting flow rate estimate—by enhanced modeling of the unknown velocity and conductivity fields. More specifically, the proposed approach is based on modeling the joint statistics of the velocity and conductivity with a cross-covariance matrix which is based on a representative ensemble of velocity–conductivity image pairs obtained, for example, from fluid dynamical modeling or empirically. The cross-covariance matrix is incorporated in the joint reconstruction within the Bayesian inverse problems framework as an additional prior model. The proposed image reconstruction approach is tested with a set of numerical simulations. The results show that the joint reconstruction approach (JRA) with a cross-covariance model is capable of improving the accuracy of the estimates compared to the approaches that are currently in use.

Errors in individual tree detection and delineation affect diameter distribution predictions based on crown attributes extracted from the detected trees. We develop a methodology for circumventing these problems. The method is based on matching cumulative distribution functions of field measured tree diameter distributions and crown radii distributions extracted from airborne laser scanning data through individual tree detection presented by Vauhkonen and Mehtatalo (2015). In this study, empirical distribution functions and a monotonic, nonlinear model curve are introduced. Tree crown radius distribution produced by individual tree detection is corrected by a method taking into account that all trees cannot be detected. The evaluation is based on the ability of the developed model sequence to predict quadratic mean diameter and total basal area. The studied data consists of 36 field plots in a typical boreal managed forest area in eastern Finland. The suggested enhancements to the model sequence produce improved results in most of the test cases. Most notably, in leaveone-out cross-validation experiments the modified models improve RMSE of basal area 13% in the full data and RMSE of quadratic mean diameter and basal area 69% and 11%, respectively, in pure pine plots. Better modeling of the crown radius distribution and improved matching between crown radii and stem diameters add the operational premises of the full distribution matching.

Electrical impedance tomography (EIT) is an imaging modality that provides cross-sectional images of objects that carry contrasts in electrical conductivity. EIT is suitable for example for monitoring industrial processes involving multiple phases with different conductivities. This paper presents a parametric level set (PLS) based reconstruction scheme for an EIT-imaging of conductivity distributions within multiphase systems. The proposed scheme involves applying a multiphase level set model to solve the inverse problem of finding interfaces between the regions having different conductivity values and estimating the conductivities of phases. The unknown conductivity to be reconstructed is assumed to be piecewise constant while the interface between the regions are represented by the two PLS functions employing Gaussian radial basis functions (GRBF). The level set-based scheme handles topological merging and breaking naturally during the evolution process. It also offers several advantages compared to the traditional pixel-based approaches. For example, the representation of the PLS function by using GRBF provides flexibility in describing a large class of shapes with fewer unknowns. Numerical simulations and phantom experiments are performed to validate the proposed method.

The uncertainty in the radiative forcing caused by aerosols and its effect on the climate change calls for research to improve knowledge of the aerosol particle formation and growth processes. While the experimental research has provided large amount of high quality data on aerosols in the last two decades, the inference of the process rates is still inadequate, mainly due to limitations in the analysis of data. This paper focuses on developing computational methods to infer aerosol process rates from size distribution measurements. In the proposed approach, the temporal evolution of aerosol size distributions is modeled with the general dynamic equation equipped with stochastic terms that account for the uncertainties of the process rates. The time-dependent particle size distribution and the rates of the underlying formation and growth processes are reconstructed based on time series of particle analyzer data using Bayesian state estimation – which not only provides (point) estimates for the process rates but also enables quantifying their uncertainties. The feasibility of the proposed computational framework is demonstrated by a set of numerical simulation studies.

Electrical resistance tomography (ERT) -based distributed surface sensing systems, or sensing skins, offer alternative sensing techniques for structural health monitoring, providing capabilities for distributed sensing of, for example, damage, strain and temperature. Currently, however, the computational techniques utilized for sensing skins are limited to planar surfaces. In this paper, to overcome this limitation, we generalize the ERT-based surface sensing to non-planar surfaces covering arbitrarily shaped three-dimensional structures; We construct a framework in which we reformulate the image reconstruction problem of ERT using techniques of Riemannian geometry, and solve the resulting problem numerically. We test this framework in series of numerical and experimental studies. The results demonstrate that the feasibility of the proposed formulation and the applicability of ERT-based sensing skins for non-planar geometries., Comment: 22 pages, 7 figures

As second-order methods, Gauss--Newton-type methods can be more effective than first-order methods for the solution of nonsmooth optimization problems with expensive-to-evaluate smooth components. Such methods, however, often do not converge. Motivated by nonlinear inverse problems with nonsmooth regularization, we propose a new Gauss--Newton-type method with inexact relaxed steps. We prove that the method converges to a set of disjoint critical points given that the linearisation of the forward operator for the inverse problem is sufficiently precise. We extensively evaluate the performance of the method on electrical impedance tomography (EIT)., Comment: 43 pages, 29 figures

In magnetic particle imaging, the concentration of magnetic nanoparticles is imaged based on their nonlinear magnetization response to an applied magnetic field. Because of security limitations regarding the magnetic field, the field of view does not always cover the entire body being imaged. However, nanoparticles outside the field of view can also be excited by the applied magnetic field and therefore contribute to the measured signal. This leads to domain truncation artifacts, which can often be observed in the reconstructed particle concentrations. In this paper, a computational method for reducing such truncation artifacts is proposed. For this aim, an approximative statistical model for the modeling errors caused by domain truncation is constructed, and the reconstruction problem in magnetic particle imaging is solved in the Bayesian framework of inverse problems. Several numerical test examples illustrate the successful recovery from truncation artifacts.

In this paper, Bayesian inversion of a physically-based forest reflectance model is investigated to estimate of boreal forest canopy leaf area index (LAI) from EO-1 Hyperion hyperspectral data. The data consist of multiple forest stands with different species compositions and structures, imaged in three phases of the growing season. The Bayesian estimates of canopy LAI are compared to reference estimates based on a spectral vegetation index. The forest reflectance model contains also other unknown variables in addition to LAI, for example leaf single scattering albedo and understory reflectance. In the Bayesian approach, these variables are estimated simultaneously with LAI. The feasibility and seasonal variation of these estimates is also examined. Credible intervals for the estimates are also calculated and evaluated. The results show that the Bayesian inversion approach is significantly better than using a comparable spectral vegetation index regression.

The need to improve the understanding of the properties of cement-based materials calls for the development of tools for visualizing and quantifying chemical reactions and flows of fluids within them. In this paper, we report the results of an experimental study where a sample of fractured cement-paste was subjected to injection of fluids (krypton, CO 2 and water) and imaged simultaneously by X-ray computed tomography (CT). Initial porosity of the sample was estimated using a subtraction method based on CT scans taken initially and during krypton injection. The CT reconstructions were segmented to visualize crack patterns and fluid flow in three-dimensions and to quantify the evolution of porosity during the experiment. The results show that CT captures the formation of a carbonate phase in the sample during CO 2 injection, and the flow of water in the fractured media. We quantify the reduction of porosity resulting from the carbonation reaction. We observe that the newly formed carbonated layer impedes water flow and, locally, can lead to crack healing. The results demonstrate the ability of CT to image reactive transport in cement-based materials, and support the feasibility of this imaging tool for their characterization.

This paper focuses on studying the effects of isotropic and anisotropic total variation (TV) regularization in electrical impedance tomography (EIT). A characteristic difference between these two widely used TV regularization methods is that the isotropic TV is rotationally invariant and the anisotropic TV is not. The rotational variance of the anisotropic TV is known to cause geometric distortions by favoring edge orientations that are aligned with co-ordinate axes. In many applications, such as transmission tomography problems, these distortions often play only a minor role in the overall accuracy of reconstructed images, because the measurement data is sensitive to the shapes of the edges in the imaged domain. In EIT and other diffusive image modalities, however, the data is severely less sensitive to the fine details of edges, and it is an open question how large impact the selection of the TV regularization variant has on the reconstructed images. In this work, this effect is investigated based on a set of experiments. The results demonstrate that the choice between isotropic and anisotropic TV regularization indeed has a significant impact on the properties of EIT reconstructions; especially, the tendency of the anisotropic TV to favor edges aligned with co-ordinate axes is shown to yield large geometric distortions in EIT reconstructions.

In this paper, we propose an approach to quantify the plot-level uncertainty in species-specific growing stock volume estimated from airborne laser scanning data and aerial imagery. This is accomplished by adopting the framework of Bayesian inference in the area-based estimation of stock volume. The results show that the proposed approach performs well in quantifying the estimate uncertainty and produces optimal interval estimates for species-specific volumes when sufficient training data are available. Also the point estimate accuracy is competitive with current state-of-the-art methods. Furthermore, we demonstrate how the quantified uncertainties of the stand attributes can be utilized to determine the uncertainty in classification done using the estimated stand attributes.

In this paper, we propose an electrical impedance tomography (EIT)-based multifunctional surface sensing system, or sensing skin, for structural health monitoring. More specifically, the EIT-based sensing skin is developed for detecting and localizing the ingress of chlorides and cracking: two phenomena which are of concern in many structures, including reinforced concrete structures. The multifunctional sensing skin is made of two layers: one layer is sensitive to both chlorides and cracking, and the other layer is sensitive to cracking only. In the experiments, the sensing skin is tested on a polymeric and concrete substrate. The results demonstrate the feasibility of using the multifunctional multi-layer sensing skin for detecting and localizing corrosive elements and cracking, and for distinguishing between them.

Objective: To evaluate the recently proposed nonlinear difference imaging approach to electrical impedance tomography (EIT) in realistic 3-D geometries. Methods: In this paper, the feasibility of nonlinear difference approach-based EIT is tested using simulation studies in 3-D geometries of thorax and larynx, and with an experimental study of a thorax-shaped water tank. All test cases exhibit severe modeling errors due to uncertainty in the boundary shape of the body. Results: In all test cases, the conductivity change reconstructed with nonlinear difference imaging outperforms the conventional reconstructions qualitatively and quantitatively. Conclusion: The results demonstrate that the nonlinear difference reconstructions tolerate geometrical modeling errors at least to the same extent as the conventional linear approach and produce quantitatively more accurate information on the conductivity change. Significance: Physiological processes that produce changes in the electrical conductivity of the body can be monitored with difference imaging based on EIT. The wide popularity of linearized difference imaging in EIT is mainly based on its good tolerance for the ubiquitous modeling errors, which are predominantly caused by inexact knowledge of the body geometry. However, the linearized difference imaging produces only qualitative information on the conductivity change, and the feasibility of the estimates also depends on the selection of the linearization point which ideally should be equal to the conductivity of the initial state. Based on the findings of this paper, these problems can be avoided by nonlinear difference imaging, and potentially the approach can enable quantitative imaging of conductivity change in medical applications.

The development of tools to monitor unsaturated moisture flow in cement-based material is of great importance, as most degradation processes in cement-based materials take place in the presence of moisture. In this paper, the feasibility of electrical impedance tomography (EIT) to monitor three-dimensional (3D) moisture flow in mortar containing fine aggregates is investigated. In the experiments, EIT measurements are taken during moisture ingress in mortar, using electrodes attached on the outer surface of specimens. For EIT, the so-called difference imaging scheme is adopted to reconstruct the change of the 3D electrical conductivity distribution within a specimen caused by the ingress of water into mortar. To study the ability of EIT to detect differences in the rate of ingress, the experiment is performed using plain water and with water containing a viscosity-modifying agent yielding a slower flow rate. To corroborate EIT, X-ray computed tomography (CT) and simulations of unsaturated moisture flow are carried out. While X-ray CT shows contrast with respect to background only in highly saturated regions, EIT shows the conductivity change also in the regions of low degree of saturation. The results of EIT compare well with simulations of unsaturated moisture flow. Moreover, the EIT reconstructions show a clear difference between the cases of water without and with the viscosity-modifying agent and demonstrate the ability of EIT to distinguish between different flow rates.

In electrical impedance tomography (EIT), two- or three-dimensional (2D or 3D) distribution of the electrical conductivity is reconstructed based on potential measurements from the surface of the object. In many industrial and medical applications of EIT, the conductivity distribution is discontinuous – due to, e.g. phase or tissue boundaries. Previous studies have shown that such features in the conductivity can be preserved by using total variation (TV) prior model in the EIT image reconstruction. Recently, both 2D and 3D TV models have been utilized in EIT simulation studies. This far, however, EIT reconstructions with TV models have been evaluated experimentally only in 2D cases or translationally invariant 3D cases. In this paper, an experimental study of EIT with a TV prior in a 3D geometry is presented. In addition, we propose the selection of the prior parameters in the TV model based on the prior information of materials in the target, and their conductivity ranges. The results demonstrate the ro...

Multitemporal land cover classification over urban areas is challenging, especially when using heterogeneous data sources with variable quality attributes. A prominent challenge is that classes with similar spectral signatures (such as trees and grass) tend to be confused with one another. In this paper, we evaluate the efficacy of image point cloud (IPC) data combined with suitable Bayesian analysis based time-series rectification techniques to improve the classification accuracy in a multitemporal context. The proposed method uses hidden Markov models (HMMs) to rectify land covers that are initially classified by a random forest (RF) algorithm. This land cover classification method is tested using time series of remote sensing data from a heterogeneous and rapidly changing urban landscape (Kuopio city, Finland) observed from 2006 to 2014. The data consisted of aerial images (5 years), Landsat data (all 9 years) and airborne laser scanning data (1 year). The results of the study demonstrate that the addition of three-dimensional image point cloud data derived from aerial stereo images as predictor variables improved overall classification accuracy, around three percentage points. Additionally, HMM-based post processing reduces significantly the number of spurious year-to-year changes. Using a set of 240 validation points, we estimated that this step improved overall classification accuracy by around 3.0 percentage points, and up to 6 to 10 percentage points for some classes. The overall accuracy of the final product was 91% (kappa = 0.88). Our analysis shows that around 1.9% of the area around Kuopio city, representing a total area of approximately 0.61 km2, experienced changes in land cover over the nine years considered.

The ability to detect cracks in structural elements is an integral component in the assessment of structural heath and integrity. Recently, Electrical Resistance Tomography (ERT) -based sensing skins have been shown to reliably image progressive surface damage on structural members. However, so far the approach has only been tested in cases of relatively simple crack patterns. Because the spatial resolution of ERT is generally low, it is an open question whether the ERT-based sensing skins are able to image complex structural cracking patterns. In this paper, we test the accuracy of ERT for reconstructing cracking patterns experimentally and computationally. In the computational study, we use a set of numerical simulations that model progressive cracking in a rectangular beam geometry. We also investigate the effect of image reconstruction methods on the crack pattern estimates: In addition to the contemporary image reconstruction method used in the recent sensing skin studies, we test the feasibility of a novel approach where model-based structural prior information on the cracking probability is accounted for in the image reconstruction. The results of this study indicate that ERT-based sensing skins are able to detect and reconstruct complex structural cracking patterns, especially when structural prior information is utilized in the image reconstruction.

The development of visualizing tools to monitor unsaturated moisture flow in cement-based materials is of great importance, as most degradation processes in cement-based materials are connected to and take place in the presence moisture. This paper investigates the ability of electrical capacitance tomography (ECT) to image two-dimensional (2D) unsaturated moisture flow in cement-based materials. In ECT, the electrical permittivity distribution within an object is reconstructed based on measured capacitances between electrodes attached on the object’s surface. In a series of experiments, mortar specimens with and without discrete cracks were imaged with ECT during a 2D moisture ingress. The results show that ECT is able to monitor the evolution of the moisture flow, and to approximate the shape and position of the moisture front. These findings indicate that ECT is a viable method for monitoring and visualizing 2D unsaturated moisture flow in cement-based materials in the presence and absence of discrete cracks.

While the analysis of airborne laser scanning (ALS) data often provides reliable estimates for certain forest stand attributes -- such as total volume or basal area -- there is still room for improvement, especially in estimating species-specific attributes. Moreover, while information on the estimate uncertainty would be useful in various economic and environmental analyses on forests, a computationally feasible framework for uncertainty quantifying in ALS is still missing. In this article, the species-specific stand attribute estimation and uncertainty quantification (UQ) is approached using Gaussian process regression (GPR), which is a nonlinear and nonparametric machine learning method. Multiple species-specific stand attributes are estimated simultaneously: tree height, stem diameter, stem number, basal area, and stem volume. The cross-validation results show that GPR yields on average an improvement of 4.6\% in estimate RMSE over a state-of-the-art k-nearest neighbors (kNN) implementation, negligible bias and well performing UQ (credible intervals), while being computationally fast. The performance advantage over kNN and the feasibility of credible intervals persists even when smaller training sets are used., Comment: Accepted for publication in IEEE Transactions on Geoscience and Remote Sensing

Electrical impedance tomography (EIT) is a label free harmless imaging method capable of imaging differences in electrical conductivity of a sample. In EIT, a low frequency current is injected into the sample, voltage differences on sample surface are measured, and from these measurements, interior conductivity distribution is reconstructed. To increase the accuracy of reconstruction, rotational EIT (rEIT) has been proposed where independent measurements are taken from multiple rotational positions around the sample. However, the benefit of conventional electrode configurations are limited to small number of rotational positions. We have presented an approach called Limited Angle Full Revolution rEIT (LAFR-rEIT) that uses a small number of electrodes and large number of rotational measurement position measurements over 360°. The results are comparable to previous rotational EIT implementations, and furthermore, the limited EIT boundary access provides space for simultaneous attachment of other measurement modalities. On the other hand, the increased number of measurement positions cause an increase in computational complexity, and optimization is required until 3D applications are feasible. This work presents modifications into finite element mesh presentation of the imaging domain and outlines an optimization, that enables sufficiently light rotation for 3D LAFR-rEIT computations. submittedVersion

Traditionally the electrically-based assessment of the moisture flow in cement-based materials relies on two- or four-point measurements. In this paper, imaging of moisture distribution with electrical resistance tomography (ERT) is considered. Especially, the aim is to study whether ERT could give information on unsaturated moisture flows in cases where the flow is non-uniform. In the experiment, the specimens are monitored with ERT during the water ingress. The ERT reconstructions are compared with neutron radiographs, which provide high resolution information on the 2D distribution of the moisture. The results indicate that ERT is able to detect the moisture movement and to show approximately the shape and position of the water front even if the flow is nonuniform.

In a typical inverse problem, a spatially distributed parameter in a physical model is estimated from measurements of model output. Since measurements are stochastic in nature, so is any parameter estimate. Moreover, in the Bayesian setting, the choice of regularization corresponds to the definition of the prior probability density function, which in turn is an uncertainty model for the unknown parameters. For both of these reasons, significant uncertainties exist in the solution of an inverse problem. Thus to fully understand the solution, quantifying these uncertainties is important. When the physical model is linear and the error model and prior are Gaussian, the posterior density function is Gaussian with a known mean and covariance matrix. However, the electrical impedance tomography inverse problem is nonlinear, and hence no closed form expression exists for the posterior density. The typical approach for such problems is to sample from the posterior and then use the samples to compute statistics (s...

This paper presents the development of a novel two-dimensional (2D) Electrical Resistance Tomography (ERT) based distributed temperature sensor. This sensor consists of a thin layer of porous metal film that is manufactured by spraying colloidal copper paint to the surface of an electrically non-conductive substrate. A local temperature change of the metal film, locally changes its electrical conductivity. This conductivity change is localized and quantified with ERT. In the present work, the proposed temperature sensor is experimentally evaluated by imposing known temperature changes on its surface using a heat source. The results indicate that the ERT-based temperature sensor provides a good qualitative estimates of the magnitude and quantitative estimates of the location of the temperature change.

Hyperspectral remote sensing data carry information on the leaf area index (LAI) of forests, and thus in principle, LAI can be estimated based on the data by inverting a forest reflectance model. However, LAI is usually not the only unknown in a reflectance model; especially, the leaf spectral albedo and understory reflectance are also not known. If the uncertainties of these parameters are not accounted for, the inversion of a forest reflectance model can lead to biased estimates for LAI. In this paper, we study the effects of reflectance model uncertainties on LAI estimates, and further, investigate whether the LAI estimates could recover from these uncertainties with the aid of Bayesian inference. In the proposed approach, the unknown leaf albedo and understory reflectance are estimated simultaneously with LAI from hyperspectral remote sensing data. The feasibility of the approach is tested with numerical simulation studies. The results show that in the presence of unknown parameters, the Bayesian LAI estimates which account for the model uncertainties outperform the conventional estimates that are based on biased model parameters. Moreover, the results demonstrate that the Bayesian inference can also provide feasible measures for the uncertainty of the estimated LAI., final draft, peerReviewed

Previously, it has been shown that Electrical Resistance Tomography (ERT) can be used for monitoring moisture flow in undamaged cement-based materials. In this work, we investigate whether ERT could be used for imaging three-dimensional (3D) unsaturated moisture flow in cement-based materials that contain discrete cracks. Novel computational methods based on the so-called absolute imaging framework are developed and used in ERT image reconstructions, aiming at a better tolerance of the reconstructed images with respect to the complexity of the conductivity distribution in cracked material. ERT is first tested using specimens with physically simulated cracks of known geometries, and corroborated with numerical simulations of unsaturated moisture flow. Next, specimens with loading-induced cracks are imaged; here, ERT reconstructions are evaluated qualitatively based on visual observations and known properties of unsaturated moisture flow. Results indicate that ERT is a viable method of visualizing 3D unsaturated moisture flow in cement-based materials with discrete cracks.

In this paper, we consider a computational method for detecting trees on the basis of airborne laser scanning (ALS) data. In the approach, locations, heights, and crown shapes of trees are tracked automatically by fitting multiple 3-D crown height models to ALS data of a field plot. The estimates are computed with an iterative reconstruction method based on Bayesian inversion paradigm. The formulation allows for utilizing prior information on tree shapes in the estimation. Here, the prior models are written based on field measurement data and allometric models for tree shapes. The feasibility of the approach is tested with ALS and field data from a managed boreal forest. The algorithm found 70.2% of the trees in the area, which is a clear improvement compared to a usual 2.5D crown delineation approach (53.1% of the trees detected).

Electrical impedance tomography (EIT) is a non-invasive imaging modality in which the internal conductivity distribution is reconstructed based on boundary voltage measurements. In this work, we consider the application of EIT to non-destructive testing (NDT) of materials and, especially, crack detection. The main goal is to estimate the location, depth and orientation of a crack in three dimensions. We formulate the crack detection task as a shape estimation problem for boundaries imposed with Neumann zero boundary conditions. We propose an adaptive meshing algorithm that iteratively seeks the maximum a posteriori estimate for the shape of the crack. The approach is tested both numerically and experimentally. In all test cases, the EIT measurements are collected using a set of electrodes attached on only a single planar surface of the target -- this is often the only realizable configuration in NDT of large building structures, such as concrete walls. The results show that with the proposed computational method, it is possible to recover the position and size of the crack, even in cases where the background conductivity is inhomogeneous.

The presence of moisture significantly affects the mechanical, hydraulic, chemical, electrical, and thermal properties of cement-based and other porous materials, and therefore, methods for detecting and quantifying the moisture ingress in these materials are needed. Recent research studies have shown that the ingress of moisture in porous materials can be qualitatively imaged with Electrical Impedance Tomography (EIT) – an imaging modality which uses electrical measurements from object’s surface to reconstruct the electrical conductivity distribution inside the object. The aim of this study is to investigate whether EIT could image the three-dimensional volumetric moisture content within cement-based materials quantitatively. For this aim, we apply the so-called absolute imaging scheme to the EIT image reconstruction, and use an experimentally developed model for converting the electrical conductivity distribution to volumetric moisture content. The results of the experimental studies support the feasibility of EIT for quantitative imaging of three-dimensional moisture flows in cement-based materials.

http://shop.tuverlag.at/de/the-world-conference-on-timber-engineering The current study investigates whether an electrical imaging modality, electrical capacitance tomography (ECT), could provide information on the moisture content in wood. In ECT, a set of electrodes are placed around the surface of the object, and based on electrical capacitance measurements from the surface, the spatial distribution of the electrical permittivity inside the object is reconstructed. In this experimental study, water is infiltrated in a wood sample for 7 days, and ECT measurements are sequentially collected during the absorption of water. The reconstructed ECT images show a constant increase of electrical permittivity in the location of water absorption. The results support the feasibility of ECT for imaging the water content in wood.

We investigate the potential of sparsity constraints in the electrical impedance tomography (EIT) inverse problem of inferring the distributed conductivity based on boundary potential measurements. In sparsity reconstruction, inhomogeneities of the conductivity are a priori assumed to be sparse with respect to a certain basis. This prior information is incorporated into a Tikhonov-type functional by including a sparsity-promoting ℓ1-penalty term. The functional is minimized with an iterative soft shrinkage-type algorithm. In this paper, the feasibility of the sparsity reconstruction approach is evaluated by experimental data from water tank measurements. The reconstructions are computed both with sparsity constraints and with a more conventional smoothness regularization approach. The results verify that the adoption of ℓ1-type constraints can enhance the quality of EIT reconstructions: in most of the test cases the reconstructions with sparsity constraints are both qualitatively and quantitatively more feasible than that with the smoothness constraint.

Electrical impedance tomography (EIT) is an imaging method that could become a valuable tool in multimodal applications. One challenge in simultaneous multimodal imaging is that typically the EIT electrodes cover a large portion of the object surface. This paper investigates the feasibility of rotational EIT (rEIT) in applications where electrodes cover only a limited angle of the surface of the object. In the studied rEIT, the object is rotated a full 360◦ during a set of measurements to increase the information content of the data. We call this approach Limited Angle Full Revolution rEIT (LAFR-rEIT). We test LAFR-rEIT setups in two-dimensional geometries with computational and experimental data. We use up to 256 rotational measurement positions, which requires a new way to solve the forward and inverse problem of rEIT. For this, we provide a modification, available for EIDORS, in the supplementary material. The computational results demonstrate that LAFR-rEIT with eight electrodes produce the same image quality as conventional 16-electrode rEIT, when data from an adequate number of rotational measurement positions are used. Both computational and experimental results indicate that the novel LAFR-rEIT provides good EIT with setups with limited surface coverage and a small number of electrodes. publishedVersion

In this paper, we consider the reconstruction of time-varying concentration distributions under nonstationary flow conditions. Previous studies have shown that the state estimation approach that is based on stochastic process evolution models, facilitates reconstructions of rapidly time-varying targets. However, only cases with stationary velocity fields, or cases in which the velocity field can be completely specified by a velocity profile, have been studied. While simultaneous estimation of the time-varying concentration and low-dimensional representations of the flow field itself has been shown to be possible to some extent, this would be computationally too heavy for on-line process estimation and control. On the other hand, using an incorrect flow model in the evolution model may induce intolerable estimation errors. In this paper, we consider an approach in which the state evolution model is written to correspond to a stationary flow, while the actual flow is nonstationary. The associated modelling errors are handled by constructing the state noise process to accommodate to this discrepancy. We carry out a numerical feasibility study with different Reynolds numbers and show that the approach yields significant reduction of estimation errors and simultaneously facilitates using computationally efficient reduced order models.