Spatio-temporal Reconstruction Research Articles

Generative models for sound field reconstruction.

This work examines the use of generative adversarial networks for reconstructing sound fields from experimental data. It is investigated whether generative models, which learn the underlying statistics of a given signal or process, can improve the spatio-temporal reconstruction of a sound field by extending its bandwidth. The problem is significant as acoustic array processing is naturally band limited by the spatial sampling of the sound field (due to the difficulty to satisfy the Nyquist criterion in space domain at high frequencies). In this study, the reconstruction of spatial room impulse responses in a conventional room is tested based on three different generative adversarial models. The results indicate that the models can improve the reconstruction, mostly by recovering some of the sound field energy that would otherwise be lost at high frequencies. There is an encouraging outlook in the use of statistical learning models to overcome the bandwidth limitations of acoustic sensor arrays. The approach can be of interest in other areas, such as computational acoustics, to alleviate the classical computational burden at high frequencies.

Spatiotemporal reconstruction of ancient road networks through sequential cost-benefit analysis.

The construction of ancient road networks spanned generations and exhibits temporal path dependence that is not fully captured by established network formation models that are used to support archaeological reasoning. We introduce an evolutionary model that captures explicitly the sequential nature of road network formation: A central feature is that connections are added successively and according to an optimal cost-benefit trade-off with respect to existing connections. In this model, the network topology emerges rapidly from early decisions, a trait that makes it possible to identify plausible road construction orders in practice. Based on this observation we develop a method to compress the search space of path-dependent optimization problems. We use this method to show that the model's assumptions on ancient decision-making allow the reconstruction of partially known road networks from the Roman era in good detail and from sparse archaeological evidence. In particular, we identify missing links in the major road network of ancient Sardinia that are in good agreement with expert predictions.

Merging Microwave, Optical, and Reanalysis Data for 1 Km Daily Soil Moisture by Triple Collocation

High-spatiotemporal resolution soil moisture (SM) plays an essential role in optimized irrigation, agricultural droughts, and hydrometeorological model simulations. However, producing high-spatiotemporal seamless soil moisture products is challenging due to the inability of optical bands to penetrate clouds and the coarse spatiotemporal resolution of microwave and reanalysis products. To address these issues, this study proposed a framework for multi-source data merging based on the triple collocation (TC) method with an explicit physical mechanism, which was dedicated to generating seamless 1 km daily soil moisture products. Current merging techniques based on the TC method often lack seamless daily optical data input. To remedy this deficiency, our study performed a spatiotemporal reconstruction on MODIS LST and NDVI, and retrieved seamless daily optical soil moisture products. Then, the optical-derived sm1, microwave-retrieved sm2 (ESA CCI combined), and reanalysis sm3 (CLDAS) were matched by the cumulative distribution function (CDF) method to eliminate bias, and their weights were determined by the TC method. Finally, the least squares algorithm and the significance judgment were adopted to complete the merging. Although the CLDAS soil moisture presented anomalies over several stations, our proposed method can detect and reduce this impact by minimizing its weight, which shows the robustness of the method. This framework was implemented in the Naqu region, and the results showed that the merged products captured the temporal variability of the SM and depicted spatial information in detail; the validation with the in situ measurement obtained an average ubRMSE of 0.046 m³/m³. Additionally, this framework is transferrable to any area with measured sites for better agricultural and hydrological applications.

GRASPNET: Fast spatiotemporal deep learning reconstruction of golden-angle radial data for free-breathing dynamic contrast-enhanced magnetic resonance imaging.

The purpose of the current study was to develop a deep learning technique called Golden-angle RAdial Sparse Parallel Network (GRASPnet) for fast reconstruction of dynamic contrast-enhanced 4D MRI acquired with golden-angle radial k-space trajectories. GRASPnet operates in the image-time space and does not use explicit data consistency to minimize the reconstruction time. Three different network architectures were developed: (1) GRASPnet-2D: 2D convolutional kernels (x,y) and coil and contrast dimensions collapsed into a single combined dimension; (2) GRASPnet-3D: 3D kernels (x,y,t); and (3) GRASPnet-2D + time: two 3D kernels to first exploit spatial correlations (x,y,1) followed by temporal correlations (1,1,t). The networks were trained using iterative GRASP reconstruction as the reference. Free-breathing 3D abdominal imaging with contrast injection was performed on 33 patients with liver lesions using a T1-weighted golden-angle stack-of-stars pulse sequence. Ten datasets were used for testing. The three GRASPnet architectures were compared with iterative GRASP results using quantitative and qualitative analysis, including impressions from two body radiologists. The three GRASPnet techniques reduced the reconstruction time to about 13 s with similar results with respect to iterative GRASP. Among the GRASPnet techniques, GRASPnet-2D + time compared favorably in the quantitative analysis. Spatiotemporal deep learning enables reconstruction of dynamic 4D contrast-enhanced images in a few seconds, which would facilitate translation to clinical practice of compressed sensing methods that are currently limited by long reconstruction times.

Genomic analyses of the scorpion mud turtle (Kinosternon scorpioides) (Linnaeus, 1766) in insular and continental Colombia: Evidence for multiple conservation and taxonomic units

The turtle genus Kinosternon is widespread with at least 25 species distributed from Mexico to northern Argentina. The taxonomy of this genus is controversial and requires a full revision using both morphological and molecular approaches. In this study, we did a genomic analysis on the species Kinosternon scorpioides distributed in insular and continental Colombia in order to define conservation units. Total DNA was extracted from 24 tissue samples and RADseq genotyping analysis was done. In addition, the intron R35 was amplified and sequenced for a subset of samples. A total of 35,507 SNPs combined with 1,047 bp of the intron were used for spatiotemporal colonization pattern reconstruction and phylogenetic analyses. In addition, SNPs were used for population structure inferences and allele frequency-based analyses. Reciprocal monophyly, significant differences in allele frequencies (Fst = 0.32 - 0.78), and evidence of reproductive isolation (no admixture/geneflow), indicate long-term divergence between groups (2-8 MYA), possibly due to geographical barriers. Four Evolutionarily Significant Units (ESUs) were defined within our samples. One ESU was represented by the insular subspecies K. scorpioides albogulare, found on San Andrés island, and three ESUs were defined for the subspecies K. s. scorpioides in continental Colombia: one trans-Andean, found in northwestern Colombia (Caribbean region) and two cis-Andean, found in eastern and southeastern Colombia in the Orinoco and Amazon regions, respectively. Colonization of this species occurred from an ancestral area on South of Central America region (~ 8.43 MYA), followed by an establishing of current populations on San Andrés Island and then, in the continent. First, in the Colombian Caribbean, next, in the Orinoco, and more recently, in the Amazon. We hypothesize that the emergence of the Panamá Isthmus, as well as the final uplift of the North Eastern Andes and Vaupes Arch, were key event leading to the differentiation of these ESUs. For management and conservation purposes, each of these ESUs should be considered as a separate management unit. A full revision of the taxonomy of the genus Kinosternon is warranted.

Prediction of apple first flowering date using daily land surface temperature spatio-temporal reconstruction and machine learning

The first flowering date (FFD) is a critical phenological parameter closely related to the apple yield, so the accurate prediction of the FFD is important for precise orchard production management. Existing methods to predict the FFD are mostly based on air temperature (Ta) measured at meteorological stations, but to great differences in meteorological variations and the ecological conditions, these methods cannot present the differences of FFD under complex meteorological conditions and provide spatially continuous FFD information at the level of a region. Therefore, we propose a method to predict spatially continuous apple FFD from remote sensing land surface temperature (LST) based on flowering prediction model. Firstly, the missing LST data were reconstructed by using spatio-temporal reconstruction (STR) approach developed. Next, new air temperature (NAT) data were generated by using the daily Ta estimation (DTE) model and the reconstructed LST. Finally, apple FFD was predicted by the NAT data and the apple flowering prediction model established based on random forest (RF) algorithm and the phenology sequential model, and the prediction accuracy was verified by comparison with the independently measured apple FFD. The LST reconstructed by using the STR approach has mean absolute error (MAE) ranging from 0.51 to 0.68 °C, and root mean square error (RMSE) ranging from 1.07 to 1.21 °C. The MAE between the NAT data and the High-Resolution Land Surface Data Assimilation System (HR-CLDAS) meteorological data ranges from 2.15 to 3.23 °C, and the RMSE ranges from 2.81 to 4.27 °C. In addition, the determination coefficient (R2) and RMSE between the predicted and measured FFD is 0.72 and 2.96 days, respectively. These results demonstrate that the developed method maximizes the potential of MODIS LST in predicting spatially continuous apple FFD, which is valuable for flower and fruit thinning, to defend against frost disasters, and in general for refined orchard production management.

Data-driven optimization of sampling patterns for MR brain T1ρ mapping.

The goal of this study was to apply a fast data-driven optimization algorithm, called bias-accelerated subset selection, for MR brain T1ρ mapping to generate optimized sampling patterns (SPs) for compressed sensing reconstruction of brain 3D-T1ρ MRI. Five healthy volunteers were recruited, and fully sampled Cartesian 3D-T1ρ MRIs were obtained. Variable density (VD) and Poisson disc (PD) undersampling was used as the input to SP optimization process. The reconstruction used 3 compressed sensing methods: spatiotemporal finite differences, low-rank plus sparse with spatial finite differences, and low rank. The performance of images and T1ρ maps using PD-SP and VD-SP and their optimized sampling patterns (PD-OSP and VD-OSP) were compared to the fully sampled reference using normalized root mean square error (NRMSE). The VD-OSP with spatiotemporal finite differences reconstruction (NRMSE=0.078) and the PD-OSP with spatiotemporal finite differences reconstruction (NRMSE=0.079) at the highest acceleration factors (AF=30) showed the largest improvement compared to the respective nonoptimized SPs (VD NRMSE=0.087 and PD NRMSE=0.149). Prospective undersampling was tested at AF=4, with VD-OSP NRMSE=0.057 versus PD-OSP NRMSE=0.060, with optimized sampling performing better that input PD or VD sampling. For brain T1ρ mapping, the VD-OSP with low rank reconstruction for AFs <10 and VD-OSP with spatiotemporal finite differences for AFs >10 perform better. The study demonstrated that the appropriate use of data-driven optimized sampling and suitable compressed sensing reconstruction technique can be employed to potentially accelerate 3D T1ρ mapping for brain imaging applications.

Spatiotemporal reconstruction and drivers of tourism-oriented towns: A case study of Jinshitan

Reconstruction of settlement spaces is the process of optimizing and reorganizing the internal resources of a settlement, which is of great significance to settlement development. In this study, we used the land-use transfer matrix, land-use dynamics, building density, mean building height, and plot ratio in four time series to explore Jinshitan 2-dimensional and 3-dimensional space, production space reconstruction and its drivers. The conclusions were as follows: 1) 82.288% of cultivated land that was lost during the study period was transformed as construction land, which was concentrated in patches; 2) showed a high degree of consistency in terms of building density, mean building height, and floor area ratio; 3) domestic tourists accounted for the majority of tourism, and the population structure exhibited a non-agricultural transformation; and 4) the spatial reconstruction of Jinshitan has benefited from natural resource endowment, government policy guidance, market orientation, and participation of social subjects. This study suggests that Jinshitan should focus on protecting the natural environment and strengthening humanistic care in the future, and hopes to provide reference for the urbanization development of other tourism-oriented settlements.

Deep neural network-based spatiotemporal heterogeneous data reconstruction for landslide detection

Deep neural network-based spatiotemporal heterogeneous data reconstruction for landslide detection

Monte Carlo Simulation and Reconstruction: Assessment of Myocardial Perfusion Imaging of Tracer Dynamics With Cardiac Motion Due to Deformation and Respiration Using Gamma Camera With Continuous Acquisition.

PurposeMyocardial perfusion imaging (MPI) with single photon emission computed tomography (SPECT) is routinely used for stress testing in nuclear medicine. Recently, our group extended its potential going from 3D visual qualitative image analysis to 4D spatiotemporal reconstruction of dynamically acquired data to capture the time variation of the radiotracer concentration and the estimated myocardial blood flow (MBF) and coronary flow reserve (CFR). However, the quality of reconstructed image is compromised due to cardiac deformation and respiration. The work presented here develops an algorithm that reconstructs the dynamic sequence of separate respiratory and cardiac phases and evaluates the algorithm with data simulated with a Monte Carlo simulation for the continuous image acquisition and processing with a slowly rotating SPECT camera.MethodsA clinically realistic Monte Carlo (MC) simulation is developed using the 4D Extended Cardiac Torso (XCAT) digital phantom with respiratory and cardiac motion to model continuous data acquisition of dynamic cardiac SPECT with slowly rotating gamma cameras by incorporating deformation and displacement of the myocardium due to cardiac and respiratory motion. We extended our previously developed 4D maximum-likelihood expectation-maximization (MLEM) reconstruction algorithm for a data set binned from a continuous list mode (LM) simulation with cardiac and respiratory information. Our spatiotemporal image reconstruction uses splines to explicitly model the temporal change of the tracer for each cardiac and respiratory gate that delineates the myocardial spatial position as the tracer washes in and out. Unlike in a fully list-mode data acquisition and reconstruction the accumulated photons are binned over a specific but very short time interval corresponding to each cardiac and respiratory gate. Reconstruction results are presented showing the dynamics of the tracer in the myocardium as it continuously deforms. These results are then compared with the conventional 4D spatiotemporal reconstruction method that models only the temporal changes of the tracer activity. Mean Stabilized Activity (MSA), signal to noise ratio (SNR) and Bias for the myocardium activities for three different target-to-background ratios (TBRs) are evaluated. Dynamic quantitative indices such as wash-in (K1) and wash-out (k2) rates at each gate were also estimated.ResultsThe MSA and SNR are higher with higher TBRs while biases were improved with higher TBRs to less than 10%. The correlation between exhalation-inhalation sequence with the ground truth during respiratory cycle was excellent. Our reconstruction method showed better resolved myocardial walls during diastole to systole as compared to the ungated 4D image. Estimated values of K1 and k2 were also consistent with the ground truth.ConclusionThe continuous image acquisition for dynamic scan using conventional two-head gamma cameras can provide valuable information for MPI. Our study demonstrated the viability of using a continuous image acquisition method on a widely used clinical two-head SPECT system. Our reconstruction method showed better resolved myocardial walls during diastole to systole as compared to the ungated 4D image. Precise implementation of reconstruction algorithms, better segmentation techniques by generating images of different tissue types and background activity would improve the feasibility of the method in real clinical environment.

Accelerated 3D myelin water imaging using joint spatio-temporal reconstruction.

To enable acceleration in 3D multi-echo gradient echo (mGRE) acquisition for myelin water imaging (MWI) by combining joint parallel imaging (JPI) and joint deep learning (JDL). We implemented a multistep reconstruction process using both advanced parallel imaging and deep learning network which can utilize joint spatiotemporal components between the multi-echo images to further accelerate 3D mGRE acquisition for MWI. In the first step, JPI was performed to estimate missing k-space lines. Next, JDL was implemented to reduce residual artifacts and produce high-fidelity reconstruction by using variable splitting optimization consisting of spatiotemporal denoiser block, data consistency block, and weighted average block. The proposed method was evaluated for MWI with 2D Cartesian uniform under-sampling for each echo, enabling scan times of up to approximately 2min for 3D coverage. The proposed method showed acceptable MWI quality with improved quantitative values compared to both JPI and JDL methods individually. The improved performance of the proposed method was demonstrated by the low normalized mean-square error and high-frequency error norm values of the reconstruction with high similarity to the fully sampled MWI. Joint spatiotemporal reconstruction approach by combining JPI and JDL can achieve high acceleration factors for 3D mGRE-based MWI.

Spatiotemporal reconstruction method of carotid artery ultrasound from freehand sonography.

4D reconstruction based on radiation-free ultrasound can provide valuable information about the anatomy. Current 4D US technologies are either faced with limited field-of-view (FoV), technical complications, or cumbersome setups. This paper proposes a spatiotemporal US reconstruction framework to enhance its ability to provide dynamic structure information. We propose a spatiotemporal US reconstruction framework based on freehand sonography. First, a collecting strategy is presented to acquire 2D US images in multiple spatial and temporal positions. A morphology-based phase extraction method after pose correction is presented to decouple the compounding image variations. For temporal alignment and reconstruction, a robust kernel regression model is established to reconstruct images in arbitrary phases. Finally, the spatiotemporal reconstruction is demonstrated in the form of 4D movies by integrating the US images according to the tracked poses and estimated phases. Quantitative and qualitative experiments were conducted on the carotid US to validate the feasibility of the proposed pipeline. The mean phase localization and heart rate estimation errors were 0.07 ± 0.04s and 0.83 ± 3.35bpm, respectively, compared with cardiac gating signals. The assessment of reconstruction quality showed a low RMSE (<0.06) between consecutive images. Quantitative comparisons of anatomy reconstruction from the generated US volumes and MRI showed an average surface distance of 0.39 ± 0.09mm on the common carotid artery and 0.53 ± 0.05mm with a landmark localization error of 0.60 ± 0.18mm on carotid bifurcation. A novel spatiotemporal US reconstruction framework based on freehand sonography is proposed that preserves the utility nature of conventional freehand US. Evaluations on in vivo datasets indicated that our framework could achieve acceptable reconstruction performance and show potential application value in the US examination of dynamic anatomy.

Spatiotemporal Kernel Reconstruction for Linear Parametric Neurotransmitter PET Kinetic Modeling in Motion Correction Brain PET of Awake Rats.

The linear parametric neurotransmitter positron emission tomography (lp-ntPET) kinetic model can be used to detect transient changes (activation) in endogenous neurotransmitter levels. Preclinical PET scans in awake animals can be performed to investigate neurotransmitter transient changes. Here we use the spatiotemporal kernel reconstruction (Kernel) for noise reduction in dynamic PET, and lp-ntPET kinetic modeling. Kernel is adapted for motion correction reconstruction, applied in awake rat PET scans. We performed 2D rat brain phantom simulation using the ntPET model at 3 different noise levels. Data was reconstructed with independent frame reconstruction (IFR), IFR with HYPR denoising, and Kernel, and lp-ntPET kinetic parameters (k2a: efflux rate, γ: activation magnitude, td: activation onset time, and tp: activation peak time) were calculated. Additionally, significant activation magnitude (γ) difference with respect to a region with no activation (rest) was calculated. Finally, [11C]raclopride experiments were performed in anesthetized and awake rats, injecting cold raclopride at 20 min after scan start to simulate endogenous neurotransmitter release. For simulated data at the regional level, IFR coefficient of variation (COV) of k2a, γ, td and tp was reduced with HYPR denoising, but Kernel showed the lowest COV (2 fold reduction compared with IFR). At the pixel level the same trend is observed for k2a, γ, td and tp COV, but reduction is larger with Kernel compared with IFR (10–14 fold). Bias in γ with respect with noise-free values was additionally reduced using Kernel (difference of 292, 72.4, and −6.92% for IFR, IFR+KYPR, and Kernel, respectively). Significant difference in activation between the rest and active region could be detected at a simulated activation of 160% for IFR and IFR+HYPR, and of 120% for Kernel. In rat experiments, lp-ntPET parameters have better confidence intervals using Kernel. In the γ, and td parametric maps, the striatum structure can be identified with Kernel but not with IFR. Striatum voxel-wise γ, td and tp values have lower variability using Kernel compared with IFR and IFR+HYPR. The spatiotemporal kernel reconstruction adapted for motion correction reconstruction allows to improve lp-ntPET kinetic modeling noise in awake rat studies, as well as detection of subtle neurotransmitter activations.

Proton therapy monitoring: spatiotemporal emission reconstruction with prompt gamma timing and implementation with PET detectors

Objective. In this study we introduce spatiotemporal emission reconstruction prompt gamma timing (SER-PGT), a new method to directly reconstruct the prompt photon emission in the space and time domains inside the patient in proton therapy. Approach. SER-PGT is based on the numerical optimisation of a multidimensional likelihood function, followed by a post-processing of the results. The current approach relies on a specific implementation of the maximum-likelihood expectation maximisation algorithm. The robustness of the method is guaranteed by the complete absence of any information about the target composition in the algorithm. Main results. Accurate Monte Carlo simulations indicate a range resolution of about 0.5 cm (standard deviation) when considering 107 primary protons impinging on an homogeneous phantom. Preliminary results on an anthropomorphic phantom are also reported. Significance. By showing the feasibility for the reconstruction of the primary particle range using PET detectors, this study provides significant basis for the development of an hybrid in-beam PET and prompt photon device.

Finding Mesolithic Sites: A Multichannel Ground-Penetrating Radar (GPR) Investigation at the Ancient Lake Duvensee

The shift to the early Holocene in northern Europe is strongly associated with major environmental and climatic changes that influenced hunter-gatherers’ activities and occupation during the Mesolithic period. The ancient lake Duvensee (10,000–6500 cal. BCE) has been studied for almost a century, providing archaeological sites consisting of bark mats and hazelnut-roasting hearths situated on small sand banks deposited by the glacier. No method is yet available to locate these features before excavation. Therefore, a key method for understanding the living conditions of hunter-gatherer groups is to reconstruct the paleoenvironment with a focus on the identification of areas that could possibly host Mesolithic camps and well-preserved archaeological artefacts. We performed a 16-channel MALÅ Imaging Radar Array (MIRA) system survey aimed at understanding the landscape surrounding the find spot Duvensee WP10, located in a hitherto uninvestigated part of the bog. Using an integrated approach of high-resolution ground radar mapping and targeted excavations enabled us to derive a 3D spatio-temporal landscape reconstruction of the investigated sector, including paleo-bathymetry, stratigraphy, and shorelines around the Mesolithic camps. Additionally, we detected previously unknown islands as potential areas for yet unknown dwelling sites. We found that the growth rates of the islands were in the order of approximately 0.3 m2/yr to 0.7 m2/yr between the late Preboreal and the Subboreal stages. The ground-penetrating radar surveying performed excellently in all aspects of near-surface landscape reconstruction as well as in identifying potential dwellings; however, the direct identification of small-scale artefacts, such as fireplaces, was not successful because of their similarity to natural structures.

Two-phase separation based spatiotemporal modeling of thermal processes with applications to lithium-ion batteries

The thermal process of a battery belongs to distributed parameter systems (DPSs). Modeling of this process is beneficial for energy prediction and conservation. When its first-principle model is not known completely, data-driven spatiotemporal modeling is necessary. Because a DPS is infinite-dimensional and time/space coupled, Karhunen–Loève decomposition (KL) is often used for time/space separation. When modeling a thermal process owning two spatial dimensions, the traditional KL considers two spatial dimensions as a whole without separation. However, in this case, the correlation function, which is critical to time/space separation, would be difficult to evaluate. To remedy this shortcoming, a two-phase separation based spatiotemporal modeling method is proposed. First, one spatial dimension is separated from the process by a set of dominant spatial basis functions (SBFs). Then, the other spatial dimension and the time dimension are separated by another set of dominant SBFs. As a result, the infinite-dimensional time/space coupled process is reduced into a low-dimensional temporal system, which is modeled by radial basis function neural networks. Finally, by spatiotemporal reconstruction, the temperature distribution on the whole space can be predicted. By separating the spatial dimensions, the proposed method is more effective than the traditional KL. Numerical simulations and experiments on the thermal process of a lithium-ion battery have demonstrated the effectiveness of the proposed model.

Poroelastic near-field inverse scattering

A multiphysics data analytic platform is established for imaging poroelastic interfaces of finite permeability (e.g., hydraulic fractures) from elastic waveforms and/or acoustic pore pressure measurements. This is accomplished through recent advances in design of non-iterative sampling methods to inverse scattering. The direct problem is formulated via the Biot equations in the frequency domain where a network of discontinuities is illuminated by a set of total body forces and fluid volumetric sources, while monitoring the induced (acoustic and elastic) scattered waves in an arbitrary near-field configuration. A thin-layer approximation is deployed to capture the rough and multiphase nature of interfaces whose spatially varying hydro-mechanical properties are a priori unknown. In this setting, the well-posedness analysis of the forward problem yields the admissibility conditions for the contact parameters. In light of which, the poroelastic scattering operator and its first and second factorizations are introduced and their mathematical properties are carefully examined. It is shown that the non-selfadjoint nature of the Biot system leads to an intrinsically asymmetric factorization of the scattering operator which may be symmetrized at certain limits. These results furnish a robust framework for systematic design of regularized and convex cost functionals whose minimizers underpin the multiphysics imaging indicators. The proposed solution is synthetically implemented with application to spatiotemporal reconstruction of hydraulic fracture networks via deep-well measurements.

The long-term (142 years) spatiotemporal reconstruction and synoptic analysis of extreme low temperatures (− 15 °C or lower) in the northwest region of Iran

The long-term (142 years) spatiotemporal reconstruction and synoptic analysis of extreme low temperatures (− 15 °C or lower) in the northwest region of Iran

Spatiotemporal Continuous Reconstruction of Pm2.5 Retrieval Data Using Automated Feature Engineering and Ensemble Model Over the Beijing-Tianjin-Hebei Region of China

Spatiotemporal Continuous Reconstruction of Pm2.5 Retrieval Data Using Automated Feature Engineering and Ensemble Model Over the Beijing-Tianjin-Hebei Region of China

Active geomorphic hazards in the Sâmbăta Valley, Făgăraș Mountains (Romania): A tree-ring based approach

The present study addresses, for the first time, the problem of spatio-temporal reconstruction of geomorphic processes using tree-rings in the Sâmbăta Valley (Romanian Carpathians). The dendrogeomorphic analysis was conducted in two different sites, one affected by snow avalanches and the other by rockfall. A total number of 130 Picea Abies were sampled in the two sites. The results yield 13 major snow avalanches between 1950 and 2020 and a return period of 3.3 years. The winters with the highest activity index were 1988, 1997 and 2012. The rockfall reconstruction highlights several years of intense activity: 1952, 1955, 2003 and 2012. Thus, the results of the present study provide evidence of active geomorphic processes in the studied area, indicating that tourists are highly exposed to geomorphic hazards, as both sites interfere with popular hiking trails. (Because Sâmbăta Valley is one of the most intensely frequented by tourists in the Făgăraș Mountains, it is a need for warning signs to be installed on the exposed trails.