scholarly journals Monitoring Tropical Forest Structure Using SAR Tomography at L- and P-Band

2019 ◽  
Vol 11 (16) ◽  
pp. 1934 ◽  
Author(s):  
Ibrahim El Moussawi ◽  
Dinh Ho Tong Minh ◽  
Nicolas Baghdadi ◽  
Chadi Abdallah ◽  
Jalal Jomaah ◽  
...  
Keyword(s):  
Forest Management ◽  
Tropical Forest ◽  
Forest Structure ◽  
Vertical Structure ◽  
Three Dimensional ◽  
Region Of Interest ◽  
Scientific Basis ◽  
Coefficient Of Determination ◽  
Phase Measurements ◽  
L Band

Our study aims to provide a comparison of the P- and L-band TomoSAR profiles, Land Vegetation and Ice Sensor (LVIS), and discrete return LiDAR to assess the ability for TomoSAR to monitor and estimate the tropical forest structure parameters for enhanced forest management and to support biomass missions. The comparison relies on the unique UAVSAR Jet propulsion Laboratory (JPL)/NASA L-band data, P-band data acquired by ONERA airborne system (SETHI), Small Footprint LiDAR (SFL), and NASA Land, Vegetation and Ice Sensor (LVIS) LiDAR datasets acquired in 2015 and 2016 in the frame of the AfriSAR campaign. Prior to multi-baseline data processing, a phase residual correction methodology based on phase calibration via phase center double localization has been implemented to improve the phase measurements and compensate for the phase perturbations, and disturbances originated from uncertainties in allocating flight trajectories. First, the vertical structure was estimated from L- and P-band corrected Tomography SAR data measurements, then compared with the canopy height model from SFL data. After that, the SAR and LiDAR three-dimensional (3D) datasets are compared and discussed at a qualitative basis at the region of interest. The L- and P-band’s performance for canopy penetration was assessed to determine the underlying ground locations. Additionally, the 3D records for each configuration were compared with their ability to derive forest vertical structure. Finally, the vertical structure extracted from the 3D radar reflectivity from L- and P-band are compared with SFL data, resulting in a root mean square error of 3.02 m and 3.68 m, where the coefficient of determination shows a value of 0.95 and 0.93 for P- and L-band, respectively. The results demonstrate that TomoSAR holds promise for a scientific basis in forest management activities.

A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sunil K. Deokar ◽  
Nachiket A. Gokhale ◽  
Sachin A. Mandavgane
Keyword(s):  
Three Dimensional ◽  
Current Approach ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Operational Parameters ◽  
Initial Concentration ◽  
Combined Application ◽  
Artificial Neural Network Ann ◽  
Relationship Of ◽  
Biomass Ashes

Abstract Biomass ashes like rice husk ash (RHA), bagasse fly ash (BFA), were used for aqueous phase removal of a pesticide, diuron. Response surface methodology (RSM) and artificial neural network (ANN) were successfully applied to estimate and optimize the conditions for the maximum diuron adsorption using biomass ashes. The effect of operational parameters such as initial concentration (10–30 mg/L); contact time (0.93–16.07 h) and adsorbent dosage (20–308 mg) on adsorption were studied using central composite design (CCD) matrix. Same design was also employed to gain a training set for ANN. The maximum diuron removal of 88.95 and 99.78% was obtained at initial concentration of 15 mg/L, time of 12 h, RHA dosage of 250 mg and at initial concentration of 14 mg/L, time of 13 h, BFA dosage of 60 mg respectively. Estimation of coefficient of determination (R 2) and mean errors obtained for ANN and RSM (R 2 RHA = 0.976, R 2 BFA = 0.943) proved ANN (R 2 RHA = 0.997, R 2 BFA = 0.982) fits better. By employing RSM coupled with ANN model, the qualitative and quantitative activity relationship of experimental data was visualized in three dimensional spaces. The current approach will be instrumental in providing quick preliminary estimations in process and product development.

scholarly journals Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems

Vibration ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 49-63
Author(s):  
Waad Subber ◽  
Sayan Ghosh ◽  
Piyush Pandita ◽  
Yiming Zhang ◽  
Liping Wang
Keyword(s):  
Dynamical Systems ◽  
Computational Cost ◽  
Three Dimensional ◽  
Region Of Interest ◽  
System Response ◽  
Computational Domain ◽  
User Interest ◽  
Numerical Resolution ◽  
Multi Scale ◽  
Operation Conditions

Industrial dynamical systems often exhibit multi-scale responses due to material heterogeneity and complex operation conditions. The smallest length-scale of the systems dynamics controls the numerical resolution required to resolve the embedded physics. In practice however, high numerical resolution is only required in a confined region of the domain where fast dynamics or localized material variability is exhibited, whereas a coarser discretization can be sufficient in the rest majority of the domain. Partitioning the complex dynamical system into smaller easier-to-solve problems based on the localized dynamics and material variability can reduce the overall computational cost. The region of interest can be specified based on the localized features of the solution, user interest, and correlation length of the material properties. For problems where a region of interest is not evident, Bayesian inference can provide a feasible solution. In this work, we employ a Bayesian framework to update the prior knowledge of the localized region of interest using measurements of the system response. Once, the region of interest is identified, the localized uncertainty is propagate forward through the computational domain. We demonstrate our framework using numerical experiments on a three-dimensional elastodynamic problem.

scholarly journals Forest Height Estimation Based on P-Band Pol-InSAR Modeling and Multi-Baseline Inversion

2020 ◽  
Vol 12 (8) ◽  
pp. 1319
Author(s):  
Xiaofan Sun ◽  
Bingnan Wang ◽  
Maosheng Xiang ◽  
Liangjiang Zhou ◽  
Shuai Jiang
Keyword(s):  
Standard Deviation ◽  
Vertical Structure ◽  
Incidence Angle ◽  
Three Dimensional ◽  
Solution Space ◽  
Model Complexity ◽  
Two Dimensional ◽  
Dimensional Parameter ◽  
Model Inversion ◽  
Forest Height

The Gaussian vertical backscatter (GVB) model has a pivotal role in describing the forest vertical structure more accurately, which is reflected by P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) with strong penetrability. The model uses a three-dimensional parameter space (forest height, Gaussian mean representing the strongest backscattered power elevation, and the corresponding standard deviation) to interpret the forest vertical structure. This paper establishes a two-dimensional GVB model by simplifying the three-dimensional one. Specifically, the two-dimensional GVB model includes the following three cases: the Gaussian mean is located at the bottom of the canopy, the Gaussian mean is located at the top of the canopy, as well as a constant volume profile. In the first two cases, only the forest height and the Gaussian standard deviation are variable. The above approximation operation generates a two-dimensional volume only coherence solution space on the complex plane. Based on the established two-dimensional GVB model, the three-baseline inversion is achieved without the null ground-to-volume ratio assumption. The proposed method improves the performance by 18.62% compared to the three-baseline Random Volume over Ground (RVoG) model inversion. In particular, in the area where the radar incidence angle is less than 0.6 rad, the proposed method improves the inversion accuracy by 34.71%. It suggests that the two-dimensional GVB model reduces the GVB model complexity while maintaining a strong description ability.

scholarly journals TomoSAR Mapping of 3D Forest Structure: Contributions of L-Band Configurations

2021 ◽  
Vol 13 (12) ◽  
pp. 2255
Author(s):  
Matteo Pardini ◽  
Victor Cazcarra-Bes ◽  
Konstantinos Papathanassiou
Keyword(s):  
Information Content ◽  
Forest Structure ◽  
Structural Information ◽  
3D Structure ◽  
Physical Structure ◽  
Structure Mapping ◽  
Structure Information ◽  
Forest Sites ◽  
L Band ◽  
Structure Indices

Synthetic Aperture Radar (SAR) measurements are unique for mapping forest 3D structure and its changes in time. Tomographic SAR (TomoSAR) configurations exploit this potential by reconstructing the 3D radar reflectivity. The frequency of the SAR measurements is one of the main parameters determining the information content of the reconstructed reflectivity in terms of penetration and sensitivity to the individual vegetation elements. This paper attempts to review and characterize the structural information content of L-band TomoSAR reflectivity reconstructions, and their potential to forest structure mapping. First, the challenges in the accurate TomoSAR reflectivity reconstruction of volume scatterers (which are expected to dominate at L-band) and to extract physical structure information from the reconstructed reflectivity is addressed. Then, the L-band penetration capability is directly evaluated by means of the estimation performance of the sub-canopy ground topography. The information content of the reconstructed reflectivity is then evaluated in terms of complementary structure indices. Finally, the dependency of the TomoSAR reconstruction and of its structural information to both the TomoSAR acquisition geometry and the temporal change of the reflectivity that may occur in the time between the TomoSAR measurements in repeat-pass or bistatic configurations is evaluated. The analysis is supported by experimental results obtained by processing airborne acquisitions performed over temperate forest sites close to the city of Traunstein in the south of Germany.

scholarly journals Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography

2017 ◽  
Vol 9 (12) ◽  
pp. 1229 ◽  
Author(s):  
Victor Cazcarra-Bes ◽  
Maria Tello-Alonso ◽  
Rico Fischer ◽  
Michael Heym ◽  
Konstantinos Papathanassiou
Keyword(s):  
Forest Structure ◽  
Structure Dynamics ◽  
L Band

Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110381
Author(s):  
Xue Bai ◽  
Ze Liu ◽  
Jie Zhang ◽  
Shengye Wang ◽  
Qing Hou ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Region Of Interest ◽  
Dice Similarity Coefficient ◽  
Two Dimensional ◽  
Dimensional Model ◽  
Dose Distributions ◽  
Convolutional Networks ◽  
Dose Prediction ◽  
Fully Convolutional Networks ◽  
Percentage Difference

Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed. Among them, 50 cases were randomly chosen to conform the training set, and the remaining 10 were to construct the test set. Two U-Net fully convolutional networks predicted the dose distributions, with two-dimensional and three-dimensional convolution kernels, respectively. Computed tomography images, delineated regions of interest, or their combination were considered as input data. The accuracy of predicted results was evaluated against the clinical dose. Most types of input data retrieved a similar dose to the ground truth for organs at risk ( p > 0.05). Overall, the two-dimensional model had higher performance than the three-dimensional model ( p < 0.05). Moreover, the two-dimensional region of interest input provided the best prediction results regarding the planning target volume mean percentage difference (2.40 ± 0.18%), heart mean percentage difference (4.28 ± 2.02%), and the gamma index at 80% of the prescription dose are with tolerances of 3 mm and 3% (0.85 ± 0.03), whereas the two-dimensional combined input provided the best prediction regarding ipsilateral lung mean percentage difference (4.16 ± 1.48%), lung mean percentage difference (2.41 ± 0.95%), spinal cord mean percentage difference (0.67 ± 0.40%), and 80% Dice similarity coefficient (0.94 ± 0.01). Statistically, the two-dimensional combined inputs achieved higher prediction accuracy regarding 80% Dice similarity coefficient than the two-dimensional region of interest input (0.94 ± 0.01 vs 0.92 ± 0.01, p < 0.05). The two-dimensional data model retrieves higher performance than its three-dimensional counterpart for dose prediction, especially when using region of interest and combined inputs.

Is the END (emulation of natural disturbance) a new beginning? A critical analysis of the use of fire regimes as the basis of forest ecosystem management with examples from the Canadian western Cordillera

2016 ◽  
Vol 24 (3) ◽  
pp. 233-243 ◽  
Author(s):  
Chris Stockdale ◽  
Mike Flannigan ◽  
Ellen Macdonald
Keyword(s):  
Forest Management ◽  
Ecosystem Management ◽  
Forest Structure ◽  
Fire Regime ◽  
Natural Disturbance ◽  
Fire Regimes ◽  
Disturbance Regime ◽  
Ecological Processes ◽  
High Severity ◽  
Management Actions

As our view of disturbances such as wildfire has shifted from prevention to recognizing their ecological necessity, so too forest management has evolved from timber-focused even-aged management to more holistic paradigms like ecosystem-based management. Emulation of natural disturbance (END) is a variant of ecosystem management that recognizes the importance of disturbance for maintaining ecological integrity. For END to be a successful model for forest management we need to describe disturbance regimes and implement management actions that emulate them, in turn achieving our objectives for forest structure and function. We review the different components of fire regimes (cause, frequency, extent, timing, and magnitude), we describe low-, mixed-, and high-severity fire regimes, and we discuss key issues related to describing these regimes. When characterizing fire regimes, different methods and spatial and temporal extents result in wide variation of estimates for different fire regime components. Comparing studies is difficult as few measure the same components; some methods are based on the assumption of a high-severity fire regime and are not suited to detecting mixed- or low-severity regimes, which are critical to END management, as this would affect retention in harvested areas. We outline some difficulties with using fire regimes as coarse filters for forest management, including (i) not fully understanding the interactions between fire and other disturbance agents, (ii) assuming that fire is strictly an exogenous disturbance agent that exerts top-down control of forest structure while ignoring numerous endogenous and bottom-up feedbacks on fire effects, and (iii) assuming by only replicating natural disturbance patterns we preserve ecological processes and vital ecosystem components. Even with a good understanding of a fire regime, we would still be challenged with choosing the temporal and spatial scope for the disturbance regime we are trying to emulate. We cannot yet define forest conditions that will arise from variations in disturbance regime; this then limits our ability to implement management actions that will achieve those conditions. We end by highlighting some important knowledge gaps about fire regimes and how the END model could be strengthened to achieve a more sustainable form of forest management.

scholarly journals Vascular phenotyping of brain tumors using magnetic resonance microscopy (μMRI)

2011 ◽  
Vol 31 (7) ◽  
pp. 1623-1636 ◽  
Author(s):  
Eugene Kim ◽  
Jiangyang Zhang ◽  
Karen Hong ◽  
Nicole E Benoit ◽  
Arvind P Pathak
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Three Dimensional ◽  
Region Of Interest ◽  
Tumor Model ◽  
Magnetic Resonance Microscopy ◽  
Vascular Phenotype ◽  
Brain Tumor Model ◽  
Novel Method

Abnormal vascular phenotypes have been implicated in neuropathologies ranging from Alzheimer's disease to brain tumors. The development of transgenic mouse models of such diseases has created a crucial need for characterizing the murine neurovasculature. Although histologic techniques are excellent for imaging the microvasculature at submicron resolutions, they offer only limited coverage. It is also challenging to reconstruct the three-dimensional (3D) vasculature and other structures, such as white matter tracts, after tissue sectioning. Here, we describe a novel method for 3D whole-brain mapping of the murine vasculature using magnetic resonance microscopy (μMRI), and its application to a preclinical brain tumor model. The 3D vascular architecture was characterized by six morphologic parameters: vessel length, vessel radius, microvessel density, length per unit volume, fractional blood volume, and tortuosity. Region-of-interest analysis showed significant differences in the vascular phenotype between the tumor and the contralateral brain, as well as between postinoculation day 12 and day 17 tumors. These results unequivocally show the feasibility of using μMRI to characterize the vascular phenotype of brain tumors. Finally, we show that combining these vascular data with coregistered images acquired with diffusion-weighted MRI provides a new tool for investigating the relationship between angiogenesis and concomitant changes in the brain tumor microenvironment.

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