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1. Fusion of physically-based registration and deformation modeling for nonrigid motion analysis

   https://doi.org/10.1109/83.967394  

   Tsap, L.V.; Goldgof, D.B.; Sarkar, S. (November 2001, IEEE Transactions on Image Processing)

   In our previous work, we used finite element models to determine nonrigid motion parameters and recover unknown local properties of objects given correspondence data recovered with snakes or other tracking models. In this paper, we present a novel multiscale approach to recovery of nonrigid motion from sequences of registered intensity and range images. The main idea of our approach is that a finite element (FEM) model incorporating material properties of the object can naturally handle both registration and deformation modeling using a single model-driving strategy. The method includes a multiscale iterative algorithm based on analysis of the undirected Hausdorff distance to recover correspondences. The method is evaluated with respect to speed and accuracy. Noise sensitivity issues are addressed. Advantages of the proposed approach are demonstrated using man-made elastic materials and human skin motion. Experiments with regular grid features are used for performance comparison with a conventional approach (separate snakes and FEM models). It is shown, however, that the new method does not require a sampling/correspondence template and can adapt the model to available object features. Usefulness of the method is presented not only in the context of tracking and motion analysis, but also for a burn scar detection application. 

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2. Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

   https://doi.org/10.3390/rs13101966  

   Smith, Christopher W; Panda, Santosh K; Bhatt, Uma S; Meyer, Franz J; Badola, Anushree; Hrobak, Jennifer L (May 2021, Remote Sensing)

   null (Ed.)

   In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent. 

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3. Soil microbiome feedbacks during disturbance-driven forest ecosystem conversion

   https://doi.org/10.1093/ismejo/wrae047  

   Nelson, Amelia R.; Fegel, Timothy S.; Danczak, Robert E.; Caiafa, Marcos V.; Roth, Holly K.; Dunn, Oliver I.; Turvold, Cosette A.; Borch, Thomas; Glassman, Sydney I.; Barnes, Rebecca T.; et al (March 2024, The ISME Journal)

   Abstract Disturbances cause rapid changes to forests, with different disturbance types and severities creating unique ecosystem trajectories that can impact the underlying soil microbiome. Pile burning—the combustion of logging residue on the forest floor—is a common fuel reduction practice that can have impacts on forest soils analogous to those following high-severity wildfire. Further, pile burning following clear-cut harvesting can create persistent openings dominated by nonwoody plants surrounded by dense regenerating conifer forest. A paired 60-year chronosequence of burn scar openings and surrounding regenerating forest after clear-cut harvesting provides a unique opportunity to assess whether belowground microbial processes mirror aboveground vegetation during disturbance-induced ecosystem shifts. Soil ectomycorrhizal fungal diversity was reduced the first decade after pile burning, which could explain poor tree seedling establishment and subsequent persistence of herbaceous species within the openings. Fine-scale changes in the soil microbiome mirrored aboveground shifts in vegetation, with short-term changes to microbial carbon cycling functions resembling a postfire microbiome (e.g. enrichment of aromatic degradation genes) and respiration in burn scars decoupled from substrate quantity and quality. Broadly, however, soil microbiome composition and function within burn scar soils converged with that of the surrounding regenerating forest six decades after the disturbances, indicating potential microbial resilience that was disconnected from aboveground vegetation shifts. This work begins to unravel the belowground microbial processes that underlie disturbance-induced ecosystem changes, which are increasing in frequency tied to climate change. 

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4. Investigation of the Homogenized Anisotropy for the Triply Periodic Minimal Surface Lattice under Varying Applied Stress Orientation

   Wei, Chongyi; Smith, DE (September 2024, 2024 Solid Freeform Fabrication Conference)

   The advance of additive manufacturing makes it possible to design spatially varying lattice structures with complex geometric configurations. The homogenized elastic properties of these periodic lattice structures are known to deviate significantly from isotropic behavior where orthotropic material symmetry is often assumed. This paper addresses the need for a robust homogenization method for evaluating anisotropy of periodic lattice structures including an understanding of how the elastic properties transform under rotation. Here, periodic boundary conditions are applied on two-material representative volume element (RVE) finite element models to evaluate the complete homogenized stiffness tensor. A constrained multi-output regression approach is proposed to evaluate the elasticity tensor components under any assumed material symmetry model. This approach is applied to various lattice structures including scaffold and surface-based Triply Periodic Minimal Surface (TPMS). Our approach is used to assess the accuracy of rotation for assumed anisotropic and orthotropic homogenized material models over a range of lattice structures. 

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5. A Robust Mesh Moving Method for Moving-Boundary Problems

   https://doi.org/10.2514/6.2024-1163  

   Tu, Shuangzhang; Jiang, Chao (January 2024, American Institute of Aeronautics and Astronautics)

   This paper presents a robust mesh moving solver developed to address moving boundary problems. Crucially, the resulting deformed mesh retains the same topology as the original mesh without being overly distorted. The mesh is treated as an elastic material, and the deformation of the computational domain resulting from moving boundaries is determined by solving the equilibrium linear elasticity equations. The linear elasticity equations are discretized by the classic Galerkin finite element method and solved by the block conjugate gradient iterative method. To maintain the quality of the mesh after motion, the Young's modulus of each element is weighted by the reciprocal of the distance between the element center and the moving boundaries. The effectiveness of this approach is demonstrated through a set of 2D and 3D test cases featuring prescribed translational and/or rotational motion of the embedded object. The method is now ready for integration into our existing in-house CFD solvers. 

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