scholarly journals Sensitivity of Bistatic TanDEM-X Data to Stand Structural Parameters in Temperate Forests

2019 ◽  
Vol 11 (24) ◽  
pp. 2966
Author(s):  
Stefan Erasmi ◽  
Malte Semmler ◽  
Peter Schall ◽  
Michael Schlund
Keyword(s):  
Sensitivity Analysis ◽  
Structural Properties ◽  
Large Scale ◽  
Vertical Structure ◽  
Temperate Forests ◽  
Forest Type ◽  
Structural Parameters ◽  
Stem Volume ◽  
Forest Stands ◽  
Canopy Volume

Synthetic aperture radar (SAR) satellite data provide a valuable means for the large-scale and long-term monitoring of structural components of forest stands. The potential of TanDEM-X interferometric SAR (InSAR) for the assessment of forest structural properties has been widely verified. However, present studies are mostly restricted to homogeneous forests and do not account for stratification in assessing model performance. A systematic sensitivity analysis of the TanDEM-X SAR signal to forest structural parameters was carried out with emphasis on different strata of forest stands (location of the study site, forest type, and development stage). Forest structure was parameterized by forest height metrics and stem volume. Results show that X-band volume coherence is highly sensitive to the forest canopy. Volume scattering within the canopy is dependent on the vertical heterogeneity of the forest stand. In general, TanDEM-X coherence is more sensitive to forest vertical structure compared to backscatter. The relations between TanDEM-X volume coherence and forest structural properties were significant at the level of a single test site as well as across sites in temperate forests in Germany. Forest type does not affect the overall relationship between the SAR signal and the forests’ vertical structure. The prediction of forest structural parameters based on the outcome of the sensitivity analysis yielded model accuracies between 15% (relative root mean square error) for Lorey’s height and 32% for stem volume. The global database of single-polarized bistatic TanDEM-X data provides an important source for mapping structural parameters in temperate forests at large scale, irrespective of forest type.

scholarly journals Monitoring Exotic Beetles with Inexpensive Attractants: A Case Study

Insects ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 462
Author(s):  
Enrico Ruzzier ◽  
Andrea Galli ◽  
Luciano Bani
Keyword(s):  
Native Species ◽  
Large Scale ◽  
Red Wine ◽  
Forest Type ◽  
Popillia Japonica ◽  
Complex Activity ◽  
Local Fauna ◽  
Apple Cider ◽  
Trap Placement

Detecting and monitoring exotic and invasive Coleoptera is a complex activity to implement, and citizen science projects can provide significant contributions to such plans. Bottle traps are successfully used in wildlife surveys and can also be adapted for monitoring alien species; however, a sustainable, large scale trapping plan must take into account the collateral catches of native species and thus minimize its impact on local fauna. In the present paper, we tested the use of bottles baited with standard food products that can be purchased in every supermarket and immediately used (apple cider vinegar, red wine, and 80% ethyl alcohol) in capturing exotic and invasive beetles in the area surrounding Malpensa Airport (Italy). In particular, we reduced the exposition type of the traps in each sampling round to three days in order to minimize native species collecting. We found a significant effect of the environmental covariates (trap placement, temperature, humidity, and forest type) in affecting the efficiency in catching target beetles. Nearly all invasive Nitidulidae and Scarabaeidae known to be present in the area were captured in the traps, with apple cider vinegar usually being the most effective attractant, especially for the invasive Popillia japonica.

Derivation of Pheromone-Based Larval Thresholds in Spruce Budworm Accounting for Distance to Defoliated Forest Stands

2021 ◽  
Author(s):  
Marc Rhainds ◽  
Ian DeMerchant ◽  
Pierre Therrien
Keyword(s):  
Boreal Forests ◽  
Large Scale ◽  
Choristoneura Fumiferana ◽  
Intervention Strategy ◽  
Spruce Budworm ◽  
Interactive Effects ◽  
The Novel ◽  
Forest Stands ◽  
General Guideline ◽  
Number Of Males

Abstract Spruce budworm, Choristoneura fumiferana Clem. (Lepidoptera: Tortricidae), is the most severe defoliator of Pinaceae in Nearctic boreal forests. Three tools widely used to guide large-scale management decisions (year-to-year defoliation maps; density of overwintering second instars [L2]; number of males at pheromone traps) were integrated to derive pheromone-based thresholds corresponding to specific intergenerational transitions in larval densities (L2i → L2i+1), taking into account the novel finding that threshold estimates decline with distance to defoliated forest stands (DIST). Estimates of thresholds were highly variable between years, both numerically and in terms of interactive effects of L2i and DIST, which limit their heuristic value. In the context of early intervention strategy (L2i+1 > 6.5 individuals per branch), however, thresholds fluctuated within relatively narrow intervals across wide ranges of L2i and DIST, and values of 40–200 males per trap may thus be used as general guideline.

Fire history on a desert mountain range: Rincon Mountain Wilderness, Arizona, U.S.A.

1990 ◽  
Vol 20 (10) ◽  
pp. 1559-1569 ◽  
Author(s):  
Christopher H. Baisan ◽  
Thomas W. Swetnam
Keyword(s):  
Tree Ring ◽  
Large Scale ◽  
Fire History ◽  
Fire Regime ◽  
Forest Type ◽  
Drought Severity ◽  
Mountain Range ◽  
Mixed Conifer ◽  
The Mean ◽  
Mean Fire Interval

Modern fire records and fire-scarred remnant material collected from logs, snags, and stumps were used to reconstruct and analyze fire history in the mixed-conifer and pine forest above 2300 m within the Rincon Mountain Wilderness of Saguaro National Monument, Arizona, United States. Cross-dating of the remnant material allowed dating of fire events to the calendar year. Estimates of seasonal occurrence were compiled for larger fires. It was determined that the fire regime was dominated by large scale (> 200 ha), early-season (May–July) surface fires. The mean fire interval over the Mica Mountain study area for the period 1657–1893 was 6.1 years with a range of 1–13 years for larger fires. The mean fire interval for the mixed-conifer forest type (1748–1886) was 9.9 years with a range of 3–19 years. Thirty-five major fire years between 1700 and 1900 were compared with a tree-ring reconstruction of the Palmer drought severity index (PDSI). Mean July PDSI for 2 years prior to fires was higher (wetter) than average, while mean fire year PDSI was near average. This 490-year record of fire occurrence demonstrates the value of high-resolution (annual and seasonal) tree-ring analyses for documenting and interpreting temporal and spatial patterns of past fire regimes.

scholarly journals Sensitivity Analysis of the Influence of Structural Parameters on Dynamic Behaviour of Highly Redundant Cable-Stayed Bridges

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
B. Asgari ◽  
S. A. Osman ◽  
A. Adnan
Keyword(s):  
Sensitivity Analysis ◽  
Finite Element ◽  
Finite Element Model ◽  
Model Updating ◽  
Structural Parameters ◽  
Element Model ◽  
Structural Behavior ◽  
Model Tuning ◽  
The Finite Element Model ◽  
Cable Stayed Bridges

The model tuning through sensitivity analysis is a prominent procedure to assess the structural behavior and dynamic characteristics of cable-stayed bridges. Most of the previous sensitivity-based model tuning methods are automatic iterative processes; however, the results of recent studies show that the most reasonable results are achievable by applying the manual methods to update the analytical model of cable-stayed bridges. This paper presents a model updating algorithm for highly redundant cable-stayed bridges that can be used as an iterative manual procedure. The updating parameters are selected through the sensitivity analysis which helps to better understand the structural behavior of the bridge. The finite element model of Tatara Bridge is considered for the numerical studies. The results of the simulations indicate the efficiency and applicability of the presented manual tuning method for updating the finite element model of cable-stayed bridges. The new aspects regarding effective material and structural parameters and model tuning procedure presented in this paper will be useful for analyzing and model updating of cable-stayed bridges.

Parameter Sensitivity Analysis of a Large-Scale System With Several Components Interacting in Series

1989 ◽  
Author(s):  
H. Torab
Keyword(s):  
Sensitivity Analysis ◽  
Large Scale ◽  
Optimization Problems ◽  
Parameter Sensitivity ◽  
Engineering Optimization ◽  
Parameter Sensitivity Analysis ◽  
Large Scale Systems ◽  
In Series ◽  
Special Case ◽  
Engineering Optimization Problems

Abstract Parameter sensitivity for large-scale systems that include several components which interface in series is presented. Large-scale systems can be divided into components or sub-systems to avoid excessive calculations in determining their optimum design. Model Coordination Method of Decomposition (MCMD) is one of the most commonly used methods to solve large-scale engineering optimization problems. In the Model Coordination Method of Decomposition, the vector of coordinating variables can be partitioned into two sub-vectors for systems with several components interacting in series. The first sub-vector consists of those variables that are common among all or most of the elements. The other sub-vector consists of those variables that are common between only two components that are in series. This study focuses on a parameter sensitivity analysis for this special case using MCMD.

scholarly journals Seasonality is more important than forest type in regulating the pool size and composition of soil soluble N in temperate forests

2020 ◽  
Vol 150 (3) ◽  
pp. 279-295
Author(s):  
Lei Gao ◽  
Paul W. Hill ◽  
Davey L. Jones ◽  
Yafen Guo ◽  
Fei Gao ◽  
...  
Keyword(s):  
Temperate Forests ◽  
Forest Type ◽  
Pool Size

scholarly journals Machine learning-based feature importance approach for sensitivity analysis of steel frames

2021 ◽  
Author(s):  
Hyeyoung Koh ◽  
Hannah Beth Blum
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Feature Selection ◽  
Large Scale ◽  
Failure Modes ◽  
Model Development ◽  
Predictive Performance ◽  
Computational Effort ◽  
Structural Systems ◽  
Feature Importance

This study presents a machine learning-based approach for sensitivity analysis to examine how parameters affect a given structural response while accounting for uncertainty. Reliability-based sensitivity analysis involves repeated evaluations of the performance function incorporating uncertainties to estimate the influence of a model parameter, which can lead to prohibitive computational costs. This challenge is exacerbated for large-scale engineering problems which often carry a large quantity of uncertain parameters. The proposed approach is based on feature selection algorithms that rank feature importance and remove redundant predictors during model development which improve model generality and training performance by focusing only on the significant features. The approach allows performing sensitivity analysis of structural systems by providing feature rankings with reduced computational effort. The proposed approach is demonstrated with two designs of a two-bay, two-story planar steel frame with different failure modes: inelastic instability of a single member and progressive yielding. The feature variables in the data are uncertainties including material yield strength, Young’s modulus, frame sway imperfection, and residual stress. The Monte Carlo sampling method is utilized to generate random realizations of the frames from published distributions of the feature parameters, and the response variable is the frame ultimate strength obtained from finite element analyses. Decision trees are trained to identify important features. Feature rankings are derived by four feature selection techniques including impurity-based, permutation, SHAP, and Spearman's correlation. Predictive performance of the model including the important features are discussed using the evaluation metric for imbalanced datasets, Matthews correlation coefficient. Finally, the results are compared with those from reliability-based sensitivity analysis on the same example frames to show the validity of the feature selection approach. As the proposed machine learning-based approach produces the same results as the reliability-based sensitivity analysis with improved computational efficiency and accuracy, it could be extended to other structural systems.

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