scholarly journals Asian needle ant (Brachyponera chinensis) and woodland ant responses to repeated applications of fuel reduction methods

Ecosphere ◽  
2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Joshua W. Campbell ◽  
Steven M. Grodsky ◽  
Dale A. Halbritter ◽  
Patrick A. Vigueira ◽  
Cynthia C. Vigueira ◽  
...  
Keyword(s):  
Fuel Reduction ◽  
Reduction Methods ◽  
Repeated Applications ◽  
Asian Needle Ant

scholarly journals The national Fire and Fire Surrogate study: effects of fuel reduction methods on forest vegetation structure and fuels

2009 ◽  
Vol 19 (2) ◽  
pp. 285-304 ◽  
Author(s):  
Dylan W. Schwilk ◽  
Jon E. Keeley ◽  
Eric E. Knapp ◽  
James McIver ◽  
John D. Bailey ◽  
...  
Keyword(s):  
Vegetation Structure ◽  
Forest Vegetation ◽  
Fuel Reduction ◽  
Reduction Methods

scholarly journals Damage to Residual Trees and Advance Regeneration from Skyline and Forwarder Yarding in Mixed-Conifer Stands of Northeastern Oregon

2000 ◽  
Vol 15 (2) ◽  
pp. 101-107 ◽  
Author(s):  
Andrew Youngblood
Keyword(s):  
Pinus Contorta ◽  
Fuel Reduction ◽  
Mixed Conifer ◽  
Larix Occidentalis ◽  
Partial Cutting ◽  
Blue Mountains ◽  
Engelmann Spruce ◽  
Advance Regeneration ◽  
Large Trees ◽  
Reduction Methods

Abstract Reducing the risk of occurrence of wildfire and outbreaks of insects and diseases through fuel reduction is a priority management objective on federal lands within the Blue Mountains in northeastern Oregon. Optimal methods to achieve desired levels of fuel in mixed conifer stands by mechanical means are as yet unknown. One factor essential in evaluating optimal fuel reduction methods is the damage to residual trees and advance regeneration associated with specific combinations of stand condition, prescription, and harvesting system. Residual stem damage on 12,899 stems was compared after partial cutting and yarding with either skyline or ground-based forwarder in mixed conifer stands of northeastern Oregon. There were 6,092 occurrences of damage on 4,074 stems after yarding; 4.1% of the damaged stems had crushed foliage, 15.4% had a broken terminal leader, 26.5% had broken branches, 28.9% were wrenched, 35.0% had scraped bark, and 38.9% had bole scars. Fir (Abies grandis and A. lasiocarpa) seedlings were more frequently damaged than nonfir (Larix occidentalis, Picea engelmannii, and Pinus contorta) seedlings, and the most frequent damage to fir seedlings occurred in units treated by the forwarder. More damage occurred to residual large trees during yarding than to seedlings. Forwarder yarding resulted in slightly more damage to trees than did skyline yarding. Wrenching was generally consistent between residual seedlings and trees. Scarring occurred more frequently to residual trees than to seedlings. Mean scar area per tree on those actually scarred was generally about 40 cm² on seedlings and 256 cm² on residual trees. Despite slight differences in stand damage, both yarding methods met the silviculture prescription of reducing fuel and protecting large western latch, Engelmann spruce, Douglas-fir, and lodgepole pine stems targeted for retention. This suggests that the decision by resource managers to use one method of yarding over the other should probably be based on considerations such as availability of equipment, costs, and soil impacts. West. J. Appl. For. 15(2):101-107.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

Sign in / Sign up

Export Citation Format

Share Document