FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2045973791> ?p ?o ?g. }

• W2045973791 endingPage "2395" @default.
• W2045973791 startingPage "2386" @default.
• W2045973791 abstract "The occurrence and spatial arrangement of post-fire alive residual stands affect the recolonization of trees and animals of burned areas in boreal ecosystems. Because the analysis of residual stands in the field is prohibitively expensive we lack understanding on how residual stands are distributed and why. Here, we explore the use of high-resolution Quickbird satellite imagery in conjunction with in-situ measurements and machine learning techniques to map basal area of spruce and fir for two fire areas in Central Siberia, and analyze the distribution of residual stands with respect to topography. First, an advanced feature selection algorithm which combines a genetic algorithm with guided local search is wrapped around the Random Forests regression technique, to identify suitable variable subsets out of a large number of candidate variables that were derived from Quickbird data. Second, we train and apply Random Forests using the derived variable subsets to the two fire areas to generate spatially explicit estimates of basal area for spruce and fir. Third, we analyze species specific differences and the relationship between basal area and topography using a high resolution digital elevation model from ASTER data. Our results show that the main gradients of species specific basal area can be reproduced using Quickbird data but stress the importance of variable selection. We find associations of residual stands with topography - depressions and channels exhibit larger prevalence of residual stands than ridges or plateaus, the latter being more often subject to severe fires. We further found that the relationship between basal area and elevation tends to be reversed inside the burned area in comparison to the surrounding unburned forest. Our results suggest that local topography may control the sensitivity of ecological processes to a changing fire regime with more severe fires, and highlight the synergistic use of high resolution satellite remote sensing and machine learning methods for fire ecological applications." @default.
• W2045973791 created "2016-06-24" @default.
• W2045973791 creator A5034705495 @default.
• W2045973791 creator A5060621645 @default.
• W2045973791 creator A5081080326 @default.
• W2045973791 creator A5088518218 @default.
• W2045973791 date "2013-01-01" @default.
• W2045973791 modified "2023-09-23" @default.
• W2045973791 title "Estimating Basal Area of Spruce and Fir in Post-fire Residual Stands in Central Siberia Using Quickbird, Feature Selection, and Random Forests" @default.
• W2045973791 cites W1988559119 @default.
• W2045973791 cites W2017724555 @default.
• W2045973791 cites W2033904036 @default.
• W2045973791 cites W2063623478 @default.
• W2045973791 cites W2065172737 @default.
• W2045973791 cites W2068431118 @default.
• W2045973791 cites W2089790291 @default.
• W2045973791 cites W2094677081 @default.
• W2045973791 cites W2104940013 @default.
• W2045973791 cites W2113242816 @default.
• W2045973791 cites W2113410727 @default.
• W2045973791 cites W2117897510 @default.
• W2045973791 cites W2129214137 @default.
• W2045973791 cites W2131291252 @default.
• W2045973791 cites W2133348937 @default.
• W2045973791 cites W2144059664 @default.
• W2045973791 cites W2148633389 @default.
• W2045973791 cites W2148782288 @default.
• W2045973791 cites W2149390613 @default.
• W2045973791 cites W2164391795 @default.
• W2045973791 cites W2911964244 @default.
• W2045973791 cites W4210895022 @default.
• W2045973791 cites W1999831533 @default.
• W2045973791 doi "https://doi.org/10.1016/j.procs.2013.05.410" @default.
• W2045973791 hasPublicationYear "2013" @default.
• W2045973791 type Work @default.
• W2045973791 sameAs 2045973791 @default.
• W2045973791 citedByCount "12" @default.
• W2045973791 countsByYear W20459737912016 @default.
• W2045973791 countsByYear W20459737912017 @default.
• W2045973791 countsByYear W20459737912018 @default.
• W2045973791 countsByYear W20459737912019 @default.
• W2045973791 countsByYear W20459737912020 @default.
• W2045973791 countsByYear W20459737912022 @default.
• W2045973791 countsByYear W20459737912023 @default.
• W2045973791 crossrefType "journal-article" @default.
• W2045973791 hasAuthorship W2045973791A5034705495 @default.
• W2045973791 hasAuthorship W2045973791A5060621645 @default.
• W2045973791 hasAuthorship W2045973791A5081080326 @default.
• W2045973791 hasAuthorship W2045973791A5088518218 @default.
• W2045973791 hasBestOaLocation W20459737911 @default.
• W2045973791 hasConcept C100537666 @default.
• W2045973791 hasConcept C100970517 @default.
• W2045973791 hasConcept C11413529 @default.
• W2045973791 hasConcept C138885662 @default.
• W2045973791 hasConcept C148483581 @default.
• W2045973791 hasConcept C154945302 @default.
• W2045973791 hasConcept C155512373 @default.
• W2045973791 hasConcept C169258074 @default.
• W2045973791 hasConcept C181843262 @default.
• W2045973791 hasConcept C18903297 @default.
• W2045973791 hasConcept C205649164 @default.
• W2045973791 hasConcept C2524010 @default.
• W2045973791 hasConcept C2776401178 @default.
• W2045973791 hasConcept C33923547 @default.
• W2045973791 hasConcept C37054046 @default.
• W2045973791 hasConcept C41008148 @default.
• W2045973791 hasConcept C41895202 @default.
• W2045973791 hasConcept C62649853 @default.
• W2045973791 hasConcept C86803240 @default.
• W2045973791 hasConcept C87621631 @default.
• W2045973791 hasConcept C91354502 @default.
• W2045973791 hasConcept C97137747 @default.
• W2045973791 hasConceptScore W2045973791C100537666 @default.
• W2045973791 hasConceptScore W2045973791C100970517 @default.
• W2045973791 hasConceptScore W2045973791C11413529 @default.
• W2045973791 hasConceptScore W2045973791C138885662 @default.
• W2045973791 hasConceptScore W2045973791C148483581 @default.
• W2045973791 hasConceptScore W2045973791C154945302 @default.
• W2045973791 hasConceptScore W2045973791C155512373 @default.
• W2045973791 hasConceptScore W2045973791C169258074 @default.
• W2045973791 hasConceptScore W2045973791C181843262 @default.
• W2045973791 hasConceptScore W2045973791C18903297 @default.
• W2045973791 hasConceptScore W2045973791C205649164 @default.
• W2045973791 hasConceptScore W2045973791C2524010 @default.
• W2045973791 hasConceptScore W2045973791C2776401178 @default.
• W2045973791 hasConceptScore W2045973791C33923547 @default.
• W2045973791 hasConceptScore W2045973791C37054046 @default.
• W2045973791 hasConceptScore W2045973791C41008148 @default.
• W2045973791 hasConceptScore W2045973791C41895202 @default.
• W2045973791 hasConceptScore W2045973791C62649853 @default.
• W2045973791 hasConceptScore W2045973791C86803240 @default.
• W2045973791 hasConceptScore W2045973791C87621631 @default.
• W2045973791 hasConceptScore W2045973791C91354502 @default.
• W2045973791 hasConceptScore W2045973791C97137747 @default.
• W2045973791 hasLocation W20459737911 @default.
• W2045973791 hasLocation W20459737912 @default.
• W2045973791 hasOpenAccess W2045973791 @default.
• W2045973791 hasPrimaryLocation W20459737911 @default.

• next