FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2165794527> ?p ?o ?g. }

• W2165794527 endingPage "47" @default.
• W2165794527 startingPage "38" @default.
• W2165794527 abstract "Forest ecosystems are subject to a variety of disturbances with increasing intensities and frequencies, which may permanently change the trajectories of forest recovery and disrupt the ecosystem services provided by trees. Fire and invasive species, especially exotic disease-causing pathogens and insects, are examples of disturbances that together could pose major threats to forest health. This study examines the impacts of fire and exotic disease (sudden oak death) on forests, with an emphasis on the assessment of post-fire burn severity in a forest where trees have experienced three stages of disease progression pre-fire: early-stage (trees retaining dried foliage and fine twigs), middle-stage (trees losing fine crown fuels), and late-stage (trees falling down). The research was conducted by applying Geographic Object-Based Image Analysis (GEOBIA) to MASTER airborne images that were acquired immediately following the fire for rapid assessment and contained both high-spatial (4 m) and high-spectral (50 bands) resolutions. Although GEOBIA has gradually become a standard tool for analyzing high-spatial resolution imagery, high-spectral resolution data (dozens to hundreds of bands) can dramatically reduce computation efficiency in the process of segmentation and object-based variable extraction, leading to complicated variable selection for succeeding modeling. Hence, we also assessed two widely used band reduction algorithms, PCA (principal component analysis) and MNF (minimum noise fraction), for the delineation of image objects and the subsequent performance of burn severity models using either PCA or MNF derived variables. To increase computation efficiency, only the top 5 PCA and MNF and top 10 PCA and MNF components were evaluated, which accounted for 10% and 20% of the total number of the original 50 spectral bands, respectively. Results show that if no band reduction was applied the models developed for the three stages of disease progression had relatively similar performance, where both spectral responses and texture contributed to burn assessments. However, the application of PCA and MNF introduced much greater variation among models across the three stages. For the early-stage disease progression, neither band reduction algorithms improved or retained the accuracy of burn severity modeling (except for the use of 10 MNF components). Compared to the no-band-reduction scenario, band reduction led to a greater level of overestimation of low-degree burns and underestimation of medium-degree burns, suggesting that the spectral variation removed by PCA and MNF was vital for distinguishing between the spectral reflectance from disease-induced dried crowns (still retaining high structural complexity) and fire ash. For the middle-stage, both algorithms improved the model R2 values by 2–37%, while the late-stage models had comparable or better performance to those using the original 50 spectral bands. This could be explained by the loss of tree crowns enabling better signal penetration, thus leading to reduced spectral variation from canopies. Hence, spectral bands containing a high degree of random noise were correctly removed by the band reduction algorithms. Compared to the middle-stage, the late-stage forest stands were covered by large piles of fallen trees and branches, resulting in higher variability of MASTER imagery. The ability of band reduction to improve the model performance for these late-stage forest stands was reduced, because the valuable spectral variation representing the actual late-stage forest status was partially removed by both algorithms as noise. Our results indicate that PCA and MNF are promising for balancing computation efficiency and the performance of burn severity models in forest stands subject to the middle and late stages of sudden oak death disease progression. Compared to PCA, MNF dramatically reduced image spectral variation, generating larger image objects with less complexity of object shapes. Whereas, PCA-based models delivered superior performance in most evaluated cases suggesting that some key spectral variability contributing to the accuracy of burn severity models in diseased forests may have been removed together with true spectral noise through MNF transformations." @default.
• W2165794527 created "2016-06-24" @default.
• W2165794527 creator A5011456727 @default.
• W2165794527 creator A5031405800 @default.
• W2165794527 creator A5075844125 @default.
• W2165794527 creator A5083224934 @default.
• W2165794527 creator A5086106525 @default.
• W2165794527 date "2015-04-01" @default.
• W2165794527 modified "2023-09-27" @default.
• W2165794527 title "Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery" @default.
• W2165794527 cites W1969560639 @default.
• W2165794527 cites W1969935516 @default.
• W2165794527 cites W1972812213 @default.
• W2165794527 cites W1985007599 @default.
• W2165794527 cites W1986504269 @default.
• W2165794527 cites W1987656037 @default.
• W2165794527 cites W1991011355 @default.
• W2165794527 cites W1998538617 @default.
• W2165794527 cites W2011688956 @default.
• W2165794527 cites W2024084309 @default.
• W2165794527 cites W2026337641 @default.
• W2165794527 cites W2027944437 @default.
• W2165794527 cites W2028991610 @default.
• W2165794527 cites W2035083333 @default.
• W2165794527 cites W2040026378 @default.
• W2165794527 cites W2045551504 @default.
• W2165794527 cites W2046339565 @default.
• W2165794527 cites W2046639742 @default.
• W2165794527 cites W2051564354 @default.
• W2165794527 cites W2054518059 @default.
• W2165794527 cites W2098514340 @default.
• W2165794527 cites W2103079830 @default.
• W2165794527 cites W2105391092 @default.
• W2165794527 cites W2110126622 @default.
• W2165794527 cites W2112186536 @default.
• W2165794527 cites W2112887935 @default.
• W2165794527 cites W2115715711 @default.
• W2165794527 cites W2116032305 @default.
• W2165794527 cites W2119196385 @default.
• W2165794527 cites W2136625467 @default.
• W2165794527 cites W2138078010 @default.
• W2165794527 cites W2139295303 @default.
• W2165794527 cites W2140643712 @default.
• W2165794527 cites W2142939943 @default.
• W2165794527 cites W2144059664 @default.
• W2165794527 cites W2144432426 @default.
• W2165794527 cites W2144606028 @default.
• W2165794527 cites W2152465183 @default.
• W2165794527 cites W2167794310 @default.
• W2165794527 doi "https://doi.org/10.1016/j.isprsjprs.2015.01.004" @default.
• W2165794527 hasPublicationYear "2015" @default.
• W2165794527 type Work @default.
• W2165794527 sameAs 2165794527 @default.
• W2165794527 citedByCount "27" @default.
• W2165794527 countsByYear W21657945272015 @default.
• W2165794527 countsByYear W21657945272016 @default.
• W2165794527 countsByYear W21657945272017 @default.
• W2165794527 countsByYear W21657945272018 @default.
• W2165794527 countsByYear W21657945272019 @default.
• W2165794527 countsByYear W21657945272020 @default.
• W2165794527 countsByYear W21657945272021 @default.
• W2165794527 countsByYear W21657945272022 @default.
• W2165794527 countsByYear W21657945272023 @default.
• W2165794527 crossrefType "journal-article" @default.
• W2165794527 hasAuthorship W2165794527A5011456727 @default.
• W2165794527 hasAuthorship W2165794527A5031405800 @default.
• W2165794527 hasAuthorship W2165794527A5075844125 @default.
• W2165794527 hasAuthorship W2165794527A5083224934 @default.
• W2165794527 hasAuthorship W2165794527A5086106525 @default.
• W2165794527 hasBestOaLocation W21657945272 @default.
• W2165794527 hasConcept C113174947 @default.
• W2165794527 hasConcept C134306372 @default.
• W2165794527 hasConcept C142724271 @default.
• W2165794527 hasConcept C154945302 @default.
• W2165794527 hasConcept C205372480 @default.
• W2165794527 hasConcept C205649164 @default.
• W2165794527 hasConcept C27438332 @default.
• W2165794527 hasConcept C2776133958 @default.
• W2165794527 hasConcept C33923547 @default.
• W2165794527 hasConcept C39432304 @default.
• W2165794527 hasConcept C41008148 @default.
• W2165794527 hasConcept C58640448 @default.
• W2165794527 hasConcept C62649853 @default.
• W2165794527 hasConcept C71924100 @default.
• W2165794527 hasConcept C89600930 @default.
• W2165794527 hasConceptScore W2165794527C113174947 @default.
• W2165794527 hasConceptScore W2165794527C134306372 @default.
• W2165794527 hasConceptScore W2165794527C142724271 @default.
• W2165794527 hasConceptScore W2165794527C154945302 @default.
• W2165794527 hasConceptScore W2165794527C205372480 @default.
• W2165794527 hasConceptScore W2165794527C205649164 @default.
• W2165794527 hasConceptScore W2165794527C27438332 @default.
• W2165794527 hasConceptScore W2165794527C2776133958 @default.
• W2165794527 hasConceptScore W2165794527C33923547 @default.
• W2165794527 hasConceptScore W2165794527C39432304 @default.
• W2165794527 hasConceptScore W2165794527C41008148 @default.
• W2165794527 hasConceptScore W2165794527C58640448 @default.
• W2165794527 hasConceptScore W2165794527C62649853 @default.

• next