FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4210874597> ?p ?o ?g. }

• W4210874597 endingPage "103510" @default.
• W4210874597 startingPage "103510" @default.
• W4210874597 abstract "The degradation of permafrost in the Earth's cold regions due to climate warming and human activities is of increasing concern because of its impact on ecosystems and land management, thus, monitoring the degradation becomes critical. This study aimed to demonstrate a method in which Sentinel-1 DInSAR and MODIS data were combined to estimate and map seasonal and long-term land surface deformation (subsidence/uplifting) and permafrost active layer thickness (ALT) in Donnelly Training Area (DTA) in Alaska. The coherence (similarity) between the paired SAR images was analyzed for their applicability. A sensitivity analysis and accuracy assessment of the estimates were conducted to account for their quality. Results showed that the estimated seasonal subsidence mainly occurring in June and July was in the range of 0 to −0.43 m, while the estimated uplifting mainly happening from September to May of next year was as great as +0.34 m. Moreover, the estimated long-term (2015 to 2018) subsidence and uplifting were mostly distributed in the southern and northern parts of DTA, respectively. The spatially variable time delays led to the ALT estimates greater along the east river and in the west and south parts, and smaller in the north parts. At the significance level of 0.05, the coherence estimates of the paired images were significantly different from zero and compared with the referenced predictions from a widely used annual prediction model, the average residual of the ALT estimates did not significantly differ from zero at the significance level of 0.05. The spatial distributions of the uncertainties for the seasonal surface deformation estimates were similar to those of the input uncertainties from the estimates of model coefficients, the phase change, the modelling error, and the image pairs. Thus, the DInSAR image based method coupled with MODIS data offered the potential of mapping and monitoring the dynamics of permafrost environment for the cold regions such as DTA in which collecting field observations is difficult and costly. This study also enhanced understanding spatiotemporal variability of permafrost deformation in DTA and provided guidelines for developing a near real-time monitoring system of the permafrost environment. • Permafrost degradation due to climate warming and human activity is of great concern. • Land surface deformation of an installation in Alaska is mapped by SAR&MODIS images. • A quality assessment of the land surface deformation estimates is conducted. • The accuracy of the estimates is compatible with that from a widely used model. • This study provides guidelines for developing a near real-time monitoring system." @default.
• W4210874597 created "2022-02-09" @default.
• W4210874597 creator A5020051427 @default.
• W4210874597 creator A5025332620 @default.
• W4210874597 creator A5030277448 @default.
• W4210874597 creator A5044444399 @default.
• W4210874597 creator A5053192302 @default.
• W4210874597 creator A5061343064 @default.
• W4210874597 creator A5072315367 @default.
• W4210874597 creator A5086878627 @default.
• W4210874597 creator A5089599217 @default.
• W4210874597 date "2022-04-01" @default.
• W4210874597 modified "2023-10-18" @default.
• W4210874597 title "Spatially characterizing land surface deformation and permafrost active layer thickness for Donnelly installation of Alaska using DInSAR and MODIS data" @default.
• W4210874597 cites W2000104946 @default.
• W4210874597 cites W2009225674 @default.
• W4210874597 cites W2014385813 @default.
• W4210874597 cites W2015702996 @default.
• W4210874597 cites W2016798699 @default.
• W4210874597 cites W2016841772 @default.
• W4210874597 cites W2019452902 @default.
• W4210874597 cites W2022855924 @default.
• W4210874597 cites W2025719935 @default.
• W4210874597 cites W2029654989 @default.
• W4210874597 cites W2031697714 @default.
• W4210874597 cites W2033061003 @default.
• W4210874597 cites W2050501721 @default.
• W4210874597 cites W2053548517 @default.
• W4210874597 cites W2060739529 @default.
• W4210874597 cites W2065989874 @default.
• W4210874597 cites W2070276566 @default.
• W4210874597 cites W2123896593 @default.
• W4210874597 cites W2129063635 @default.
• W4210874597 cites W2141414687 @default.
• W4210874597 cites W2144803728 @default.
• W4210874597 cites W2146257778 @default.
• W4210874597 cites W2148505991 @default.
• W4210874597 cites W2152657318 @default.
• W4210874597 cites W2156311995 @default.
• W4210874597 cites W2159625559 @default.
• W4210874597 cites W2174009612 @default.
• W4210874597 cites W2174625957 @default.
• W4210874597 cites W2519016947 @default.
• W4210874597 cites W2557459010 @default.
• W4210874597 cites W2583639353 @default.
• W4210874597 cites W2756270744 @default.
• W4210874597 cites W2791962623 @default.
• W4210874597 cites W2888773612 @default.
• W4210874597 cites W2889156065 @default.
• W4210874597 cites W2904418510 @default.
• W4210874597 cites W2943415823 @default.
• W4210874597 doi "https://doi.org/10.1016/j.coldregions.2022.103510" @default.
• W4210874597 hasPublicationYear "2022" @default.
• W4210874597 type Work @default.
• W4210874597 citedByCount "3" @default.
• W4210874597 countsByYear W42108745972022 @default.
• W4210874597 countsByYear W42108745972023 @default.
• W4210874597 crossrefType "journal-article" @default.
• W4210874597 hasAuthorship W4210874597A5020051427 @default.
• W4210874597 hasAuthorship W4210874597A5025332620 @default.
• W4210874597 hasAuthorship W4210874597A5030277448 @default.
• W4210874597 hasAuthorship W4210874597A5044444399 @default.
• W4210874597 hasAuthorship W4210874597A5053192302 @default.
• W4210874597 hasAuthorship W4210874597A5061343064 @default.
• W4210874597 hasAuthorship W4210874597A5072315367 @default.
• W4210874597 hasAuthorship W4210874597A5086878627 @default.
• W4210874597 hasAuthorship W4210874597A5089599217 @default.
• W4210874597 hasConcept C100970517 @default.
• W4210874597 hasConcept C109007969 @default.
• W4210874597 hasConcept C111368507 @default.
• W4210874597 hasConcept C11413529 @default.
• W4210874597 hasConcept C114793014 @default.
• W4210874597 hasConcept C127313418 @default.
• W4210874597 hasConcept C132651083 @default.
• W4210874597 hasConcept C134306372 @default.
• W4210874597 hasConcept C15098985 @default.
• W4210874597 hasConcept C155512373 @default.
• W4210874597 hasConcept C159985019 @default.
• W4210874597 hasConcept C178790620 @default.
• W4210874597 hasConcept C180851071 @default.
• W4210874597 hasConcept C185592680 @default.
• W4210874597 hasConcept C187320778 @default.
• W4210874597 hasConcept C192562407 @default.
• W4210874597 hasConcept C204323151 @default.
• W4210874597 hasConcept C205649164 @default.
• W4210874597 hasConcept C22286887 @default.
• W4210874597 hasConcept C2776026197 @default.
• W4210874597 hasConcept C2779227376 @default.
• W4210874597 hasConcept C2780030769 @default.
• W4210874597 hasConcept C33923547 @default.
• W4210874597 hasConcept C39432304 @default.
• W4210874597 hasConcept C41008148 @default.
• W4210874597 hasConcept C49204034 @default.
• W4210874597 hasConcept C62649853 @default.
• W4210874597 hasConcept C76886044 @default.
• W4210874597 hasConcept C87359718 @default.
• W4210874597 hasConcept C87360688 @default.
• W4210874597 hasConceptScore W4210874597C100970517 @default.

• next