FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3172104521> ?p ?o ?g. }

• W3172104521 endingPage "100023" @default.
• W3172104521 startingPage "100023" @default.
• W3172104521 abstract "We evaluated the performance of a variety of time series models, that include the harmonic (HR) model, autoregressive (AR) model, linear Gaussian state-space (LGSS) model, cubic spline (SP) model, double logistic (DL) model, and asymmetric Gaussian (AG) model, for reconstructing (all six models) and forecasting (HR, AR, and LGSS models) dense Landsats 5–7 time series based on 4562 samples. To remove the impact of land change and human interventions on data reconstruction and forecasting, this evaluation excluded croplands and samples changed between 2000 and 2011. Results show that the widely used HR model is not a good model for data reconstruction but outperforms other models in data forecasting. The DL and AG models have the best performance in data reconstruction but cannot forecast Landsat observations. The AR and LGSS models shared similar performance in reconstructing Landsat data but are less ideal for data forecasting, particularly for the LGSS model. Integrating the HR (for forecasting) and DL or AG (for reconstruction) is recommended to improve land change detection and land cover classification results. We also evaluated the impact of data density and irregularity on reconstructing and forecasting Landsat observations. When the data density is low (<7 clear observations per year), the increase of data density can substantially improve the performance of data reconstruction and forecasting, but when the data density is higher than 7 and less than 17, the noise in the data dominates the results, and slightly lower reconstruction and forecasting accuracy is observed. When the data density is higher than 17, model performance improves with the increase of data density again. Therefore, we recommend analyzing Landsat time series for places with data density higher than 7 clear observations per year if possible. On the other hand, data irregularity has a moderate impact on data reconstruction and forecasting. When the irregularity is less than 1, the smaller the irregularity the better the performance in both data reconstruction and forecasting, and when the irregularity is higher than 2.5, it will have a more substantial negative impact. Therefore, time series analysis using Landsat data with an irregularity less than 1 is generally recommended, and time series with an irregularity larger than 2.5 should be avoided if possible." @default.
• W3172104521 created "2021-06-22" @default.
• W3172104521 creator A5004054930 @default.
• W3172104521 creator A5006812496 @default.
• W3172104521 creator A5019050529 @default.
• W3172104521 creator A5062571096 @default.
• W3172104521 creator A5071584772 @default.
• W3172104521 date "2021-12-01" @default.
• W3172104521 modified "2023-10-14" @default.
• W3172104521 title "Evaluating the impacts of models, data density and irregularity on reconstructing and forecasting dense Landsat time series" @default.
• W3172104521 cites W1968718753 @default.
• W3172104521 cites W1971637299 @default.
• W3172104521 cites W1973410094 @default.
• W3172104521 cites W1974635621 @default.
• W3172104521 cites W1979532661 @default.
• W3172104521 cites W1982121855 @default.
• W3172104521 cites W1989098521 @default.
• W3172104521 cites W1993438546 @default.
• W3172104521 cites W1996905547 @default.
• W3172104521 cites W1999123671 @default.
• W3172104521 cites W2001510610 @default.
• W3172104521 cites W2001564322 @default.
• W3172104521 cites W2010806274 @default.
• W3172104521 cites W2020402473 @default.
• W3172104521 cites W2025745000 @default.
• W3172104521 cites W2028240797 @default.
• W3172104521 cites W2030864384 @default.
• W3172104521 cites W2036632898 @default.
• W3172104521 cites W2043142216 @default.
• W3172104521 cites W2055718260 @default.
• W3172104521 cites W2067542652 @default.
• W3172104521 cites W2070224379 @default.
• W3172104521 cites W2076265041 @default.
• W3172104521 cites W2076447280 @default.
• W3172104521 cites W2085316211 @default.
• W3172104521 cites W2092141993 @default.
• W3172104521 cites W2098418965 @default.
• W3172104521 cites W2117199485 @default.
• W3172104521 cites W2127070009 @default.
• W3172104521 cites W2129331467 @default.
• W3172104521 cites W2181171301 @default.
• W3172104521 cites W2221744192 @default.
• W3172104521 cites W2273147317 @default.
• W3172104521 cites W2560558291 @default.
• W3172104521 cites W2589890164 @default.
• W3172104521 cites W2735042947 @default.
• W3172104521 cites W2738665160 @default.
• W3172104521 cites W2739505165 @default.
• W3172104521 cites W2761672078 @default.
• W3172104521 cites W2766496462 @default.
• W3172104521 cites W2777168572 @default.
• W3172104521 cites W2794195626 @default.
• W3172104521 cites W2916848715 @default.
• W3172104521 cites W2918120629 @default.
• W3172104521 cites W2920930972 @default.
• W3172104521 cites W2922152173 @default.
• W3172104521 cites W2950314938 @default.
• W3172104521 cites W2953950759 @default.
• W3172104521 cites W2971093460 @default.
• W3172104521 cites W3003421670 @default.
• W3172104521 cites W3007407753 @default.
• W3172104521 cites W3010594708 @default.
• W3172104521 cites W3028832936 @default.
• W3172104521 cites W3096431188 @default.
• W3172104521 doi "https://doi.org/10.1016/j.srs.2021.100023" @default.
• W3172104521 hasPublicationYear "2021" @default.
• W3172104521 type Work @default.
• W3172104521 sameAs 3172104521 @default.
• W3172104521 citedByCount "14" @default.
• W3172104521 countsByYear W31721045212022 @default.
• W3172104521 countsByYear W31721045212023 @default.
• W3172104521 crossrefType "journal-article" @default.
• W3172104521 hasAuthorship W3172104521A5004054930 @default.
• W3172104521 hasAuthorship W3172104521A5006812496 @default.
• W3172104521 hasAuthorship W3172104521A5019050529 @default.
• W3172104521 hasAuthorship W3172104521A5062571096 @default.
• W3172104521 hasAuthorship W3172104521A5071584772 @default.
• W3172104521 hasBestOaLocation W31721045211 @default.
• W3172104521 hasConcept C105795698 @default.
• W3172104521 hasConcept C119857082 @default.
• W3172104521 hasConcept C121332964 @default.
• W3172104521 hasConcept C127313418 @default.
• W3172104521 hasConcept C127413603 @default.
• W3172104521 hasConcept C143724316 @default.
• W3172104521 hasConcept C147176958 @default.
• W3172104521 hasConcept C151406439 @default.
• W3172104521 hasConcept C151730666 @default.
• W3172104521 hasConcept C159877910 @default.
• W3172104521 hasConcept C163716315 @default.
• W3172104521 hasConcept C2780648208 @default.
• W3172104521 hasConcept C33923547 @default.
• W3172104521 hasConcept C41008148 @default.
• W3172104521 hasConcept C4792198 @default.
• W3172104521 hasConcept C62520636 @default.
• W3172104521 hasConceptScore W3172104521C105795698 @default.
• W3172104521 hasConceptScore W3172104521C119857082 @default.
• W3172104521 hasConceptScore W3172104521C121332964 @default.
• W3172104521 hasConceptScore W3172104521C127313418 @default.

• next