FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2775900035> ?p ?o ?g. }

• W2775900035 abstract "Lakes are vital components of the global biosphere, supporting complex ecosystems and playing important roles in the global biogeochemical cycle. However, they are vulnerable to the threat from climate change and their responses to climate forcing, eutrophication and other pressures, and their possibly confounding interactions, are not yet well understood. Monitoring lake health is therefore essential, in order to understand the changing patterns over space and time.Traditionally, in-situ data, which are collected directly from within lakes and analysed in laboratories, have been available for analysis. However, although these data are assumed to be accurate within measurement error, they are expensive to collect, so that few, if any, in-situ sampling locations are available for each lake, often with infrequent sampling at each location. On the other hand, remotely-sensed data, which are derived from reflectance measurements of the Earth's surface, obtained from satellites, have recently become widely available. These data have good spatial coverage of up to 300 metre resolution, covering entire lakes, often with a monthly-average time-scale, but they must firstly be calibrated with the in-situ data to ensure accuracy, before inferences are made.The data for this research were provided by the GloboLakes project (www.globolakes.ac.uk), which is a consortium research project that is investigating the state of lakes and their responses to environmental drivers on a global scale. The research primarily focusses on log(chlorophyll-a) data for Lake Balaton, in Hungary, and for the Great Lakes of North America.The key question of interest for this research is: ``How can data fusion be performed for in-situ and remotely-sensed lake water quality data, accounting for the spatiotemporal change of support between the point-location, point-time in-situ data and the grid-cell-scale, monthly-averaged remotely-sensed data, producing a fused dataset that takes accuracy from the in-situ data and spatial and temporal information from the remotely-sensed data?In order to answer this question, this thesis presents the following work:An initial analysis of the data for Lake Balaton motivates the following work, by demonstrating the spatial and temporal patterns in the data, using mixed-effects models, generalised additive models, kriging and principal components analysis.Following the identification of statistical downscaling as an appropriate method for fusion of the data, statistical downscaling models are developed, specifically in the framework of Bayesian hierarchical models with spatially-varying coefficients, for the novel application to data for log(chlorophyll-a), producing fully calibrated maps of fused data across lake surfaces, with associated comprehensive uncertainty measures.Bivariate and multiple-lakes statistical downscaling models are developed and applied, motivated by the assumption that sharing information between variables and between lakes can improve the accuracy of model predictions.The statistically novel method of nonparametric statistical downscaling is developed, to account for both the spatial and temporal aspects of the change of support between the in-situ and remotely-sensed data. Using methodology from both functional data analysis and statistical downscaling, the model treats in-situ and remotely-sensed data at each location as observations of smooth functions over time, estimated using bases, with the basis coefficients related via a spatially-varying coefficient regression. This is computed within a Bayesian hierarchical model, enabling the calculation of comprehensive uncertainties.This thesis presents the background, motivation, model development and application of the novel method of nonparametric statistical downscaling, filling the gap in the literature of accounting for changing temporal support in statistical downscaling modelling. Results are presented throughout this thesis, to demonstrate the utility of the method for real lake water quality data." @default.
• W2775900035 created "2018-01-05" @default.
• W2775900035 creator A5047926636 @default.
• W2775900035 date "2017-01-01" @default.
• W2775900035 modified "2023-09-23" @default.
• W2775900035 title "Nonparametric statistical downscaling for the fusion of in-lake and remote sensing data" @default.
• W2775900035 cites W146619314 @default.
• W2775900035 cites W1491833257 @default.
• W2775900035 cites W1524392826 @default.
• W2775900035 cites W1549853756 @default.
• W2775900035 cites W1556631438 @default.
• W2775900035 cites W1595243343 @default.
• W2775900035 cites W1599710294 @default.
• W2775900035 cites W1603304768 @default.
• W2775900035 cites W1822370477 @default.
• W2775900035 cites W1831804202 @default.
• W2775900035 cites W1853715423 @default.
• W2775900035 cites W1874972434 @default.
• W2775900035 cites W1970609391 @default.
• W2775900035 cites W1984125303 @default.
• W2775900035 cites W1994462451 @default.
• W2775900035 cites W2000233768 @default.
• W2775900035 cites W2008505918 @default.
• W2775900035 cites W2012109845 @default.
• W2775900035 cites W2014707926 @default.
• W2775900035 cites W2016183155 @default.
• W2775900035 cites W2019972036 @default.
• W2775900035 cites W2020999234 @default.
• W2775900035 cites W2025720061 @default.
• W2775900035 cites W2026156015 @default.
• W2775900035 cites W2027106381 @default.
• W2775900035 cites W2038277379 @default.
• W2775900035 cites W2038420319 @default.
• W2775900035 cites W2040371781 @default.
• W2775900035 cites W2056760934 @default.
• W2775900035 cites W2058540316 @default.
• W2775900035 cites W2060138232 @default.
• W2775900035 cites W2063578633 @default.
• W2775900035 cites W2077788881 @default.
• W2775900035 cites W2085210969 @default.
• W2775900035 cites W2104794450 @default.
• W2775900035 cites W2106201557 @default.
• W2775900035 cites W2114164280 @default.
• W2775900035 cites W2129412583 @default.
• W2775900035 cites W2130761473 @default.
• W2775900035 cites W2138309709 @default.
• W2775900035 cites W2140308441 @default.
• W2775900035 cites W2144898279 @default.
• W2775900035 cites W2145554228 @default.
• W2775900035 cites W2145874683 @default.
• W2775900035 cites W2158449659 @default.
• W2775900035 cites W2159253889 @default.
• W2775900035 cites W2160551663 @default.
• W2775900035 cites W2160858172 @default.
• W2775900035 cites W2162870748 @default.
• W2775900035 cites W2172191993 @default.
• W2775900035 cites W2326628520 @default.
• W2775900035 cites W2549994478 @default.
• W2775900035 cites W2582867690 @default.
• W2775900035 cites W2615151247 @default.
• W2775900035 cites W264320815 @default.
• W2775900035 cites W3000332379 @default.
• W2775900035 cites W3020204246 @default.
• W2775900035 cites W3099514962 @default.
• W2775900035 cites W393154239 @default.
• W2775900035 cites W397373641 @default.
• W2775900035 cites W402407836 @default.
• W2775900035 cites W585085135 @default.
• W2775900035 cites W621325926 @default.
• W2775900035 cites W68436435 @default.
• W2775900035 cites W966232302 @default.
• W2775900035 hasPublicationYear "2017" @default.
• W2775900035 type Work @default.
• W2775900035 sameAs 2775900035 @default.
• W2775900035 citedByCount "0" @default.
• W2775900035 crossrefType "dissertation" @default.
• W2775900035 hasAuthorship W2775900035A5047926636 @default.
• W2775900035 hasConcept C106131492 @default.
• W2775900035 hasConcept C107054158 @default.
• W2775900035 hasConcept C107218244 @default.
• W2775900035 hasConcept C107826830 @default.
• W2775900035 hasConcept C132651083 @default.
• W2775900035 hasConcept C140779682 @default.
• W2775900035 hasConcept C153294291 @default.
• W2775900035 hasConcept C18903297 @default.
• W2775900035 hasConcept C205649164 @default.
• W2775900035 hasConcept C2778755073 @default.
• W2775900035 hasConcept C31972630 @default.
• W2775900035 hasConcept C39432304 @default.
• W2775900035 hasConcept C41008148 @default.
• W2775900035 hasConcept C41156917 @default.
• W2775900035 hasConcept C58640448 @default.
• W2775900035 hasConcept C62649853 @default.
• W2775900035 hasConcept C86803240 @default.
• W2775900035 hasConceptScore W2775900035C106131492 @default.
• W2775900035 hasConceptScore W2775900035C107054158 @default.
• W2775900035 hasConceptScore W2775900035C107218244 @default.
• W2775900035 hasConceptScore W2775900035C107826830 @default.
• W2775900035 hasConceptScore W2775900035C132651083 @default.
• W2775900035 hasConceptScore W2775900035C140779682 @default.

• next