Use this URL to cite or link to this record in EThOS:
Title: Fusion of airborne LiDAR, multispectral imagery and spatial modelling for understanding saltmarsh response to sea-level rise
Author: Fernandez-Nunez, M.
ISNI:       0000 0004 8498 7980
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2017
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Coastal ecosystems are considered to be sensitive to changes in environmental forcing, particularly sea-level rise. Saltmarshes occupy a discrete lateral and vertical position that is fundamentally controlled by the position of sea level, but the nature of other factors such as broader scale shoreline dynamics and anthropogenic ensure that the nature and extent of sea-level rise impacts on saltmarshes are globally variable, and locally complex. Thus, there is a need to understand these controls and to predict the potential response of saltmarsh systems to sea-level change at the local scale. The present research presents a multifaceted methodology for investigating the response of saltmarshes due to sea-level rise at local scales with application to the Odiel saltmarshes (SW-Spain), using elevation data derived from Light detection and ranging (LiDAR), high spatial resolution multispectral imagery and spatial modelling, that in combination with historical estuary evolution and field observation can be applied for effective management and conservation of saltmarshes in the context of sea-level change. SLAMM (Sea Level Affecting Marshes Model) has been used to evaluate coastal wetland habitat response to sea-level rise Accurate model spatial model inputs such as digital elevation models (DEMs) and saltmarsh habitat map are essential to reduce uncertainties in the model outputs, and part of this thesis has been focused on improving accuracy in saltmarsh elevation and habitat maps. Additionally, a sensitivity and uncertainty analysis was undertaken to explore first the relative importance of data quality and resolution (spatial and vertical) in the elevation data and saltmarsh habitat classification layers, and then the global uncertainty of the model outputs using a Monte Carlo approach. Our findings suggested that model is sensitive to DEM and habitat map resolution, and that historical sea-level trend and saltmarsh accretion rates are the predominant factors that influence uncertainty in predictions of change in saltmarsh habitats.
Supervisor: Burningham, H. ; French, J. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available