TY - THES

T1 - Determining the accuracy and repeatability of citizen-derived imagery as a source for Structure-from-Motion photogrammetry

AU - Lusty, Michael

PY - 2019/2/1

Y1 - 2019/2/1

N2 - Globally, sea levels are rising and continue to rise at an accelerating rate. Developments built near the coast are vulnerable from coastal flooding due to a direct rise in sea level and an increase in storm severity, persistence and frequency. As storm events become more prevalent and powerful they will consequently exacerbate the effects from rising sea levels and increase coastal flooding. It is therefore relevant for coastal managers to build and maintain a comprehensive understanding of the coast to predict what a future heightened sea level might bring. Building understanding at a time when resources are limited due to budget cuts is often difficult requiring cost-effective monitoring approaches. Citizen Science is a rapidly developing research method whereby scientific projects utilise public input at one or more stages of the research process. CS projects can tackle scientific research which often cannot be done by scientists alone due to human, financial, time and spatial constraints. Alongside the benefits afforded to scientific research, CS projects help in building scientific understanding within the public domain. By increasing public understanding of the coastal environment, citizens become more empowered to contribute towards coastal decisions.This project takes on the framework defined by CS by engaging a community group with data collection methods for coastal monitoring. Focus is placed on the Structure-from-Motion (SfM) photogrammetric workflow to build 3D models of the coastal environment using citizens and their personal standalone cameras or inbuilt smartphone cameras. This project aims to assess the accuracy of point clouds derived from citizen-derived imagery of a coastal environment and thus determine its potential as a source of data for coastal practitioners. It also aims to recognise the response from participating members of the public towards the SfM imaging procedure.

AB - Globally, sea levels are rising and continue to rise at an accelerating rate. Developments built near the coast are vulnerable from coastal flooding due to a direct rise in sea level and an increase in storm severity, persistence and frequency. As storm events become more prevalent and powerful they will consequently exacerbate the effects from rising sea levels and increase coastal flooding. It is therefore relevant for coastal managers to build and maintain a comprehensive understanding of the coast to predict what a future heightened sea level might bring. Building understanding at a time when resources are limited due to budget cuts is often difficult requiring cost-effective monitoring approaches. Citizen Science is a rapidly developing research method whereby scientific projects utilise public input at one or more stages of the research process. CS projects can tackle scientific research which often cannot be done by scientists alone due to human, financial, time and spatial constraints. Alongside the benefits afforded to scientific research, CS projects help in building scientific understanding within the public domain. By increasing public understanding of the coastal environment, citizens become more empowered to contribute towards coastal decisions.This project takes on the framework defined by CS by engaging a community group with data collection methods for coastal monitoring. Focus is placed on the Structure-from-Motion (SfM) photogrammetric workflow to build 3D models of the coastal environment using citizens and their personal standalone cameras or inbuilt smartphone cameras. This project aims to assess the accuracy of point clouds derived from citizen-derived imagery of a coastal environment and thus determine its potential as a source of data for coastal practitioners. It also aims to recognise the response from participating members of the public towards the SfM imaging procedure.

U2 - 10.17635/lancaster/thesis/751

DO - 10.17635/lancaster/thesis/751

M3 - Master's Thesis

PB - Lancaster University

ER -