Modeling Biophysical Variables in the Canadian High Arctic Using Synthetic Aperture Radar Data
Thesis (Ph.D, Geography) -- Queen's University, 2014-02-03 16:52:26.856 The estimation or modeling of biophysical variables such as surface roughness, vegetation phytomass, and soil moisture in the Arctic is an important step towards understanding arctic energy fluxes, effects of changing clima...
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ftqueensuniv:oai:qspace.library.queensu.ca:1974/8618 2023-05-15T14:46:42+02:00 Modeling Biophysical Variables in the Canadian High Arctic Using Synthetic Aperture Radar Data Collingwood, Adam Treitz, Paul Geography 2014-02-02 15:17:42.865 http://hdl.handle.net/1974/8618 eng eng Canadian theses http://hdl.handle.net/1974/8618 This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner. Remote Sensing SAR Arctic thesis 2014 ftqueensuniv 2020-12-29T09:06:53Z Thesis (Ph.D, Geography) -- Queen's University, 2014-02-03 16:52:26.856 The estimation or modeling of biophysical variables such as surface roughness, vegetation phytomass, and soil moisture in the Arctic is an important step towards understanding arctic energy fluxes, effects of changing climate, and hydrological patterns. This research uses Synthetic Aperture Radar (SAR) data, along with ancillary optical and environmental data, to create models that estimate these biophysical variables across different High Arctic landscapes, with the goal of applying the models across even larger areas. Field work was conducted at two High Arctic locations on Melville Island, Nunavut, Canada. At each location, surface roughness values were measured at a number of randomized plot locations using a pin meter. Soil moisture values were measured using a time domain reflectometry (TDR) instrument within six hours of multiple overpasses of the RADARSAT-2 SAR sensor. Surface roughness models were generated with multi-incidence angle and fully polarimetric SAR data, with resulting R2 values ranging between 0.39 and 0.66, and normalized root mean squared error (N_RMSE) values of 14% - 22%. The output from the final surface roughness model was used as an input to the soil moisture models. Vegetation phytomass was modeled with multi-angular SAR data, using a soil adjusted vegetation index (SAVI) derived from optical data across the study area as a measure of verification. The resulting model had a significant (p <0.05) relationship to the SAVI values, with an R2 of 0.60. This model was then compared to field-collected above-ground phytomass values, and a model was derived that related SAR data directly to phytomass. This model again showed a strong relationship, with an R2 value of 0.87. The final biophysical variable that was modeled, soil moisture, showed moderate agreement to field-measured soil moisture values (R2 = 0.46, N_RMSE = 0.15%), but much stronger relationships were found for relative moisture values at fine scales across the landscape. These models, when taken together, demonstrate that SAR data is capable of modeling biophysical variables across high latitude environments. These models will help address larger questions, such as how SAR can be used to better understand moisture and energy exchanges over regional areas in high arctic environments. PhD Thesis Arctic Nunavut Melville Island Queen's University, Ontario: QSpace Arctic Canada Nunavut |
institution |
Open Polar |
collection |
Queen's University, Ontario: QSpace |
op_collection_id |
ftqueensuniv |
language |
English |
topic |
Remote Sensing SAR Arctic |
spellingShingle |
Remote Sensing SAR Arctic Collingwood, Adam Modeling Biophysical Variables in the Canadian High Arctic Using Synthetic Aperture Radar Data |
topic_facet |
Remote Sensing SAR Arctic |
description |
Thesis (Ph.D, Geography) -- Queen's University, 2014-02-03 16:52:26.856 The estimation or modeling of biophysical variables such as surface roughness, vegetation phytomass, and soil moisture in the Arctic is an important step towards understanding arctic energy fluxes, effects of changing climate, and hydrological patterns. This research uses Synthetic Aperture Radar (SAR) data, along with ancillary optical and environmental data, to create models that estimate these biophysical variables across different High Arctic landscapes, with the goal of applying the models across even larger areas. Field work was conducted at two High Arctic locations on Melville Island, Nunavut, Canada. At each location, surface roughness values were measured at a number of randomized plot locations using a pin meter. Soil moisture values were measured using a time domain reflectometry (TDR) instrument within six hours of multiple overpasses of the RADARSAT-2 SAR sensor. Surface roughness models were generated with multi-incidence angle and fully polarimetric SAR data, with resulting R2 values ranging between 0.39 and 0.66, and normalized root mean squared error (N_RMSE) values of 14% - 22%. The output from the final surface roughness model was used as an input to the soil moisture models. Vegetation phytomass was modeled with multi-angular SAR data, using a soil adjusted vegetation index (SAVI) derived from optical data across the study area as a measure of verification. The resulting model had a significant (p <0.05) relationship to the SAVI values, with an R2 of 0.60. This model was then compared to field-collected above-ground phytomass values, and a model was derived that related SAR data directly to phytomass. This model again showed a strong relationship, with an R2 value of 0.87. The final biophysical variable that was modeled, soil moisture, showed moderate agreement to field-measured soil moisture values (R2 = 0.46, N_RMSE = 0.15%), but much stronger relationships were found for relative moisture values at fine scales across the landscape. These models, when taken together, demonstrate that SAR data is capable of modeling biophysical variables across high latitude environments. These models will help address larger questions, such as how SAR can be used to better understand moisture and energy exchanges over regional areas in high arctic environments. PhD |
author2 |
Treitz, Paul Geography |
format |
Thesis |
author |
Collingwood, Adam |
author_facet |
Collingwood, Adam |
author_sort |
Collingwood, Adam |
title |
Modeling Biophysical Variables in the Canadian High Arctic Using Synthetic Aperture Radar Data |
title_short |
Modeling Biophysical Variables in the Canadian High Arctic Using Synthetic Aperture Radar Data |
title_full |
Modeling Biophysical Variables in the Canadian High Arctic Using Synthetic Aperture Radar Data |
title_fullStr |
Modeling Biophysical Variables in the Canadian High Arctic Using Synthetic Aperture Radar Data |
title_full_unstemmed |
Modeling Biophysical Variables in the Canadian High Arctic Using Synthetic Aperture Radar Data |
title_sort |
modeling biophysical variables in the canadian high arctic using synthetic aperture radar data |
publishDate |
2014 |
url |
http://hdl.handle.net/1974/8618 |
geographic |
Arctic Canada Nunavut |
geographic_facet |
Arctic Canada Nunavut |
genre |
Arctic Nunavut Melville Island |
genre_facet |
Arctic Nunavut Melville Island |
op_relation |
Canadian theses http://hdl.handle.net/1974/8618 |
op_rights |
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner. |
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1766317893911314432 |