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Sitka spruce (Picea sitchensis (Bong.) Carr) drought monitoring using remotely sensed Vegetation Indices (VI) at multiple spatiotemporal scales / GERRARD ENGLISH

Swansea University Author: GERRARD ENGLISH

  • E-Thesis under embargo until: 1st April 2026

DOI (Published version): 10.23889/SUThesis.69869

Abstract

The environmental threats to UK forests due to the changing climate are unprecedented. However, the potential for remote sensing to assess, model, and monitor these threats at previously unattainable spatiotemporal scales has never been greater. Drought stress is among the greatest threats to forest...

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Published: Swansea University, Wales, UK 2025
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Rosette, J., and Suarez, J.
URI: https://cronfa.swan.ac.uk/Record/cronfa69869
first_indexed 2025-07-03T09:38:23Z
last_indexed 2025-07-04T06:42:52Z
id cronfa69869
recordtype RisThesis
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spelling 2025-07-03T10:42:11.6883313 v2 69869 2025-07-03 Sitka spruce (Picea sitchensis (Bong.) Carr) drought monitoring using remotely sensed Vegetation Indices (VI) at multiple spatiotemporal scales da2768af2b3d55c34788438b4950fb14 GERRARD ENGLISH GERRARD ENGLISH true false 2025-07-03 The environmental threats to UK forests due to the changing climate are unprecedented. However, the potential for remote sensing to assess, model, and monitor these threats at previously unattainable spatiotemporal scales has never been greater. Drought stress is among the greatest threats to forests, and Sitka spruce (Picea sitchensis (Bong.) Carr.), UK forestry’s most important conifer species, can be susceptible to it. The present thesis explores potential ways that the UK forestry sector could adopt remotely sensed Vegetation Indices (VI) and transition towards more drought-resilient management. In the analysis presented in this thesis, VI associated with the xanthophyll cycle and anthocyanin concentrations (PRI, ARI,ARI2) detected early drought responses in Sitka spruce needles after about 10 days of mild drought stress. At the stand level, the xanthophyll cycle-associated VI (CCI and GPPVI) detected drought-induced annual reductions during the 2018 drought year. Depending on the time interval, these reductions ranged between 5% and 15%,compared to the 5% GPP reduction estimated by the flux tower. Gross Primary Productivity (GPP) (within 0.5% of flux tower GPP) which were delectable from space. These VI also act as Vcmax proxies to improve model realism in process-based biophysical models. Traditional VI such as NDVI generally performed poorly at the needle and canopy scale for drought detection however, successfully tracked seasonal GPP patterns (R2 > 0.80) when combined with a measure of PAR in the Light Use Efficiency Model (LUE). For the first time, intraspecific clonal differences in drought responses were detected with VI in conifers. This demonstrated the possibility of high-throughput phenotyping for drought tolerance and highlighted potential discrepancies in the drought tolerance of existing breeding population Sitka spruce trees. The results demonstrate how VI can directly aid in transitioning UK forestry to a drought-resilient sector. Xanthophyll reflectance, detectable with MODIS satellites at countywide scales, could facilitate Sitka spruce health monitoring and stress assessments, as well as be incorporated into the breeding program to future-proof progeny. E-Thesis Swansea University, Wales, UK Remote sensing, Forest Health, Drought Stress 25 3 2025 2025-03-25 10.23889/SUThesis.69869 A selection of content is redacted or is partially redacted from this thesis to protect sensitive and personal information. COLLEGE NANME COLLEGE CODE Swansea University Rosette, J., and Suarez, J. Doctoral Ph.D Royal Society and Forest Research Royal Society and Forest Research 2025-07-03T10:42:11.6883313 2025-07-03T10:30:44.1670887 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography GERRARD ENGLISH 1 Under embargo Under embargo 2025-07-03T10:35:45.3972240 Output 67059523 application/pdf E-Thesis true 2026-04-01T00:00:00.0000000 Copyright: The author, Gerrard English, 2024 true eng
title Sitka spruce (Picea sitchensis (Bong.) Carr) drought monitoring using remotely sensed Vegetation Indices (VI) at multiple spatiotemporal scales
spellingShingle Sitka spruce (Picea sitchensis (Bong.) Carr) drought monitoring using remotely sensed Vegetation Indices (VI) at multiple spatiotemporal scales
GERRARD ENGLISH
title_short Sitka spruce (Picea sitchensis (Bong.) Carr) drought monitoring using remotely sensed Vegetation Indices (VI) at multiple spatiotemporal scales
title_full Sitka spruce (Picea sitchensis (Bong.) Carr) drought monitoring using remotely sensed Vegetation Indices (VI) at multiple spatiotemporal scales
title_fullStr Sitka spruce (Picea sitchensis (Bong.) Carr) drought monitoring using remotely sensed Vegetation Indices (VI) at multiple spatiotemporal scales
title_full_unstemmed Sitka spruce (Picea sitchensis (Bong.) Carr) drought monitoring using remotely sensed Vegetation Indices (VI) at multiple spatiotemporal scales
title_sort Sitka spruce (Picea sitchensis (Bong.) Carr) drought monitoring using remotely sensed Vegetation Indices (VI) at multiple spatiotemporal scales
author_id_str_mv da2768af2b3d55c34788438b4950fb14
author_id_fullname_str_mv da2768af2b3d55c34788438b4950fb14_***_GERRARD ENGLISH
author GERRARD ENGLISH
author2 GERRARD ENGLISH
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publishDate 2025
institution Swansea University
doi_str_mv 10.23889/SUThesis.69869
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
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description The environmental threats to UK forests due to the changing climate are unprecedented. However, the potential for remote sensing to assess, model, and monitor these threats at previously unattainable spatiotemporal scales has never been greater. Drought stress is among the greatest threats to forests, and Sitka spruce (Picea sitchensis (Bong.) Carr.), UK forestry’s most important conifer species, can be susceptible to it. The present thesis explores potential ways that the UK forestry sector could adopt remotely sensed Vegetation Indices (VI) and transition towards more drought-resilient management. In the analysis presented in this thesis, VI associated with the xanthophyll cycle and anthocyanin concentrations (PRI, ARI,ARI2) detected early drought responses in Sitka spruce needles after about 10 days of mild drought stress. At the stand level, the xanthophyll cycle-associated VI (CCI and GPPVI) detected drought-induced annual reductions during the 2018 drought year. Depending on the time interval, these reductions ranged between 5% and 15%,compared to the 5% GPP reduction estimated by the flux tower. Gross Primary Productivity (GPP) (within 0.5% of flux tower GPP) which were delectable from space. These VI also act as Vcmax proxies to improve model realism in process-based biophysical models. Traditional VI such as NDVI generally performed poorly at the needle and canopy scale for drought detection however, successfully tracked seasonal GPP patterns (R2 > 0.80) when combined with a measure of PAR in the Light Use Efficiency Model (LUE). For the first time, intraspecific clonal differences in drought responses were detected with VI in conifers. This demonstrated the possibility of high-throughput phenotyping for drought tolerance and highlighted potential discrepancies in the drought tolerance of existing breeding population Sitka spruce trees. The results demonstrate how VI can directly aid in transitioning UK forestry to a drought-resilient sector. Xanthophyll reflectance, detectable with MODIS satellites at countywide scales, could facilitate Sitka spruce health monitoring and stress assessments, as well as be incorporated into the breeding program to future-proof progeny.
published_date 2025-03-25T06:43:14Z
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score 11.096913

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