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Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery / Alberto Hornero

Swansea University Author: Alberto Hornero

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DOI (Published version): 10.23889/SUthesis.57329

Abstract

Large-scale monitoring of vegetation dynamics by remote sensing is key to detecting early signs of vegetation decline. Spectral-based indicators of phys-iological plant traits (PTs) have the potential to quantify variations in pho-tosynthetic pigments, chlorophyll fluorescence emission, and structur...

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Published: Swansea 2021
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: North Peter R.J. ; Zarco-Tejada, Pablo J.
URI: https://cronfa.swan.ac.uk/Record/cronfa57329
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first_indexed 2021-07-15T09:52:31Z
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spelling 2021-07-21T15:25:39.4828931 v2 57329 2021-07-15 Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery 3140d9cb2dde2c093d42d5bf3b85d05e Alberto Hornero Alberto Hornero true false 2021-07-15 FGSEN Large-scale monitoring of vegetation dynamics by remote sensing is key to detecting early signs of vegetation decline. Spectral-based indicators of phys-iological plant traits (PTs) have the potential to quantify variations in pho-tosynthetic pigments, chlorophyll fluorescence emission, and structural changes of vegetation as a function of stress. However, the specific response of PTs to disease-induced decline in heterogeneous canopies remains largely unknown, which is critical for the early detection of irreversible damage at different scales. Four specific objectives were defined in this research: i) to assess the feasibility of modelling the incidence and severity of Phytophthora cinnamomi and Xylella fastidiosa based on PTs and biophysical properties of vegetation; ii) to assess non-visual early indicators, iii) to retrieve PT using radiative transfer models (RTM), high-resolution imagery and satellite observations; and iv) to establish the basis for scaling up PTs at different spatial resolutions using RTM for their retrieval in different vegetation co-vers. This thesis integrates different approaches combining field data, air- and space-borne imagery, and physical and empirical models that allow the retrieval of indicators and the evaluation of each component’s contribution to understanding temporal variations of disease-induced symptoms in heter-ogeneous canopies. Furthermore, the effects associated with the understory are introduced, showing not only their impact but also providing a compre-hensive model to account for it. Consequently, a new methodology has been established to detect vegetation health processes and the influence of biotic and abiotic factors, considering different components of the canopy and their impact on the aggregated signal. It is expected that, using the presented methods, existing remote sensors and future developments, the ability to detect and assess vegetation health globally will have a substantial impact not only on socio-economic factors, but also on the preservation of our eco-system as a whole. E-Thesis Swansea High-resolution imagery, Hyperspectral, Thermal, HyPlant, Satellite data, Sentinel-2, Radiative transfer modelling, Understory, Chlorophyll fluorescence, SIF, Heterogeneous canopies, Temporal change, Disease monitoring, Forest dieback, Xylella fastidiosa, Phytophthora cinnamomi 9 7 2021 2021-07-09 10.23889/SUthesis.57329 ORCiD identifier https://orcid.org/0000-0002-8434-2168 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University North Peter R.J. ; Zarco-Tejada, Pablo J. Doctoral Ph.D Science Doctoral Training Centre (Swansea University); DTC GEO 29 “Detection of global photosynthesis and forest health from space” 2021-07-21T15:25:39.4828931 2021-07-15T10:49:28.2602742 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Alberto Hornero 1 57329__20410__a6b0d428762446b0930e02a7a6de63f6.pdf Hornero_Alberto_PhD_Thesis_Final_Copyright_Statement.pdf 2021-07-15T11:20:50.6490935 Output 12411087 application/pdf E-Thesis – open access true Copyright: The author, Alberto Hornero Luque, 2021. true eng
title Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery
spellingShingle Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery
Alberto Hornero
title_short Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery
title_full Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery
title_fullStr Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery
title_full_unstemmed Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery
title_sort Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery
author_id_str_mv 3140d9cb2dde2c093d42d5bf3b85d05e
author_id_fullname_str_mv 3140d9cb2dde2c093d42d5bf3b85d05e_***_Alberto Hornero
author Alberto Hornero
author2 Alberto Hornero
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publishDate 2021
institution Swansea University
doi_str_mv 10.23889/SUthesis.57329
college_str Faculty of Science and Engineering
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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
document_store_str 1
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description Large-scale monitoring of vegetation dynamics by remote sensing is key to detecting early signs of vegetation decline. Spectral-based indicators of phys-iological plant traits (PTs) have the potential to quantify variations in pho-tosynthetic pigments, chlorophyll fluorescence emission, and structural changes of vegetation as a function of stress. However, the specific response of PTs to disease-induced decline in heterogeneous canopies remains largely unknown, which is critical for the early detection of irreversible damage at different scales. Four specific objectives were defined in this research: i) to assess the feasibility of modelling the incidence and severity of Phytophthora cinnamomi and Xylella fastidiosa based on PTs and biophysical properties of vegetation; ii) to assess non-visual early indicators, iii) to retrieve PT using radiative transfer models (RTM), high-resolution imagery and satellite observations; and iv) to establish the basis for scaling up PTs at different spatial resolutions using RTM for their retrieval in different vegetation co-vers. This thesis integrates different approaches combining field data, air- and space-borne imagery, and physical and empirical models that allow the retrieval of indicators and the evaluation of each component’s contribution to understanding temporal variations of disease-induced symptoms in heter-ogeneous canopies. Furthermore, the effects associated with the understory are introduced, showing not only their impact but also providing a compre-hensive model to account for it. Consequently, a new methodology has been established to detect vegetation health processes and the influence of biotic and abiotic factors, considering different components of the canopy and their impact on the aggregated signal. It is expected that, using the presented methods, existing remote sensors and future developments, the ability to detect and assess vegetation health globally will have a substantial impact not only on socio-economic factors, but also on the preservation of our eco-system as a whole.
published_date 2021-07-09T04:12:59Z
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