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Record W7020926764

Monitoring productivity of plant ecosystems: integration of optical, flux and ecophysiological measurements

2017· dissertation· en· W7020926764 on OpenAlexfundaboutno aff

Bibliographic record

VenueUnitusOpen (Tuscia University) · 2017
Typedissertation
Languageen
FieldSocial Sciences
TopicGeography and Education Methods
Canadian institutionsnot available
FundersFP7 SpaceErasmus+Natural Sciences and Engineering Research Council of CanadaDipartimento per la Innovazione nei Sistemi Biologici, Agroalimentari e Forestali, Università degli Studi della TusciaHorizon 2020 Framework ProgrammeUniversity of AlbertaMinistero dell’Istruzione, dell’Università e della RicercaAlberta Innovates - Technology Futures
KeywordsEddy covariancePhotosynthetically active radiationEcosystemVegetation (pathology)ProductivityDeciduousGrasslandFlux (metallurgy)Terrestrial ecosystem
DOInot available

Abstract

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Monitoring productivity of plant ecosystems is essential to evaluate the response of different ecosystems to ongoing disturbance and climate change. Always more studies focus on the integration of different techniques for monitoring ecosystems dynamics. Eddy covariance greatly improved the understanding of carbon exchanges between terrestrial ecosystems and the atmosphere. At the same time, the advent of remote sensing offered new possibilities for monitoring broader vegetation patterns over continental regions and yearly timescale. Between these two widespread approaches, the integration of proximal sensing within the flux tower sites currently represents a tool to understand physiological details operating at finer temporal and spatial scales. In any case, ground truthing at the experimental sites keep providing a critical validation of different techniques across biomes. The general aim of this research is exploiting the combination of different methodologies to describe vegetation productivity and plant status using mainly three approaches: 1) eddy covariance technique, 2) remote and proximal sensing and 3) field sampling. The research is carried out in two very different ecosystems, a grassland site in Alberta, Canada and a deciduous broadleaf forest in central Italy. The specific objectives of the study are to: 1) evaluate the seasonal productivity of the prairie grassland using a combination of remote sensing, eddy covariance, and field sampling (Chapter 2); 2) investigate the functionality of the deciduous broadleaf forest using simultaneous determinations of optical measurements, carbon flux data, leaf eco-physiological and biochemical traits during two growing season with different meteorological conditions (Chapter 3) and 3) validate three fAPAR (the fraction of photosynthetically active radiation absorbed) satellite products against ground fAPAR references to determine their accuracy in the deciduous beech forest site (Chapter 4).\nIn Chapter 2, we evaluated different ways of parameterizing the light-use efficiency (LUE) model for assessing net ecosystem fluxes at a two grassland sites in Alberta during 2012 and 2013. Three variations on the NDVI (Normalized Difference Vegetation Index), differing by formula and footprint, were derived and all three NDVIs provided good estimates of dry green biomass, confirming their utility as metrics of productivity. NDVI values from the different methods were\nalso calibrated against fAPARgreen (the fraction of photosynthetically active radiation absorbed by green vegetation) measurements to parameterize the APARgreen (absorbed PAR) term of the LUE (light use efficiency) model for comparison with measured fluxes. The best results were obtained by splitting the data into two stages, a greening and senescence phase, and applying separate fits to these two periods. By incorporating the dynamic irradiance regime, the model based on APARgreen rather than NDVI best captured the high variability of the fluxes and provided a more realistic depiction of missing fluxes.\nThe experiment presented in Chapter 3, was carried out in the Mediterranean beech (Fagus sylvatica L.) forest of Collelongo and is focused on two growing seasons (2014-2015) having different meteorological conditions, with July 2015 characterized by higher monthly temperature and reduced precipitations compared to July 2014. Spectral indices computed at canopy level were used to track changes in CO2 fluxes and in the physiological status. Mainly optical indices related to structure were found to better track carbon fluxes variation for both 2014 and 2015, thus suggesting that structural parameters are essential drivers at the forest site. Moreover, seasonal patterns of chlorophylls (Chl a and Chl b), carotenoids (b-carotene, lutein, neoxanthin and xanthophyll cycle components) and fluorescence parameters were investigated to evaluate which optical indices better predict changes in photosynthetic pigment levels and energy dissipation mechanisms. Optical indices related to carotenoids composition were indicators of the shifting pigment composition related to stress (July) and senescence (October) during 2015. Thus, spectral indices resulted to be reliable proxies for monitoring carbon fluxes and vegetation dynamics in healthy and stressed vegetation.\nChapter 4 was aimed to validate three fAPAR satellite products, GEOV1, MODIS C5, and MODIS C6, against ground references at the same beech forest in Italy during 2014 and 2015. Three ground reference fAPAR, differing for temporal (continuous or campaign mode) and spatial sampling (single points or Elementary Sampling Units-ESUs), were collected using different devices: 1) Apogee (defined as benchmark in this study); 2) PASTIS; and 3) Digital cameras for collecting hemispherical photographs (DHP). A bottom-up approach for the upscaling process was used. Radiometric values of satellite images were extracted over the ESUs and used to develop empirical transfer functions for upscaling the ground measurements. The resulting high-resolution ground-based maps were aggregated to the spatial resolution of the satellite product to be validated considering the equivalent point spread function of the satellite sensors, and a correlation analysis was performed to accomplish the accuracy assessment. The temporal courses of the three satellite products were found to be consistent with both Apogee and PASTIS, except at the end of the summer season when ground data were more affected by senescent leaves, with both MODIS C5\nand C6 displaying larger short-term variability due to their shorter temporal composite period. The three green fAPAR satellite products under study showed good agreement with ground-based maps of canopy fAPAR at 10 h and very low systematic differences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.106
GPT teacher head0.344
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2017
Admission routes2
Has abstractyes

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