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

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.