A remote sensing study of forests to estimate biophysical indicators and monitor CO2 fluxes in Spain: the ÁRTEMIS project. (Contributo in atti di convegno)

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  • A remote sensing study of forests to estimate biophysical indicators and monitor CO2 fluxes in Spain: the ÁRTEMIS project. (Contributo in atti di convegno) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
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  • Gilabert M.A., Maselli F., Martínez B., Moreno A., Camacho F., Chiesi M., García-Haro F.J., Meliá J., Pérez-Hoyos A., Verger A. (2010)
    A remote sensing study of forests to estimate biophysical indicators and monitor CO2 fluxes in Spain: the ÁRTEMIS project.
    in Third international symposium on “RECENT ADVANCES IN QUANTITATIVE REMOTE SENSING”, Auditori-Torrent (Valencia), S
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Gilabert M.A., Maselli F., Martínez B., Moreno A., Camacho F., Chiesi M., García-Haro F.J., Meliá J., Pérez-Hoyos A., Verger A. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • Forests have an important role in regulating both water and carbon cycles. The estimation of forest ecosystem processes and their validation in time and space is one of the main objectives of applied ecological studies. The aim of the ÁRTEMIS project is to develop optimized remote sensing-based procedures to estimate fAPAR (the fraction of absorbed photosynthetically active radiation) in Spanish forests, and to monitor the CO2 fluxes between these ecosystems and the atmosphere. These procedures combine the use of remote sensing data and ecosystem simulation models. The remote sensing data provide direct estimates of vegetation conditions (e.g. fAPAR). The simulation models allow for the estimation of carbon fluxes between vegetation (forests) and atmosphere. The fAPAR values not only characterize the vegetation state but they are also required inputs to estimate carbon fluxes. As the carbon budget uncertainty depends on the fAPAR uncertainties, the improvement of fAPAR estimation procedures based on remote sensing is a crucial task of this project. The statistical method proposed by Roujean & Bréon (1995) has been selected to produce daily fAPAR images from MODIS data at 500 m spatial resolution. An inter-comparison analysis between inputs (available images from different sensors) and outputs (fAPAR products) was previously carried out. At this first stage of the project, the C-Fix model is applied to estimate daily GPP (gross primary production) at regional scale. This model is based on the relationship between fAPAR and the productivity of the canopies and is an efficient means to combine data from different sources for a more complete characterization of vegetation processes (which determine carbon fluxes). Fluxes are calculated by taking into account also the daily maximum and minimum temperatures and incoming global radiation. The model estimates GPP by means of the general relation GPP = µ APAR, where µ is a radiation use efficiency factor, and APAR (absorbed photosynthetically active radiation) is obtained from the product of fAPAR and PAR (photosynthetically active radiation), which in turns is obtained from temperature and precipitation data by means of an artificial neural network procedure (see the contribution of A. Moreno et al. in these proceedings). The original C-Fix methodology is improved by using more abundant ancillary information and operational and robust strategies (calibrated for the study area) to derive fAPAR from new generation sensor facilities. The procedure is applied to forest areas in Spain by using an optimized land cover map generated from available land cover maps at coarse and medium spatial resolutions. The CORINE Land Cover (100m), GLOBCOVER (300m) and ECOCLIMAP V2 (1 km) land cover classifications have been used. A sensitivity analysis will be carried out to determine the impact of the different input variables to the output of the ecosystem model. In particular, the impact of the existing uncertainty in the operational fAPAR products will be assessed. On the other hand, a validation of the product will be carried out. This validation includes the comparison with ground truth provided by micrometeorological tower sites (direct validation) as well as the inter-comparisons with similar products currently operational (indirect validation). As a result of this control, quality flags (e.g., optimal, normal, poor) will be assigned on a pixel per pixel base with regard to the uncertainty and retrieval quality. Globally, the procedure proves to be of easy and objective implementation that allows for the evaluation of mean productivity levels of existing forests on Spain. (literal)
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  • Gilabert M.A., Martínez B., Moreno A, García-Haro F.J., Meliá J., Pérez-Hoyos A., Verger A.: Remote Sensing Unit, Facultat de Física, Universitat de València, Dr. Moliner, 50. 46100-Burjassot, Spain Camacho F.: EOLAB, Parc Cientific Universitat de Valencia. 46980-Paterna, Spain (literal)
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  • A remote sensing study of forests to estimate biophysical indicators and monitor CO2 fluxes in Spain: the ÁRTEMIS project. (literal)
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