Schauman S., Verger A., Filella I., Peñuelas J. (2018) Characterisation of functional-trait dynamics at high spatial resolution in a mediterranean forest from sentinel-2 and ground-truth data. Remote Sensing. 10: 0-0.LinkDoi: 10.3390/rs10121874
The characterisation of functional-trait dynamics of vegetation from remotely sensed data complements the structural characterisation of ecosystems. In this study we characterised for the first time the spatial heterogeneity of the intra-annual dynamics of the fraction of absorbed photosynthetically active radiation (FAPAR) as a functional trait of the vegetation in Prades Mediterranean forest in Catalonia, Spain. FAPAR was derived from the Multispectral Instrument (MSI) on the Sentinel-2 satellite and validated by comparison with the ground measurements acquired in June 2017 at the annual peak of vegetation activity. The validation results showed that most of points were distributed along the 1:1 line, with no bias nor scattering: R2 = 0.93, p < 0.05; with a root mean square error of 0.03 FAPAR (4.3%). We classified the study area into nine vegetation groups with different dynamics of FAPAR using a methodology that is objective and repeatable over time. This functional classification based on the annual magnitude (FAPAR-M) and the seasonality (FAPAR-CV) from the data on one year (2016-2017) complements structural classifications. The internal heterogeneity of the FAPAR dynamics in each land-cover type is attributed to the environmental and to the specific species composition variability. A spatial autoregressive (SAR) model for the main type of land cover, evergreen holm oak forest (Quercus ilex), indicated that topographic aspect, slope, height, and the topographic aspect x slope interaction accounted for most of the spatial heterogeneity of the functional trait FAPAR-M, thus improving our understanding of the explanatory factors of the annual absorption of photosynthetically active radiation by the vegetation canopy for this ecosystem. © 2018 by the authors.
Verger A., Filella I., Baret F., Peñuelas J. (2016) Vegetation baseline phenology from kilometric global LAI satellite products. Remote Sensing of Environment. 178: 1-14.LinkDoi: 10.1016/j.rse.2016.02.057
Land surface phenology derived from remotely sensed satellite data can substantially improve our macroecological knowledge and the representation of phenology in earth system models. We characterized the baseline phenology of the vegetation at the global scale from the GEOCLIM climatology of leaf area index (LAI) estimated from 1-km SPOT-VEGETATION time series for 1999-2010. The phenological metrics were calibrated over an ensemble of ground observations of the timing of leaf unfolding and autumnal colouring of leaves. The start and end of season were best identified using respectively 30% and 40% threshold of LAI amplitude values. The accuracy of the derived phenological metrics, evaluated using available ground observations for birch forests over Europe (and lilac shrubs over North America), improved as compared to those derived from MODIS-EVI and produced an overall root mean square error of 7 days (19 days) for the timing of the start of season, 15 for the end of season, and 16 for the length of season. The spatial patterns of the derived LAI phenology agreed well with those from MODIS-EVI and -NDVI, although the timing of the start, end, and length of season differed by about one month at the global scale, with higher uncertainties in areas of limited seasonality of the satellite signal and systematic biases due to the differences in the methodologies and datasets. The baseline LAI phenology was spatially consistent with the global distributions of climatic drivers and biome land cover. © 2016 Elsevier Inc.
Vicca S., Balzarolo M., Filella I., Granier A., Herbst M., Knohl A., Longdoz B., Mund M., Nagy Z., Pintér K., Rambal S., Verbesselt J., Verger A., Zeileis A., Zhang C., Peñuelas J. (2016) Remotely-sensed detection of effects of extreme droughts on gross primary production. Scientific Reports. 6: 0-0.LinkDoi: 10.1038/srep28269
Severe droughts strongly impact photosynthesis (GPP), and satellite imagery has yet to demonstrate its ability to detect drought effects. Especially changes in vegetation functioning when vegetation state remains unaltered (no browning or defoliation) pose a challenge to satellite-derived indicators. We evaluated the performance of different satellite indicators to detect strong drought effects on GPP in a beech forest in France (Hesse), where vegetation state remained largely unaffected while GPP decreased substantially. We compared the results with three additional sites: a Mediterranean holm oak forest (Puéchabon), a temperate beech forest (Hainich), and a semi-arid grassland (Bugacpuszta). In Hesse, a three-year reduction in GPP following drought was detected only by the Enhanced Vegetation Index (EVI). The Photochemical Reflectance Index (PRI) also detected this drought effect, but only after normalization for absorbed light. In Puéchabon normalized PRI outperformed the other indicators, while the short-term drought effect in Hainich was not detected by any tested indicator. In contrast, most indicators, but not PRI, captured the drought effects in Bugacpuszta. Hence, PRI improved detection of drought effects on GPP in forests and we propose that PRI normalized for absorbed light is considered in future algorithms to estimate GPP from space.
Verger A., Baret F., Weiss M., Filella I., Penuelas J. (2015) GEOCLIM: A global climatology of LAI, FAPAR, and FCOVER from VEGETATION observations for 1999-2010. Remote Sensing of Environment. 166: 126-137.LinkDoi: 10.1016/j.rse.2015.05.027
Land-surface modelling would benefit significantly from improved characterisation of the seasonal variability of vegetation at a global scale. GEOCLIM, a global climatology of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR)-both essential climate variables-and fraction of vegetation cover (FCOVER), is here derived from observations from the SPOT VEGETATION programme. Interannual average values from the GEOV1 Copernicus Global Land time series of biophysical products at 1-km resolution and 10-day frequency are computed for 1999 to 2010. GEOCLIM provides the baseline characteristics of the seasonal cycle of the annual vegetation phenology for each 1-km. pixel on the globe. The associated standard deviation characterises the interannual variability. Temporal consistency and continuity is achieved by the accumulation of multi-year observations and the application of techniques for temporal smoothing and gap filling. Specific corrections are applied over cloudy tropical regions and high latitudes in the Northern Hemisphere where the low number of available observations compromises the reliability of estimates. Artefacts over evergreen broadleaf forests and areas of bare soil are corrected based on the expected limited seasonality. The GEOCLIM data set is demonstrated to be consistent, both spatially and temporally. GEOCLIM shows absolute differences lower than 0.5 compared with MODIS (GIMMS3g) climatology of LAI for more than 80% (90%) of land pixels, with higher discrepancies in tropical and boreal latitudes. ECOCLIMAP systematically produces higher LAI values. The phenological metric for the date of maximum foliar development derived from GEOCLIM is spatially consistent (correlation higher than 0.9) with those of MODIS, GIMMS3g, ECOCLIMAP and MCD12Q2 with average differences within 14. days at the global scale. © 2015 Elsevier Inc.
Garbulsky M.F., Filella I., Verger A., Penuelas J. (2014) Photosynthetic light use efficiency from satellite sensors: From global to Mediterranean vegetation. Environmental and Experimental Botany. 103: 3-11.LinkDoi: 10.1016/j.envexpbot.2013.10.009
Recent advances in remote-sensing techniques for light use efficiency (LUE) are providing new possibilities for monitoring carbon uptake by terrestrial vegetation (gross primary production, GPP), in particular for Mediterranean vegetation types. This article reviews the state of the art of two of the most promising approaches for remotely estimating LUE: the use of the photochemical reflectance index (PRI) and the exploitation of the passive chlorophyll fluorescence signal. The theoretical and technical issues that remain before these methods can be implemented for the operational global production of LUE from forthcoming hyperspectral satellite data are identified for future research. © 2013 Elsevier B.V.
Subscribe to our Newsletter to get the lastest CREAF news.
BOARD OF TRUSTEES
WITH SUPPORT FROM
© 2016 CREAF | Legal notice