Résumé
Classifiers trained on airborne hyperspectral imagery are proficient in identifying tree species in hyperdiverse tropical rainforests. However, spectral fluctuations, influenced by intrinsic and environmental factors, such as the heterogeneity of individual crown properties and atmospheric conditions, pose challenges for large-scale mapping. This study proposes an approach to assess the instability of airborne imaging spectroscopy reflectance in response to environmental variability. Through repeated overflights of two tropical forest sites in French Guiana, we explore factors that affect the spectral similarity between dates and acquisitions. By decomposing acquisitions into subsets and analyzing different sources of variability, we analyze the stability of reflectance and various vegetation indices with respect to specific sources of variability. Factors such as the variability of the viewing and sun angles or the variability of the atmospheric state shed light on the impact of sources of spectral instability, informing processing strategies. Our experiments conclude that the environmental factors that affect the canopy reflectance the most vary according to the considered spectral domain. In the short wave infrared (SWIR) domain, solar angle variation is the main source of variability, followed by atmospheric and viewing angles. In the visible and near infrared (VNIR) domain, atmospheric variability dominates, followed by solar angle and viewing angle variabilities. Despite efforts to address these variabilities, significant spectral instability persists, highlighting the need for more robust representations and improved correction methods for reliable species-specific signatures.
Auteurs, date et publication :
Auteurs Colin Prieur , Antony Laybros , Giovanni Frati , Daniel Schläpfer , Jocelyn Chanussot , Grégoire Vincent
Publication : IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Date : 2025
Volume : 17
Pages : 18751-18768
Catégorie(s)
#CIRAD #CNRS #FORET Nouragues #FORET ParacouRésumé
Warmer and drier climates over Amazonia have been predicted for the next century with expected changes in regional water and carbon cycles. We examined the impact of interannual and seasonal variations in climate conditions on ecosystem-level evapotranspiration (ET) and water use efficiency (WUE) to determine key climatic drivers and anticipate the response of these ecosystems to climate change. We used daily climate and eddyflux data recorded at the Guyaflux site in French Guiana from 2004 to 2014. ET and WUE exhibited weak interannual variability. The main climatic driver of ET and WUE was global radiation (Rg), but relative extractable water (REW) and soil temperature (Ts) did also contribute. At the seasonal scale, ET and WUE showed a modal pattern driven by Rg, with maximum values for ET in July and August and for WUE at the beginning of the year. By removing radiation effects during water depleted periods, we showed that soil water stress strongly reduced ET. In contrast, drought conditions enhanced radiation-normalized WUE in almost all the years, suggesting that the lack of soil water had a more severe effect on ecosystem evapotranspiration than on photosynthesis. Our results are of major concern for tropical ecosystem modeling because they suggest that under future climate conditions, tropical forest ecosystems will be able to simultaneously adjust CO2 and H2O fluxes. Yet, for tropical forests under future conditions, the direction of change in WUE at the ecosystem scale is hard to predict, since the impact of radiation on WUE is counterbalanced by adjustments to soil water limitations. Developing mechanistic models that fully integrate the processes associated with CO2 and H2O flux control should help researchers understand and simulate future functional adjustments in these ecosystems.
Auteurs, date et publication :
Auteurs Maricar Aguilos , Clément Stahl , Benoit Burban , Bruno Hérault , Elodie Courtois , Sabrina Coste , Fabien Wagner , Camille Ziegler , Kentaro Takagi , Damien Bonal
Publication : Forests
Date : 2019
Volume : 10
Issue : 1
Pages : 14
Catégorie(s)
#ANR-Citation #CIRAD #FORET ParacouRésumé
A new processing technique, i.e., ground cancellation, which removes the ground signal from a pair of interferometric synthetic aperture radar (SAR) images, is used to emphasize the response from above-ground targets. This technique is of particular interest when studying forest canopies using low-frequency signals able to reach the underlying ground, in which case the portion of the signal coming from the ground interferes with the recovery of information about the vegetation. We demonstrate that the power in ground-canceled P-band HV SAR data gives significantly higher correlations with above-ground biomass (AGB) than the interferometric images considered separately. In addition, a significant increase in the sensitivity of backscatter to AGB is observed. Ground-canceled power may then be modeled or regressed to estimate AGB; these possibilities are not discussed here as they will be the topic of forthcoming publications. The effectiveness of this technique is proven through simulations and analysis of real data gathered on tropical forests. The stability of the technique is analyzed under the digital terrain model and baseline control errors, and compensation strategies for these errors are presented.
Auteurs, date et publication :
Auteurs M. Mariotti d’Alessandro , S. Tebaldini , S. Quegan , M. J. Soja , L. M. H. Ulander , K. Scipal
Publication : IEEE Transactions on Geoscience and Remote Sensing
Date : 2020
Volume : 58
Issue : 9
Pages : 6410-6419
Catégorie(s)
#CIRAD #FORET ParacouAuteurs, date et publication :
Auteurs Julie Bossu , Romain Lehnebach , Stephane Corn , Arnaud Regazzi , Jacques Beauchêne , Bruno Clair
Publication : Trees
Date : 2018
Pages : 1–13
Catégorie(s)
#CIRAD #FORET ParacouRésumé
Resource control over abundance, structure and functional diversity of soil microbial communities is a key determinant of soil processes and related ecosystem functioning. Copiotrophic organisms tend to be found in environments which are rich in nutrients, particularly carbon, in contrast to oligotrophs, which survive in much lower carbon concentrations. We hypothesized that microbial biomass, activity and community structure in nutrient‐poor soils of an Amazonian rain forest are limited by multiple elements in interaction. We tested this hypothesis with a fertilization experiment by adding C (as cellulose), N (as urea) and P (as phosphate) in all possible combinations to a total of 40 plots of an undisturbed tropical forest in French Guiana. After 2 years of fertilization, we measured a 47% higher biomass, a 21% increase in substrate‐induced respiration rate and a 5‐fold higher rate of decomposition of cellulose paper discs of soil microbial communities that grew in P‐fertilized plots compared to plots without P fertilization. These responses were amplified with a simultaneous C fertilization suggesting P and C colimitation of soil micro‐organisms at our study site. Moreover, P fertilization modified microbial community structure (PLFAs) to a more copiotrophic bacterial community indicated by a significant decrease in the Gram‐positive : Gram‐negative ratio. The Fungi : Bacteria ratio increased in N fertilized plots, suggesting that fungi are relatively more limited by N than bacteria. Changes in microbial community structure did not affect rates of general processes such as glucose mineralization and cellulose paper decomposition. In contrast, community level physiological profiles under P fertilization combined with either C or N fertilization or both differed strongly from all other treatments, indicating functionally different microbial communities. While P appears to be the most critical from the three major elements we manipulated, the strongest effects were observed in combination with either supplementary C or N addition in support of multiple element control on soil microbial functioning and community structure. We conclude that the soil microbial community in the studied tropical rain forest and the processes it drives is finely tuned by the relative availability in C, N and P. Any shifts in the relative abundance of these key elements may affect spatial and temporal heterogeneity in microbial community structure, their associated functions and the dynamics of C and nutrients in tropical ecosystems.
Auteurs, date et publication :
Auteurs Nicolas Fanin , Stephan Hättenschwiler , Heidy Schimann , Nathalie Fromin , Joseph K. Bailey
Publication : Functional Ecology
Date : 2015
Volume : 29
Issue : 1
Pages : 140–150
Catégorie(s)
#CIRAD #FORET ParacouRésumé
The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth's radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m−2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.
Auteurs, date et publication :
Auteurs Francis M Lopes , Emanuel Dutra , Isabel F. Trigo
Publication : Remote Sensing
Date : 2025
Volume : 14
Issue : 7
Catégorie(s)
#CIRAD #FORET ParacouAuteurs, date et publication :
Auteurs M. Fournier , J. Dlouhá , G. Jaouen , T. Almeras
Publication : Journal of Experimental Botany
Date : 2013
Volume : 64
Issue : 15
Pages : 4793–4815
Catégorie(s)
#CIRAD #FORET ParacouRésumé
Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system 1 . Remote-sensing estimates to quantify carbon losses from global forests 2–5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced 6 and satellite-derived approaches 2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea 2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.
Auteurs, date et publication :
Auteurs Lidong Mo , Constantin M. Zohner , Peter B. Reich , Jingjing Liang , Sergio de Miguel , Gert-Jan Nabuurs , Susanne S. Renner , Johan van den Hoogen , Arnan Araza , Martin Herold , Leila Mirzagholi , Haozhi Ma , Colin Averill , Oliver L. Phillips , Javier G. P. Gamarra , Iris Hordijk , Devin Routh , Meinrad Abegg , Yves C. Adou Yao , Giorgio Alberti
Publication : Nature
Date : 2025
Catégorie(s)
#CIRAD #FORET ParacouRésumé
LiDAR technology has been widely used to characterize structural parameters of forest ecosystems, which in turn are valuable information for forest monitoring. GEDI is a spaceborne LiDAR system specifically designed to measure vegetation’s vertical structure, and it has been acquiring waveforms on a global scale since April 2019. In particular, canopy height is an important descriptor of forest ecosystems, as it allows for quantifying biomass and other inventory information. This paper analyzes the accuracy of canopy height estimates from GEDI data over tropical forests in French Guiana and Gabon. The influence of various signal acquisition and processing parameters is assessed to highlight how they impact the estimation of canopy heights. Canopy height models derived from airborne LiDAR data are used as reference heights. Several linear and non-linear approaches are tested given the richness of the available GEDI information. The results show that the use of regression models built on multiple GEDI metrics allows for reaching improved accuracies compared to a direct estimation from a single GEDI height metric. In a notable way, random forest improves the canopy height estimation accuracy by almost 80% (in terms of RMSE) compared to the use of rh_95 as a direct proxy of canopy height. Additionally, convolutional neural networks calibrated on GEDI waveforms exhibit similar results to the ones of other regression models. Beam type as well as beam sensitivity, which are related to laser penetration, appear as parameters of major influence on the data derived from GEDI waveforms and used as input for canopy height estimation. Therefore, we recommend the use of only power and high-sensitivity beams when sufficient data are available. Finally, we note that regression models trained on reference data can be transferred across study sites that share identical environmental conditions.
Auteurs, date et publication :
Auteurs Kamel Lahssini , Nicolas Baghdadi , Guerric le Maire , Ibrahim Fayad
Publication : Remote Sensing
Date : 2022
Volume : 14
Issue : 24
Pages : 6264
Catégorie(s)
#CIRAD #CNRS #FORET Nouragues #FORET ParacouAuteurs, date et publication :
Auteurs Clément Stahl , Benoit Burban , Fabien Wagner , Jean-Yves Goret , Félix Bompy , Damien Bonal
Publication : Biotropica
Date : 2013
Volume : 45
Issue : 2
Pages : 155–164