Auteurs, date et publication :
Auteurs P. Hinsinger , A. Brauman , N. Devau , F. Gérard , C. Jourdan , J. P. Laclau , E. Le Cadre , B. Jaillard , C. Plassard
Publication : Plant Soil
Date : 2025
Volume : 348
Pages : 29-61
Catégorie(s)
#CIRAD #FORET Itatinga #INRAERésumé
Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (textgreater150Mg/ha, and textgreater300Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean textgreater300Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter- and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R2=0.54, RMSE=48.3Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain “wall-to-wall” AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ∼50Mg/ha and R2=0.66 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values.
Auteurs, date et publication :
Auteurs Ibrahim Fayad , Nicolas Baghdadi , Stéphane Guitet , Jean-Stéphane Bailly , Bruno Hérault , Valéry Gond , Mahmoud El Hajj , Dinh Ho Tong Minh
Publication : International Journal of Applied Earth Observation and Geoinformation
Date : 2016
Volume : 52
Pages : 502–514
Catégorie(s)
#CIRAD #FORET ParacouRésumé
textlessptextgreater Ripley's textlessmath id="M2"textgreater textlessmrowtextgreater textlessmitextgreaterKtextless/mitextgreater textless/mrowtextgreater textless/mathtextgreater function is the classical tool to characterize the spatial structure of point patterns. It is widely used in vegetation studies. Testing its values against a null hypothesis usually relies on Monte-Carlo simulations since little is known about its distribution. We introduce a statistical test against complete spatial randomness (CSR). The test returns the textlessmath id="M3"textgreater textlessmrowtextgreater textlessmitextgreaterPtextless/mitextgreater textless/mrowtextgreater textless/mathtextgreater value to reject the null hypothesis of independence between point locations. It is more rigorous and faster than classical Monte-Carlo simulations. We show how to apply it to a tropical forest plot. The necessary R code is provided. textless/ptextgreater
Auteurs, date et publication :
Auteurs Eric Marcon , Stéphane Traissac , Gabriel Lang
Publication : Ecology
Date : 2025
Volume : 2013
Pages : 1–9
Catégorie(s)
#CIRAD #FORET ParacouAuteurs, date et publication :
Auteurs Flora Weissgerber , Elise Colin-Koeniguer , Nicolas Trouve , Jean-Marie Nicolas
Publication : IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Date : 2016
Volume : 9
Issue : 8
Pages : 3809–3820
Catégorie(s)
#CIRAD #FORET ParacouRésumé
The relationship between species abundance, the variance of the number of individuals, and species occupancy is a fundamental ecological characteristic of a community. Moreover, this relationship varies across scales, and any model for the variance-occupancy-abundance (VOA) relationship has to address its scale dependency in a consistent way. In this study, point-process theory was used to define a multiscale model that jointly predicts the VOA relationship across scales in a consistent way. This provides a tool to jointly analyze data sets collected at different scales and to give insights into the biological processes underlying the VOA relationship. This model can also account for different types of individual spatial pattern (clustered, random, or regular). Three stand-mapping data sets of tree species in tropical rain forests were used to assess the relevance of this model. When compared with four existing models, the model based on point-process theory provided the best fit to the data and was the most often ranked as the model with the best predictive performance.
Auteurs, date et publication :
Auteurs Nicolas Picard , Charly Favier
Publication : The American naturalist
Date : 2011
Volume : 178
Issue : 3
Pages : 383–96
Catégorie(s)
#CIRAD #FORET ParacouRésumé
Climate change and fast extension in climatically suboptimal areas threaten the sustainability of rubber tree cultivation. A simple framework based on reduction factors of potential transpiration was tested to evaluate the water constraints on seasonal transpiration in tropical sub-humid climates, according pedoclimatic conditions. We selected a representative, mature stand in a drought-prone area. Tree transpiration, evaporative demand and soil water availability were measured every day over 15 months. The results showed that basic relationships with evaporative demand, leaf area index and soil water availability were globally supported. However, the implementation of a regulation of transpiration at high evaporative demand whatever soil water availability was necessary to avoid large overestimates of transpiration. The details of regulation were confirmed by the analysis of canopy conductance response to vapor pressure deficit. The final objective of providing hierarchy between the main regulation factors of seasonal and annual transpiration was achieved. In the tested environmental conditions, the impact of atmospheric drought appeared larger importance than soil drought contrary to expectations. Our results support the interest in simple models to provide a first diagnosis of water constraints on transpiration with limited data, and to help decision making toward more sustainable rubber plantations.
Auteurs, date et publication :
Auteurs Jessada Sopharat , Frederic Gay , Philippe Thaler , Sayan Sdoodee , Supat Isarangkool Na Ayutthaya , Charlchai Tanavud , Claude Hammecker , Frederic C. Do
Publication : Frontiers in Plant Science
Date : 2015
Volume : 5
Catégorie(s)
#CIRAD #FORET RubberfluxRésumé
There are strong uncertainties regarding LAI dynamics in forest ecosystems in response to climate change. While empirical growth & yield models (G&YMs) provide good estimations of tree growth at the stand level on a yearly to decennial scale, process-based models (PBMs) use LAI dynamics as a key variable for enabling the accurate prediction of tree growth over short time scales. Bridging the gap between PBMs and G&YMs could improve the prediction of forest growth and, therefore, carbon, water and nutrient fluxes by combining modeling approaches at the stand level. Our study aimed to estimate monthly changes of leaf area in response to climate variations from sparse measurements of foliage area and biomass. A leaf population probabilistic model (SLCD) was designed to simulate foliage renewal. The leaf population was distributed in monthly cohorts, and the total population size was limited depending on forest age and productivity. Foliage dynamics were driven by a foliation function and the probabilities ruling leaf aging or fall. Their formulation depends on the forest environment. The model was applied to three tree species growing under contrasting climates and soil types. In tropical Brazilian evergreen broadleaf eucalypt plantations, the phenology was described using 8 parameters. A multi-objective evolutionary algorithm method (MOEA) was used to fit the model parameters on litterfall and LAI data over an entire stand rotation. Field measurements from a second eucalypt stand were used to validate the model. Seasonal LAI changes were accurately rendered for both sites ( R2=0.898 adjustment, R2=0.698 validation). Litterfall production was correctly simulated ( R2=0.562, R2=0.4018 validation) and may be improved by using additional validation data in future work. In two French temperate deciduous forests (beech and oak), we adapted phenological sub-modules of the CASTANEA model to simulate canopy dynamics, and SLCD was validated using LAI measurements. The phenological patterns were simulated with good accuracy in the two cases studied. However, LAImax was not accurately simulated in the beech forest, and further improvement is required. Our probabilistic approach is expected to contribute to improving predictions of LAI dynamics. The model formalism is general and suitable to broadleaf forests for a large range of ecological conditions.
Auteurs, date et publication :
Auteurs J. Sainte-Marie , L. Saint-Andre , Y. Nouvellon , J. P. Laclau , O. Roupsard , G. le Maire , N. Delpierre , A. Henrot , M. Barrandon
Publication : Ecological Modelling
Date : 2025
Volume : 290
Pages : 121-133
Catégorie(s)
#CIRAD #FORET CoffeeFluxRésumé
Systematic biases in eddy covariance measurements of net ecosystem-atmosphere carbon dioxide exchange (NEE) are ubiquitous in forests when turbulence is low at night. We propose an alternative to the conventional bias correction, the friction velocity (u*) filter, by hypothesizing that these biases have two separate, concurrent causes: (1) a subcanopy CO2 storage pool that eludes typical storage measurements, creating a turbulence-dependent bias, and (2) advective divergence loss of CO2, creating a turbulence-independent bias. We correct for (1) using a simple parametric model of missing storage (MS). Prior experiments have inferred (2) directly from atmospheric measurements (DRAINO). For sites at which DRAINO experiments have not been performed or are infeasible, we estimate (2) empirically using a PAR-extrapolated advective respiration loss (PEARL) approach. We compare u* filter estimates of advection and NEE to MS-PEARL estimates at one temperate forest and two tropical forest sites. We find that for tropical forests, u* filters can produce a range of extreme NEE estimates, from long-term forest carbon emission to sequestration, that diverge from independent assessments and are not physically sustainable. Our MS model eliminates the dependence of nighttime NEE on u*, consistent with findings from DRAINO studies that nighttime advective losses of CO2 are often not dependent on the strength of turbulence. Our PEARL estimates of mean advective loss agree with available DRAINO measurements. The MS-PEARL correction to long-term NEE produces better agreement with forest inventories at all three sites. Moreover, the correction retains all nighttime eddy covariance data and is therefore more widely applicable than the u* filter approach, which rejects substantial nighttime data—up to 93% at one of the tropical sites. The full MS-PEARL NEE correction is therefore an equally defensible and more practical alternative to the u* filter, but leads to different conclusions about the resulting carbon balance. Our results therefore highlight the need to investigate which approach's underlying hypotheses are more physically realistic.
Auteurs, date et publication :
Auteurs Matthew N. Hayek , Richard Wehr , Marcos Longo , Lucy R. Hutyra , Kenia Wiedemann , J. William Munger , Damien Bonal , Scott R. Saleska , David R. Fitzjarrald , Steven C. Wofsy
Publication : Agricultural and Forest Meteorology
Date : 2018
Volume : 250-251
Pages : 90–101
Catégorie(s)
#CIRAD #FORET ParacouRésumé
Tree vigor is often used as a covariate when tree mortality is predicted from tree growth in tropical forest dynamic models, but it is rarely explicitly accounted for in a coherent modeling framework. We quantify tree vigor at the individual tree level, based on the difference between expected and observed growth. The available methods to join nonlinear tree growth and mortality processes are not commonly used by forest ecologists so that we develop an inference methodology based on an MCMC approach, allowing us to sample the parameters of the growth and mortality model according to their posterior distribution using the joint model likelihood. We apply our framework to a set of data on the 20‐year dynamics of a forest in Paracou, French Guiana, taking advantage of functional trait‐based growth and mortality models already developed independently. Our results showed that growth and mortality are intimately linked and that the vigor estimator is an essential predictor of mortality, highlighting that trees growing more than expected have a far lower probability of dying. Our joint model methodology is sufficiently generic to be used to join two longitudinal and punctual linked processes and thus may be applied to a wide range of growth and mortality models. In the context of global changes, such joint models are urgently needed in tropical forests to analyze, and then predict, the effects of the ongoing changes on the tree dynamics in hyperdiverse tropical forests.
Auteurs, date et publication :
Auteurs Mélaine Aubry-Kientz , Vivien Rossi , Jean-Jacques Boreux , Bruno Hérault
Publication : Ecology and Evolution
Date : 2015
Volume : 5
Issue : 12
Pages : 2457–2465
Catégorie(s)
#CIRAD #FORET ParacouRésumé
Synthetic aperture radar tomography (TomoSAR) is an important way of obtaining underlying topography and forest height for long-wavelength datasets such as L-band and P-band radar. It is usual to apply nonparametric spectral estimation methods with a large number of snapshots over forest areas. The nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES) can obtain a high resolution; however, it only tends to work well with a small number of snapshots. To overcome this problem, this paper proposes the nonparametric iterative adaptive approach based on maximum likelihood estimation (IAA-ML) for the application over forest areas. IAA-ML can be directly used in forest areas, without any prior information or preprocessing. Moreover, it can work well in the case of a large number of snapshots. In addition, it mainly focuses on the backscattered power around the phase centers, helping to detect their locations. The proposed IAA-ML estimator was tested in simulated experiments and the results confirmed that IAA-ML obtains a higher resolution than IAA-APES. Moreover, six P-band fully polarimetric airborne SAR images were applied to acquire the structural parameters of a forest area. It was found that the results of the HH polarization are suitable for analyzing the ground contribution and the results of the HV polarization are beneficial when studying the canopy contribution. Based on this, the underlying topography and forest height of a test site in Paracou, French Guiana, were estimated. With respect to the Light Detection and Ranging (LiDAR) measurements, the standard deviation of the estimations of the IAA-ML TomoSAR method was 2.11 m for the underlying topography and 2.80 m for the forest height. Furthermore, compared to IAA-APES, IAA-ML obtained a higher resolution and a higher estimation accuracy. In addition, the estimation accuracy of IAA-ML was also slightly higher than that of the SKP-beamforming technique in this case study.
Auteurs, date et publication :
Auteurs Xing Peng , Xinwu Li , Changcheng Wang , Haiqiang Fu , Yanan Du
Publication : Sensors
Date : 2018
Volume : 18
Issue : 8