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This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed individually for both a small forest area containing a set of 29 permanent sample plots that were damaged in Storm Martin in December 1999, and from a much larger set of 235 forest inventory data damaged in Storm Klaus in January 2009. Both data sets are within the Landes de Gascogne Forest in Nouvelle Aquitaine, France. The models were tested both against the data from which they were developed, and against the data set from the other storm. For comparison with an earlier study using the same data, logistic regression models were also developed. In addition, the ability of machine learning techniques to substitute for a mechanistic wind damage risk model by training them with previous mechanistic model predictions was tested. All models were accurate at identifying whether trees would be damaged or not damaged but the random forests models were more accurate, had higher discriminatory power, and were almost totally unaffected by the removal of any individual input variable. However, if all information relating to a stand was removed the random forests model lost accuracy and discriminatory power. The other models were similarly affected by the removal of all site information but none of the models were affected by removal of all tree information, suggesting that damage in the Landes de Gascogne Forest occurs at stand scale and is not controlled by individual tree characteristics. The models developed with the large comprehensive database were also accurate in identifying damaged trees when applied to the small forest data damaged in the earlier storm. However, none of the models developed with the smaller forest data set could successfully discriminate between damaged and undamaged trees when applied across the whole landscape. All models were very successful in replicating the predictions of the mechanistic wind risk model and using them as a substitute for the mechanistic model predictions of critical wind speed did not affect the damage model results. Overall the results suggest that random forests provide a significant advantage over other statistical modelling techniques and the random forest models were found to be more robust in their predictions if all input variables were not available. In addition, the ability to replace the mechanistic wind damage model suggests that random forests could provide a powerful tool for damage risk assessment at the stand or single tree level over large regions and provide rapid assessment of the impact of different management strategies or be used in the development of optimised forest management with multiple objectives and constraints including the risk of wind damage.

The Above-Ground Biomass (AGB) is a key parameter used for the modeling of the carbon cycle. The aim of this study is to make an experimental assessment of the sensitivity of Global Navigation Satellite System (GNSS) reflected signals to forest AGB. This is based on the analysis of the data recorded during several GLORI airborne campaigns in June and July 2015, over the Landes Forest (France). Ground truth measurements of tree height, density and diameter at breast height (DBH), as well as AGB, were carried out for 100 maritime pine forest plots of various ages. The GNSS-R data were used to obtain the right-left (ΓRL) and right-right (ΓRR) reflectivity observables, which are geo-referenced in accordance with the known positions of relevant GPS satellites and the airborne receiver. The correlations between forest AGB and the GNSS-R observables yield the highest sensitivity at high elevation angles (70°-90°). In this case, for (ΓRL) and the reflectivity polarization ratio (PR = ΓRL/ΓRR) estimated with a coherent integration time Tc = 20 ms, the coefficients of determination R2 are equal to 0.67 and 0.51, with a sensitivity of −0.051 dB/[106g (Mg) ha−1], and −0.053 dB/[Mg ha−1], respectively. The relationships between AGB and the observables are confirmed through the use of a 5-fold cross validation approach, with several different coherent integration times.

International audience; The GLORI Campaign performed in June-July 2015 to investigate the sensitivity of airborne GNSS-R measurements to land parameters is presented. In this paper data obtained on forest areas are analyzed. Ground truth measurements of tree height, density and diameter at breast height, AGB etc were measured over 100 maritime pine forest plots of various ages. The correlation between forest parameters and GNSS reflectivity in LHCP polarization or polarization ratio (PR) yields to high sensitivity for high elevation angles (70°-90°). Results show that PR illustrates highest potential than the reflectivity in LHCP.

16 pages; International audience; Temperature is a driving climate variable for grapevine development and grape ripening kinetics. The current study first reports interpolation of daily minimum and maximum temperature data by a weather station network from 2001 to 2005 in the Bordeaux (France) region by means of regression kriging using terrain, satellite and land-cover derived covariates. Second it analyses the interpolation procedure errors in agroclimatic indices by means of cross validation and then it compares the field observations of grapevine phenology to temperature-based predicted phenology applied to interpolated data. Finally it proposes a simple method to perform a zoning of Bordeaux vineyards based upon the spatialized prediction of the day on which grape sugar content reaches 200 g.L-1. The zoning performed shows large potential differences in grape maturity date (up to 20 days) induced by temperature spatial variability in a low relief area.

Paper Code: TH3.L9.4Paper Number: 2155; The GLORIE GNSS-R airborne campaign was conducted in the late spring 2015 with the GLORI polarimetric receiver. More than 15 hours or raw data was gathered during 5 flights that spanned over a 3-week period. The aircraft flew over several areas if interest including: 1) agricultural plots with coincident in-situ measurements of soil moisture, vegetation biomass and roughness, 2) in situ monitored forest plots with a wide range of above ground biomass values and 3) inland water bodies in order to test phase altimetry retrievals. Apparent reflectivity was computed from the data, showing a good dynamics above various types of terrains. Phase altimetry was performed over calm water, showing a precision in the range of the centimeter level.

International audience; Maritime pine (Pinus pinaster Ait.) forests in the Aquitaine region, south-west France, suffered catastrophic damage from Storms Martin (1999) and Klaus (2009), and more damage is expected in the future due to forest structural change and climate change. Thus, developing risk assessment methods is one of the keys to finding forest management strategies to reduce future damage. In this paper we evaluated two approaches to calculating wind damage risk to individual trees using data from different damage data sets from two storm events. Airflow models were coupled either with a mechanistic model (GALES) or a bias-reduced logistic regression model, in order to discriminate between damaged and undamaged trees. The mechanistic approach was found to successfully discriminate the trees for different storms, but only in locations with soil conditions similar to where the model parameters were obtained from previous field experiments. The statistical approach successfully discriminated the trees only when applied to similar data as that used for creating the models, but it did not work at an acceptable level for other data sets. One variable, decade of stand establishment, was a significant variable in all statistical models, suggesting that site preparation and tree establishment could be a key factor related to wind damage in this region.; Les forêts d’Aquitaine de Pin maritime (Pinus pinaster Ait.) du sud-ouest de la France ont souffert de deux tempêtes majeures, Martin (1999) et Klaus (2009) et de nouveaux dégâts sont attendus dans le futur en raison de changements dans la structure forestière et à cause du changement climatique. C’est pour cela que le développement d’une méthode d’évaluation des risques liés aux tempêtes est une des clés pour développer de nouvelles stratégies d’aménagement pour réduire les risques à venir. Dans ce papier, nous avons évalué deux approches pour calculer le risque au niveau des arbres individuels en utilisant deux jeux de données provenant des deux dernières tempêtes. Des modèles de simulation atmosphériques ont été couplés soit à un modèle mécaniste de prévision des dégâts (GALES) soit à un modèle de régression logistique avec réduction du biais de façon à distinguer les arbres endommagés ou non. L’approche mécaniste permet de distinguer avec succès les catégories d’arbres lors des deux tempêtes mais seulement pour les conditions pédologiques des données qui ont servi à la calibration des paramètres du modèle. L’approche statistique permet elle aussi de discriminer les catégories d’arbres avec succès uniquement pour les conditions de calibration mais elle ne fonctionne pas pour d’autres jeux de données. Une variable, la décade d’installation des peuplements, est significative pour tous les différents modèles statistiques, suggérant que les techniques de préparation du sol et de régénération pourraient faire parties des facteurs clés en relation avec les dégâts dus au vent dans cette région.

This letter evaluates the potential of wavelet-based texture modeling for the classification of stand age in a managed maritime pine forest using very high resolution panchromatic and multispectral PLEIADES data. A cross-validation approach based on stand age reference data is used to compare classification performances obtained from different multivariate models (multivariate Gaussian, spherically invariant random vector (SIRV)-based models, and Gaussian copulas) and from co-occurrence matrices. Results show that the multivariate modeling of the spatial dependence of wavelet coefficients (particularly when using the Gaussian SIRV model) outperforms the use of features derived from co-occurrence matrices. Simultaneously adding features representing the color dependence and leveling the dominant orientation in anisotropic forest stands enhances the classification performances. These results confirm the ability of such wavelet-based multivariate models to efficiently capture the textural properties of very high resolution forest data and open up perspectives for their use in the mapping of monospecific forest structure variables.

Plant phenological development is orchestrated through subtle changes in photoperiod, temperature, soil moisture and nutrient availability. Presently, the exact timing of plant development stages and their response to climate and management practices are crudely represented in land surface models. As visual observations of phenology are laborious, there is a need to supplement long-term observations with automated techniques such as those provided by digital repeat photography at high temporal and spatial resolution. We present the first synthesis from a growing observational network of digital cameras installed on towers across Europe above deciduous and evergreen forests, grasslands and croplands, where vegetation and atmosphere CO2 fluxes are measured continuously. Using colour indices from digital images and using piecewise regression analysis of time-series, we explored whether key changes in canopy phenology could be detected automatically across different land use types in the network. The piecewise regression approach could capture the start and end of the growing season, in addition to identifying striking changes in colour signals caused by flowering and management practices such as mowing. Exploring the dates of green up and senescence of deciduous forests extracted by the piecewise regression approach against dates estimated from visual observations we found that these phenological events could be detected adequately (RMSE < 8 and 11 days for leaf out and leaf fall respectively). We also investigated whether the seasonal patterns of red, green and blue colour fractions derived from digital images could be modelled mechanistically using the PROSAIL model parameterised with information of seasonal changes in canopy leaf area and leaf chlorophyll and carotenoid concentrations. From a model sensitivity analysis we found that variations in colour fractions, and in particular the late spring "green hump" observed repeatedly in deciduous broadleaf canopies across the network, are essentially dominated by changes in the respective pigment concentrations. Using the model we were able to explain why this spring maximum in green signal is often observed out of phase with the maximum period of canopy photosynthesis in ecosystems across Europe. Coupling such quasi-continuous digital records of canopy colours with co-located CO2 flux measurements will improve our understanding of how changes in growing season length are likely to shape the capacity of European ecosystems to sequester CO2 in the future.

National audience; Une étude comparative entre des acquisitions Pléiades et par drone a été menée pour des applications de viticulture de précision dans la région Aquitaine, sur le vignoble de Buzet et deux domaines du Bordelais. Cette étude montre une bonne cohérence entre les résultats obtenus à partir des différents capteurs, à la fois pour la caractérisation des paramètres parcellaires et pour l’estimation de la vigueur de la vigne. En particulier, Pléiades met en évidence les variabilités et hétérogénéités intra-parcellaires. Pléiades est donc pertinent pour caractériser des domaines viticoles à l’échelle de l’appellation. Cependant, la résolution du satellite Pléiades limite la détection des plus petites structures à l’intérieur des rangs et empêche la caractérisation des parcelles pour lesquelles les rangs sont plus resserrés (écarts inférieurs à deux mètres). Le recours à des technologies de meilleure résolution (notamment le drone) est complémentaire afin d’obtenir des caractérisations plus précises à l’échelle d’un domaine.

International audience; The potential of very high resolution Pléiades image texture for forest structure mapping was assessed on maritime pine stands in south-western France. A preliminary step showed that multi-linear regressions allow a reliable prediction of forest variables (such as crown diameter or tree height) from a set of features automatically selected among a huge number of Haralick texture features with various spatial parameterizations. In a second step, to assess Pléiades image texture contribution for classification, Random Forests (RF) classification was performed to discriminate four forest structure classes from recent reforestation to mature stand. Two texture feature selection strategies are compared: (1) the previous regression-based modelling using in situ tree measurements (2) the RF-variable importance using a visual photo-interpretation. Both methods produced comparable classification accuracies. The results highlight the contribution of processes automation and the need for using both Pléiades image resolutions (panchromatic and multispectral) to derive the best performing texture features.

International audience; Since several years, more and more studies aim at developing phenology products from satellite time-series at high temporal frequency such as those provided by the VEGETATION or MODIS sensors. Reflectance times-series at high spatial resolution, such as those that will be obtained from SENTINEL2, will soon available to monitor the phenology response of forests under climate change at the level of the forest stand or the tree species. There is a great need for continuous in situ monitoring of phenology to calibrate and validate the current and future remotely-sensed phenology products. In this context, we proposed a method based on near surface remote sensing techniques to monitor the seasonal change in LAI (Leaf area Index) and date key stages of the leaf phenology. As it is important to use a network of sensors with autonomous recording systems at a minimal cost in order to maximize the spatial sampling, we selected a method based on the transmittance continuous measurement of photosynthetic active radiation (PAR). We evaluated and validated the method for a deciduous forest species (Quercus petraea) over two sites encompassing a large variation in the timing of spring leafing, where direct visual phenology observations were performed. A specific preprocessing and modeling of the measured temporal signal was developed. The performances for dating the leaf unfolding in spring are satisfying: the bias is lower than ∼1 day and the RMSE is (generally) lower than ∼ 4 days.

This paper presents a new feature selection method which aims to effectively and efficiently map remote sensing data. An automated texture-based modelling procedure of forest structure variables is at the core of our approach. We show that texture features that are highly correlated to genuine physical parameters of forest structure have potential for building reliable classifiers. We demonstrate the effectiveness of our modelling-based texture feature selection method in performing mapping of very high resolution forest images. Our method outperforms Random Forest variable importance in terms of classification accuracy and computational complexity.

In most of the ecological models, simplified methods are still being used to describe the canopy radiative transfer mechanism (CRTM). Largest uncertainties in the conceptualization of CRTM lies in the adequate description of the two main canopy architectural parameters: (1) the projection function (G) that describes the foliage inclination distribution in space and (2) the clumping function (Ω) that describes the aggregative nature of canopy elements. Herein, we explore the effects of different (simple to complex) representations of the G and Ω functions on simulations of photosynthetically active radiation (PAR) transmitted through a middle-aged maritime pine canopy, using a scenario-based numerical experiment. Initially we studied the nature of different G and Ω functions for a maritime pine stand using the data obtained using a cohort of ground-based instruments that measures canopy architecture (LAI-2000, TRAC, Digital Hemispherical Photography (DHP)). Further, we evaluated the performances of 15 CRTM scenarios that had various combinations of the G and Ω functions (or values) to predict the transmitted PAR below the overstory canopy (Runder). The performances of the scenarios were explored by comparing the simulated and measured daily average Runder for 293 days in 2008. We found that the best performance was showed by scenario S6 (bias +1%, RMSE = 32.42 μmol m−2 s−1, NSE = 0.90) wherein the Ω function was described using the empirical function derived using the TRAC data, and the G function calculated using a leaf angle distribution function (LADF) employing the Goel and Strebel (1984) approach. The scenarios that used the Ellipsoidal LADF for the G-function showed a general tendency to overestimate Runder with poorer performances. Assumption of a spherical leaf angle distribution (G = 0.5) along with a Ω function developed by Kucharik et al. (1999) (scenario S3) showed a rather satisfying performance (bias = 2% RMSE = 33.31 μmol m−2 s−1, NSE = 0.89). The scenario (S4) that assumed a random foliage distribution (Ω = 1) and a spherical leaf angle distribution (G = 0.5), the most common method used in ecological models, showed the worst performance to predict the Runder (bias −34%, RMSE = 72.8 μmol m−2 s−1, NSE = 0.40). With these observations we suggest that the dynamics of G and Ω function be explicitly defined in the CRTM modules that are used within ecological models.

By overemphasizing the role of overstory trees in forest studies, researchers, managers and policy makers could well ignore an important functional component of the forest ecosystem: the understory. This could be particularly true in forests with relatively open canopies. In maritime pine (Pinus pinaster Ait.) plantation forests in the southwest of France, a considerable amount of the direct radiation is transmitted through the canopy and is available for the growth of understory species. This study focused on five dominant understory species, including three woody species (Calluna vulgaris L., Erica cinerea L. and Cytisus scoparius L.) and two herbaceous perennials species (Molinia caerulea L. (Moench) and Pteridium aquilinum L. Khun). For each species five pine stands with varying amounts of understory were selected to predict biomass for a wide range of values. The number of pine trees and their dimensions were recorded enabling us to estimate the aboveground and root biomass of the pines using allometric relationships. Biometrical measurements of the understory and destructive harvests were carried out in each stand to measure aboveground, coarse and fine root biomass. The results showed that the understory represented an average of 6.8% of total ecosystem aboveground biomass and 27.4% of total ecosystem root biomass, underlining the need for a broader view of this component of the forest ecosystem. Allometric relationships derived from the biometrical data enabled satisfactory predictions of aboveground biomass for all five understory species and for belowground biomass of three species (M. caerulea, P. aquilinum, C. scoparius); allometric relationships failed to predict root biomass for two woody understory species (E. cinerea and C. vulgaris).

chapitre 6; International audience; Recent evidence has shown that maize pollen can travel and remain viable at regional scales, which opens the possibility for long-distance cross-pollination. Here, we first report results obtained on small plots of white-kernel maize used as pollen traps, in a region with extensive maize cultivation, demonstrating that cross-pollination can indeed occur at several kilometres from the nearest source field. We also used the atmospheric Meso-NH model to better understand long-range dispersal of maize pollen. Simulations were performed over Southwest France during the maize pollination period. When compared with airborne measurements, the model provides good estimates of pollen concentration throughout the atmospheric boundary layer. It allows the pollen plume to be characterized during the day and permits the production of deposition maps of accumulated viable pollen. These results quantify long-distance pollen deposition; they show that background levels of cross-pollination are unavoidable at the regional scale and support the use of low purity thresholds by supply chain operators.

International audience; Over the last decade, a growing number of Very High Resolution (VHR) remote sensing data from various spatial sensors has become available. They provide interesting information for forest inventory applications thanks to the strong relationship between forest spatial structure and image texture at stand level. The main goal of this study is to find the most relevant image features to describe forest structure from very high resolution satellite imagery. The emphasis is placed on the automatisation of this process, exploiting both spectral and spatial information. Compared to High Resolution images, VHR imagery provides a very rich textural information that has to be thoroughly investigated for the forest structure characterization. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick's texture features (derived from Grey Level Co-occurrence Matrix). The main drawback of this well-known texture representation is the underlying parameters (window size, displacement length and orientation, quantification level) which are extremely difficult to set due to the spatial complexity of the forest structure. To tackle this major issue, probably the main cause of poor texture analysis in practice, we propose an automatic feature selection process whose originality lies on the use of test frames of adequate forest samples where the structure variables were measured from ground. This method, inspired by camera calibration protocols, selects the best image descriptors via statistical modelling, exploring a wide range of parameter values. Hence, just a few samples are required to build up the test frames but allow a fast assessment of thousands of descriptors, given the large number of tested combinations of parameters values. This method has been successfully applied to the modelling of 7 typical forest structure variables (age, tree height, crown diameter, diameter at breast hight, basal area, density and tree spacing) over maritime pine stands in South West of France from QuickBird panchromatic and multi-spectral images. Overall results are good, multi-spectral and panchromatic images show similar performances to provide well-suited features to estimate the forest variables. The coefficient of determination, R², of the best models for 6 of the forest variables of interest, estimated from the test frames, ranges from 0.89 to 0.97. Only the basal area was weakly correlated to the considered image features (0.64). To improve the results, various linear combinations of panchromatic descriptors or multi-spectral descriptors or a combination of both, were tested using multiple linear regressions. As collinearity is a very perturbing problem in multi-linear regression, this issue is carefully addressed. Different variables subset selection methods are tested. A stepwise method, derived from LARS (Least Angular Regression), turned out the most convincing, significantly improving the quality of estimation for all the forest structure variables, including the basal area (R²>0.98). The best estimation results are obtained from subsets combining multi-spectral and panchromatic features, highlighting the potential of a multi-scale approach for retrieving forest structure variables from VHR satellite images.

Insect related damage has severe ecological and socio-economic impacts by causing large-scale mortality of vegetation and thereby affecting biogeochemical cycles and forest industry. The objective of this study was to evaluate the potential use of 250 m MODIS data time-series to monitor damage caused by the pine processionary moth (PPM), which is the most important pine defoliator of the maritime-pine forest in southwest France. We compared the in situ observed defoliation rates (DR) and the multi-seasonal variations of the MODIS-sensor derived MOD13Q1 product. We exploited the winter anomalies in the EVI signals to empirically detect and quantify the PPM defoliation. Different EVI-based anomaly metrics were tested by linear regression over a test site of 18 km2. The best correlated relationship (R2=0.908, p

International audience; Spatial datasets of biophysical parameters at multiple scales can be important for the modeling of landsurface processes. In this study, we compared how decametric resolution and kilometric resolution LAI retrievals vary over a maritime pine dominated ecosystem in Southern France. Firstly, we used atmospherically corrected Landsat ETM+ and SPOT4 HRVIR reflectances along with ground-based LAI measurements to derive empirical relationships between vegetation indices and measured LAI. These algorithms were later inverted to map LAI over the landscape. RSR-based algorithms showed the best performance for both ETM+ (r² = 0.788) and HRVIR (r² = 0.780) sensors and were more stable than SR and NDVI. Further, after upscaling to 1km, comparison with global LAI products revealed that the CYCLOPES product was more robust in capturing the fine scale signatures. These results show that modeling of landsurface processes could be improved by adopting a multiscale and multisensor approach.

article i nfo Plant phenology characterises the seasonal cyclicity of biological events such as budburst, flowering, fructifi- cation, leaf senescence and leaf fall. These biological events are genetically pre-determined but also strongly modulated by climatic conditions, particularly temperature, daylength and water availability. Therefore, the timing of these events is considered as a good indicator of climate change impacts and as a key parameter for understanding and modelling vegetation-climate interactions. In situ observations, empirical or bioclimatic models and remotely sensed time-series data constitute the three possible ways for monitoring the timing of plant phenological events. Remote sensing has the advantage of being the only way of surface sampling at high temporal frequency and, in the case of satellite-based remote sensing, over large regions. Neverthe- less, exogenous factors, particularly atmospheric conditions, lead to some uncertainties on the seasonal course of surface reflectance and cause bias in the identification of vegetation phenological events. Since 2005, a network of forest and herbaceous sites has been equipped with laboratory made NDVI sensors to monitor the temporal dynamics of canopy structure and phenology at an intra-daily time step. In this study, we present recent results obtained in several contrasting biomes in France, French Guiana, Belgium and Congo. These sites represent a gradient of vegetation ecosystems: the main evergreen and deciduous for- est ecosystems in temperate climate region, an evergreen tropical rain forest in French Guiana, an herbaceous savanna ecosystem in Congo, and a succession of three annual crops in Belgium. In this paper, (1) we provide an accurate description of the seasonal dynamics of vegetation cover in these different ecosystems (2) we identify the most relevant remotely sensed markers from NDVI time-series for determining the dates of the main phenological events that characterize these ecosystems and (3) we discuss the relationships be- tween temporal canopy dynamics and climate factors. In addition to its importance for phenological studies, this ground-based Network of NDVI measurement provides data needed for the calibration and direct valida- tion of satellite observations and products.

International audience; The SMOS (Soil Moisture and Ocean Salinity) mission was launched on November 2, 2009. Over the land surfaces, simultaneous retrievals of surface soil moisture (SM) and vegetation characteristics are made from the multi-angular and dual polarization SMOS observations by inverting the L-MEB (L-band Microwave Emission of the Biosphere) model. Preliminary analyses evaluating the SMOS observations in terms of Brightness Temperatures (TB) and Level-2 products (SM and vegetation optical depth TAU) were carried out over several sites of all continents. Here, the study is based on SMOS observations and in situ measurements carried out in 2010 over one of the main SMOS calibration/validation sites in Europe: the VAS (Valencia Anchor Station) site in the region of Utiel–Requena, close to Valencia, Spain. The main vegetation types in the region are vineyards (cover fraction ~60%), natural Mediterranean vegetation (pine trees and shrubs ~30%) and orchards. The SMOS observations were analysed in conjunction with those carried out by the L-band ELBARA II radiometer over a vineyard which is representative of the main land use of the VAS site. Retrievals of SM and TAU based on the SMOS observations (Level-2 products) and the ELBARA II instrument were compared and evaluated against in situ measurements and simulations of SM by atmospheric models. A good agreement was found between the time variations in SM over the site retrieved from the SMOS and ELBARA II observations. However, it was found the SMOS Level-2 products underestimated SM by about 0.15 %, even though it is difficult to estimate SM values at large scale that can be considered as a "reference" to evaluate the SMOS SM retrievals. Retrievals computed from SMOS data using a simplified L-MEB version, that did not consider the pixel heterogeneity and the WEF function, produced very satisfying results (R2=66% when compared to retrievals computed from the ELBARA II observations) The time variations in the retrieved TAU computed from the ELBARA II observations could be closely related to those of the NDVI vegetation index, obtained from the MODIS observations in the optical domain. Conversely, the time variations in the SMOS Level-2 TAU product seemed to be strongly affected by microwave interferences over the site.

A simple approach for correcting for the effect of vegetation in the estimation of the surface soil moisture (wS) from L-band passive microwave observations is presented in this study. The approach is based on semi-empirical relationships between soil moisture and the polarized reflectivity including the effect of the vegetation optical depth which is parameterized as a function of the normalized vegetation difference index (NDVI). The method was tested against in situ measurements collected over a grass site from 2004 to 2007 (SMOSREX experiment). Two polarizations (horizontal/vertical) and five incidence angles (20◦, 30◦, 40◦, 50◦, and 60◦) were considered in the analysis. The best wS estimations were obtained when using both polarizations at an angle of 40◦. The average accuracy in the soil moisture retrievals was found to be approximately 0.06 m3/m3, improving the estimations by 0.02 m3/m3 with respect to the case in which the vegetation effect is not considered. The results indicate that information on vegetation (through a vegetation index such as NDVI) is useful for the estimation of soil moisture through the semi-empirical regressions.

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International audience; Data on forest variables are required for environmental and forest management applications. Numerous authors have shown significant correlations between mean radar response intensity and forest variables (age, height or biomass) but few studies have explored the spatial characteristics of the radar response for varying forest states. In this Letter, variation in the most commonly used texture features is shown as a function of an indicator of forest growth (age) for a controlled homogeneous test site (monospecific, even-aged forest, with identical sylvicultural practices and a sampling that covers all forest stages from sowing to harvest). Significant linear relationships between some texture features and stand age are observed. Moreover, the quality of some fits indicates that texture could be used instead of the usual intensity-age relationships that saturate for mature stands.

International audience; Due to their synoptic and monitoring capacities, Earth observation satellites could prove useful for the assessment and evaluation of drought effects in forest ecosystems. The objectives of this article are: to briefly review the existing sources of remote sensing data and their potential to detect drought damage; to review the remote sensing applications and studies carried out during the last two decades aiming at detecting and quantifying disturbances caused by various stress factors, and especially those causing effects similar to drought; to explore the possibility to use some of the different available systems for setting up a strategy more adapted to monitoring of drought effects in forests.; Grâce à leurs capacités de surveillance continue, les satellites d'observation de la Terre pourraient s'avérer utiles pour l'évaluation des effets de la sécheresse sur les écosystèmes forestiers. Les objectifs de cet article sont : de passer en revue rapidement les sources actuelles de données de télédétection et leur potentiel pour la détection des dommages dus à la sécheresse ; de passer en revue les études et applications de télédétection conduites pendant les 2 dernières décennies et visant à détecter et quantifier les perturbations induites par différents facteurs de stress, et en particulier ceux causant des effets semblables à ceux de la sécheresse ; d'explorer la possibilité d'utiliser certains des systèmes disponibles pour définir une stratégie adaptée au suivi continu des effets de la sécheresse sur les forêts.

International audience; In the microwave region, discrete physical models are often used to simulate surface emission and backscattering of forests. These models require detailed information on the geometry of the forest constituents, typically represented by canonical shapes. This paper presents a forest geometric description (FGD) of a Maritime pine tree forest (Pinus pinaster AÏt) suitable for discrete models at low microwave frequencies. The FGD contains a number of allometric equations developed from the analysis of a very large set of ground-based measurements of the forest structure. These allometric equations reproduce the density, shape, and dimension of branches, trunks, and needles, represented by cylinders in the FGD. An original approach to describe the tree architecture is presented: primary branches (branches inserted in the trunk) are split into segments or growth units (GUs) so that the full branch curvature can be accounted for, and probability distribution functions are given for the orientation and diameter of the GUs. The FGD uses only two variables, tree density (number of trees per ha) and tree age, as input information to simulate the forest geometric characteristics at three layers: the upper crown, the lower crown, and a crown-free layer. Input parameters to compute the dielectric constant of the forest constituents are also given.

We carried out a multi-taxa study to investigate the responses of species assemblages to stand and landscape variables in maritime pine plantation forests in the Landes de Gascogne (southwestern France). Breeding birds, carabid beetles, and ground-dwelling spiders were sampled simultaneously in a balanced set of 27 stands varying in tree species composition, age, and vertical structure. The composition and structure of surrounding landscapes were quantified within a 500-m radius around the stands. Species responses to a combination of 12 stand and landscape variables were estimated using multiple stepwise regression. Bird, carabid, and spider assemblages were related to the same explanatory variables using a set of co-inertia analyses. Tree height was the best predictive variable for all taxa at species richness, species assemblage, and individual species levels. Landscape variables were important as secondary factors, especially landscape fragmentation, spatial heterogeneity, and shape and spatial distribution of deciduous forest patches in the surrounding landscape. The occurrence of deciduous forest patches and an increasing landscape heterogeneity appeared to be beneficial to forest birds and spiders but not to carabids, probably because forest carabids are generally poor dispersers, more sensitive to habitat fragmentation, Nous avons réalisé une étude avec plusieurs taxa pour analyser la réponse d’assemblages d’espèces aux variables stationnelles et paysagères dans les forêts de plantations de pin maritime des Landes de Gascogne (sud-ouest de la France). Nous avons échantillonné simultanément les oiseaux nicheurs, les coléoptères carabiques et les araignées épigées dans un échantillon de 27 parcelles différant selon leur composition en espèces d’arbres, leur âge et leur structure verticale. La composition et la structure du paysage environnant ont été quantifiées dans un rayon de 500 m autour des parcelles. La réponse des espèces à une combinaison de 12 variables stationnelles et paysagères a été analysée par régression multiple pas à pas. La relation entre les assemblages d’oiseaux, de carabiques et d’araignées et les mêmes variables environnementales a été étudiée par une série d’analyses de co-inertie. La hauteur moyenne des arbres est la meilleure variable explicative pour tous les taxa à la fois au niveau de la richesse spécifique, de la composition des assemblages et de l’espèce. Les variables paysagères constituent des facteurs secondaires importants, en particulier la fragmentation du paysage, l’hétérogénéité spatiale et la forme et la distribution spatiale des îlots de feuillus dans le paysage environnant. La présence d’îlots de feuillus et l’hétérogénéité du paysage sont favorables aux oiseaux et aux araignées des habitats forestiers, mais ne favorisent pas les carabiques, sans doute en raison de la moindre capacité de dispersion des carabiques forestiers

International audience; Epidemiololgical investigations were performed in a 3-ha maritime pine (Pinus pinaster) plantation established on a site heavily infested by Armillaria ostoyae. Geostatistics were used to examine the density and the distribution of the initial inoculum. Disease dynamics were monitored for 17 years after planting. On the whole site, the cumulative mortality rate reached 35% over this period, plateauing at 12 years. Disease progress curves differed according to the density of the initial inoculum, although in all the cases, the Gompertz model described the epidemics, well. The epidemiological contributions of both primary (initially colonized Stumps) and secondary inoculum (newly dead pines) were evaluated by analyzing their spatial relation to annual mortality. Newly dead pines inoculum from year 3 and their role increased with acted as secondary time. When the initial inculum density was low, the contribution of secondary inoculum to epidemic development increased faster and halted sooner than when the density of primary inoculum was high. Regardless of its density, the primary inoculum acted throughout the dynamic phase of the epidemic. When the inoculum density was low, the probability of mortality during the first 6 years of the epidemic depended on the tree distance from the nearest stumps colonized by Armillaria sp. When the inculum density was high, the probability of mortality was higher and not related to the distance between trees and colonized stumps

International audience; This letter presents preliminary results of a study concerning the influence of forest thinning on the backscattering coefficient (sigma) at L-band by using data from the JPL-NASA campaign MAESTRO-1 and MacEurope on Les Landes forest, France. The backscattering coefficient of thinned and unthinned stands were compared and interpreted with the help of model simulations. It is shown that thinning significantly influences the backscattering coefficient of adult stands, particularly at HV polarisation where sigma was reduced by up to 4 dB. Moreover, the density variation of the stand did not explain all this decrease in sigma. Secondary structure modifications induced by the thinning are likely to have considerable influence on the backscattering coefficient.

International audience; The objective of this study is to analyze the potential interest of microwave radiometry to monitor coniferous forests. Microwave data were acquired by the airborne multifrequency radiometer PORTOS (5 GHz, 10.6 GHz, 23.8 GHz, 36.5 GHz, and 90 GHz) over a test site in Les Landes Forest (France). The test site consists of large homogeneous stands of maritime pines (Pinus pinaster). The stands are monospecies and even-aged and cover a large range of age and biomass conditions (the above-ground dry biomass ranges from 0 to about 180 tons/ha). The stands were flown over twice in May and August 1994. The microwave response to the coniferous stand characteristics is analyzed. A statistical study is carried out to investigate the potential interest of the PORTOS data to discriminate between the different stand categories. The sensitivity of the stand emissivity to view angle, frequency, measurement date, and stand characteristics is analyzed. If is found that the range of forest stand microwave emissivity is relatively narrow for a vegetation coverage exceeding 95%. Although different stand categories can be distinguished from the PORTOS data, the level of discrimination is not sufficient to allow the development of accurate rules of classification. No relationship could be found between low frequency measurements and the stand biovolume. On the other hand, a linear negative relationship is found between emissivity at 10.6 GHz and average tree variables of the stands (age, height, trunk diameter) for well-developed pine stands. Also, potential interest of PORTOS to estimate forest stand characteristics seems to be significant at 90 GHz. For this frequency channel, good correlation is found between emissivity and tree basal area B-A. The results presented in this work contribute to better assess the potential applications of the future ESA/MIMR instrument over coniferous areas.

It is shown that investigators can use an airborne radar with high range resolution to measure the height and planting density of trees in forests. Based on C-band, nadir looking airborne radar data from a site in Southwest France, a single-scattering model is developed and verified to aid in the interpretation of such data.