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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.

International audience; The GLORIE Campaign was performed in June-July 2015 in order to investigate the sensitivity of airborne GNSS-R measurements to land parameters. In this paper we present the first results focusing on agricultural areas. For this purpose ground truth measurements of soil moisture, roughness, plant water content and plant height were measured over 20 agricultural plots of various crops (cereals, vegetables, bare soil). The correlation with GNSS reflectivity in LHCP polarization confirms noticeable sensitivity to soil moisture, and plant related parameters such as NDVI, vegetation water content and plant height.

The main goal of this study is to design a method to describe the structure of forest stands from Very High Resolution satellite imagery, relying on some typical variables such as crown diameter, tree height, trunk diameter, tree density and tree spacing. The emphasis is placed on the automatization of the process of identification of the most relevant image features for the forest structure retrieval task, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick’s texture features. The main drawback of this well-known texture representation is the underlying parameters which are extremely difficult to set due to the spatial complexity of the forest structure. To tackle this major issue, an automated feature selection process is proposed which is based on statistical modeling, exploring a wide range of parameter values. It provides texture measures of diverse spatial parameters hence implicitly inducing a multi-scale texture analysis. A new feature selection technique, we called Random PRiF, is proposed. It relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables. Our automated forest variable estimation scheme was tested on Quickbird and Pleiades panchromatic and multispectral images, acquired at different periods on the maritime pine stands of two sites in South-Western France. It outperforms two well-established variable subset selection techniques. It has been successfully applied to identify the best texture features in modeling the five considered forest structure variables. The RMSE of all predicted forest variables is improved by combining multispectral and panchromatic texture features, with various parameterizations, highlighting the potential of a multi-resolution approach for retrieving forest structure variables from VHR satellite images. Thus an average prediction error of ∼ 1.1 m is expected on crown diameter, ∼ 0.9 m on tree spacing, ∼ 3 m on height and ∼ 0.06 m on diameter at breast height.

Measurements of directional brightness surface temperature were performed over vineyards using an airborne thermal infrared camera. These measurements reveal differences of up to 15 °C according to the viewing direction and are critically impacted by vine row orientation. The contribution of each element of the canopy (sunlit/shaded soil and vegetation) to the directional anisotropy is illustrated using a simple 3-D model and the POV-Ray ray-tracing tool.

The Soil Moisture and Ocean Salinity (SMOS) mission provides multi-angular, dual-polarised brightness temperatures at 1.4 GHz, from which global soil moisture and vegetation optical depth (tau) products are retrieved. This paper presents a study of SMOS' tau product in 2010 and 2011 for crop zones of the USA. Retrieved tau values for 504 crop nodes were compared to optical/IR vegetation indices from the MODES (Moderate Resolution Imaging Spectroradiometer) satellite sensor, including the Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVE), Leaf Area Index (LAI), and a Normalised Difference Water Index (NOW!) product. tau values were observed to increase during the growing season and decrease during senescence in these areas, as did MODIS vegetation indices. SMOS' tau values generally peaked later than MODES LAI values, with an estimated time difference of about 19 days. A linear regression between tau and the MODIS products was carried out for each node and values of the determination coefficient, R-2, slope, b' and intercept, b '' were found. The average R-2 value varied from 0.32 to 035 for the different vegetation indices. The linear regression between LAI and tau produced an average slope of b' = 0.06, and an average intercept of b '' = 0.14. The effects of crop fraction and dominant crop type were investigated and crop fraction was found to have a low effect on R-2 values. R-2 values appeared to be lower for wheat and hay and higher for corn. b' and b '' values had higher standard deviations for wheat but were generally close to the mean values for corn, soybean and hay. (C) 2013 Published by Elsevier Inc. (Less)

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International audience; In mountain forest ecosystems where elevation gradients are prominent, temperature gradient-based phonological variability can be high. However, there are few studies that assess the capability of remote sensing observations to monitor ecosystem phenology along elevation gradients, despite their relevance under climate change. We investigated the potential of medium resolution remotely sensed data to monitor the elevation variations in the seasonal dynamics of a temperate deciduous broadleaf forested ecosystem. Further, we explored the impact of elevation on the onset of spring leafing. This study was based on the analysis of multi-annual time-series of VEGETATION data acquired over the French Pyrenees Mountain Region (FPMR), in conjunction with simultaneous ground-based observations of leaf phenology made for two dominant tree species in the region (oak and beech). The seasonal variations in the perpendicular vegetation index (PVI) were analyzed during a five-year period (2002 to 2006). The five years of data were averaged into a one sole year in order to fill the numerous large spatio-temporal gaps due to cloud and snow presence frequent in mountains - without altering the temporal resolution. Since a VEGETATION pixel (1 km(2)) includes several types of land cover, the broadleaf forest-specific seasonal dynamics of PVI was reconstructed pixel-by-pixel using a temporal unmixing method based on a non-parametric statistical approach. The spatial pattern of the seasonal response of PVI was clearly consistent with the relief. Nevertheless the elevational or geographic range of tree species, which differ in their phenology sensitivity to temperature, also has a significant impact on this pattern. The reduction in the growing season length with elevation was clearly observable from the delay in the increase of PVI in spring and from the advance of its decrease in the fall. The elevation variations in leaf flushing timing were estimated from the temporal change in PVI in spring over the study area. They were found to be consistent with those measured in situ (R2>0.95). It was deduced that, over FPMR, the mean delay of leaf flushing timing for every 100 m increase in elevation was estimated be approximately 2.3 days. The expected estimation error of satellite-based leaf unfolding date for a given elevation was approximately 2 days. This accuracy can be considered as satisfactory since it would allow us to detect changes in leafing timing of deciduous broadleaf forests with a magnitude equivalent to that due to an elevation variation of 100 m (2.3 days on average), or in other words, to that caused by a variation in the mean annual air temperature of 0.5 degrees C. Although averaging the VEGETATION data over five years led to a loss of interannual information, it was found to be a robust approach to characterise the elevation variations in spring leafing and its long-term trends. (C) 2010 Elsevier Inc. All rights reserved.

International audience; L- and P-band airborne polarimetric synthetic aperture radar (SAR) data acquired by the RAMSES system over different height maritime pine (Pinus Pinaster Ait.) stands of the Nezer forest (Landes, France) have been evaluated for forest biophysical parameter estimation. A pseudolinear correlation has been brought to evidence at P-band between polarimetric anisotropy and mean tree height, which is also linked to other biophysical parameters in the Nezer forest, meaning that SAR polarimetry constitutes a promising tool for forest parameter retrieval at low frequency. The spatial conditions have been evaluated through the quantification of the impact of signal-to-noise ratio diminution and resolution degradation on the forest height inversion. It has been shown that the inversion accuracy remains acceptable for N Esigma0, representing the noise level of the SAR image, which is lower than -15 dB, and for spatial resolution increasing up to 15 m.

Chapitre 7; International audience; Optical imaging, which makes use of the reflective properties of short wavelength solar radiation (400 to 2,500 nm), is the oldest source of spatial data on which its applications in forestry have been developed.

International audience; Measuring turbulent fluxes with the eddy covariance method has become a widely accepted and powerful tool for the determination of long term data sets for the exchange of momentum, sensible and latent heat, and trace gases such as CO2 between the atmosphere and the underlying surface. Several flux networks developed continuous measurements above complex terrain, e.g. AmeriFlux and EUROFLUX, with a strong focus on the net exchange of CO2 between the atmosphere and the underlying surface. Under many conditions basic assumptions for the eddy covariance method in its simplified form, such as stationarity of the flow, homogeneity of the surface and fully developed turbulence of the flow field, are not fulfilled. To deal with non-ideal conditions which are common at many FLUXNET sites, quality tests have been developed to check if these basic theoretical assumptions are valid. In the framework of the CARBOEUROFLUX project, we combined quality tests described by Foken and Wichura (1996) with the analytical footprint model of Schmid (1997). The aim was to identify suitable wind sectors and meteorological conditions for flux measurements. These tools were used on data of 18 participating sites. Quality tests were applied on the fluxes of momentum, sensible and latent heat, and on the CO2-flux, respectively. The influence of the topography on the vertical wind component was also checked. At many sites the land use around the flux towers is not homogeneous or the fetch may not be large enough. So the relative contribution of the land use type intended to be measured was also investigated. Thus the developed tool allows comparative investigations of the measured turbulent fluxes at different sites if using the same technique and algorithms for the determination of the fluxes as well as analyses of potential problems caused by influences of the surrounding land use patterns.

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.

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.

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS; International audience; Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a remote sensing tool, which is complementary to traditional monostatic radars, for the retrieval of geophysical parameters related to surface properties. In the present paper, we describe a new polarimetric GNSS-R system, referred to as the GLObal navigation satellite system Reflectometry Instrument (GLORI), dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass) and inland water bodies. This system was installed as a permanent payload on a French ATR42 research aircraft, from which simultaneous measurements can be carried out using other instruments, when required. Following initial laboratory qualifications, two airborne campaigns involving nine flights were performed in 2014 and 2015 in the Southwest of France, over various types of land cover, including agricultural fields and forests. Some of these flights were made concurrently with in situ ground truth campaigns. Various preliminary applications for the characterisation of agricultural and forest areas are presented. Initial analysis of the data shows that the performance of the GLORI instrument is well within specifications, with a cross-polarization isolation better than 15 dB at all elevations above 45, a relative polarimetric calibration accuracy better than 0.5 dB, and an apparent reflectivity sensitivity better than 30 dB, thus demonstrating its strong potential for the retrieval of land surface characteristics.

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.

International audience; Numerous studies showed the impact of droughts events on the seasonal dynamic of satellite-based vegetation index but validation and relation to ground observations is still lacking. Focusing on the impacts of 2003 and 2011 droughts events, we defined and validated forest health indicators using extensive in-situ damage inventories carried out on all French forests. Analysis of vegetation indices dynamics (EVI, LAI) at the national scale was performed using MODIS and SPOT VEGETATION products (MOD13A2 V5 and CYCLOPES V3.1). Satellite-based indicators of (i) crown condition - which is itself a commonly used forest health indicator - and (ii) leaf phenology anomalies were calibrated and validated. In our study, average of CYCLOPES LAI during the growing season is a good proxi of crown condition (r2=0.79, p

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.

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.

The authors present the results of an experiment defined to demonstrate the use of radar to retrieve forest biomass. The SAR data were acquired by the NASA/JPL SAR over the Landes pine forest during the 1989 MAESTRO-1 campaign. The SAR data, after calibration, were analyzed together with ground data collected on forest stands from a young stage (eight years) to a mature stage (46 years). The dynamic range of the radar backscatter intensity from forest was found to be greatest at P-band and decreased with increasing frequencies. Cross-polarized backscatter intensity yielded the best sensitivities to variations of forest biomass. L-band data confirmed past results on good correlation with forest parameters. The most striking observation was the strong correlation of P-band backscatter intensity to forest biomass. >

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; 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.

The results of the Portos-94 aircraft measurement campaign over coniferous stands of the Landes forest (France) are presented. PORTOS is a five-frequency microwave radiometer developed by CNES and Matra Marconi Space. The objective of the study is to analyse the interest of passive microwave radiometry to estimate land surface characteristics of the forest canopy. The frequency channels of PORTOS are identical or very close to the channels of the planned ESA microwave radiometer MIMR. Therefore, the present study contributes to better assess the potential applications of MIMR over forest areas. The results, based on a principal component analysis, are in good agreement with results obtained from active microwave measurements. They show the interest of "short" wavelengths (5, 10.65, 23.8 and 36.5 GHz) for discriminating between categories of coniferous stands.

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.

Interpreting remote sensing data on forest canopies demands an adequate knowledge of factors affecting their optical properties. After a short analysis of the optical properties of a forest canopy, a review of the factors acting on the forest reflectance is presented. These factors can be external or internal. The five external factors considered are: size of the viewed area, orientation and inclination of the view axis, sun elevation, nebulosity and wind speed. Three internal factors can also affect forest reflectance: row orientation (for young artificial forests), optical properties of the background (soil and understory), and canopy geometry. The effects of these different factors are analysed and discussed.

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.