biodivMapR: An r package for α‐ and β‐diversity mapping using remotely sensed images

International audienceThe accelerated erosion of biodiversity is a critical environmental challenge. Operational methods for the monitoring of biodiversity taking advantage of remotely sensed data are needed in order to provide information to ecologists and decision‐makers.We present an R package designed to compute a selection of α‐ and β‐diversity indicators from optical imagery, based on spectral variation hypothesis. This package builds upon previous work on biodiversity mapping using airborne imaging spectroscopy, and has been adapted in order to process broader range of data sources, including Sentinel‐2 satellite images.biodivMapR is able to produce α‐diversity maps including Shannon and Simpson indices, as well as β‐diversity maps derived from Bray–Curtis dissimilarity. It is able to process large images efficiently with moderate computational requirements on a personal computer. Additional functions allow computing diversity indicators directly from field plots defined as polygon shapefiles for easy comparison with ground data and validation.The package biodivMapR should contribute to improved standards for biodiversity mapping using remotely sensed data. It should also contribute to the identification of relevant Remotely Sensed enabled Essential Biodiversity Variables

PROSPECT-PRO: a leaf radiative transfer model for estimation of leaf protein content and carbon-based constituents

International audienceLeaf nitrogen content is key information for ecological and agronomic processes. A number of studies aiming at estimation of leaf nitrogen content used chlorophyll content as a proxy due to a moderate to strong correlation between chlorophyll and nitrogen content during vegetative growth stages. Since leaf nitrogen content is directly linked to leaf protein content, the capacity to accurately estimate leaf protein content may improve robustness of an operational nitrogen monitoring. In the past, the introduction of proteins - as an absorbing input constituent of the PROSPECT leaf model - has been attempted numerous times. Yet, the attempts suffered from a certain number of shortcomings, including limited applicability to both fresh and dry vegetation, inaccurate definition of the specific absorption coefficients, or incomplete accounting for different constituents of leaf dry matter.</p><p>Here, we introduce PROSPECT-PRO, a new version of the PROSPECT model simulating leaf optical properties based on their biochemical properties and including protein and carbon-based constituents (CBC) as new input variables. These two additional chemical constituents correspond to two complementary constituents of LMA. Specific absorption coefficients for proteins and CBC were produced splitting LOPEX dataset into 50% for calibration and 50%for validation. Both data sets included fresh and dry samples. Our objective is to keep compatibility between PROSPECT-PRO and PROSPECT-D, the previous version of the model, and to ensure the same performances for the estimation of LMA even through its decomposition into two constituents. Therefore, the full validation consisted of two steps:</p><p>1) PROSPECT-PRO inversion using an iterative optimization approach to retrieve proteins and CBC from LOPEX data</p><p>2) Testing the compatibility with PROSPECT-D by estimating LMA as the sum of protein and CBC content from independent datasets</p><p>The capacity of PROSPECT-PRO for the accurate estimation of leaf proteins and CBC on LOPEX could be evidenced, with slightly higher performances for the estimation of fresh leaf proteins (NRMSE = 17.3%, R<sup>2</sup> = 0.75) than of dry leaf proteins (NRMSE =24.0%, R<sup>2</sup> = 0.62). Good overall performances were obtained for the estimation of CBC (NRMSE<15%, R<sup>2</sup>>0.90). Based on these results, the carbon/nitrogen ratio of leaves could be modelled accurately.</p><p>The indirect estimation of LMA through PROSPECT-PRO inversion led to similar or slightly improved results when compared to the estimation of LMA with PROSPECT-D. Hence, PROSPECT-PRO might be of particular interest for precision agriculture applications in the context of nitrogen sensing using observations of current and forthcoming satellite imaging spectroscopy missions.</p&gt

PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents

International audienceModels of radiative transfer (RT) are important tools for remote sensing of vegetation, allowing for forward simulations of remotely sensed data as well as inverse estimation of biophysical and biochemical traits from vegetation optical properties. Estimation of foliar protein content is the key to monitor the nitrogen cycle in terrestrial ecosystems, in particular to assess the photosynthetic capacity of plants and to improve nitrogen management in agriculture. However, until now physically based leaf RT models have not allowed for proper spectral decomposition and estimation of leaf dry matter as nitrogen-based proteins and other carbon-based constituents (CBC) from optical properties of fresh and dry foliage. Such an achievement is the key for subsequent upscaling to canopy level and for development of new Earth observation applications.Therefore, we developed a new version of the PROSPECT model, named PROSPECT-PRO, which separates the nitrogen-based constituents (proteins) from CBC (including cellulose, lignin, hemicellulose, starch and sugars). PROSPECT-PRO was calibrated and validated on subsets of the LOPEX dataset, accounting for both fresh and dry broadleaf and grass samples. We applied an iterative model inversion optimization algorithm and identified the optimal spectral ranges for retrieval of proteins and CBC. When combining leaf reflectance and transmittance within the selected optimal spectral domains, PROSPECT-PRO inversions revealed similarly accurate CBC estimates of fresh and dry leaf samples (respective validation R2 = 0.96 and 0.95, NRMSE = 9.6% and 13.4%), whereas a better performance was obtained for fresh than for dry leaves when estimating proteins (respective validation R2 = 0.79 and 0.57, NRMSE = 15.1% and 26.1%). The accurate estimation of leaf constituents for fresh samples is attributed to the optimal spectral feature selection procedure.We further tested the ability of PROSPECT-PRO to estimate leaf mass per area (LMA) as the sum of proteins and CBC using independent datasets acquired for numerous plant species. Results showed that both PROSPECT-PRO and PROSPECT-D inversions were able to produce comparable LMA estimates across an independent dataset gathering 1685 leaf samples (validation R2 = 0.90 and NRMSE = 16.5% for PROSPECT-PRO, and R2 = 0.90 and NRMSE = 18.3% for PROSPECT-D). Findings also revealed that PROSPECT-PRO is capable of assessing the carbon-to‑nitrogen ratio based on the retrieved CBC-to-proteins ratio (R2 = 0.87 and NRMSE = 15.7% for fresh leaves, and R2 = 0.65 and NRMSE = 28.1% for dry leaves). The performance assessment of newly designed PROSPECT-PRO demonstrates a promising potential for its involvement in precision agriculture and ecological applications aiming at estimation of leaf carbon and nitrogen contents from observations of current and forthcoming airborne and satellite imaging spectroscopy sensors

Simulating Imaging Spectroscopy in Tropical Forest with 3D Radiative Transfer Modeling

International audienceOptical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART (Discrete Anisotropic Radiative Transfer) to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in a complex tropical forest, and to reproduce spectral dissimilarity within and among species, as well as species discrimination based on spectral information. We focused on two factors contributing to these canopy reflectance properties: the horizontal variability in leaf optical properties (LOP) and the fraction of non-photosynthetic vegetation (NPVf). The variability in LOP was induced by changes in leaf pigment content, and defined for each pixel based on a hybrid approach combining radiative transfer modeling and spectral indices. The influence of LOP variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. We incorporated NPVf into simulations following two approaches, either considering NPVf as a part of wood area density in each voxel or using leaf brown pigments. We validated the different scenarios by comparing simulated scenes with experimental airborne imaging spectroscopy using statistical metrics, spectral dissimilarity (within crowns, within species, and among species dissimilarity) and supervised classification for species discrimination. The simulation of NPVf based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. The definition of LOP at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. Therefore, we recommend future research on forest biodiversity using physical modeling of remote-sensing data to account for LOP variability within crowns and species. Our simulation framework could contribute to better understanding of performances of species discrimination and the relationship between spectral variations and taxonomic and functional dimensions of biodiversity. This work contributes to the improved integration of physical modeling tools for applications, focusing on remotely sensed monitoring of biodiversity in complex ecosystems, for current sensors, and for the preparation of future multispectral and hyperspectral satellite mission

Stability in time and consistency between atmospheric corrections: Assessing the reliability of Sentinel-2 products for biodiversity monitoring in tropical forests

International audienceEarth observation satellite imagery is increasingly accessible, and has become a key component for vegetation mapping and monitoring. Sentinel-2 satellites acquire optical images with five days' revisit frequency, which is an important feature to increase the probability of acquisition with reasonable cloud cover in tropical regions. Regular and reliable satellite observations open perspectives for the monitoring of vegetation properties and biodiversity. Atmospheric correction methods (ACMs) producing bottom-of-atmosphere (BOA) reflectance are critical to ensure temporal consistency of higher-level products and optimal sensitivity to changes in vegetation properties. Still their application in tropical regions remains challenging due to complex atmospheric issues. This study aims at performing ACM inter-comparison in the context of tropical forest monitoring. We produced BOA reflectance for a set of Sentinel-2 acquisitions corresponding to a forested area in Cameroon, using four atmospheric correction methods: Sen2cor, MAJA, Overland and LaSRC. We selected five successive acquisitions with moderate to no cloud cover, and computed a set of spectral indices and spectral diversity metrics in order to compare the consistency of these products through time, under the hypothesis that they should remain stable over a short period. We also assessed the agreement between atmospheric correction methods. Two spatial extents were used for the computation of spectral diversity metrics to assess the robustness of the data-driven processes applied to compute spectral diversity. We found that the choice of an ACM did have a significant impact on BOA reflectance and higher-level products. In the visible domain, Overland and LaSRC produced consistent BOA reflectance values, while MAJA and Sen2Cor showed strong variability which could not be explained by changes in surface properties. This directly influenced the temporal consistency of NDVI. Yet, the influence on the temporal consistency for EVI and NDWI was moderate. Spectral diversity metrics were consistent through time for all methods, but to a lesser degree than vegetation indices. When comparing the mean values over the period considered, vegetation indices were stable across methods, but not diversity metrics. Spatial context changes had an impact on the Shannon index, but not on Bray-Curtis dissimilarity. These results suggest that the choice of ACM has major potential implications for tropical forest monitoring

Topsoil clay content mapping in croplands from Sentinel-2 data: Influence of atmospheric correction methods across a season time series

International audienceRecent studies demonstrated the capability of Sentinel-2 (S2) data to estimate topsoil properties and highlighted the sensitivity of these estimations to soil surface conditions depending on the S2 acquisition date. These estimations are based on Bottom of Atmosphere (BOA) reflectance images, obtained from Top of Atmosphere (TOA) reflectance values using Atmospheric Correction (AC) methods. AC of optical satellite imagery is an important pre-processing stage before estimating biophysical variables, and several AC methods are currently operational to perform such conversion. This study aims at evaluating the sensitivity of topsoil clay content estimation to atmospheric corrections along an S2 time series. Three AC methods were tested (MAJA, Sen2Cor, and LaSRC) on a time series of eleven Sentinel-2 images acquired over a cultivated region in India (Karnataka State) from February 2017 to June 2017. Multiple Linear Regression models were built using clay content analyzed from topsoil samples collected over bare soil pixels and corresponding BOA reflectance data. The influence of AC methods was also analysed depending on bare soil pixels selections based on two spectral indices and several thresholds: the normalized difference vegetation index (NDVI below 0.25, 0.3 and 0.35) and the combination of NDVI (below 0.3) and Normalized Burned Ratio 2 index (NBR2 below 0.09, 0.12 and 0.15) for masking green vegetation, crop residues and soil moisture. First, this work highlighted that regression models were more sensitive to acquisition date than to AC method, suggesting that soil surface conditions were more influent on clay content estimation models than variability among atmospheric corrections. Secondly, no AC method outperformed other methods for clay content estimation, and the performances of regression models varied mostly depending on the bare soil pixels selection used to calibrate the regression models. Finally, differences in BOA reflectance among AC methods for the same acquisition date led to differences in NDVI and NBR2, and hence in bare soil coverage identification and subsequent topsoil clay content mapping coverage. Thus, selecting S2 images with respect to the acquisition date appears to be a more critical step than selecting an AC method, to ensure optimal retrieval accuracy when mapping topsoil properties assumed to be relatively stable over time

Spectral subdomains and prior estimation of leaf structure improves PROSPECT inversion on reflectance or transmittance alone

International audienceLeaf biochemical and structural traits are vegetation characteristics related to various physiological processes. Taking advantage of the physical relationship between optical properties and leaf biochemistry, field-based spectroscopy has allowed for the rapid estimation of leaf biochemical constituents and repeated non-destructive measurements through time. Leaf constituent retrieval from leaf optical properties following inversion of the physically-based radiative transfer model PROSPECT is now a popular method, but some cases prompt poor retrieval success and this approach requires a strict inversion procedure. We investigated the performances of different inversion procedures for the estimation of leaf constituents, specifically chlorophyll a and b, carotenoids, water (EWT), and dry matter (LMA) from >1400 broadleaf samples, including the definition of optimal spectral subdomains, and the use of leaf reflectance or transmittance alone. We also developed a strategy to obtain prior information on the leaf structure parameter (N) in PROSPECT, when only reflectance or transmittance is measured, and examined the influence of this prior information in combination with different inversion procedures. We found that using the full domain of reflectance or transmittance only systematically leads to suboptimal estimation of chlorophyll a and b, carotenoids, EWT, and LMA, due to either the combined absorption of multiple constituents or inaccurate estimation of the N parameter. Our study confirms that the selection of optimal spectral subdomains leads to improved estimation of all leaf constituents, from 700 to 720 nm for chlorophyll a and b, 520–560 nm for carotenoids, and from 1700 to 2400 nm for EWT and LMA. Prior information on N, computed directly from the spectra, leads to systematic improved estimation of leaf constituents when only reflectance or transmittance is measured, with reductions in normalized root mean square error from 8 to 37%. We strongly recommend using optimal subdomains when inverting PROSPECT to retrieve leaf constituents, and with the availability of only reflectance or transmittance we further recommend the use of prior information on the N parameter

Simulating Spectral Heterogeneity In Tropical Forest Canopy Reflectance With 3d Radiative Transfer Modeling

International audienceModeling radiative transfer is useful for simulating spectral heterogeneity, which can meaningfully characterize environmental heterogeneity. In this study, we analyze the effect of leaf optical properties (LOPs) variability among trees on the simulation of spectral heterogeneity of tropical forest canopy reflectance. Simulations are performed on 3D scenes. LOPs are integrated in simulations following two approaches: either using unique LOP for each individual tree crown (ITC) (ITC scale approach), or for all individual pixels of the whole scene (pixel scale approach). Heterogeneity among ITCs is then computed independently for simulated and measured data using spectral angle as similarity metric. Spectral heterogeneity obtained from pixel scale approach produces stronger similarity with measured data (r = 0.41) than ITC scale approach (r = 0.30). For a large number of individuals, spectral dissimilarity observed in simulated data is correlated with that observed in measured data. Spectral dissimilarity among ITC is overestimated for ITC scale approach and underestimated for pixels’ scale

Validation of the DART Model for Airborne Laser Scanner Simulations on Complex Forest Environments

International audienceWith the recent progresses in lidar technology for Earth remote sensing, the development of a reliable lidar simulator is becoming central in order to define specifications for new sensors, perform intercomparisons, train machine learning algorithms, and help transferring information from one scale to another. The discrete anisotropic radiative transfer (DART) model includes such a lidar simulator. Although already tested on several virtual scenes, the DART outputs still need to be rigorously evaluated against actual sensor acquisitions, especially on real complex scenes of various forest types, such as dense tropical forests. That is the purpose of the present study. A real airborne laser scanner (ALS) with full-waveform capacity was first radiometrically calibrated on targets of measured reflectance. The properties of the ALS system were then introduced in the DART model, along with a 3-D virtual scene built from terrestrial laser scans and spectroscopic measurements acquired on a forest plot near the calibration site. Finally, an ALS acquisition was simulated and the shape and magnitude of the waveforms were compared with real acquisitions. The comparison between measured and simulated data was performed at different scales by aggregating waveform samples into a 3-D grid with a vertical resolution of 1 m and a horizontal resolution ranging from 2 to 80 m. Results showed a high similarity between simulated and measured waveforms at all scales with R 2 >0.9 and NRMSE<10%. These promising results open up numerous perspectives for improved spaceborne and airborne lidar data processing and for the development of new system

Discussion au sujet du renvoi au comité de sûreté générale d’une pétition des citoyens de la Meuse contre un arrêté du représentant Mallarmé ordonnant la déportation des prêtres de ce département (Rapporteur : Harmand), lors la séance du 7 fructidor an II (24 août 1794)

Harmand Jean-Baptiste, Roux Louis Félix, Thuriot Jacques Alexis, Durand de Maillane Pierre Toussaint, Boissieu Pierre Joseph Didier, Louchet Louis, Mallarmé François René Auguste. Discussion au sujet du renvoi au comité de sûreté générale d’une pétition des citoyens de la Meuse contre un arrêté du représentant Mallarmé ordonnant la déportation des prêtres de ce département (Rapporteur : Harmand), lors la séance du 7 fructidor an II (24 août 1794). In: Archives Parlementaires de 1787 à 1860 - Première série (1787-1799) Tome XCV - Du 26 thermidor au 9 fructidor an II (13 au 26 août 1794) Paris : Librairie Administrative P. Dupont, 1987. p. 407