Binary partition tree as a hyperspectral segmentation tool for tropical rainforests

International audienceIndividual tree crown delineation in tropical forests is of great interest for ecological applications. In this paper we propose a method for hyperspectral image segmentation based on binary tree partitioning. The initial partition is obtained from a watershed transformation in order to make the method computationally more efficient. Then we use a non-parametric region model based on histograms to characterize the regions and the diffusion distance to define the region merging order. The pruning strategy is based on the discontinuity of size increment observed when iteratively merging the regions. The segmentation quality is assessed visually and appears to perform well on most cases, but tree delineation could be improved by including structural information derived from LiDAR data

Measuring beta-diversity by remote sensing: a challenge for biodiversity monitoring

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, overall when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this view, airborne or satellite remote sensing allow to gather information over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta-diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure beta-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Rao's Q diversity. Each of these measures allow to solve one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta-diversity from remotely sensed imagery and potentially relate them to species diversity in the field

Measuring beta-diversity by remote sensing: a challenge for biodiversity monitoring

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, overall when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this view, airborne or satellite remote sensing allow to gather information over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta-diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure beta-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Rao's Q diversity. Each of these measures allow to solve one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta-diversity from remotely sensed imagery and potentially relate them to species diversity in the field

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

Apport de la modélisation pour l'estimation de la teneur en pigments foliaires par télédétection

Le modèle PROSPECT-5 est applicable en télédétection pour estimer la teneur en chlorophylle et en caroténoïdes foliaire à partir de mesures de réflectance et transmittance. Il est plus précis que les indices spectraux publiés dans la littérature, qui n utilisent que la réflectance. La validation des résultats effectuée sur près de 1500 feuilles démontre aussi la capacité de PROSPECT-5 à synthétiser des propriétés optiques réalistes utilisables pour calculer de nouveaux indices spectraux plus performants que les précédents. A l échelle de la canopée, le couplage de PROSPECT-5 avec SAIL et le calcul d indices pour prédire la teneur en chlorophylle et le LAI diminue la dépendance vis-à-vis des données expérimentales. L utilisation des indices comme information a priori améliore la précision de l inversion pour l estimation de ces variables par rapport à une inversion simple ou des indices. L estimation de la teneur en caroténoïdes nécessiterait une étude plus approfondie et des données terrain supplémentaires. Enfin le modèle et la méthode d inversion sont appliqués sur les données caractéristiques de capteurs opérationnels ou en projet.PARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

Regularization of discriminant analysis for the study of biodiversity in humid tropical forests

The performance of two supervised classifiers, linear and regularized discriminant analysis (LDA and RDA), is compared here for canopy species discrimination in humid tropical forest, based on airborne hyperspectral imagery acquired with the sensor Carnegie Airborne Observatory Alpha System (CAO-Alpha). Classification is performed to identify 13 species at pixel scale, crown scale, and using an object-based approach. The results show that for each scale of study, 70% to 75% overall accuracy is obtained withLDA. RDA allows improved classification for more than half species, and 5% increase of overall accuracy compared to LDA. The extended spectral range of the forthcoming CAO AToMS system (380-2500 nm) will allow for even more accurate classifications of tropical canopy species

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