Using genotyping-by-sequencing to understand Musa diversity

Poster presented at Plant and Animal Genome, PAG XXII. San Diego (USA), 11-15 Jan 201

Comparing small-footprint lidar and forest inventory data for single strata biomass estimation: a case study over a multi-layered mediterranean forest

Current methods for accurately estimating vegetation biomass with remote sensing data require extensive, representative and time consuming field measurements to calibrate the sensor signal. In addition, such techniques focus on the topmost vegetation canopy and thus they are of little use over multi-layered forest ecosystems where the underneath strata hold considerable amounts of biomass. This work is the first attempt to estimate biomass by remote sensing without the need for massive in situ measurements. Indeed, we use small-footprint airborne laser scanning (ALS) data to derive key forest metrics, which are used in allometric equations that were originally established to assess biomass using field measurements. Field- and ALS-derived biomass estimates are compared over 40 plots of a multi-layered Mediterranean forest. Linear regression models explain up to 99% of the variability associated with surface vegetation, understory, and overstory biomass

Citrus of the world: a Citrus directory

De nombreuses classifications ont tenté de structurer le genre citrus qui constitue un groupe végétal complexe. L'importance des noms locaux, issus de la tradition orale, et plus récemment, l'apparition de dénominations commerciales augmentent encore le nombre des appellations. Cet annuaire représente une tentative d'identification et de standardisation du groupe. Il s'appuie sur la classification très détaillée du Japonais Tanaka, les équivalences avec celle de l'Américain Swingle sont données en annexes. Les tableaux indiquent pour chaque nom rencontré (nom local, appellation commerciale, variante orthographique...) son binôme latin et son nom standardisé. Un synthèse des appellations hybrides complète cet annuair

A review of applying second-generation wavelets for noise removal from remote sensing data.

The processing of remotely sensed data includes compression, noise reduction, classification, feature extraction, change detection and any improvement associated with the problems at hand. In the literature, wavelet methods have been widely used for analysing remote sensing images and signals. The second-generation of wavelets, which is designed based on a method called the lifting scheme, is almost a new version of wavelets, and its application in the remote sensing field is fresh. Although first-generation wavelets have been proven to offer effective techniques for processing remotely sensed data, second-generation wavelets are more efficient in some respects, as will be discussed later. The aim of this review paper is to examine all existing studies in the literature related to applying second-generation wavelets for denoising remote sensing data. However, to make a better understanding of the application of wavelet-based denoising methods for remote sensing data, some studies that apply first-generation wavelets are also presented. In the part of hyperspectral data, there is a focus on noise removal from vegetation spectrum

The Laegeren site: an augmented forest laboratory combining 3-D reconstruction and radiative transfer models for trait-based assessment of functional diversity

Given the increased pressure on forests and their diversity in the context of global change, new ways of monitoring diversity are needed. Remote sensing has the potential to inform essential biodiversity variables on the global scale, but validation of data and products, particularly in remote areas, is difficult. We show how radiative transfer (RT) models, parameterized with a detailed 3-D forest reconstruction based on laser scanning, can be used to upscale leaf-level information to canopy scale. The simulation approach is compared with actual remote sensing data, showing very good agreement in both the spectral and spatial domains. In addition, we compute a set of physiological and morphological traits from airborne imaging spectroscopy and laser scanning data and show how these traits can be used to estimate the functional richness of a forest at regional scale. The presented RT modeling framework has the potential to prototype and validate future spaceborne observation concepts aimed at informing variables of biodiversity, while the trait-based mapping of diversity could augment in situ networks of diversity, providing effective spatiotemporal gap filling for a comprehensive assessment of changes to diversity

Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations

Plant pathogens cause significant losses to agricultural yields, and increasingly threaten food security, ecosystem integrity, and societies in general. Xylella fastidiosa (Xf) is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment. Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates a dramatically broadened geographic range. The Xf pathogen has thus re-emerged as a global threat, with its poorly contained expansion in Europe creating a socio-economic, cultural, and political disaster. Xf represents a threat of global proportion because it can infect over 350 plant species worldwide, and the early detection of Xf has been identified as a critical need for its eradication. Here, we show that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography reveal Xf infection in trees before symptoms are visible. We obtained accuracies of disease detection exceeding 80% when high-resolution solar-induced fluorescence quantified by 3D simulations and thermal-based stress indicators were coupled with photosynthetic traits sensitive to rapid pigment dynamics and degradation. Moreover, we found that the visually asymptomatic trees originally scored as affected via spectral plant trait alterations (presumed false positives) developed Xf symptoms four months later at almost double the rate of the asymptomatic trees classified as not affected by remote sensing. We demonstrate that spectral plant trait alterations caused by Xf infection are detectable at the landscape scale before symptoms are visible, a critical requirement to help eradicate some of the most devastating plant diseases worldwide.JRC.D.1-Bio-econom