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

Meta-analysis of the detection of plant pigment concentrations using hyperspectral remotely sensed data

Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550–560nm) and red edge (680–750nm) regions; chlorophyll b on the red, (630–660nm), red edge (670–710nm) and the near-infrared (800–810nm); carotenoids on the 500–580nm region; and anthocyanins on the green (550–560nm), red edge (700–710nm) and near-infrared (780–790nm). For total chlorophyll the optimal wavelengths are valid across canopy and landscape scales and there is some evidence that the same applies for chlorophyll a

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Early Diagnosis of Vegetation Health From High-Resolution Hyperspectral and Thermal Imagery: Lessons Learned From Empirical Relationships and Radiative Transfer Modelling

[Purpose of Review] We provide a comprehensive review of the empirical and modelling approaches used to quantify the radiation–vegetation interactions related to vegetation temperature, leaf optical properties linked to pigment absorption and chlorophyll fluorescence emission, and of their capability to monitor vegetation health. Part 1 provides an overview of the main physiological indicators (PIs) applied in remote sensing to detect alterations in plant functioning linked to vegetation diseases and decline processes. Part 2 reviews the recent advances in the development of quantitative methods to assess PI through hyperspectral and thermal images.[Recent Findings] In recent years, the availability of high-resolution hyperspectral and thermal images has increased due to the extraordinary progress made in sensor technology, including the miniaturization of advanced cameras designed for unmanned aerial vehicle (UAV) systems and lightweight aircrafts. This technological revolution has contributed to the wider use of hyperspectral imaging sensors by the scientific community and industry; it has led to better modelling and understanding of the sensitivity of different ranges of the electromagnetic spectrum to detect biophysical alterations used as early warning indicators of vegetation health.[Summary] The review deals with the capability of PIs such as vegetation temperature, chlorophyll fluorescence, photosynthetic energy downregulation and photosynthetic pigments detected through remote sensing to monitor the early responses of plants to different stressors. Various methods for the detection of PI alterations have recently been proposed and validated to monitor vegetation health. The greatest challenges for the remote sensing community today are (i) the availability of high spatial, spectral and temporal resolution image data; (ii) the empirical validation of radiation–vegetation interactions; (iii) the upscaling of physiological alterations from the leaf to the canopy, mainly in complex heterogeneous vegetation landscapes; and (iv) the temporal dynamics of the PIs and the interaction between physiological changes.The authors received funding provided by the FluorFLIGHT (GGR801) Marie Curie Fellowship, the QUERCUSAT and ESPECTRAMED projects (Spanish Ministry of Economy and Competitiveness), the Academy of Finland (grants 266152, 317387) and the European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE-P.Peer reviewe

Extraction and analysis of oxylipins from macroalgae illustrated on the example Gracilaria vermiculophylla

Oxylipins are natural products that are derived by oxidative transformations of unsaturated fatty acids. These metabolites are found in a wide range of organisms from the animal kingdom to plants and algae. They represent an important class of signaling molecules, mediating intra- and intercellular processes such as development, inflammation, and other stress responses. In addition, these metabolites directly function as chemical defense against grazers and pathogens. In the red alga Gracilaria vermiculophylla, oxylipin production is initiated by mechanical tissue disruption and can also be induced in intact algae in response to external stress signals. The defense metabolites mostly result from the lipase- and lipoxygenase-mediated conversion of phospho- and galactolipids. Oxylipins can vary greatly in their size, degree of unsaturation, oxidation state, and functional groups. But also isomers with only subtle chemical differences are found. A variety of methods have been developed for separation, detection, and identification of oxylipins. This chapter focuses on the analysis of oxylipins in macroalgae and covers all aspects from sample preparation (including protocols for the investigation of oxylipins in wounded and intact algal tissue), extraction, purification, and subsequent analysis using liquid chromatography coupled to a UV detector or a mass spectrometer. The protocols developed for G. vermiculophylla can be readily adapted to the investigation of other macroalgae

Quantification of L-band InSAR coherence over volcanic areas using LiDAR and in situ measurements

International audienceInterferometric Synthetic Aperture Radar (InSAR) is a powerful tool tomonitor large-scale ground deformation at active volcanoes. However, vegetation and pyroclastic deposits degrade the radar coherence and therefore the measurement of 3-D surface displacements. In this article, we explore the complementarity between ALOS–PALSAR coherence images, airborne LiDAR data and in situ measurements acquired over the Piton de La Fournaise volcano (Reunion Island, France) to determine the sources of errors that may affect repeat-pass InSAR measurements. We investigate three types of surfaces: terrains covered with vegetation, lava flows (a′a, pahoehoe or slabby pahoehoe lava flows) and pyroclastic deposits (lapilli). To explain the loss of coherence observed over the Dolomieu crater between 2008 and 2009, we first use laser altimetry data to map topographic variations. The LiDAR intensity, which depends on surface reflectance, also provides ancillary information about the potential sources of coherence loss. In addition, surface roughness and rock dielectric properties of each terrain have been determined in situ to better understand how electromagnetic waves interact with such media: rough and porous surfaces, such as the a′a lava flows, produce a higher coherence loss than smoother surfaces, such as the pahoehoe lava flows. Variations in dielectric properties suggest a higher penetration depth in pyroclasts than in lava flows at L-band frequency. Decorrelation over the lapilli is hence mainly caused by volumetric effects. Finally, a map of LAI (Leaf Area Index) produced using SPOT 5 imagery allows us to quantify the effect of vegetation density: radar coherence is negatively correlated with LAI and is unreliable for values higher than 7.5