Intégration des données spectrales et géomorphométriques pour la caractérisation de la dégradation des sols et l'identification des zones desusceptibilité à l'érosion hydrique

In the field of spatial observations of arid and semiarid ecosystems, the interest of remote sensing has long been recognised. The present work investigates the use of remote sensing techniques and digital elevation model analysis to characterise land degradation processes. The main objective is to evaluate the potential of spectral data and geomorphometric variables to discriminate different levels of soil degradation, and to assess ecosystems fragility and their susceptibility to degradation and desertification phenomena. The methodology adopted uses an integrated approach that combines spectral measurements, provided by remote sensing images, and geomorphometric variables, derived from a digital elevation model, in an attempt to define a set of indicators (spectral and topographic) of geomorphic processes and land degradation. Remote sensing techniques are based on two approaches: spectral mixture analysis that deals with heterogeneity at the sub-pixel level, and a set of indices describing the spectrum shape, which are sensitive to soil surface conditions. Integration procedures were involved in two ways. The first is in the correction of terrain-induced image distortions, which provide images free from relief displacement effects (ortho-rectification), and in the removal of topographically induced effects on TM images through a combined atmospheric and topographic correction. The second is in a parametric integration of spectral data and geomorphometric attributes to assess land susceptibility to degradation and desertification processes. Two types of data were collected for this research: satellite optical imagery and ground-based spectro-radiometric measurements. While indices describing soil colour (corresponding to colour parameters Intensity, Hue and Saturation) were used to discriminate different levels of soil degradation based on both ground and satellite data, the spectral mixture analysis was performed on the image to derive relative abundance of scene components. The results show that the spectral indices have enough potential to discriminate different levels of degradation, particularly when bands from the short-wave infrared domain are included (TM5 and TM7). They demonstrate results similar to those generated by spectral unmixing for the assessment of land degradation features in general, and soil erosion in particular. Concerning terrain analysis, this study points up the interest of the integrated use of local topographic attributes and combined topographic indices to characterise the hydrologic behaviour of terrain units, and to understand its effects on landscape evolution. These topographic variables quantify the contextual nature of points and characterise the spatial variability of processes occurring in the landscape. They are the major factors controlling the direction and the intensity of hillslope and hydrologic processes. The latter are responsible for the landscape evolution and its exposure to degradation risks by water erosion. Compared to the method based on curvatures analysis, our approach allows a better identification of homogeneous response units to hydrologic processes. These units are in agreement with flow directions and with the principles governing water and substance motion on the hillslope. Finally, we carried out an integrated analysis of geo-ecological parameters describing the studied ecosystem. It consists in defining hydrologic response units through an integration of spectral information and geomorphometric attributes. This allows us to determine the ecosystem fragility and to evaluate its susceptibility to land degradation and desertification processes

Leaf Pigment retrievals from DAISEX data for crops at BARRAX: Effects of sun-angle and view-angle on inversion results

In Proceedings of the First International Sysmposium on Recent Advances in Quantitative Remote Sensing, Valencia, Spain, 16-20 September, 2002.The use of combined leaf and canopy models to retrieve biophysical crop variables are increasingly thought to provide an effective means of providing quantitative input needed to determine stress condition and improve crop yield predictions based on physiological condition. Nevertheless, the sensitivity of such retrieval results to changes in view and sun angle are needed if efficient single-view optical image data are to attain operational agriculture use. Although some studies have been carried out using synthetic model data, similar studies using real data have been very limited due to the unavailability of such data sets. In this research the focus is on the retrieval of leaf pigment (chlorophyll a+b). Some recent studies have demonstrated modelbased retrievals of leaf chlorophyll with RMSEs <5 mg/cm2 by comparison with field sampling and subsequent laboratory chemical analysis. The research reported here uses the extensive DAISEX data set acquired at Barrax, Spain in 1999 and 2000. Airborne data collection strategies provided DAIS, ROSIS and HyMap hyperspectral data in which various field study plots have been observed under widely varying view angles and also at significantly different solar zenith angle. Nearly simultaneously, a comprehensive field data set was acquired on specific crop plots which provided measurements of the following relevant crop variables among others: LAI, percent vegetation cover, leaf chlorophyll content, biomass, leaf and canopy water content, and soil reflectance. We use a combined modeling and indices-based approach, which predicts the leaf chlorophyll content while minimizing LAI influence and underlying soil effects. The sensitivity of leaf chlorophyll predictions with changes in view and sun angle are reported and analyzed through modeling studies for a range of plots in the DAISEX data set.Peer reviewe

Caractérisation de l'état de dégradation des sols du bassin versant de Zagota (Maroc) à l'aide d'indicateurs spectraux

Les milieux arides et semi-arides sont vulnérables au processus de dégradation et d’extension de la désertification. Dans un tel environnement où la végétation est éparse, l’information spectrale générée par l’image satellitaire est souvent dominée par les propriétés spectrales du sol. La variabilité observée de ces propriétés peut être perçue comme le changement des états de surface du sol; lequel peut représenter une modification des propriétés physico-chimiques et texturales du dit sol. Ce travail consiste en l’utilisation des techniques de télédétection afin de caractériser l’état de dégradation du couvert végétal et des sols dans le bassin versant de Zagota, situé au nord de Meknès au Maroc. Pour ce faire, nous disposons de mesures spectroradiométriques effectuées sur le terrain ainsi que d’images satellitaires du capteur ETM+ (Enhanced Thematic Mapper Plus) du satellite 7 de Landsat, ainsi que du capteur ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) du satellite Terra. L’approche adoptée a consisté en la détermination des produits images à l’aide de méthodes basées sur la similarité spectrale : AMS (analyse de mixture spectrale), SAM (Spectral Angle Mapper), MTMF (Mixture Tuned Matched Filtering), et les indices d’intensité, indice de coloration et indice de forme. Les résultats ont montré l’atout des méthodes basées sur la similarité spectrale à discriminer différents niveaux de dégradation des sols; elles possèdent un potentiel important pour l’identification des unités de terrain en fonction du niveau de dégradation du sol

Relationship between hyperspectral indices, agronomic parameters and phenolic composition ofVitis viniferacv Tempranillo grapes

[EN] BACKGROUND: The phenolic composition of grapes is key when making decisions about harvest date and ensuring the quality of grapes. The present study aimed to investigate the relationship between the detailed phenolic composition of grapes and the agronomic parameters and hyperspectral indices, with the latter being measured via field radiometry techniques. RESULTS: Good correlations were found between phenolic composition (both anthocyanin and flavanol composition) and some hyperspectral indices related to vigor, such as the NDVI (normalized difference vegetation index) and the SAVI (soil adjusted vegetation index). The strongest correlations were observed between the phenolic composition of grape skin at harvest time and variables measured from grapes at veraison time, as well as variables determined from grapevines at harvest time. The potential usefulness of these hyperspectral indices calculated from measurements performed directly on grapes or grapevines for estimating the anthocyanin and flavanol composition of grape skins was indicated by the high coefficients of determination (R2 = 0.7955 and R2 = 0.8594, respectively) as obtained by means of principal component regression. CONCLUSION: According to the results of the present study, hyperspectral indices calculated from measurements performed directly on grapes at veraison time or on grapevines at harvest time may be useful for estimating the anthocyanin and flavanol composition of grape skins. This suggests that field radiometry might provide valuable information for estimating the phenolic composition of grapes, which may prove to be very useful when establishing strategies for harvest planning

Sequential application of hyperspectral indices for delineation of stripe rust infection and nitrogen deficiency in wheat

© 2015, Springer Science+Business Media New York. Nitrogen (N) fertilization is crucial for the growth and development of wheat crops, and yet increased use of N can also result in increased stripe rust severity. Stripe rust infection and N deficiency both cause changes in foliar physiological activity and reduction in plant pigments that result in chlorosis. Furthermore, stripe rust produce pustules on the leaf surface which similar to chlorotic regions have a yellow color. Quantifying the severity of each factor is critical for adopting appropriate management practices. Eleven widely-used vegetation indices, based on mathematic combinations of narrow-band optical reflectance measurements in the visible/near infrared wavelength range were evaluated for their ability to discriminate and quantify stripe rust severity and N deficiency in a rust-susceptible wheat variety (H45) under varying conditions of nitrogen status. The physiological reflectance index (PhRI) and leaf and canopy chlorophyll index (LCCI) provided the strongest correlation with levels of rust infection and N-deficiency, respectively. When PhRI and LCCI were used in a sequence, both N deficiency and rust infection levels were correctly classified in 82.5 and 55 % of the plots at Zadoks growth stage 47 and 75, respectively. In misclassified plots, an overestimation of N deficiency was accompanied by an underestimation of the rust infection level or vice versa. In 18 % of the plots, there was a tendency to underestimate the severity of stripe rust infection even though the N-deficiency level was correctly predicted. The contrasting responses of the PhRI and LCCI to stripe rust infection and N deficiency, respectively, and the relative insensitivity of these indices to the other parameter makes their use in combination suitable for quantifying levels of stripe rust infection and N deficiency in wheat crops under field conditions

Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels

Scaling up Semi-Arid Grassland Biochemical Content from the Leaf to the Canopy Level: Challenges and Opportunities

Remote sensing imagery is being used intensively to estimate the biochemical content of vegetation (e.g., chlorophyll, nitrogen, and lignin) at the leaf level. As a result of our need for vegetation biochemical information and our increasing ability to obtain canopy spectral data, a few techniques have been explored to scale leaf-level biochemical content to the canopy level for forests and crops. However, due to the contribution of non-green materials (i.e., standing dead litter, rock, and bare soil) from canopy spectra in semi-arid grasslands, it is difficult to obtain information about grassland biochemical content from remote sensing data at the canopy level. This paper summarizes available methods used to scale biochemical information from the leaf level to the canopy level and groups these methods into three categories: direct extrapolation, canopy-integrated approach, and inversion of physical models. As for semi-arid heterogeneous grasslands, we conclude that all methods are useful, but none are ideal. It is recommended that future research should explore a systematic upscaling framework which combines spatial pattern analysis, canopy-integrated approach, and modeling methods to retrieve vegetation biochemical content at the canopy level

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