Antioxidant and antimicrobial phenolic compounds from extracts of cultivated and wild-grown Tunisian Ruta chalepensis

The antioxidant and antibacterial activities of phenolic compounds from cultivated and wild Tunisian Ruta chalepensis L. leaves, stems, and flowers were assessed. The leaves and the flowers exhibited high but similar total polyphenol, flavonoid, and tannin content. Moreover, two organs showed strong, although not significantly different, total antioxidant activity, 2,2-diphenyl-1-picrylhydrazyl scavenging ability, and reducing power. Investigation of the phenolic composition showed that vanillic acid and coumarin were the major compounds in the two organs, with higher percentages in the cultivated organs than in the spontaneous organs. Furthermore, R. chalepensis extracts showed marked antibacterial properties against human pathogen strains, and the activity was organ- and origin-dependent. Spontaneous stems had the strongest activity against Pseudomonas aeruginosa. From these results, it was concluded that domestication of Ruta did not significantly affect its chemical composition and consequently the possibility of using R. chalpensis organs as a potential source of natural antioxidants and as an antimicrobial agent in the food industry

A global spectral library to characterize the world's soil

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of

MORPHO-SPECTRAL RECOGNITION OF DENSE URBAN OBJECTS BY HYPERSPECTRAL IMAGERY

This paper presents a methodology for recognizing, identifying and classifying built objects in dense urban areas, using a morphospectral approach applied to VNIR/SWIR hyperspectral image (HySpex). This methodology contains several image processing steps: Principal Components Analysis and Laplacian enhancement, Feature Extraction of segmented build-up objects, and supervised classification from a morpho-spectral database (i.e. spectral and morphometric attributes). The Feature Extraction toolbox automatically generates a vector map of segmented buildings and an urban object-oriented morphometric database which is merged with an independent spectral database of urban objects. Each build-up object is spectrally identified and morphologically characterized thanks to the built-in morpho-spectral database

Applying blind source separation on hyperspectral data for clay content estimation over partially vegetated surfaces

Hyperspectral imagery has proven to be a useful technique for mapping soil surface properties. However vegetation cover has a significant influence on spectral reflectance and the applicability of hyperspectral images for soil property estimations decreases when surfaces are partially covered by vegetation. To maximize information extraction from hyperspectral data, we apply a "double-extraction" technique: 1) extraction of a soil reflectance spectrum s, using blind source separation (BSS) techniques from mixed hyperspectral spectra without any information about the proportion of the components in the mixture nor the original spectra that composed the mixed spectra and 2) extraction of soil property contents from the soil reflectance spectrum s by classical chemometric methods. The Infomax algorithm is used as the BSS algorithm for this approach, and the chemometric method is the partial least squares regression (PLSR). The estimated soil property after soil signals extraction is the clay content, and the hyperspectral datasets are from Hymap airborne data. First, experiments were performed using simulated linear spectral mixtures of one soil spectrum and one vegetation spectrum (vineyards). Second, the "double-extraction" method was applied to grids of 3 x 3 Hymap mixed spectra, which were centered on surfaces partially covered by vineyards. Our simulated experiments and applications to Hymap data show that the BSS concept provides accurate soil reflectance spectra for clay content estimation. The clay content estimations are accurate compared to physico-chemical values (the mean error of estimation is always inferior to 50 g/kg in simulated experiments and predominantly inferior to 90 g/kg in Hymap mixed pixels treatments). We conclude that the "double-extraction" method, which requires no a priori information is a promising method for soil property prediction using hyperspectral imagery over partially vegetated surfaces

SEMI-BLIND SOURCE SEPARATION FOR ESTIMATION OF CLAY CONTENT OVER SEMI-VEGETATED AREAS, FROM VNIR/SWIR HYPERSPECTRAL AIRBORNE DATA

The applicability of Visible, Near-Infrared and Short Wave Infrared (VNIR/SWIR) hyperspectral imagery for soil property mapping decreases when surfaces are partially covered by vegetation. The objective of this research was to develop and evaluate a methodology based on the “double-extraction” technique, for clay content estimation over semi-vegetated surfaces using VNIR/SWIR hyperspectral airborne data. The “double-extraction” technique initially proposed by Ouerghemmi et al. (2011) consists of 1) an extraction of a soil reflectance spectrum ssoil from semi-vegetated spectra using a Blind Source Separation technique, and 2) an extraction of clay content from the soil reflectance spectrum ssoil, using a multivariate regression method. In this paper, the Source Separation approach is Semi-Blind thanks to the integration of field knowledge in Source Separation model. And the multivariate regression method is a partial least squares regression (PLSR) model. This study employed VNIR/SWIR HyMap airborne data acquired in a French Mediterranean region over an area of 24 km2. Our results showed that our methodology based on the “double-extraction” technique is accurate for clay content estimation when applied to pixels under a specific Cellulose Absorption Index threshold. Finally the clay content can be estimated over around 70% of the semi-vegetated pixels of our study area, which may offer an extension of soil properties mapping, at the moment restricted to bare soils

Semi-blind source separation for the estimation of the clay content over semi-vegetated areas using VNIR/SWIR hyperspectral airborne data

International audienceVisible, near-infrared and short wave infrared (VNIR/SWIR) hyperspectral imagery has proven to be a useful technique for mapping the soil surface properties over bare soils pixels. Multivariate regression models are usually built linking a set of soil surface properties (response Y-variables) to a set of imaging reflectance spectra over bare soil pixels (predictor X-variables), and then, they are applied to all bare soil pixels to map the soil surface properties. The applicability of VNIR/SWIR hyperspectral imagery for soil properties mapping decreases when surfaces are partially covered by vegetation. The objective of this research was to develop a "Double-Extraction" approach for clay content estimation over semi-vegetated surfaces and to evaluate its performance using VNIR/SWIR HyMap airborne data acquired in a Mediterranean region over an area of 24 km(2). The "Double-Extraction" approach consists of 1) an extraction of a soil reflectance spectrum, S-soil, using a semi-blind source separation (SBSS) technique applied to couples of semi-vegetated spectra and 2) an extraction of clay content from the soil reflectance spectrum S-soli using a multivariate regression method. The source separation approach is semi blind due to the use of available knowledge about expected soil and vegetation spectra. The multiplicative algorithm of Lee & Seung, belonging to the family of non-negative matrix factorization (NMF) methods, is used to solve the blind source separation (BSS) problem. The multivariate regression method used in this study is the partial least squares regression (PLSR) method. The "Double-Extraction" approach was compared to a "Direct" approach consisting of the application of the multivariate regression model built from bare soil spectra over the semi-vegetated area. Our results showed poor prediction performances for both approaches when applied to all pixels; however, a slight improvement was observed when correcting the bias prediction that occurs when using the PLSR model. Conversely, satisfactory prediction performances were obtained by restricting the prediction to the weakly vegetated area (NDVI < 0.55) that covered 63% of the study area. The resulting clay map over this restricted vegetated area exhibited patterns of variations that matched the previous expertise acquired on the spatial structures of soils in this area

Assessing Context-Specific Factors to Increase Tree Survival for Scaling Ecosystem Restoration Efforts in East Africa

Increasing tree cover in agricultural lands can contribute to achieving global and national restoration goals, more so in the drylands where trees play a key role in enhancing both ecosystem and livelihood resilience of the communities that depend on them. Despite this, drylands are characterized by low tree survival especially for tree species preferred by local communities. We conducted a study in arid and semi-arid areas of Kenya and Ethiopia with 1773 households to assess how different tree planting and management practices influence seedling survival. Using on-farm planned comparisons, farmers experimented and compared tree survival under different planting and management practices as well as under varying socioeconomic and biophysical contexts in the two countries. Seedling survival was monitored at least six months after planting. Results show that watering, manure application, seedling protection by fencing and planting in a small hole (30 cm diameter and 45 cm depth) had a significant effect on tree seedling survival in Kenya, while in Ethiopia, mulching, watering and planting niche were significant to tree survival. Household socioeconomics and farms’ biophysical characteristics such as farm size, education level of the household head, land tenure, age of the household head had significant effects on seedling survival in both Ethiopia and Kenya while presence of soil erosion on the farm had a significant effect in Kenya. Soil quality ranking was positively correlated with tree survival in Ethiopia, regardless of species assessed. Current findings have confirmed effects of context specific variables some involving intrahousehold socioeconomic status such education level of the household head, and farm size that influence survival

Vis-NIR-SWIR remote sensing products as new soil data for digital soil mapping

International audienc