Use of Remote Sensing for Mapping and Evaluation of Mining Waste Anomalies at National to Multi-Country Scale. A Case Study to Integrate Remote Sensing Information with Thematic Data Layers and National Inventories on Mining Features in Pre-Accession ...

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Characterization of Pan-Mediterranean Riparian Areas by Remote Sensing Derived Phenological Indices

This report aimed at inventorying characteristics of Mediterranean riparian-use zones using statistical analysis of some phenological indices calculated from remote sensing time series. Riparian areas are focused because of their prime importance in offering potential for adapted agricultural landuse and their ecosystem services. The quantity of vegetation cover present in these wider riparian-use zones has been proven to be directly dependent to adjacent landuse and related to the functioning of the zone as wider riparian buffer. Phenological indices derived from low resolution remote sensing time series can be used in complement with other data to assess and monitor dynamics and stresses of the riparian-use zones.JRC.H.7-Land management and natural hazard

Correspondence of Satellite Measured Phenology to European Farmland Bird Distribution Patterns

This report presents research in establishing linkages between remotely sensed information of vegetation cover and biological diversity, specifically focusing on farmland birds. The vegetation cover was investigated via phenological indices derived from time series of satellite images. The quantification of phenological processes is very important for understanding ecosystems and ecological development. Such factors determine population growth and influence species-species interactions (competition, predation, reproduction) and species distribution. Birds have long been used to provide early warning of environmental problems, because they are the best known and documented major taxonomic group, especially in terms of the sizes and trends of populations and distributions. Common farmland birds are in decline throughout Europe, with the cumulative populations of all 33 species of farmland birds suffering a decline of 44 per cent between 1980 and 2005. For the link between vegetation dynamics and farmland birds distribution phenological indices and their spatial statistical characteristics were calculated from the time series of the SPOT Vegetation images. The farmland birds species data were selected from the European Bird Census Counsel (EBCC) Atlas of European breeding birds. Both datasets were then statistically analyzed using the Environmental Stratification of Europe. The study shows that this stratification is very appropriate to describe the spatial distribution of farmland birds. Furthermore it was shown that phenological indicators, especially the start of the growing season, the first greening up measures and the productivity measures are good indicators of the distribution of the European farmland birds and that these indicators are comparable to climatic measures. The importance of using phenological indicators is argued by the illustrated fact that phenological indicators can deliver information on the habitat on a higher spatial resolution that cannot be obtained through climatic data. This combination of information supplies indispensible measures to monitor those environmental changes that most probably lead to the reported dramatic decrease of the species.JRC.DDG.H.7-Land management and natural hazard

Chemometric Modelling and Remote Sensing of Arable Land Soil Organic Carbon as Mediterranean Land Degradation Indicator - A Case Study in Southern Italy

The application of chemometric models for the quantitative estimation of soil organic matter (SOM) from laboratory reflectance data from samples taken on the regional/national level from Italian sites is explored in Part 1 of this report. In addition, the possibility to transfer the developed models from the spectral resolution of lab/field instrumentation to the one of operational satellite systems has been evaluated, by using the laboratory spectra to simulate the respective soil reflectance signatures of Landsat-TM, MODIS and MERIS. Soil physical and chemical laboratory analyses results were provided by the JRC-IES SOIL action (formerly JRC FP6 MOSES action). The 376 soil samples, used in this study, were collected for previous projects of the IES SOIL action and its partners within a wide range of environmental settings in Italy. Reflectance measurements were obtained on disturbed soil samples using an ASD Field Spec Pro spectro-radiometer. Data transformation methods (standardisation, vector-normalisation and first and second order derivatives) have been applied on the spectral data. The transformed spectral data have been used for the prediction of SOM and carbonate content using the partial least squares regression (PLSR). The results (R2 between 0.57 and 0.8) demonstrate the successful application of reflectance spectroscopy combined with chemometric modelling for the estimation of SOM and carbonate content. The calibration models demonstrated a tolerable stability over a variety of different soil types, which is a positive factor for opening the opportunity to use this methodology for monitoring larger areas. Furthermore it could be shown, that the spectral resolution of the MERIS sensor is sufficient for approximation of the SOC/SOM content from pure soil spectra. Consequently the second part of the study focused on the use of MERIS satellite data for the estimation of soil organic carbon content of bare soils at regional scale. The study concentrated on the Apulia region, where we had high density of available field sampling sites, and on parts of the coastal areas of the Abruzzi region South of Pescara, which are known to be amongst the more critical areas in Italy suffering from land degradation problems and desertification risk. For specific morphological-lithological units simple spectral models, based on soil colour and spectral shape attributes, were built to derive soil organic carbon content. In order to apply these models to MERIS satellite data, a time series of images covering the years 2003 and 2004 were acquired for Southern Italy. Pre-processing of image data aimed at extracting those pixels with negligible vegetation abundance at least at one date of observation per year, i.e. practically showing pure bare soil signatures only, and consisted of: ¿ geometrical co-registration and superposition of images from different acquisition dates ¿ the derivation of minimum vegetation composites for each year applying simple minimum value criteria for MERIS vegetation indices ¿ the determination of soil and vegetation abundance at sub-pixel level based on spectral mixture modelling. ¿ the removal of residual vegetation influence from image spectra Soil colour attributes (soil lightness, R coordinate of R-G-B model) and coefficients of a second order polynomial fitted through the pixel reflectance signatures were derived from the minimum vegetation composites of both years. The spatial distribution of soil organic carbon was estimated for each year within specific morphological-lithological units in the Apulia region. In addition models could be applied to other regions in Southern Italy. Estimation results showed good agreement with independent field data and the pedo-transfer rules based estimations of Jones et. al. (2004, 2005).JRC.H.7-Land management and natural hazard

Mediterranean-wide Green Vegetation Abundance for Land Degradation Assessment Derived from AVHRR NDVI and Surface Temperature 1989 to 2005

NOAA AVHRR data stemming from the MEDOKADS archive and ranging from 1989 to 2005 was processed and decomposed into their fractions of the vegetated, non-vegetated and the so called ¿cold¿ endmember. Decomposition occurred via Linear Unmixing within a triangle spanned up by NDVI (y-axis) and surface temperature (x-axis), separately for each of the 612 10-day composites. Endmembers were derived statistically using percentiles and the inverse relationship between NDVI and Ts. The cold endmember was fixed at -20 degrees Celsius, the vegetated endmember at NDVI = 0.7, the latter was then empirically corrected for illumination effects. Linear Unmixing occurred for the whole Mediterranean area, separately for a western and eastern window. Outcomes are the vegetation abundance, soil abundance and ¿cold¿ abundance, indicating the individual coverage of a pixel by each of these. The vegetation abundance was re-scaled to the so-called Grenn Vegetation Fraction (GVF), re-distributing the ¿cold¿ abundance on vegetation and soil abundance proportionally. Unmixing led to a higher stability of GVF data in comparison to NDVI data with regard to atmospheric effects. The data was post-processed for missing values and outliers and it was filtered. The GVF shows close parallelism on several test sites in comparison to a re-scaled NDVI within the endmember limits. The positive effect of the cold abundance, which is amongst other accounting for negative effects from poor atmospheric conditions and which was used to improve the GVF, could be clearly shown. Comparison with high and low resolution SPOT data shows a linear relationship and higher values for GVF. Squared GVF values were found to be closely correlated with independently derived high and low resolution vegetation cover (fCover), confirming this relationship known from literature. Coefficients of determination (R2), slope and offset of linear relations between squared GVF on one side and the two validation data sets on the other side were 0.69, 0.91, 0.07 and 0.58, 1.27, 0.06, respectively. In addition to the ¿per se¿ value of the derived abundances, validation results indicate that squared GVF may be used as approximation for vegetation cover.JRC.H.7-Land management and natural hazard

Synergy between CD26/DPP-IV Inhibition and G-CSF Improves Cardiac Function after Acute Myocardial Infarction

SummaryIschemic cardiomyopathy is one of the main causes of death, which may be prevented by stem cell-based therapies. SDF-1α is the major chemokine attracting stem cells to the heart. Since SDF-1α is cleaved and inactivated by CD26/dipeptidylpeptidase IV (DPP-IV), we established a therapeutic concept—applicable to ischemic disorders in general—by combining genetic and pharmacologic inhibition of DPP-IV with G-CSF-mediated stem cell mobilization after myocardial infarction in mice. This approach leads to (1) decreased myocardial DPP-IV activity, (2) increased myocardial homing of circulating CXCR-4+ stem cells, (3) reduced cardiac remodeling, and (4) improved heart function and survival. Indeed, CD26 depletion promoted posttranslational stabilization of active SDF-1α in heart lysates and preserved the cardiac SDF-1-CXCR4 homing axis. Therefore, we propose pharmacological DPP-IV inhibition and G-CSF-based stem cell mobilization as a therapeutic concept for future stem cell trials after myocardial infarction

Land Degradation Addressed by Satellite Based Long Term Vegetation Phenological Trends over Africa: Preliminary Results

Land degradation is the reduction of the capacity of land to sustain its ecosystem functions and services that support society and development. In this study we derive phenological indices from time series of Spot VEGETATON images. For the African continent, we illustrate that such metrics show observable trends that can help explaining the state and evolution of vegetative land cover due to climatic and/or anthropogenic influences and can be considered a large scale proxy adding to assessment of land degradationJRC.DDG.H.7-Land management and natural hazard

Ecosystem Functional Units characterized by satellite observed phenology and productivity gradients: a case study for Europe

The present study demonstrates remote sensing derived phenological and productivity indicators of ecosystem functional dynamism. The indices were derived from SPOT VEGETATION NDVI data on 1 km spatial resolution across the pan-European continent using the Phenolo approach. The phenological and productivity indices explained 78% of the variance in the European ecosystem gradient measured by bio-climatic zones. Along this gradient climatic predictors could only explain 57% of the variance in the satellite metrics. Reclassification of the bio-climatic zones into phenology and productivity related Ecosystem Functional Units (EFUs) selected five metrics related to the Cyclic and Permanent Fraction of productivity, to the Background, to the growing season start and the timing of the maximum NDVI value. Along the EFU gradient the climatic predictors explained over 90% of the variance of the remote sensing variables, 30% more than along the bio-climatic gradient. The EFUs showed strong correspondence to 14 land-cover types in Europe and the selected remote sensing metrics explained 86% of the variation in the land-cover classes. These results show that remote sensing derived parameters have tremendous potential for the quantification of ecosystem functional dynamism. Phenological and productivity metrics offer an indicator system for ecosystems that climatic indicators alone cannot manifest. Their potential to monitor the spatial pattern, status and inter-annual variability of ecosystems and vegetation cover can deliver reference status information for future assessments of the impacts of human or climate change induced ecosystem changes.JRC.H.5-Land Resources Managemen