Projection of Meteosat Images into World Geodetic System WGS-84 Matching Spot/Vegetation Grid

Products derived from MSG/SEVIRI and Spot/VEGETATION are routinely provided to a large audience of users, i.a. through the EumetCast broadcasting system. Although MSG/SEVIRI has a significantly coarser resolution than Spot/VEGETATION, its thermal channels and higher rate of observation (every 15 minutes) extends the information extracted from Spot/VEGETATION images (1km resolution). MSG/SEVIRI being a geostationary satellite and Spot/VEGETATION a polar orbiting system, the combination of both sources of information requires to re-project the images in a unique cartographic system. This document shows how to project MSG images onto the Spot/VEGETATION grid, in a WGS84 plate carrée representation. The document also analyses the image distortion of the geostationary image and of its reprojection. The results have a sufficient geometric quality to allow the combination of re-projected MSG images with Spot/VEGETATION S10 products.JRC.H.3-Global environement monitorin

CarbEF: a software for reporting carbon emissions from deforestation and forest degradation under REDD+

This document describes a carbon emission reporting method based on maps of forest changes. The method is based on the Intergovermental Panel on Climate Changes (IPCC) guidelines to define forests, deforestation, and forest degradation from multi-temporal tree cover change maps. The CarbEF module of the IMPACT toolbox software implements this method and produces reports. CarbEF module reports ‘activity’ data and carbon emissions from deforestation and forest degradation using: (i) A map of tree cover loss, usually generated from multi-temporal tree cover maps at fine spatial resolution (i.e. maps of tree cover loss at <30m resolution), (ii) a national forest definition and (iii) forest carbon stock values (from national data or other scientific literature).JRC.D.1-Bio-econom

Ensemble mean of CMIP5 Sea Surface Temperature projections under climate change and their reference climatology

A software was developed in the framework of the GEOWOW project for computing the mean of the output of an ensemble of climate change models from the World Climate Research Programme (WCRP) Coupled Model Intercomparaison Project Phase 5 (CMIP5). The ensemble mean for the time projections of the Sea Surface Temperature (SST) under climate change and the corresponding climatology were computed: this paper describes the data set and its properties. The generated datasets are of interest for ecologists willing to assess future changes of marine ecosystems, and can be used under Creative Common Attributions license.

Detection of Surface Water with Spot/Vegetation. Monitoring and Assessing CILSS Countries Surface Water Availability

The detection of surface water with Spot/VEGETATION, at 1km resolution, is done every 10-days. The quality of the detection in arid and semi-arid regions in western Africa, and the regular time step of the observations, allow monitoring the surface water availability. The seasonal surface water can be mapped and its date of avail-ability is known. From this information, some indicators were generated for assessing the relative amount of re-plenishment and delays in availability between two years. The overall information, detections and dates assessments based on Spot/VEGETATION is broadcasted every 10-days to African users thanks to the EumetCAST system. The processing of the water availability indicators, such as those demonstrated in this document, can thus be implemented at the user level.JRC.H.3-Global environement monitorin

Suivi de la dégradation des forêts d’Afrique centrale et de la cartographie des routes dans la région

Un atelier a eu lieu au Centre Commun de Recherche (CCR), à Ispra (Italie), du 27 au 31 Mars 2017. Les activités de cet atelier se sont appuyées sur le projet ReCaREDD financé par la DG DEVCO et le Projet Roadless financé par DG CLIMA. L'atelier a réuni un groupe d'experts des pays partenaires du bassin du Congo en provenance du Cameroun, de la République Démocratique du Congo, et de la République du CongoJRC.D.1-Bio-econom

Ensemble mean of CMIP5 TOS, for the period 1971 to 2000

<p>Ensemble mean of the variable TOS (temperature of surface, i.e. Sea Surface Temperature), from CMIP5 control run.</p> <p>Data are independant of any RCP (before 2006), and can thus be used for computing an SST climatology.</p

Retrieving Phenological Stages from Low Resolution Earth Observation Data

This paper illustrates the usefulness of retrieving from Earth Observation data information on phenological stages, i. e. the date of occurrence of key development stages of the vegetation. It provides a short review of existing methods to retrieve such information from 1-km resolution data time series from the SPOTVEGETATION instrument, it gives indications about key properties that a processing chain must include to retrieve and update such information throughout the season and it supplies the description of the algorithms successfully tested and implemented at continental scale for Africa.JRC.DDG.H.3-Global environement monitorin

Monthly climatology of CMIP5 models historical run, for 1971-2000

<p>Monthly climatology (12 months, and the annual climatology), from CMIP5 historical run (1971 to 2000)</p

Climate, vegetation phenology and forest fires in Siberia

A time series of 18 years of fAPAR (fraction of photosynthetically active radiation absorbed by the green parts of vegetation) data from the NOAA AVHRR instrument series was analyzed for interannual variations in the start, peak, end and length of the season of vegetation photosynthetic activity in Central and East Siberia. Variations in these indicators of seasonality can give important information on interactions between the biosphere and atmosphere. A second order local moving window regression model called the “camel back method” was developed to determine the dates of phenological events at subcontinental scale. The algorithm was validated by comparing the estimated dates to phenological field observations. Using spatial correlations with temperature and recipitation data and climatic oscillation indices, we postulate two geographically distinct mechanisms in the system of climatic controls of the biosphere in Siberia: Central Siberia is controlled by an “Arctic Oscillation/temperature mechanism” while East Siberia is controlled by an “El Niño/precipitation mechanism”. While the analysis of data from 1982 to 1991 indicates a slight increase in the length of the growing season for some land cover types due to an earlier beginning of the growing season, the overall trend from 1982 to 1999 is towards a slightly shorter season for some land cover types caused by an earlier end of season. The Arctic Oscillation tended towards a more positive phase in the Eighties leading to enhanced high pressure system prevalence but towards a less positive phase in the Nineties. We suggest that the two mechanisms also control the fire regimes in Central and East Siberia. Several extreme fire years in Central Siberia were associated with a highly positive Arctic Oscillation phase, while several years with high fire damage in East Siberia occurred in El Niño years. An analysis of remote sensing data of forest fire partially supports this hypothesis

A Map of Temporary Water Bodies in Western Africa

An algorithm developed for the SPOT VEGETATION (VGT) sensor makes it possible to detect temporary small water bodies in arid and semi-arid areas by analysing the spectral contrast of each pixel compared to its neighbourhood. This algorithm captures surface water and humid areas, the last category being likely to include vegetation. These maps are generated every 10 days allowing to monitor the small temporary water bodies and humid area seasonality. In addition, a reference map of the temporary water bodies was prepared by validating the historical record of the small water body detection. The procedure consists in checking individually any SPOT VGT water bodies detection that lasted for more than 3 consecutive dekads between 1999 and late 2004. The validation has been done over Western Africa. Several sources of information have been used to validate the detections: the Global Lakes and Wetlands Database (GLWD), the Drainage Network of the Digital Chart of the World, Spot VGT colour composite images, the SRTM digital elevation model, Landsat quicklook images and Google Earth. Pixels of the history of occurrence that were not referenced in the GLWD or DCW database have been checked manually. This validation process resulted in a map of three categories: validated water bodies, positive vegetation anomaly with no obvious sign of water but indicating water availability in arid areas and false detections.JRC.H.3-Global environement monitorin