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  • 30+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2001-December 31, 2012
    This dataset updates: As needed
    This layer contains information about the land degradation phenomenon - by second-level administrative area - observed during the Integrated Context Analysis (ICA) run in Democratic Republic of Congo in 2017. The analysis was performed in three hot-spot provinces in the north-east part of the country (Ituri, Nord-Kivu and Sud-Kivu) because of the recent conflict outbreak and increasing food insecurity levels. Data source: HQ OSEP GIS Analysis of NASA MODIS, 2001-2012 The main indicators used for the analysis were the average ecological change observed within the time window considered and the percentage of prone-erosion district surface. Cette couche contient les données necessaires pour déterminer le niveau de dégradation de terres - par unité administrative de deuxième niveau - observé pendant l'Analyse Integrée de Contexte (AIC) executée en République Démocratique du Congo en 2017. Source des données: HQ OSEP GIS Analyse de NASA MODIS, 2001-2012 Les indicateurs principaux utilisés pour l'analyse étaient les changements moyens de couverture du sol observés entre 2001 et 2012 et la pourcentage de surface ayant une propension à l'érosion significative. Original dataset title: ICA Democratic Republic of Congo, 2017 - Land Degradation, 2001-2012
  • 30+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2016-December 31, 2016
    This dataset updates: As needed
    This layer contains information about the most predominant livelihood zone - by second-level administrative area - identified during the Integrated Context Analysis (ICA) run in Democratic Republic of Congo in 2017. The analysis was performed in three hot-spot provinces in the north-east part of the country (Ituri, Nord-Kivu and Sud-Kivu) because of the recent conflict outbreak and increasing food insecurity levels. Data source: Fewsnet, 2016. Cette couche contient informations regard le zones de moyens d'existence plus prédominant - par unité administrative de deuxième niveau - identifiés pendant l'Analyse Integrée du Contexte (AIC) executée en République Démocratique du Congo en 2017. L'analyse a été executée dans trois provinces critiques dans le secteur nord-est du pays (Ituri, Nord-Kivu et Sud-Kivu) en raison du récent déclenchement des conflits et des croissants niveaux d'insécurité alimentaire. Source des données: Fewsnet, 2016. Original dataset title: ICA Democratic Republic of Congo, 2017 - Most Predominant Livelihood Zones, 2016
  • 20+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2001-December 31, 2012
    This dataset updates: As needed
    This layer contains information about the land degradation phenomenon - by second-level administrative area - observed for the purposes of the Integrated Context Analysis (ICA) run in Chad in 2017. The analysis was a joint effort between the Regional Bureau in Dakar (RBD) and the HQ GIS Unit and Programme division. Data sources: HQ OSEP GIS Analysis of NASA MODIS 2001-2012, WorldClim 1970-2000, FAO data and NASA SRTM Digital Elevation Model. The main indicators used for the analysis were the average ecological changes observed between 2001 and 2012 and the percentage of erosion-prone surface. Cette couche contient les données necessaires pour determiner le niveau de dégradation des terres – par unité administrative de deuxième niveau – observé pendant l’Analyse Integrée du Contexte (AIC) executée en Tchad en 2017. L’analyse a été executée grâce à la collaboration entre le Bureau Régional de Dakar (RBD), l’unité GIS et la division de Programme au quartier générale du PAM. Source des données: HQ OSEP GIS Analyse des données NASA MODIS 2001-2012, WorldClim 1970-2000, FAO et NASA SRTM Digital Elevation Model. Les indicateurs principaux utilisés pour l'analyse étaient les changements moyens de couverture du sol observés entre 2001 et 2012 et la pourcentage de surface ayant une propension à l'érosion significative. Original dataset title: ICA Chad, 2017 - Land Degradation, 2001-2012
  • 10+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2011-December 31, 2011
    This dataset updates: As needed
    This layer contains information about the most predominant livelihood zones - by second-level administrative area - identified during the Integrated Context Analysis (ICA) run in Chad in 2017. The analysis was a joint effort between the Regional Bureau in Dakar (RBD) and the HQ GIS Unit and Programme division. Data source: Fewsnet, 2011. Cette couche contient informations regard les zones de moyens d’existence prédominant – par unité administrative de deuxième niveau – identifiées pendant l’Analyse Integrée du Contexte (AIC) executée en Tchad en 2017. L’analyse a été executée grâce à la collaboration entre le Bureau Régional de Dakar (RBD), l’unité GIS et la division de Programme au quartier générale du PAM. Source des données: Fewsnet, 2011. Original dataset title: ICA Chad, 2017 - Most Predominant Livelihood Zones, 2011
  • 10+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2011-December 31, 2011
    This dataset updates: As needed
    This layer contains information about the land degradation phenomenon - by livelihood zone - observed during the Integrated Context Analysis (ICA) run in Burundi between 2014 and 2015. Data source: ICPAC & Regional Centre for Mapping of Resources for Development (RCMRD), 2011. The key indicator used for the analysis was the percentage of surface affected by a high or very high level of degradation. Cette couche contient les données necessaires pour déterminer le niveau de dégradation de terres - par zones de moyens d'existence - observé pendant l'Analyse Integrée de Contexte (AIC) executée au Burundi entre 2014 et 2015. Source des données: ICPAC & RCMRD, 2011. L'indicateur principale utilisé pour l'analyse était la pourcentage de surface avec une dégradation elevée ou très elevée. Original dataset title: ICA Burundi, 2014 - Land Degradation, 2011
  • 30+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2009-December 31, 2009
    This dataset updates: As needed
    This layer contains information about the most predominant livelihood zones - by first-level administrative area - identified during the Integrated Context Analysis (ICA) run in Burkina Faso in 2018. The analysis was a joint effort between the Regional Bureau in Dakar (RBD) and the HQ GIS Unit and Programme division. Data source: Fewsnet, 2009. Cette couche contient informations regard les zones de moyens d’existence prédominant – par unité administrative de première niveau – identifiées pendant l’Analyse Integrée du Contexte (AIC) executée en Burkina Faso en 2018. L’analyse a été executée grâce à la collaboration entre le Bureau Régional de Dakar (RBD), l’unité GIS et la division de Programme au quartier générale du PAM. Source des données: Fewsnet, 2009. Original dataset title: ICA Burkina Faso, 2018 - Most Predominant Livelihood Zones, 2009
  • 70+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2001-December 31, 2012
    This dataset updates: As needed
    This layer contains information about the land degradation phenomenon - by first-level administrative area - observed for the purposes of the Integrated Context Analysis (ICA) run in Burkina Faso in 2018. The analysis was a joint effort between the Regional Bureau in Dakar (RBD) and the HQ GIS Unit and Programme division. Data sources: HQ OSEP GIS Analyse des données NASA MODIS 2001-2012, WorldClim 1970-2000, FAO et NASA SRTM Digital Elevation Model. The main indicators used for the analysis were the average ecological changes observed between 2001 and 2012 and the percentage of erosion-prone surface. Cette couche contient les données necessaires pour determiner le niveau de dégradation des terres – par unité administrative de deuxième niveau – observé pendant l’Analyse Integrée du Contexte (AIC) executée en Burkina Faso en 2018. L’analyse a été executée grâce à la collaboration entre le Bureau Régional de Dakar (RBD), l’unité GIS et la division de Programme au quartier générale du PAM. Source des données: HQ OSEP GIS Analyse des données NASA MODIS 2001-2012, WorldClim 1970-2000, FAO et NASA SRTM Digital Elevation Model. Les indicateurs principaux utilisés pour l'analyse étaient les changements moyens de couverture du sol observés entre 2001 et 2012 et la pourcentage de surface ayant une propension à l'érosion significative. Original dataset title: ICA Burkina Faso, 2018 - Land Degradation, 2001-2012
  • 30+ Downloads
    Updated 22 January 2020 | Dataset date: January 01, 2002-December 31, 2014
    This dataset updates: As needed
    Geotiff download link : http://bit.ly/2VOvrlD Metadata Download link: http://geonode.themimu.info/documents/27 A Landsat-based classification of forest cover in Kachin State and Sagaing Region with a focus on changes in intact forest between 2002 and 2014. This dataset expands upon the Myanmar 2002-2014 Forest Cover Change raster*, by differentiating mining from other non-forest land cover classes. Developed by: ALARM, Smithsonian Institution, GMAP and American Museum of Natural History *http://geonode.themimu.info/layers/geonode%3Amyanmar_forestcoverchange See the recommended style file (.txt) in the documents section for associating raster values and creating the legend. Original dataset title: Kachin State and Sagaing Region 2002-2014 Forest Cover Change
  • 10+ Downloads
    Updated 22 January 2020 | Dataset date: January 01, 2002-December 31, 2014
    This dataset updates: As needed
    Geotiff download link : http://bit.ly/2wq7KFy Metadata download link: http://geonode.themimu.info/documents/26 A Landsat-based classification of forest cover in Tanintharyi Region with a focus on changes in intact forest between 2002 and 2014. This dataset expands upon the Myanmar 2002-2014 Forest Cover Change raster*, by differentiating oil palm plantations from other plantations. Developed by: ALARM, Smithsonian Institution, GMAP and American Museum of Natural History *http://geonode.themimu.info/layers/geonode%3Amyanmar_forestcoverchange See the recommended style file (.txt) in the documents section for associating raster values and creating the legend. Original dataset title: Tanintharyi Region 2002-2014 Forest Cover Change
  • 20+ Downloads
    Updated 22 January 2020 | Dataset date: April 20, 2016-April 20, 2016
    This dataset updates: As needed
    Geotiff download link : http://bit.ly/2PHtJyf This is a Landsat 8-based classification of the extent and condition of mangrove forests in Tanintharyi Region. The classification is derived from satellite images which were acquired in 2014. The classification is based on ground truth data, which was collected in May and June 2015. In addition, further training polygons were digitized based on high-resolution Google Maps and Bing Maps data. This dataset is a filtered version of the initial classification result. The classified mangrove classes are ‘Intact to slightly degraded mangroves’, ‘Degraded mangroves’, ‘Heavily degraded mangroves’ and ‘Nipa monoculture’. suggested citiation: Stephani, A.; Oswald, P.; Koellner, T.; Wegmann, M., 2016. Tanintharyi Region Mangrove Forest dataset (2014). Point of contact: annastephani@gmx.de and patrickoswald@fauna-flora.org
  • 10+ Downloads
    Updated 22 January 2020 | Dataset date: March 01, 2016-March 31, 2016
    This dataset updates: As needed
    Geotiff download link: http://bit.ly/2IjLZtw This dataset is the result of a land cover analysis for Myanmar's Tanintharyi Region based on March, 2016 Landsat 8 OLI imagery. The primary purpose of the study was to map natural forest for each of four ecological forest types (Mangrove, Mixed Deciduous, Lowland Evergreen, Upland Evergreen). A number of other land use/land cover types are also included in the dataset, including human settlement areas, rice paddyfields, and agroforestry plantations. This dataset is the original version generated according to the methodology outlined in the corresponding manuscript. [Citation: Connette, G., P. Oswald, M. Songer, and P. Leimgruber. 2016. Mapping distinct forest types improves overall forest identification based on multi-spectral Landsat imagery. Remote Sensing 8: 882.] [Spatial reference: WGS84 UTM47N] Original dataset title: Tanintharyi Region Land Cover - March 2016 (Original)
  • 10+ Downloads
    Updated 22 January 2020 | Dataset date: March 01, 2016-March 31, 2016
    This dataset updates: As needed
    Geotiff download link: http://bit.ly/2ToMIjL This dataset is the result of a land cover analysis for Myanmar's Tanintharyi Region based on March, 2016 Landsat 8 OLI imagery. The primary purpose of the study was to map natural forest for each of four ecological forest types (Mangrove, Mixed Deciduous, Lowland Evergreen, Upland Evergreen). A number of other land use/land cover types are also included in the dataset, including human settlement areas, rice paddyfields, and agroforestry plantations. This dataset is a REVISED version of the land cover map generated according to the methodology outlined in the corresponding manuscript (see below for citation). This version has been manually edited to fill cloud holes using 2015 data and to fix a number of obvious mis-classifications, particularly for oil palm and settlement areas. [Citation: Connette, G., P. Oswald, M. Songer, and P. Leimgruber. 2016. Mapping distinct forest types improves overall forest identification based on multi-spectral Landsat imagery. Remote Sensing 8: 882.] [Spatial reference: WGS84 UTM47N] Original dataset title: Tanintharyi Region Land Cover - March 2016 (Improved)
  • 100+ Downloads
    Updated 22 January 2020 | Dataset date: January 01, 2002-December 31, 2014
    This dataset updates: As needed
    Geotiff download link: http://bit.ly/32L9Fk4 Metadata Download link: http://geonode.themimu.info/documents/30 A Landsat-based classification of Myanmar’s forest cover with a focus on changes in intact forest between 2002 and 2014. Developed by: ALARM, Smithsonian Institution, GMAP and American Museum of Natural History LAND COVER CLASSES: Intact Forest (>80% canopy cover); Degraded Forest (10%-80% canopy cover); New** Degraded Forest; Non-Forest (<10% canopy cover); New Non-Forest; Plantations; New Plantations; Water; Snow or Ice *In dry deciduous forest areas, intact forest was defined as >60% canopy cover and degraded forest was defined as 10%-60% canopy cover. All classes defined as “new” indicate that the class converted from intact forest in 2002 to a “new” land cover class in 2014. Original dataset title: Myanmar 2002-2014 Forest Cover Change
  • 200+ Downloads
    Updated 3 September 2019 | Dataset date: June 01, 2019-August 01, 2019
    This dataset updates: Every three months
    These data are collected by sentinel sites in Mali, Burkina-Faso, Senegal and Niger. They provide weekly data on water availability, market prices, animal diseases and pasture conditions. The data are agregated at the end of a 2-months period and published via (1) a bulletin available on the sigsahel.info platform and maps available on a mapping platform : geosahel.info. These dataset are the summary of the data collected by sentinel sites during June and July 2019.
  • 600+ Downloads
    Updated 17 July 2019 | Dataset date: July 17, 2019-July 17, 2019
    This dataset updates: As needed
    Palestinian Communities in the West Bank and the Gaza Strip
  • 1500+ Downloads
    Updated 4 December 2018 | Dataset date: October 01, 1998-October 01, 2018
    This dataset updates: Every year
    Biomass Production (in tonnes) for Admin 2 Level. Source: 10-day images of Dry Matter Productivity (DMP) from Proba V Satellite. Data processed by Flemish Institute of Technology (VITO) through the Copernicus Global Land Service.
  • 300+ Downloads
    Updated 7 September 2018 | Dataset date: September 01, 2018-September 01, 2018
    This dataset updates: Never
    Bulletin sur la Production de Biomasse et l’Eau de Surface sur le Sahel, mi-saison d’hivernage 2018. Les données satellitaires utilisées pour cette étude sont issues des mesures provenant depuis 1998 de la série des satellites SPOT-VEGETATION 4 & 5, remplacés en 2014 par PROBA-V. Ces satellites appartiennent au programme de l’agence spatiale européenne ESA. Les données brutes sont traitées et distribuées par l’Institut Flamande pour la recherche Technologique VITO (Belgique) et ensuite analysées à l’aide des outils développés par ACF : BioGenerator et HydroGenerator. BioGenerator permet de quantifier les productions totales annuelles de biomasse végétale, exprimées en kg de matière sèche à l’hectare kg/ha, ainsi que l’anomalie de production en comparaison avec la moyenne calculée sur l’ensemble des années disponibles depuis 1998 sans discontinuité. HydroGenerator permet de suivre statistiquement la présence des points d’eau de surface, et de calculer l’indice d’accessibilité à l’eau de surface et son anomalie par comparaison à la moyenne calculée sur la période 1998 à 2018. Les prévisions des précipitations se base sur les données produites par le CPC (Climate Prediction Center) de la NOAA (National Oceanic and Atmospheric Administration), administration des Etats-Unis d’Amérique. Le CPC donne accessible publiquement au téléchargement une série de prévisions climatiques sur le globe. Les prévisions de précipitations utilisées sont issues d’une combinaison de plusieurs modèles climatiques. Ces prévisions de moyennes mensuelles des précipitations concernent une période de 4 mois suivant la période initiale, ici août 2018.
  • 500+ Downloads
    Updated 16 August 2018 | Dataset date: October 05, 2015-October 05, 2015
    This dataset updates: Every year
    Donées sur la végétation de la RDC
  • 3900+ Downloads
    Updated 9 July 2018 | Dataset date: January 31, 2000-December 31, 2015
    This dataset updates: Never
    Green area per capita is calculated from Green area per million people and city population data source from OECD.
  • 100+ Downloads
    Updated 30 January 2018 | Dataset date: May 02, 2016-May 02, 2016
    This dataset updates: Every year
    El indicador contiene la información sobre el avance de la deforestación de los bosques en Guatemala y el avance en el rescate o reforestación de los bosques.
  • 500+ Downloads
    Updated 8 March 2017 | Dataset date: September 01, 2013-September 01, 2013
    This dataset updates: Every year
    This spatial dataset provides Forest and natural parks data of Bangladesh. The original source of the data is Local Government Engineering Department (LGDE) of Bangladesh. Dataset updated by WFP, Map Action and OCHA.
  • 1000+ Downloads
    Updated 25 November 2015 | Dataset date: July 24, 2015-July 24, 2015
    This dataset updates: Every year
    Información de aspectos culturales de Perú. Información Cartográfica Básica generalizada a partir de los archivos de Carta Nacional Escala 1/100 000. Con períodos de actualización diferentes.
  • 300+ Downloads
    Updated 24 November 2015 | Dataset date: January 01, 2001-January 01, 2001
    This dataset updates: Every year
    Built-up Areas in Nepal as polygons.
  • 100+ Downloads
    Updated 24 November 2015 | Dataset date: March 20, 2015-March 20, 2015
    This dataset updates: Never
    This dataset consists of one ESRI Shapefile with outlines of all the building footprints of Vanuatu before Cyclone Pam in 2015.
  • 300+ Downloads
    Updated 16 October 2015 | Dataset date: December 31, 2009-December 31, 2009
    This dataset updates: Every year
    The dataset shows the Area reforested by the government and the private sectors