WMS
GeoNode Local GeoServer
This is a description of your Web Map Server.
WFS
WMS
GEOSERVER
NONE
NONE
text/xml
image/png
application/atom+xml
application/json;type=utfgrid
application/pdf
application/rss+xml
application/vnd.google-earth.kml+xml
application/vnd.google-earth.kml+xml;mode=networklink
application/vnd.google-earth.kmz
image/geotiff
image/geotiff8
image/gif
image/jpeg
image/png; mode=8bit
image/svg+xml
image/tiff
image/tiff8
image/vnd.jpeg-png
image/vnd.jpeg-png8
text/html; subtype=openlayers
text/html; subtype=openlayers2
text/html; subtype=openlayers3
text/plain
application/vnd.ogc.gml
text/xml
application/vnd.ogc.gml/3.1.1
text/xml; subtype=gml/3.1.1
text/html
text/javascript
application/json
XML
INIMAGE
BLANK
JSON
JSONP
GeoNode Local GeoServer
This is a description of your Web Map Server.
EPSG:4326
EPSG:3785
EPSG:3857
EPSG:900913
EPSG:32647
EPSG:32736
CRS:84
-3.40282E38
5723315.0
-3.40282E38
1335360.25
geonode:Afgooye_PotentialHigherGround_forFloodEvacuat
Afgooye District Suitable Potential Higher Grounds For Evacuation During Flooding
Datasets used for the analysis are :- Flood Extent, Elevation, Slope, Topographic Wetness Index (TWI),Drainage Density, Strategic Boreholes, Settlements, Land Cover and Roads
Based on a predefined criteria, each of these datasets were first clipped to areas outside the flood extent but within a maximum distance of 10 Km from the flood extent boundary. The outputs were then reclassified into two classes namely; Suitable and Not Suitable. Using an iterative approach, each of these datasets was used to assign a weight to the suitability of a site. These weights are indicated on table 2 below. Thereafter, a multi-criteria evaluation weighted overlay operation was undertaken to determine the potential areas for evacuation sites. An area was then computed for the potential sites and their distance from the flood extent established.
The output was then post – processed to remove invalid geometries and each site was ranked based on the distance from the flood prone area. Sites that are closer in distance were deemed to be most suitable hence a higher rank.
The final layer was then visually checked based on the density of the topographic Wetness Index (TWI) which is based on runoffs during floods. Areas with dense TWI were eliminated to remain with only continuous areas.
Afgooye_PotentialHigherGround_forFloodEvacuation_02102023
EPSG:4326
CRS:84
44.7123031616211
45.3428611755371
1.95874118804932
2.26165652275085
other
other
text/xml
other
other
other
other
geonode:Africa_Adm0_Country
Africa_Country Administrative Boundaries
This dataset is about Africa Admin boundaries level 0(country level).
africa
boundary
administrative
country
EPSG:4326
CRS:84
-17.5352382659912
56.2795066833496
-34.8398284912109
37.3496131896973
other
other
text/xml
other
other
other
other
geonode:Agri_Mask_Bay_Region_30102019_UTM
Som_Bay_Region_Agrimask_30102019UTM
This classification was done using LCCS3 legend as a framework for classifying agriculture. LCCS3 is the last version of the Land Cover Classification System (LCCS) developed by FAO and UNEP in 1998 to facilitate the understanding of theclasses of land cover regardless of the scale of mapping, the type of coverage,method of data collection, or geographic location.Sentinel 2 images were downloaded and segmented using ecognition software.The segments were then classified using LCCS legend.During the classification, photo keys of different agricultural classes created on google earth was used as visual verification of class types before classification, given that sentinel 2 images has a resolution of 30m and therefore not clear enough to visually detect differences between classes.In additon , High Resolution Google and Esri satellite basemaps were used for verification.The scale of editing and digitization of polygons was 1:10000
somalia
EPSG:32638
CRS:84
42.410443903048325
44.65109718166896
1.497744260208828
3.922109285591186
other
other
text/xml
other
other
other
other
geonode:Agrimask_Galgaduud_20th_Sept
Agrimask_Galgaduud_20th_Sept
No abstract provided
feature
EPSG:32638
CRS:84
46.46184559197604
47.60361319044937
3.5861937116202958
5.9521132160653005
other
other
text/xml
other
other
other
other
geonode:Agrimask_Galgaduud_29th_Aug_2022
Agrimask_Galgaduud_29th_Aug_2022
No abstract provided
feature
EPSG:32638
CRS:84
46.44998573733093
47.60035691464002
3.5861937116202958
5.213146661884031
other
other
text/xml
other
other
other
other
geonode:Airfields_UNSOS
Airfields_UNSOS
This layer contains data about airfields locations in Somalia. The layer is updated by the United Nations Support Office in Somalia (UNSOS)
somalia
SOM_airfields_Feb2007_GCS
EPSG:4326
CRS:84
41.029109954834
51.3264274597168
-0.389333009719849
11.9474401473999
other
other
text/xml
other
other
other
other
geonode:Aquifer_of_Somaliland_Puntland
GeologijaSomalijaLinijeNovo_1
REQUIRED: A brief narrative summary of the data set.
Aquifer_of_Somaliland_Puntland
features
REQUIRED: Common-use word or phrase used to describe the subject of the data set.
EPSG:4326
CRS:84
42.6608543395996
51.4098434448242
6.72866678237915
11.9814615249634
other
other
text/xml
other
other
other
other
geonode:Baardheere_Potential_HigherGroundsSuitablefor
Baardheere District Suitable Potential Higher Grounds For Evacuations During Flooding
Datasets used for the analysis are :- Flood Extent, Elevation, Slope, Topographic Wetness Index (TWI),Drainage Density, Strategic Boreholes, Settlements, Land Cover and Roads
Based on a predefined criteria, each of these datasets were first clipped to areas outside the flood extent but within a maximum distance of 10 Km from the flood extent boundary. The outputs were then reclassified into two classes namely; Suitable and Not Suitable. Using an iterative approach, each of these datasets was used to assign a weight to the suitability of a site. These weights are indicated on table 2 below. Thereafter, a multi-criteria evaluation weighted overlay operation was undertaken to determine the potential areas for evacuation sites. An area was then computed for the potential sites and their distance from the flood extent established.
The output was then post – processed to remove invalid geometries and each site was ranked based on the distance from the flood prone area. Sites that are closer in distance were deemed to be most suitable hence a higher rank.
The final layer was then visually checked based on the density of the topographic Wetness Index (TWI) which is based on runoffs during floods. Areas with dense TWI were eliminated to remain with only continuous areas.
Bar Dheere
EPSG:32638
CRS:84
42.121193119371235
42.42570444583845
1.9534162888563567
2.5799297714105376
other
other
text/xml
other
other
other
other
geonode:Bakool_AgriMask_Isse_Benard
Bakool_AgriMask_Isse_Benard
No abstract provided
feature
EPSG:32638
CRS:84
43.83381312467169
44.31242111051386
3.872282243665415
4.961087346036617
other
other
text/xml
other
other
other
other
geonode:Bakool_Suufi_Agrimask_Julie
Bakool_Suufi_Agrimask_Julie
No abstract provided
feature
EPSG:32638
CRS:84
44.09694845615015
44.785380052705435
3.3380461850752945
4.958336524825602
other
other
text/xml
other
other
other
other
geonode:BeletWeyne_Potential_Suitable_Higher_Grounds_0
Belet Weyne District Suitable Potential Higher Grounds for Evacuation During Flooding
Datasets used for the analysis are :- Flood Extent, Elevation, Slope, Topographic Wetness Index (TWI),Drainage Density, Strategic Boreholes, Settlements, Land Cover and Roads
Based on a predefined criteria, each of these datasets were first clipped to areas outside the flood extent but within a maximum distance of 10 Km from the flood extent boundary. The outputs were then reclassified into two classes namely; Suitable and Not Suitable. Using an iterative approach, each of these datasets was used to assign a weight to the suitability of a site. These weights are indicated on table 2 below. Thereafter, a multi-criteria evaluation weighted overlay operation was undertaken to determine the potential areas for evacuation sites. An area was then computed for the potential sites and their distance from the flood extent established.
The output was then post – processed to remove invalid geometries and each site was ranked based on the distance from the flood prone area. Sites that are closer in distance were deemed to be most suitable hence a higher rank.
The final layer was then visually checked based on the density of the topographic Wetness Index (TWI) which is based on runoffs during floods. Areas with dense TWI were eliminated to remain with only continuous areas.
elnino
floods
somalia
evacuation
beletweyne
higher
grounds
EPSG:4326
CRS:84
44.8455009460449
45.4153709411621
4.34055042266846
5.06559038162231
other
other
text/xml
other
other
other
other
geonode:Beletweyne_Openbuildings_dataset_assessment_g
Beletweyne_Openbuildings_dataset_assessment_g
No abstract provided
Beletweyne_Openbuildings_dataset_assessment_grid_polygon
features
EPSG:32638
CRS:84
45.15594717202501
45.25967452300951
4.689022236578883
4.7840432986208805
other
other
text/xml
other
other
other
other
geonode:Beletweyne_Openbuildings_dataset_assessment_g0
Beletweyne_Openbuildings_dataset_assessment_g0
No abstract provided
Beletweyne_Openbuildings_dataset_assessment_grid
features
EPSG:32638
CRS:84
45.15594738100767
45.25966494153103
4.689964501421549
4.7840432986208805
other
other
text/xml
other
other
other
other
geonode:Beletweyne_Openbuildings_dataset_clipped_filt
Beletweyne_Openbuildings_dataset_clipped_filt
No abstract provided
Beletweyne_Openbuildings_dataset_clipped_filtered_UTM
features
EPSG:32638
CRS:84
45.155934785418
45.259739900165556
4.690343865877316
4.7840370819384646
other
other
text/xml
other
other
other
other
geonode:Charcoal_Kilns_Final
Charcoal_Kilns_Final
No abstract provided
Charcoal_Kilns_Final
features
EPSG:4326
CRS:84
43.8493270874023
45.2187919616699
2.78926014900208
3.92365670204163
other
other
text/xml
other
other
other
other
geonode:Charcoal_Kilns_Final_14032022
Charcoal_Kilns_Final_14032022
No abstract provided
Charcoal_Kilns_Final_14032022
features
EPSG:4326
CRS:84
43.8493270874023
45.2325439453125
2.74165892601013
3.92365670204163
other
other
text/xml
other
other
other
other
geonode:Charcoal_Kilns_Final_Extended
Charcoal_Kilns_Final_Extended
No abstract provided
Charcoal_Kilns_Final_Extended
features
EPSG:4326
CRS:84
43.8493270874023
45.2325439453125
2.74165892601013
3.92365670204163
other
other
text/xml
other
other
other
other
geonode:Climate_Zones_in_Somalia
Climate Zones in Somalia
Data on climate from pre-war meteorological stations (1961-1990) Data on topography by NASA - SRTM, 2000.The climate of Somalia is classified into four general climatic zones, Desert, Very arid, Semi arid and Humid - Semi arid.
zones
climate
somalia
EPSG:4326
CRS:84
40.989501953125
51.4141273498535
-1.66006135940552
11.9859428405762
other
other
text/xml
other
other
other
other
geonode:Dollow_PotentialSuitableHigherGround_Evacuati
Dolloow District Suitable Potential Higher Grounds for Evacuation During Flooding
Datasets used for the analysis are :- Flood Extent, Elevation, Slope, Topographic Wetness Index (TWI),Drainage Density, Strategic Boreholes, Settlements, Land Cover and Roads
Based on a predefined criteria, each of these datasets were first clipped to areas outside the flood extent but within a maximum distance of 10 Km from the flood extent boundary. The outputs were then reclassified into two classes namely; Suitable and Not Suitable. Using an iterative approach, each of these datasets was used to assign a weight to the suitability of a site. These weights are indicated on table 2 below. Thereafter, a multi-criteria evaluation weighted overlay operation was undertaken to determine the potential areas for evacuation sites. An area was then computed for the potential sites and their distance from the flood extent established.
The output was then post – processed to remove invalid geometries and each site was ranked based on the distance from the flood prone area. Sites that are closer in distance were deemed to be most suitable hence a higher rank.
The final layer was then visually checked based on the density of the topographic Wetness Index (TWI) which is based on runoffs during floods. Areas with dense TWI were eliminated to remain with only continuous areas.t provided
higher grounds
EPSG:4326
CRS:84
41.8631591796875
42.341365814209
3.89865040779114
4.17933082580566
other
other
text/xml
other
other
other
other
geonode:Drainage_Networkline
Drainage_Networkline
No abstract provided
features
Drainage_Networkline
EPSG:4326
CRS:84
43.5129318237305
47.8076591491699
8.23493671417236
10.9096593856812
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_April_2021
Somalia Drought Conditions - April 2021
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
drought
somalia
EPSG:4326
CRS:84
40.9943161010742
51.4130325317383
-1.66491448879242
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_April_2022
Somalia Drought Conditions - April 2022
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
drought
somalia
EPSG:4326
CRS:84
40.9943161010742
51.4130325317383
-1.66491448879242
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_August_2021
Somalia Drought Conditions - August 2021
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
drought
somalia
EPSG:4326
CRS:84
40.9943161010742
51.4130325317383
-1.66491448879242
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_August_2022
Somalia Drought Conditions - August 2022
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factores in infromation from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
drought
somalia
EPSG:4326
CRS:84
40.9943161010742
51.4130325317383
-1.66491448879242
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_December_2017
Somalia Drought Conditions - December 2017
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
drought
somalia
EPSG:4326
CRS:84
40.9896812438965
51.4143142700195
-1.65970516204834
11.9867792129517
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_December_2021
Somalia Drought Conditions - December 2021
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
drought
somalia
EPSG:4326
CRS:84
40.9863204956055
51.4126968383789
-1.6557343006134
11.9790239334106
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_February_2022
Somalia Drought Conditions - February 2022
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
drought
february
somalia
EPSG:4326
CRS:84
40.9863204956055
51.3877639770508
-1.62313187122345
11.9737529754639
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_January_2019
Somalia Drought Conditions - January 2019
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
january
drought
somalia
EPSG:4326
CRS:84
40.9953765869141
51.4191360473633
-1.64918065071106
11.980583190918
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_January_2022
Somalia Drought Conditions - January 2022
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
january
drought
somalia
EPSG:4326
CRS:84
40.9863204956055
51.4064483642578
-1.62313187122345
11.9737529754639
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_March_2019
Somalia Drought Conditions - March 2019
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
drought
march
somalia
EPSG:4326
CRS:84
40.9948425292969
51.4194679260254
-1.66008341312408
11.9859209060669
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_March_2022
Somalia Drought Conditions - March 2022
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
drought
march
somalia
EPSG:4326
CRS:84
40.9943161010742
51.4130325317383
-1.66491448879242
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_May_2022
Somalia Drought Conditions - May 2022
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
may
drought
somalia
EPSG:4326
CRS:84
40.9943161010742
51.4130325317383
-1.66491448879242
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_October_2021
Somalia Drought Conditions - October 2021
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
drought
october
somalia
EPSG:4326
CRS:84
40.9943161010742
51.4130325317383
-1.66491448879242
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:Drought_Conditions_September_2021
Somalia Drought Conditions - September 2021
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
drought
somalia
EPSG:4326
CRS:84
40.9943161010742
51.4130325317383
-1.66491448879242
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:Flash_floods_information_IMC_Puntland
Flash_floods_information_IMC_Puntland
No abstract provided
Flash_floods_information_IMC_Puntland
features
EPSG:4326
CRS:84
47.4327964782715
50.6055030822754
7.2033896446228
11.2784700393677
other
other
text/xml
other
other
other
other
geonode:FloodProneAreas_ElninoFloodForecast_HQ100Floo
Historical Flood Prone Areas , Elnino Forecast and HQ Flood Model Extent Merge
Layer was derived by merging the Historical flood extent accessible at https://spatial.faoswalim.org/layers/geonode:Som_Flood_Prone_areas_20200#/, Elnino flood forecast retrieved from GLOFAS and the Output of the flood model of the 100 year return period (HQ 100) along river shabelle.
Flood Prone Areas
EPSG:4326
CRS:84
37.1648178100586
46.9578094482422
-4.66613531112671
6.45769882202148
other
other
text/xml
other
other
other
other
geonode:ForestSuitability_TamarindusIndica_North
ForestSuitability_TamarindusIndica_North
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species Tamarindus Indicain selected areas innorthern part of Somalia (Garbiley and Boroma areas in Somaliland).
ForestSuitability_TamarindusIndica_North
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:Galmudug_Region_Agrimask_Merge_17102022
Galmudug_Region_Agrimask_Merge_17102022
No abstract provided
AgriMask_Ahmed_AoI
EPSG:32638
CRS:84
46.17065649045546
47.63793945552033
3.586100624066094
6.306398093566703
other
other
text/xml
other
other
other
other
geonode:Geology_Abbate_et_al_1993
Geological map - Abbate et al. 1993
General geological map of Somalia
geology
EPSG:4326
CRS:84
40.9912338256836
51.4092979431152
-1.71470081806183
11.9819564819336
other
other
text/xml
other
other
other
other
geonode:GrazeSuitabilityCamels_North
Land_GrazeSuitability_Cattle_N
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for animal grazing (Cattle) in selected areas innorthern part of Somalia (Garbiley and Boroma areas in Somaliland).
SOM_Cropland_GFSAD_30m_2015
northern
graze
somalia
cattle
agri-suitability
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:GrazeSuitabilityCattle_N
Land_GrazeSuitability_Cattle_N
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for animal grazing (Cattle) in selected areas innorthern part of Somalia (Garbiley and Boroma areas in Somaliland).
GrazeSuitabilityCattle_N
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:GrazeSuitabilityCattle_South
Land_GrazeSuitability_Cattle_S
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for animal grazing (Cattle) in selected areas insouthern part of Somalia (Juba and Shabelle riverine areas).
SOM_Cropland_GFSAD_30m_2015
southern
graze
somalia
cattle
agri-suitability
EPSG:4326
CRS:84
41.8916931152344
46.1500358581543
-0.266810089349747
5.06004571914673
other
other
text/xml
other
other
other
other
geonode:GrazeSuitabilityGoats_N
Land_GrazeSuitability_Goats_N
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for animal grazing (Goats) in selected areas in northern part of Somalia (Garbiley and Boroma areas in Somaliland).
SOM_Cropland_GFSAD_30m_2015
graze
agri-suitability
goats
northern
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:GrazeSuitabilitySheep_North
Land_GrazeSuitability_Sheep_N
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for animal grazing (Sheep) in selected areas in northern part of Somalia (Garbiley and Boroma areas in Somaliland).
SOM_Cropland_GFSAD_30m_2015
sheep
graze
agri-suitability
north
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:Growing_Period_Length
Growing Periods of Crops in Somalia
Somalia Crops Growing Length Periods based on the two rainy seasons in Somalia GU and Deyr.
Credits : FAOSWALIM
growing
lengths
period
somalia
EPSG:4326
CRS:84
40.9899444580078
51.4141273498535
-1.6491996049881
11.9859428405762
other
other
text/xml
other
other
other
other
geonode:Harbors_UNSOS
Harbors_UNSOS
The source of this data is United Nations Support Office in Somalia (UNSOS)
somalia
harbors
EPSG:4326
CRS:84
42.5462608337402
49.8177337646484
-0.360290020704269
11.2836608886719
other
other
text/xml
other
other
other
other
geonode:Horn_of_Africa_Roads_UNSOS
Horn_of_Africa_Roads_UNSOS
The layer contains roads data for the Horn of Africa. United Nations Support Office in Somalia (UNSOS) has collected this data from sources such as ICPAC_IGAD_UNOSAT, then UNSOS has made some updates.
mogadishu_city_roads
Horn_of_Africa_Roads_UNSOS
africa
horn
EPSG:4326
CRS:84
33.5427780151367
51.3665084838867
-4.6031813621521
14.7212266921997
other
other
text/xml
other
other
other
other
geonode:HydroMet_Stations_July_2021
Somalia Hydromet Stations 2021
There are only two perennial rivers in Somalia; the Juba and the Shabelle. Due to the arid and semi-arid nature of the northern parts of the country, the rivers are dry and flashy with water flowing for few hours and there are no river gauging stations in these rivers. Before 1991, the Ministry of Agriculture was mandated to operate the river flow and climate gauging stations. However, the network collapsed following the break of civil war in the early nineties. No data was collected between 1991 and 2001/2. SWALIM re-established the network in 2001/2 and by installing new stations in some areas and the network has continued to expend progressively. Out of the 14 river gauging stations that were operational before the war only eight stations are currently operational; four on the Shabelle and four on the Juba. The climate monitoring network has over 100 stations across Somalia. FAO SWALIM has partnered with the Ministry of Energy and Water Resources (MOEWR) and Ministry of Agriculture (MOA) in river and climate data collection respectively. However, under the current context with federal governments and regions, the Puntland ministry of Environment, Agriculture and climate change and the Somaliland Ministry of Agricultural development are responsible for climate data in their respective regions.
Hydromet
EPSG:4326
CRS:84
42.0793800354004
50.8117980957031
-0.35004335641861
11.9653205871582
other
other
text/xml
other
other
other
other
geonode:Jowhar_Potential_Suitable_Higher_Grounds_for_
Jowhar District Suitable Potential Higher Grounds for Evacuation During Floods
Datasets used for the analysis are :- Flood Extent, Elevation, Slope, Topographic Wetness Index (TWI),Drainage Density, Strategic Boreholes, Settlements, Land Cover and Roads
Based on a predefined criteria, each of these datasets were first clipped to areas outside the flood extent but within a maximum distance of 10 Km from the flood extent boundary. The outputs were then reclassified into two classes namely; Suitable and Not Suitable. Using an iterative approach, each of these datasets was used to assign a weight to the suitability of a site. These weights are indicated on table 2 below. Thereafter, a multi-criteria evaluation weighted overlay operation was undertaken to determine the potential areas for evacuation sites. An area was then computed for the potential sites and their distance from the flood extent established.
The output was then post – processed to remove invalid geometries and each site was ranked based on the distance from the flood prone area. Sites that are closer in distance were deemed to be most suitable hence a higher rank.
The final layer was then visually checked based on the density of the topographic Wetness Index (TWI) which is based on runoffs during floods. Areas with dense TWI were eliminated to remain with only continuous areas.
elnino
floods
jowhar
somalia
evacuation
higher
grounds
EPSG:4326
CRS:84
45.1299209594727
45.9387321472168
2.73575496673584
3.23830485343933
other
other
text/xml
other
other
other
other
geonode:Land_CoverNorth
Land Cover of part of Somaliland
The Methodology adopted for land cover classes involved processing of satellite images of Landsat, Aster and Ikonos.This was followed by photointerpretation and and production of basemaps by SWALIM. Field data collection was then carried out and the maps then revised. Please refer to FAO-SWALIM Technical report number 03 for more information on this.
Land_CoverNorth
EPSG:32638
CRS:84
43.007251089913694
44.46284752986155
9.162947876314933
10.691058244859452
other
other
text/xml
other
other
other
other
geonode:Land_ForestSuitability_AcaciaNilotica_N
Forest Suitability of AcaciaNilotica in North Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species Acacia Niloticain selected areas in northern part of Somalia (Garbiley and Boroma areas in Somaliland).
north
agri-suitability
Land_ForestSuitability_AcaciaNilotica_N
forest
somalia
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:Land_ForestSuitability_BalanitesAegyptiaca_N
Forest Suitability of BalanitesAegyptiaca in North Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species Balanites Aegyptiacain selected areas in northern part of Somalia (Garbiley and Boroma areas in Somaliland).
somalia
agri-suitability
north
Land_ForestSuitability_BalanitesAegyptiaca_N
forest
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:Landcover_2000
Landcover_FAO_Africover2000
The land cover has been produced from visual interpretation of digitally enhanced LANDSAT TM images (Bands 4,3,2) acquired mainly in the year 1995 - 1998. The land cover classes have been developed using the FAO/UNEP international standard LCCS classification system. The purpose of the Africover landcover database is to provide the information required for natural resource assessment and management, environmental modeling and decision-making. The LCCS legend for the country, a list of the LCCS classifiers used in the interpretation, LCCS glossary of terms, a list of thematic classes and their frequency in a mixed unit, and a report on the frequency and scale adequacy, can be found in the full resolution landcover database report found in the same folder as the dataset. FAO Project code GCP/RAF/287/ITA
africover
Landcover_2000
lancover
EPSG:4326
CRS:84
40.9886245727539
51.4127235412598
-1.66718971729279
11.9921894073486
other
other
text/xml
other
other
other
other
geonode:Landcover_IGAD_FAO_Gedo_B1_2016
Landcover of Gedo Region_B1_2016
During the implementation of the FAO-IGAD partnership, SWALIM developed a trans-boundary land cover map of the Gedo region.
Landcover_IGAD_FAO_Gedo_B1_2016
EPSG:4326
CRS:84
41.8427505493164
42.8222885131836
3.86505961418152
4.26749706268311
other
other
text/xml
other
other
other
other
geonode:Landcover_IGAD_FAO_Gedo_B2_2016
Landcover of Gedo Region_B2_2016
During the implementation of the FAO-IGAD partnership, SWALIM developed a trans-boundary land cover map of the Gedo region.
Landcover_IGAD_FAO_Gedo_B2_2016
EPSG:4326
CRS:84
41.0674057006836
42.3123207092285
2.88408231735229
3.62710285186768
other
other
text/xml
other
other
other
other
geonode:Landcover_MangroveForest0
Mangrove Forest Land Cover
Patches of mangroves are found in Zeylac, Berbera and Calula on the northern coastline and in Kismayo on the southern coast. Mangrove swamp communities are also situated at the tidal estuaries of the seasonal rivers towards the Indian Ocean coast and Gulf of Aden. They areas include Bushbush, Caanoole and Lag Badanaa. The mangroves, like the Golis forest, are facing increased pressure of commercial exploitation.
landcover
forest
mangrove
EPSG:4326
CRS:84
41.9597282409668
50.8089790344238
-1.01547586917877
11.9874649047852
other
other
text/xml
other
other
other
other
geonode:Landcover_NE_1988
Landcover North East Somalia 1988
The land cover has been produced from visual interpretation of LANDSAT images.
Landcover_NE_1988
landcover
east
north
somalia
EPSG:4326
CRS:84
48.0100288391113
49.4115791320801
7.05845880508423
9.90776824951172
other
other
text/xml
other
other
other
other
geonode:Landcover_NE_2007
Landcover North East Somalia 2007
Land cover classes were created using the Land Cover Classification System (LCSS) of FAO, Landsat satellite image interpretation, and field validation.
landcover
east
north
somalia
Landcover_NE_2007
EPSG:4326
CRS:84
48.0100250244141
49.4115791320801
7.05845880508423
9.90776824951172
other
other
text/xml
other
other
other
other
geonode:Landcover_NW_2007
Landcover North West Somalia 2007
Land cover classes were created using the Land Cover Classification System (LCSS) of FAO, Landsat satellite image interpretation, and field validation.
landcover
west
north
somalia
Landcover_NE_2007
EPSG:4326
CRS:84
43.0110092163086
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:Landcover_Owdweyne_Burao
Landcover_Owdweyne_Burao
Land cover classes were created using the Land Cover Classification System (LCSS) of FAO, satellite image interpretation, and field validation. The data contain a description of the main land cover types and vegetation units.
landcover
burao
owdwenye
EPSG:4326
CRS:84
44.6907386779785
46.2508697509766
8.25667667388916
10.0998477935791
other
other
text/xml
other
other
other
other
geonode:Landcover_Riverine_2007
Landcover of Riverine Areas - 2007
Land cover is the physical material at the surface of the earth. Land cover includes grass, asphalt, trees, bare ground, water, etc. The land cover map of the riverine areas forms the most recent, most detailed and most consistent data set produced by SWALIM. The map is result of exhaustive aerial photograph interpretation by SWALIM experts. However, the map gives more attention to the cropped areas, giving the crop type and acreage. The final map has been aggregated into few manageable land cover classes. Among the striking features of the map are the areas that were once cropped and now remain abandoned.
landcover
EPSG:4326
CRS:84
41.2801971435547
46.1500358581543
-0.510218977928162
5.06004571914673
other
other
text/xml
other
other
other
other
geonode:LowerShabelle_Agrimask_UTM_Edited_13102022
LowerShabelle_Agrimask_UTM_Edited_13102022
No abstract provided
Agrimask_Galgaduud_20th_Sept
EPSG:32638
CRS:84
44.09901775715686
45.38770497597518
1.244572994188305
3.2577344659402065
other
other
text/xml
other
other
other
other
geonode:Lower_shabeelle_shappfille_Hersi
Lower_shabeelle_shappfille_Hersi
No abstract provided
features
Lower_shabeelle_shappfille_Hersi
EPSG:32638
CRS:84
44.08747890050593
44.64504225655606
1.2445686439330668
1.7129801624621503
other
other
text/xml
other
other
other
other
geonode:Luuq_PotentialSuitableHigherGround_forEvacuat
Luuq District Potential Suitable Higher Grounds For Evacuation During Flooding
Datasets used for the analysis are :- Flood Extent, Elevation, Slope, Topographic Wetness Index (TWI),Drainage Density, Strategic Boreholes, Settlements, Land Cover and Roads
Based on a predefined criteria, each of these datasets were first clipped to areas outside the flood extent but within a maximum distance of 10 Km from the flood extent boundary. The outputs were then reclassified into two classes namely; Suitable and Not Suitable. Using an iterative approach, each of these datasets was used to assign a weight to the suitability of a site. These weights are indicated on table 2 below. Thereafter, a multi-criteria evaluation weighted overlay operation was undertaken to determine the potential areas for evacuation sites. An area was then computed for the potential sites and their distance from the flood extent established.
The output was then post – processed to remove invalid geometries and each site was ranked based on the distance from the flood prone area. Sites that are closer in distance were deemed to be most suitable hence a higher rank.
The final layer was then visually checked based on the density of the topographic Wetness Index (TWI) which is based on runoffs during floods. Areas with dense TWI were eliminated to remain with only continuous areas.t provided
features
Luuq_PotentialSuitableHigherGround_forEvacuationFromFloods
EPSG:4326
CRS:84
42.3321418762207
42.6777610778809
3.48064088821411
4.08309459686279
other
other
text/xml
other
other
other
other
geonode:Middle_Juba_Agrimask_11302020utm
Som_Middle_Juba_Agrimask_11302020utm
This classification was done using LCCS3 legend as a framework for classifying agriculture. LCCS3 is the last version of the Land Cover Classification System (LCCS) developed by FAO and UNEP in 1998 to facilitate the understanding of theclasses of land cover regardless of the scale of mapping, the type of coverage,method of data collection, or geographic location.Sentinel 2 images were downloaded and segmented using ecognition software.The segments were then classified using LCCS legend.During the classification, photo keys of different agricultural classes created on google earth was used as visual verification of class types before classification, given that sentinel 2 images has a resolution of 30m and therefore not clear enough to visually detect differences between classes.In additon , High Resolution Google and Esri satellite basemaps were used for verification.The scale of editing and digitization of polygons was 1:10000
feature
EPSG:32638
CRS:84
41.990804286629
43.43235765662859
0.31172370241605246
1.9537598682419517
other
other
text/xml
other
other
other
other
geonode:Middle_Shabelle_Region_UTM38_17102022_V2
Middle_Shabelle_Region_UTM38_17102022_V2
No abstract provided
AgriMask_Ahmed_AoI
EPSG:32638
CRS:84
45.10117340126191
46.8571186723154
2.150586486094565
3.866666438533094
other
other
text/xml
other
other
other
other
geonode:Middle_Shabelle_T38NNH_20180130T071129_Liban_
Middle_Shabelle_T38NNH_20180130T071129_Liban_
No abstract provided
feature
EPSG:32638
CRS:84
45.636161082784916
45.95959852939548
2.2063218040057633
2.715708444718259
other
other
text/xml
other
other
other
other
geonode:Middle_Shabelle_T38NNJ_20180130T071129_Liban_
Middle_Shabelle_T38NNJ_20180130T071129_Liban_
No abstract provided
feature
EPSG:32638
CRS:84
45.42473543001519
45.890848072074284
2.6503646945103094
3.226048904973538
other
other
text/xml
other
other
other
other
geonode:Mogadishu_Adminbnda_Districts_UNOCHA
Administrative Districts Within Mogadishu
Digital city map of Mogadishu based on local city maps, NIMA city maps, Landsat ETM+ and QB-images
districts
mogadishu
EPSG:4326
CRS:84
45.2295188903809
45.4330406188965
1.97436988353729
2.09987998008728
other
other
text/xml
other
other
other
other
geonode:Pre_GU_2024_FRMMIS_March_2024
Status of River Breakages along Juba and Shabelle Rivers - Issued March 2024
FAO-SWALIM has updated the status of the river breakages along the Juba and Shabelle Rivers using available Very High Resolution (VHR) satellite imagery and a Digital Elevation Model (DEM). Four types of breakages have been identified, namely; open, overflow, Canal Flooding Point, and closed with sandbags. The open breakages are those that are currently open as observed on the latest VHR image available. All the observations reported refers to the latest suitable VHR satellite image available, which is indicated in the online database.
299 Open breakage points have been identified, 190 on the Shabelle River and 109 on the Juba River which require immediate action. Additionally, 97 Canal Overflow points and 747 Overflows were also identified during this season.
Users are advised that the methodology is biased towards Remote Sensing (RS) interpretation with only limited “ground truthing” due to access constraints. Open breakages might have been omitted in some cases where satellite images may not have been very clear (e.g. heavy cloud cover) or were not available.
august
breakages
juba
Pre_GU_2024_FRMMIS_March_2024
hirshabelle
somalia
river
EPSG:4326
CRS:84
1.46237993240356
45.6786727905273
-0.096370004117489
4.89294004440308
other
other
text/xml
other
other
other
other
geonode:Proscal_Charcoal_sites_FAOSWALIM2018
Proscal Charcoal sites - 2018
The dataset shows the dynamics of charcoal production in Somalia from 2011 to 2017. Multi-temporal datasets of very high-resolution remote sensing images such as WorldView-1, 2 and 3 were used to map kiln locations in the study area. The acquisition dates of the images range from 2011 to 2017. Charcoal production sites can be seen on satellite images as dark round/ almost round patches. Many small tracks/ paths are a common feature at these sites. They are used for access and transport. The average radius of the kilns is 3.3m but some are as big as 6m in radius.Multi temporal analysis of very high resolution images reveals a tremendous increase of charcoal sites over the years.
proscal
charcoalsites_2018_2019_Benard
EPSG:4326
CRS:84
41.177375793457
43.1609954833984
-1.57558763027191
1.37009334564209
other
other
text/xml
other
other
other
other
geonode:Relief_NW_FAOSWALIM
Relief_NW_FAOSWALIM
This data is about Somalia relief and landscape which represents information about elevations and landforms of north western part of Somalia. Data on topography from NASA SRTM 90 m.
west
north
relief
somalia
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:River_Juba_Shabelle_Breakage_Apr2023_18042022
Status of River Breakages along Juba and Shabelle Rivers - Issued March 2023
SWALIM has updated the status of the river breakages along the Juba and Shabelle Rivers using available Very High Resolution (VHR) satellite imagery acquired from World View 2/World View 3 and PLEIADES and a Digital Elevation Model (DEM).
Six types of breakages have been identified, namely; Canal Flooding Point, open, overflow, potential overflows, potential breakages and closed with sandbags. The open breakages are those that are currently open as observed on the latest VHR image available. All the observations reported refers to the latest suitable VHR satellite image available, which is indicated in the online database.
65 Open breakage points have been identified, 39 on the Shabelle River and 26 on the Juba River which require immediate action. 15 Overflows were also identified during this season.
Users are advised that the methodology is biased towards Remote Sensing (RS) interpretation with only limited “ground truthing” due to access constraints. Open breakages might have been omitted in some cases where satellite images may not have been very clear (e.g. heavy cloud cover) or were not available.
River_Juba_Shabelle_Breakage_Apr2023_18042022
Rivers
EPSG:4326
CRS:84
42.0680885314941
45.6786727905273
-0.070970006287098
4.89294004440308
other
other
text/xml
other
other
other
other
geonode:River_Juba_Shabelle_Breakage_Aug2021_26082021
Status of River Breakages Along Juba and Shabelle Rivers - Issued August 2021
SWALIM has been mapped twice a year the river breakages along the two rivers since 2015. This had been made possible with the use of World View Very High-Resolution (VHR) satellite imagery provided by Digital Globe. Due to lower temporal resolution of images present in Digital Globe archive, Pleiades Images provided by Airbus supplemented the image gaps in some areas. Where possible the preliminary findings from the images were verified with field observations to confirm the status of river breakages. In this case, the field river breakages surveys were carried out in Belet Weyne, Jowhar, Balcad and Afgooye districts all along the Shabelle River. In other cases, breakages which had been verified on VHR in March 2021 and there was either cloudy images or no images at all in the current assessment have been marked as ‘Not verified’.
Along the Juba River, 32 open points, 6 overflows and another 123 potential breakages and 75 potential overflows were identified. Further, 103 open points, 106 overflows and 103 potential breakages and 322 potential overflows were identified along the Shabelle River.
It has been observed that the number of points either open or overflows has been increasing over the years probably due to the continuous weakening of the river banks following three years of successive heavy floods in 2018, 2019 and 2020. These points need immediate closure or reinforcement before the 2021 Deyr rainy season which is expected to start in Mid-October 2021.
Revised_middlejuba_Mohamed_Sufi1
august
rivers
somalia
Shabellebreakages_Aug2019
Middal_shabelle_LYR2
HydroMet_Stations_July_2021
EPSG:4326
CRS:84
42.0680885314941
45.6786727905273
-0.070970006287098
4.89294004440308
other
other
text/xml
other
other
other
other
geonode:River_Juba_Shabelle_Breakage_September_2023
Status of River Breakages along Juba and Shabelle Rivers - Issued October 2023
FAO-SWALIM has updated the status of the river breakages along the Juba and Shabelle Rivers using available Very High Resolution (VHR) satellite imagery and a Digital Elevation Model (DEM). Four types of breakages have been identified, namely; Open, Overflow, Canal Flooding Point, and Closed with sandbags. The open breakages are those that are currently open as observed on the latest VHR image available. All the observations reported refers to the latest suitable VHR satellite image available, which is indicated in the online database.
196 Open breakage points have been identified, 170 on the Shabelle River and 26 on the Juba River which require immediate action. 168 Overflows were also identified during this season.
features
River_Juba_Shabelle_Breakage_September_2023
EPSG:4326
CRS:84
42.0680885314941
45.6786727905273
-0.070970006287098
4.89294004440308
other
other
text/xml
other
other
other
other
geonode:River_Juba_Shabelle_Breakages_Feb_2021
Status of River Breakages Along Juba and Shabelle Rivers - Issued March 2021
Along the Juba River, 46 open points, 8 overflows and another 65 potential overflows were identified. The Juba River assessment also identified over 100 potential breakage points. Further, the team have identified 57 open points, 225 overflows and 74 potential overflows along the Shabelle River. These points need immediate closure or reinforcement before the Gu rainy season which is expected to start in Mid-April 2021.
It is worth noting that due to limited availability of VHR images, there has been a delay in completing the assessment of Shabelle River breakages. FAO SWALIM is in the process of procuring images to cover the assessment gap and partners will be notified once the finalized product is ready.
Revised_middlejuba_Mohamed_Sufi1
march
rivers
Shabellebreakages_Aug2019
Middal_shabelle_LYR2
HydroMet_Stations_July_2021
EPSG:4326
CRS:84
42.0680885314941
45.6786727905273
-0.070970006287098
4.89294004440308
other
other
text/xml
other
other
other
other
geonode:SOM_ASTER_ImageIndex_Granule
ASTER_ImageIndex_Granule_Somalia
Image Index: it contains feature classes that contain information about the path/row, scene boundaries and the geographic coordinates of the imageries acquired by satellites for Somalia. These indices help in identifying the numbers of images covering Somaliaand are easy guides for identifying searching for the required images.
index
image
SOM_ASTER_ImageIndex_Granule
granule
somalia
EPSG:4326
CRS:84
40.9323272705078
48.464958190918
-0.779859006404877
11.7152366638184
other
other
text/xml
other
other
other
other
geonode:SOM_Adminbnda_Adm0_Country_UNOCHA
Somalia Administrative Boundary
Somaliacountry administrative boundaryadmin 0 (Country of Somalia). The source of this data is United Nation Development Programme (UNEP) 1998. UNOCHA is a contributor.
boundary
administrative
somalia
EPSG:4326
CRS:84
40.9943161010742
51.4130363464355
-1.66491448879242
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:SOM_Adminbnda_Adm1_States_UNOCHA
Administrative States Within Somalia
Somalia country administrative boundary admin 1(States of Somalia). The source of this data is United Nation Development Programme (UNEP) 1998. UNOCHA is contributor.
states
somalia
EPSG:4326
CRS:84
40.9886322021484
51.415153503418
-1.65423834323883
11.9883642196655
other
other
text/xml
other
other
other
other
geonode:SOM_Adminbnda_Adm2_Regions_UNOCHA
Administrative Regions Within Somalia
Somalia country administrative boundary admin2 (Regionsof Somalia). The source of this data is United Nation Development Programme (UNEP) 1998. UNOCHA is contributor.
SOM_Adminbnda_Adm2_Regions_UNOCHA
features
EPSG:4326
CRS:84
40.9943161010742
51.4130363464355
-1.66491448879242
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:SOM_Adminbnda_Adm3_Districts_UNOCHA
Administrative Districts of Somalia
Somalia country administrative boundary admin3 (Districts of Somalia). The source of this data is United Nation Development Programme (UNEP) 1998. UNOCHA is contributor.
Mogadishu_Adminbnda_Districts_UNOCHA
Somalia
EPSG:4326
CRS:84
40.9943161010742
51.4130363464355
-1.66491448879242
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:SOM_Adminbnda_Neighbouring_Countries
Countries Neighboring Somalia
Administrative boundaries of Somalia neighbouring countries
Neighboring
Somalia
countries
EPSG:4326
CRS:84
32.9999389648438
54.5305290222168
-4.67804670333862
19.0023326873779
other
other
text/xml
other
other
other
other
geonode:SOM_Contours_UNSOS
SOM_Contours_UNSOS
The Shuttle Radar Topography Mission (SRTM) obtained 90-meter (3 arc-second) resolution data on a near-global scale (between 56 degrees South and 60 degrees North latitude) and 30 meter (1 arc-second) resolution over United States, providing a valuable global topographic dataset. The SRTM data were collected during an 11-day mission in February of 2000 from a radar system onboard the Space Shuttle Endeavor.
somalia
SOM_Airfields_UNSOS
contours
EPSG:4326
CRS:84
40.9890441894531
51.4123802185059
-1.6579167842865
11.9800539016724
other
other
text/xml
other
other
other
other
geonode:SOM_Cropland_GFSAD_30m_2015
Somalia Cropland GFSAD 30m 2015
The GFSAD30 is a NASA funded project to provide high resolution global cropland data and their water use that contributes towards global food security in the twenty-first century. The Global Food Security-support Analysis Data (GFSAD) 30meter Cropland Extent Africa Nominal 2015 products are derived through multi-sensor remote sensing data (e.g., Landsat, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland dynamics from 1990 to 2017
landcover
cropland
EPSG:4326
CRS:84
41.0053596496582
50.4005165100098
-1.62801682949066
11.2848615646362
other
other
text/xml
other
other
other
other
geonode:SOM_DJ_KE_ETH_Airstrips
SOM_DJ_KE_ETH_Airstrips
No abstract provided
SOM_DJ_KE_ETH_Airstrips
features
EPSG:4326
CRS:84
-3.40282E38
51.3264274597168
-3.40282E38
12.4404792785645
other
other
text/xml
other
other
other
other
geonode:SOM_Drainage_Network_Extracted_From_OSM
Somalia Drainage Network (Extracted From OpenStreetMap)
Drainage network extracted from OpenStreetMap. For more information about the data, visit --> https://wiki.openstreetmap.org/wiki/Tag:waterway%3Driver
openstreetmap
network
SOM_Drainage_Network_Extracted_From_OSM
rivers
somalia
drainage
EPSG:4326
CRS:84
40.9890975952148
51.321174621582
-1.32933104038239
11.960428237915
other
other
text/xml
other
other
other
other
geonode:SOM_FLOODS_ELNINO_DEYR_2023
Juba Shabelle River Floods - Elnino - Deyr 2023
This data contains modified flood extent from:
1. Global Flood Monitoring retrieved on 14/11/2023,
2. World View 3 Image of 16/11/2023,UNOSAT Analysis 05 Nov - 03 Dec 2023
3. Sentinel 2 image of (15,20,30)/11 and (5,10) /12/ 2023.
The Flood Extent may be smaller than the actual due to cloud cover on analysis images that hindered assessment in some areas and limitations of radar to detect flooding within urban areas
deyr
floods
`elnino
`2023
somalia
EPSG:4326
CRS:84
40.9956474304199
48.3404655456543
-1.62330758571625
6.01075601577759
other
other
text/xml
other
other
other
other
geonode:SOM_Flood_Prone_Areas_FAOSWALIM2018
Somalia Flood Prone Areas (2018)
Somalia experiences two types of flooding: river floods and flash floods. River floods occur along the Juba and Shabelle rivers in Southern Somalia, whereas flash floods are common along the intermittent streams in the northern part of the country.in the recent past, the country has experienced an increasing severity and frequency of floods. The historically most recent severe floods were those of the Deyr in 1961, 1977, 1997, and 2006, and the floods of the Gu in 1981 and 2005. These floods resulted in human casualties and major economic damage.Whereas flash floods in Somalia result from localised rains, river flooding along the Juba and Shabelle rivers are primarily due to drainage from catchment areas located in the Ethiopian highlands, which normally experience heavier and more frequent rainfall than what occurs in Somalia.
floods
Agricultural_Flooded_Areas_25May_3June2018
prone areas
EPSG:4326
CRS:84
42.0785179138184
45.7423629760742
-0.105086475610733
5.01130962371826
other
other
text/xml
other
other
other
other
geonode:SOM_Flooded_Agriculture_Areas_FAOSWALIM2018
Flooded Agricultural Areas in Somalia (2018)
Somalia river floods occur along the Juba and Shabelle rivers in Southern Somalia. The layer shows the agricultural areas affected by floods in 2018.
floods
Agricultural_Flooded_Areas_25May_3June2018
agriculture
EPSG:4326
CRS:84
42.0846633911133
45.7423629760742
0.289786845445633
4.99804258346558
other
other
text/xml
other
other
other
other
geonode:SOM_Functioning_Boreholes_2010
Functioning Boreholes - 2010
Somalia Functioning Boreholes as of 2010
SOM_Functioning_Boreholes_2010
features
EPSG:4326
CRS:84
41.336498260498
51.0722236633301
0.95007997751236
11.8141660690308
other
other
text/xml
other
other
other
other
geonode:SOM_Hydro1K_GTOPO30_Basins
SOM_Hydro1K_GTOPO30_Basins
HYDRO1k is a geographic database developed to provide comprehensive and consistent global coverage of topographically derived data sets, including streams, drainage basins and ancillary layers derived from the USGS' 30 arc-second digital elevation model of the world (GTOPO30). HYDRO1k provides a suite of geo-referenced data sets, both raster and vector, which will be of value for all users who need to organize, evaluate, or process hydrologic information on a continental scale developed at the U.S. Geological Survey's EROS Center, the HYDRO1k project's goal is to provide to users, on a continent by continent basis, hydrologically correct DEMs along with ancillary data sets for use in continental and regional scale modeling and analyses.
features
SOM_Hydro1K_GTOPO30_Basins
EPSG:4326
CRS:84
39.4746780395508
51.4504890441895
-2.07890725135803
11.9967565536499
other
other
text/xml
other
other
other
other
geonode:SOM_Hydro_CrossBorder_Basins
SOM_Hydro_CrossBorder_Basins
Somalia cross boundary basin data, is a geographic database developed to provide comprehensive and consistent global coverage of topographically derived data sets, including streams, drainage basins and ancillary layers derived from the USGS' 30 arc-second digital elevation model of the world (GTOPO30). HYDRO1k provides a suite of geo-referenced data sets, both raster and vector, which will be of value for all users who need to organize, evaluate, or process hydrologic information on a continental scale developed at the U.S. Geological Survey's EROS Center, the HYDRO1k project's goal is to provide to users, on a continent by continent basis, hydrologically correct DEMs along with ancillary data sets for use in continental and regional scale modeling and analyses.
SOM_Hydro_CrossBorder_Basins
features
EPSG:4326
CRS:84
36.2761077880859
51.330753326416
-1.81083428859711
11.9967555999756
other
other
text/xml
other
other
other
other
geonode:SOM_IDPs_UNHCR_2007
Somalia_IDPs_UNHCR_2007
Somalia IDPs camp locations compiled by UNHCR 2007
SOM_IDPs_UNHCR_2007
idps
somalia
unhcr
EPSG:4326
CRS:84
43.6321983337402
49.1979217529297
2.00118279457092
11.2864084243774
other
other
text/xml
other
other
other
other
geonode:SOM_International_Disputed_Border
Somalia International Disputed Border
Somalia International Disputed Border
disputed
border
somalia
international
EPSG:4326
CRS:84
32.9999389648438
54.5305290222168
-4.67648553848267
19.0023326873779
other
other
text/xml
other
other
other
other
geonode:SOM_Irrigation_Canals_FAOSWALIM2018
Irrigation Canals in Somalia (2018)
In pre-war Somalia (before 1990), large-scale irrigation schemes existed along the Juba and Shabelle basins. These controlled irrigation systems consisting of barrages, canals and other infrastructure, were constructed in the middle and lower reaches of both rivers. The canal system comprised primary and secondary canals, and numerous tertiary canals, with water flow controlled by barrages or weirs. Pumped irrigation systems also existed, especially along the Juba river where large pumps were used to take water from the river to other network of canals. Before the collapse of the government, the Somali Ministry of Agriculture estimated that 112,950ha was under controlled irrigation, while 110,000ha was under flood recession irrigation (cultivation along the edge of rivers or other water bodies using water from receding floods), giving a total irrigated area in the country of 222,950ha. Currently, the majority of this infrastructure is not functioning, and the area under irrigation has been significantly reduced
Agricultural_Flooded_Areas_25May_3June2018
irrigation
canals
somalia
EPSG:4326
CRS:84
42.7330169677734
45.6817932128906
0.1646728515625
4.96943092346191
other
other
text/xml
other
other
other
other
geonode:SOM_Irrigation_Schemes_FAOSWALIM0
Somalia Irrigation Schemes
In pre-war Somalia (before 1990), large-scale irrigation schemes existed along the Juba and Shabelle basins. These controlled irrigation systems consisting of barrages, canals and other infrastructure, were constructed in the middle and lower reaches of both rivers. Before the collapse of the government, the Somali Ministry of Agriculture estimated that 112,950ha
SOM_Irrigation_Schemes_FAOSWALIM
features
EPSG:4326
CRS:84
43.6981086730957
45.6001205444336
1.15576255321503
4.82818841934204
other
other
text/xml
other
other
other
other
geonode:SOM_Jilib_Charcoal_Production_FAOSWALIM
Charcoal Production Sites in Jilib_Somalia
The data of charcoal production sites in Jilib - Somalia is produced by Somalia Water and Land Information Management (SWALIM) project. Very high resolution satelite images of 2011 - 2013 were used for mapping the charcoal production sites.
jilib
Charcoalsites_2018_2019_Antony
production
charcoalsites_2018_2019_Benard
EPSG:4326
CRS:84
42.8607406616211
42.8797569274902
0.520925223827362
0.532836496829987
other
other
text/xml
other
other
other
other
geonode:SOM_Juba_Shabelle_River_Floods_Deyr_2013
Juba Shabelle River Floods - Deyr 2013
This data was produced through visual interpretation of Landsat 8 satellite images acquired between 25th September 2013 – 28th November 2013
SOM_Juba_Shabelle_River_Floods_Deyr_2013
features
EPSG:4326
CRS:84
42.0880928039551
45.6903800964355
-0.078708976507187
4.18193578720093
other
other
text/xml
other
other
other
other
geonode:SOM_Juba_Shabelle_River_Floods_Deyr_20180
Juba Shabelle River Floods - Deyr 2018
This data was produced through visual interpretation of Sentinel 2 satellite images acquired between 20th October 2018– 04th November 2018
SOM_Juba_Shabelle_River_Floods_Deyr_2018
features
EPSG:4326
CRS:84
42.0785179138184
45.7423629760742
-0.105086475610733
5.07024192810059
other
other
text/xml
other
other
other
other
geonode:SOM_Juba_Shabelle_River_Floods_Deyr_2019
Juba Shabelle River Floods - Deyr 2019
This data was produced through visual interpretation of Sentinel 2 satellite images acquired between 10th October 2019 to 16th November 2019.
SOM_Juba_Shabelle_River_Floods_Deyr_2019
features
EPSG:4326
CRS:84
42.0889892578125
45.7178192138672
-0.088775739073753
5.01130962371826
other
other
text/xml
other
other
other
other
geonode:SOM_Juba_Shabelle_River_Floods_Gu_2015
Juba Shabelle River Floods - Gu 2015
This data was produced through visual interpretation of Landsat 8 satellite images acquired between 8th April 2015– 26th May 2015
SOM_Juba_Shabelle_River_Floods_Gu_2015
features
EPSG:4326
CRS:84
42.0899276733398
45.6255798339844
-0.23110793530941
4.66833543777466
other
other
text/xml
other
other
other
other
geonode:SOM_Juba_Shabelle_River_Floods_Gu_2016
Juba Shabelle River Floods - Gu 2016
This data was produced through visual interpretation of Sentinel 2 satellite images acquired between 26th May 2016 -13th June 2016
SOM_Juba_Shabelle_River_Floods_Gu_2016
features
EPSG:4326
CRS:84
42.0529479980469
45.7257194519043
-0.15198515355587
5.01072788238525
other
other
text/xml
other
other
other
other
geonode:SOM_Juba_Shabelle_River_Floods_Gu_20180
Juba Shabelle River Floods - Gu 2018
This data was produced through visual interpretation of Sentinel 2 satellite images acquired between 23rd April 2018 to 10th May 2018
SOM_Juba_Shabelle_River_Floods_Gu_2018
features
EPSG:4326
CRS:84
42.0785179138184
45.7423629760742
-0.105086475610733
5.07024192810059
other
other
text/xml
other
other
other
other
geonode:SOM_Juba_Shabelle_River_Floods_Gu_2019
Juba Shabelle River Floods - Gu 2019
This data was produced through visual interpretation of Sentinel 2 satellite images acquired between 3rd May 2019 to 14th June 2019.
SOM_Juba_Shabelle_River_Floods_Gu_2019
features
EPSG:4326
CRS:84
42.2324714660645
45.6088180541992
-0.071370653808117
4.9763560295105
other
other
text/xml
other
other
other
other
geonode:SOM_KE_ET_DJ_Somalis_Clan
Somali Clan Distribution in Neighboring Countries
Clans are mainly self-governing bodies ruled by a council of elders. Most people respect this system way more than any modern-style central government. This data shows distribution of the Somali clans in Somalia, Kenya, Ethiopia and Djibouti
Data was collected in 1999
ethiopia
djibouti
somalia
kenya
clans
distribution
EPSG:4326
CRS:84
38.4809455871582
51.4141311645508
-2.65280437469482
11.9859428405762
other
other
text/xml
other
other
other
other
geonode:SOM_Land_AgriSuitability_Citrus_S_FAOSWALIM0
Agri-Suitability of Citrus in South Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for Citrus in selected areas in southern part of Somalia (Juba and Shabelle riverine areas).
citrus
agri-suitability
somalia
south
EPSG:4326
CRS:84
41.8916931152344
46.1500358581543
-0.266810089349747
5.06004571914673
other
other
text/xml
other
other
other
other
geonode:SOM_Land_AgriSuitability_Cowpea_N_FAOSWALIM0
Agri-Suitability of Cowpea in North Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for Cowpea in selected areas inNorthern part of Somalia (Garbiley and Boroma areas in Somaliland)
SOM_Cropland_GFSAD_30m_2015
north
cowpea
suitability
somalia
agricultural
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:SOM_Land_AgriSuitability_Cowpea_N_FAOSWALIM00
SOM_Land_AgriSuitability_Cowpea_N_FAOSWALIM
features
SOM_Land_AgriSuitability_Cowpea_N_FAOSWALIM
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
geonode:SOM_Land_AgriSuitability_Maize_N_FAOSWALIM0
Agri-Suitability of Maize North Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for Maize in selected areas in Northern part of Somalia (Garbiley and Boroma areas in Somali land)
maize
agri-suitability
north
somalia
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:SOM_Land_AgriSuitability_Rice_S_FAOSWALIM0
Agri-Suitability of Rice in South Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for Rice in selected areas inSouthern part of Somalia (Juba and Shabelle riverine areas).
rice
agri-suitability
somalia
south
EPSG:4326
CRS:84
41.8916931152344
46.1500358581543
-0.266810089349747
5.06004571914673
other
other
text/xml
other
other
other
other
geonode:SOM_Land_AgriSuitability_SorghumR1_N_FAOSWALI
Agri-Suitability of SorghumR1 in North Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for SorghumR1 in selected areas in Northern part of Somalia (Garbiley and Boroma areas in Somaliland)
sorghum
agri-suitability
north
somalia
SOM_Land_AgriSuitability_SorghumR1_N_FAOSWALIM
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:SOM_Land_AgriSuitability_SorghumR2_N_FAOSWALI
Agri-Suitability of SorghumR2 in North of Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for SorghumR2in selected areas in northern part of Somalia (Garbiley and Boroma areas in Somaliland)
SOM_Land_AgriSuitability_SorghumR2_N_FAOSWALIM
agri-suitability
north
somalia
sorghum
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:SOM_Land_AgriSuitability_Sugarcane_S_FAOSWALI
Agri-Suitability of Sugarcane in South Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for Sugarcane in selected areas in southern part of Somalia (Juba and Shabelle riverine areas).
sugarcane
agri-suitability
somalia
south
EPSG:4326
CRS:84
41.8916931152344
46.1500358581543
-0.266810089349747
5.06004571914673
other
other
text/xml
other
other
other
other
geonode:SOM_Land_Degradation_ADESO_FAOSWALIM2016
Land_Degradation_ADESO_(FAOSWALIM2016)
FAO-SWALIM has carried out a national assessment in Somalia to establish the types, extent, and causes of land degradation in the country. SWALIM also studied several control efforts carried out by Somalis and their development partners, documented opportunities for upscaling successful control measures, and made recommendations for implementing land degradation monitoring in the country.
SOM_Cropland_GFSAD_30m_2015
SOM_Land_Degradation_ADESO_FAOSWALIM2016
degradation
EPSG:4326
CRS:84
46.1641311645508
51.1669692993164
6.8808069229126
11.9589567184448
other
other
text/xml
other
other
other
other
geonode:SOM_Land_Degradation_CARE_FAOSWALIM2015
Land_Degradation_CARE_(FAOSWALIM2015)
FAO-SWALIM has carried out a national assessment in Somalia to establish the types, extent, and causes of land degradation in the country. SWALIM also studied several control efforts carried out by Somalis and their development partners, documented opportunities for upscaling successful control measures, and made recommendations for implementing land degradation monitoring in the country.
SOM_Cropland_GFSAD_30m_2015
degredation
somalia
EPSG:4326
CRS:84
48.217399597168
50.1644401550293
7.91068983078003
10.0055732727051
other
other
text/xml
other
other
other
other
geonode:SOM_Land_Degradation_FAOSWALIM
Somalia Land Degradation FAOSWALIM
The data of land degradation of Somalia is produced by The Food and Agriculture Organization’s Somalia Water and Land Information Management (FAO-SWALIM) project
features
SOM_Land_Degradation_FAOSWALIM
EPSG:4326
CRS:84
40.9895057678223
51.4141273498535
-1.65278005599976
11.9886503219604
other
other
other
other
text/xml
other
other
geonode:SOM_Land_ForestSuitability_AcaciaNilotica_S_F
Forest Suitability of AcaciaNilotica in South Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species Acacia Nilotica in selected areas in southernpart of Somalia (Juba and Shabelle riverine areas).
somalia
agri-suitability
acacia
nilotica
south
forest
EPSG:4326
CRS:84
41.8916893005371
46.150032043457
-0.266810089349747
5.06004524230957
other
other
text/xml
other
other
other
other
geonode:SOM_Land_ForestSuitability_AcaciaTortilis_N_F
Forest Suitability of AcaciaTortilis in North Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species Acacia Tortilis in selected areas in northern part of Somalia (Garbiley and Boroma areas in Somaliland).
SOM_Land_ForestSuitability_AcaciaTortilis_N_FAOSWALIM
north
forest
agri-suitability
somalia
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:SOM_Land_ForestSuitability_AzardirachtaIndica
Forest Suitability of AzardirachtaIndica in North Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species Azardirachta Indica in selected areas innorthern part of Somalia (Garbiley and Boroma areas in Somaliland).
somalia
agri-suitability
north
SOM_Land_ForestSuitability_AzardirachtaIndica_N_FAOSWALIM
forest
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:SOM_Land_ForestSuitability_AzardirachtaIndica0
Forest Suitability of AzardirachtaIndica in South Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species Azardirachta Indicain selected areas in southern part of Somalia (Juba and Shabelle riverine areas).
somalia
agri-suitability
SOM_Land_ForestSuitability_AzardirachtaIndica_N_FAOSWALIM
south
forest
EPSG:4326
CRS:84
41.8916893005371
46.150032043457
-0.266810089349747
5.06004524230957
other
other
text/xml
other
other
other
other
geonode:SOM_Land_ForestSuitability_CasuarinaEquisetif
Forest Suitability of CasuarinaEquisetifolia in North Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species Casuarina Equisetifolia in selected areas in northern part of Somalia (Garbiley and Boroma areas in Somaliland).
suitability
north
forest
SOM_Land_ForestSuitability_CasuarinaEquisetifolia_N_FAOSWALIM
somalia
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:SOM_Land_ForestSuitability_ConocarpusLancifol
Forest Suitability of ConocarpusLancifolius in North Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species Conocarpus Lancifolius in selected areas innorthern part of Somalia (Garbiley and Boroma areas in Somaliland).
suitability
north
Land_ForestSuitability_AcaciaNilotica_N
somalia
SOM_Land_ForestSuitability_ConocarpusLancifolius_N_FAOSWALIM
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:SOM_Land_ForestSuitability_ConocarpusLancifol0
Forest Suitability of ConocarpusLancifolius in South Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species Conocarpus Lancifoliusin selected areas in southern part of Somalia (Juba and Shabelle riverine areas).
south
suitability
forest
SOM_Land_ForestSuitability_ConocarpusLancifolius_N_FAOSWALIM
somalia
EPSG:4326
CRS:84
41.8916893005371
46.150032043457
-0.266810089349747
5.06004524230957
other
other
text/xml
other
other
other
other
geonode:SOM_Land_ForestSuitability_DoberaGlabra_N_FAO
Forest Suitability of DoberaGlabra in North Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species Dobera Glabrain selected areas innorthern part of Somalia (Garbiley and Boroma areas in Somaliland).
suitability
somalia
north
SOM_Land_ForestSuitability_DoberaGlabra_N_FAOSWALIM
forest
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:SOM_Land_ForestSuitability_FaidherbiaAlbida_N
Forest Suitability of FaidherbiaAlbida in North Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species Faidherbia Albidain selected areas innorthern part of Somalia (Garbiley and Boroma areas in Somaliland).
suitability
SOM_Land_ForestSuitability_FaidherbiaAlbida_N_FAOSWALIM
north
forest
somalia
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:SOM_Land_ForestSuitability_N_FAOSWALIM
Forest Suitability in North Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species in selected areas innorthern part of Somalia (Garbiley and Boroma areas in Somaliland).
suitability
north
forest
somalia
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:SOM_Land_ForestSuitability_S_FAOSWALIM
Forest Suitability in South Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for forestry species in selected areas in southern part of Somalia (Juba and Shabelle riverine areas).
suitabilty
forest
south
somalia
EPSG:4326
CRS:84
41.8916931152344
46.1500358581543
-0.266810089349747
5.06004571914673
other
other
text/xml
other
other
other
other
geonode:SOM_Land_GrazeSuitability_Camels_S_FAOSWALIM
Land Graze Suitability of Camels in South Somalia
The data defines the capacity of the study area to support specific land use types. It gives the suitable areas for animal grazing (Camels) in selected areas insouthern part of Somalia (Juba and Shabelle riverine areas).
SOM_Cropland_GFSAD_30m_2015
graze
camels
suitability
somalia
south
EPSG:4326
CRS:84
41.8916931152344
46.1500358581543
-0.266810089349747
5.06004571914673
other
other
text/xml
other
other
other
other
geonode:SOM_Landcover_OasisFarms_FAOSWALIM
Oasis Farms in Puntland
Oasis farming is very common in the northern part of Somalia. Crop production by irrigation from springs and well popularly known as Oasis farming the main sources of livelihood in these areas.
Landcover_2000
EPSG:4326
CRS:84
47.5043182373047
51.0768775939941
7.97710371017456
11.3718271255493
other
other
text/xml
other
other
other
other
geonode:SOM_Landcover_Owdweyne_Burao_FAOSWALIM
Somalia Owdweyne & Burao Districts Land Cover FAOSWALIM
Land cover classes were created using the Land Cover Classification System (LCSS) of FAO, satellite image interpretation, and field validation. The data contain a description of the main land cover types and vegetation units.
SOM_Landcover_Owdweyne_Burao_FAOSWALIM
features
EPSG:4326
CRS:84
44.6907386779785
46.2508697509766
8.25667667388916
10.0998477935791
other
other
text/xml
other
other
other
other
geonode:SOM_Landcover_ProsopisPoints_NE_FAOSWALIM2017
North East Somalia Land Cover Prosopis Points 2017 FAOSWALIM
GPS Points for Prosopis Tree collected from Somaliland.FAO SWALIM has been mapping and studying the spread of Prosopis in Somalia. More recently, a survey was carried in Januray 2017 together with the Somaliland Ministry of Environment and Rural Development to map out the extent of the spread of Proposis in Somaliland. Five areas of interest were selected covering the main landform units of Somaliland that include flat lying areas, plateaus, hills, mountain ranges and coastal areas. The climate of these landscapes vary from desert (annual rainfall of 100mm and temperature of 28-35 oC) in the coastal areas to semi humid (annual rainfall of 500 - 600 mm annual rainfall and temperature of 20-22 °C) in the mountain ranges.
The survey observed that despite the extremely dry condition; Prosopis plants were exceptionally green and resistant to drought and were often flowering and with pods in most of the areas. Prosopis was found growing as trees or shrubs and also forms pure stand of forest thickets in the invaded rangelands and cropland. It was also scattered as shrubs in stony or gravelly surfaces inter growing with cactus. In the wetter alluvial deposits flanking the watercourses in the northern escarpment of the Golis Mountain, it was found to form very dense impenetrable thickets.
SOM_Landcover_ProsopisPoints_NE_FAOSWALIM2017
features
EPSG:4326
CRS:84
43.1923866271973
45.7604179382324
9.36438846588135
10.4324169158936
other
other
text/xml
other
other
other
other
geonode:SOM_Landforms_FAOSWALIM20080
Somalia Landforms (2008)
Data of landforms have been produced from FAO Africover 2000, combined with geomorphometry. Data on topography from NASA SRTM 90 m.
SOM_Cropland_GFSAD_30m_2015
SOM_Landforms_FAOSWALIM2008
somalia
EPSG:4326
CRS:84
40.9921417236328
51.4127235412598
-1.66718971729279
11.9921894073486
other
other
text/xml
other
other
other
other
geonode:SOM_Landsat8_ImageIndex_Granule
Landsat8_ImageIndex_Granule_Somalia
Image Index:it contains feature classes that contain information about the path/row, scene boundaries and the geographic coordinates of the imageries acquired by satellites for Somalia. These indices help in identifying the numbers of images covering Somaliaand are easy guides for identifying searching for the required images.
somalia
index
image
granule
landsat8
EPSG:4326
CRS:84
39.1727981567383
52.9005012512207
-2.32698011398315
12.4509000778198
other
other
text/xml
other
other
other
other
geonode:SOM_Landscape_NW_FAOSWALIM20070
Landscape_NorthWest_Somalia (2007)
The landscape data of north western part of Somalia defines the classes of topography and landscape units based on SRTM 90 m.
SOM_IDPs_UNHCR_2007
west
north
somalia
landscape
EPSG:4326
CRS:84
43.0109252929688
44.4621047973633
9.16651248931885
10.6897487640381
other
other
text/xml
other
other
other
other
geonode:SOM_Landscape_S_FAOSWALIM20070
Landscape_South Somalia (2007)
The landscape data of southern part of Somalia defines the classes of topography and landscape units based on SRTM 90 m.
south
somalia
landscape
EPSG:4326
CRS:84
41.8916931152344
46.1500358581543
-0.266810089349747
5.06004571914673
other
other
text/xml
other
other
other
other
geonode:SOM_Landuse_Suitability_FAOSWALIM0
Landuse Suitability in Somalia
Landusesuitability assessment is the process of estimating the potential of land for alternative kinds of landuse. SWALIM has undertaken detailed land suitability studies and produced a set of land suitability datafor different land usesin Somalia.
SOM_Cropland_GFSAD_30m_2015
suitability
SOM_Landuse_Suitability_FAOSWALIM
somalia
EPSG:4326
CRS:84
40.9895057678223
51.4141273498535
-1.65278005599976
11.9886503219604
other
other
text/xml
other
other
other
other
geonode:SOM_Landuse_System_FAOSWALIM2007
Somalia Landuse System (2007)
This landuse systems data is produced from FAO Landcover data for Somalia 2000.
SOM_Cropland_GFSAD_30m_2015
SOM_IDPs_UNHCR_2007
system
SOM_Landuse_Suitability_FAOSWALIM
somalia
EPSG:4326
CRS:84
40.9895057678223
51.4141273498535
-1.65278005599976
11.9886503219604
other
other
text/xml
other
other
other
other
geonode:SOM_Major_Towns
Somalia Major Towns
DATA AND INFORMATION MANAGEMENT UNIT (DIMU) ORGANIZATION: UNITED NATIONS DEVELOPMENT PROGRAMME (UNDP SOMALIA) MODIFIED BY: DATA AND INFORMATION MANAGEMENT UNIT UNDP SOMALIA MODIFICATION DATES: 22/03/2001 The towns were extratced from settlements digitized from Somalia topographic maps at a scale of 1: 100,000. The maps were produced by stereo photography using aerial photography 1974.Printed 1976 by Bureau of cartography, Ministry of Defence, Somalia Democratic government. Reprinted by Canadian Forces Mapping and Charting Establishment from reprographic material of native Somalia mapping by the British Director of Military Survey. Other settlements were collated from gazetteer data ( Defense Mapping Agency USA) and UNDOS, populations surveys 1997/98 of N.W Somalia- Middle and lower Shabelle. STATUS COMPLETE TABLES SOURCE: DATA AND INFORMATION MANAGEMENT UNIT UNDP SOMALIA SOURCE PUBLICATION DATE: 2000 ATTRIBUTE RELIABILITY: Good STATUS: COMPLETE FIELDS DESCRIPTION SETT_ID Internal Feature Number HUTS Number of huts in a settlement HOUSES Number of houses in a settlement NAME Name of the settlement PCODE Alphanumeric coding system for coding the settlements based on the region and district they belong to. CODE Code assigned to the settlements to classify them. Code definition 1 = Settlement 5 = Other town 10 = District town 100 = Regional Capital 1000 = Capital town REG_CODE Codes assigned to the regions. From 1 to 17 REGION Name of the region DISTRICT Name of the district SOURCE Source of the settlement either gazetteer, topomap or survey. PPRWARES Population pre-war estimated using number of nomadic huts. PPRWANS Population pre-war estimated using number of houses. TOP_POP Population from the topomaps. SUR_POP Field Survey populations. X COOD X coordinate of the settlement or the easting Y COOD Y coordinate of the settlement or the northing.
major
towns
somalia
EPSG:4326
CRS:84
40.998291015625
50.8122291564941
-1.03536999225616
11.965311050415
other
other
text/xml
other
other
other
other
geonode:SOM_Mogadishu_City_POI
Mogadishu Points of Interest
Digital city map of Mogadishu based on local city maps, NIMA city maps, Landsat ETM+ and QB-images etc.
of
points
mogadishu
interest
EPSG:4326
CRS:84
45.2353744506836
45.3933296203613
1.98062944412231
2.08343315124512
other
other
text/xml
other
other
other
other
geonode:SOM_Police_Stations_UNSOS2018
Police Stations in Somalia_UNSOS2018
Somalia Police Stations location as of 2018
police
somalia`
stations
EPSG:4326
CRS:84
41.0069885253906
51.2610168457031
-0.531843066215515
11.9142150878906
other
other
text/xml
other
other
other
other
geonode:SOM_Population_PESS_2014
Somalia Population_PESS_2014
Population Estimation Survey (PESS) gathered basic critical information on the Somalis living in urban, rural and nomadic areas (interviewed at water points during the peak of the long, dry season), and in settlements for internally displaced persons. One standard questionnaire was used in selected enumeration areas or pre-identified areas. Data was collected in three main phases: cartographic field mapping, household listing in the sampled areas, and the interviewing of households using the standard questionnaire.PESS report by UNFPA had only the population estimate at regional level (Admin level 1). With the demand to get this data disaggregated to district level to enhance assessment and in particular assessments of people in food insecure by FAO-FSNAU, the district data was interpolated using FSNAU livelihood information embedded in the 2005 UNDP district level population data
somalia
SOM_Population_PESS_2014
population
EPSG:4326
CRS:84
40.989501953125
51.415153503418
-1.65155220031738
11.9885330200195
other
other
text/xml
other
other
other
other
geonode:SOM_Prewar_Rain_Stations_1963_1990_FAOSWALIM
Somalia Prewar Rainfall Stations (1963-1990)
The data is about pre-war 1963 - 1990 Somalia rainfall record stations.
rainfall
stations
prewar
somalia
EPSG:4326
CRS:84
42.0599975585938
51.2500038146973
-0.360000014305115
11.960000038147
other
other
text/xml
other
other
other
other
geonode:SOM_Primary_Roads_UNOCHA
Somalia_Primary_Roads_UNOCHA
The layer contains primary roads data for Somalia. UNOCHA has collected this data from sources such as ICPAC_IGAD_UNOSAT, then UNOCHA has made some updates.
roads
somalia
SOM_Primary_Roads_UNOCHA
primary
EPSG:4326
CRS:84
40.9329032897949
51.1394195556641
-1.15030813217163
11.9586057662964
other
other
text/xml
other
other
other
other
geonode:SOM_Proposed_Charcoal_Production_Areas
Proposed Charcoal Production Areas in Somalia
This map shows the areas that are considered to be potential for charcoal production in Somalia. The selection of the areasis based on environmental aspects and FAO SWALIM accumulted field experience in Somalia.
charcoalsites_2018_2019_Benard
EPSG:4326
CRS:84
41.0000038146973
50.0038909912109
-1.65155220031738
10.603385925293
other
other
text/xml
other
other
other
other
geonode:SOM_Proscal_Study_Area_FAOSWALIM2018
Somalia_Proscal_Study_Area_FAOSWALIM2018
The dataset shows the boundary of Proscal (Charcoal Production) Study Area in South and Central Somalia. The dynamics of charcoal production in Somalia from 2011 to 2017.
swalim
prone areas
study
Agricultural_Flooded_Areas_25May_3June2018
somalia
proscal
fao
EPSG:4326
CRS:84
41.0000038146973
43.2181434631348
-1.65150237083435
1.68449187278748
other
other
text/xml
other
other
other
other
geonode:SOM_Rainfall_Stations_FAOSWALIM_2007
Rainfall Stations in Somalia_FAOSWALIM_2007
Currently, there are over 100 manual rainfall stations, eight synoptic weather stations and 11 automatic weather stations in all Somalia. The stations record a variety of weather elements, including rainfall, temperature, relative humidity, atmospheric pressure, wind speed, wind direction and solar radiation. Data from these automatic stations is received by SWALIM in near-real-time through satellite feeds every four hours. The Somalia rainfall performance layer is generated using data from these stations and other data sources.
rainfall
SOM_Landcover_NW_FAOSWALIM2007
somalia
stations
EPSG:4326
CRS:84
42.0999984741211
49.1760520935059
0.049999997019768
11.2826509475708
other
other
text/xml
other
other
other
other
geonode:SOM_Relief_S_FAOSWALIM
SOM_Relief_S_FAOSWALIM
This data is about Somalia relief and landscape which represents information about elevations and landforms of southern part of Somalia. Data on topography from NASA SRTM 90 m.
relief
south
somalia
EPSG:4326
CRS:84
41.8916931152344
46.1500358581543
-0.266810089349747
5.06004571914673
other
other
text/xml
other
other
other
other
geonode:SOM_River_Barrage_Bridge_FAOSWALIM
SOM_River_Barrage_Bridge_FAOSWALIM
Data is collected and compiled by FAO Somalia Water and Land Information Management (SWALIM)In recent years, there have been several intervention activities going on in the irrigation sector in southern Somalia, mainly aiming to rehabilitate broken down barrages and clogged irrigation canals. To support these interventions, SWALIM has developed a well-structured tool, for collecting and managing past and present information on the irrigation infrastructure: the Irrigation Information Management System (IIMS), a stand-alone PC system which is available upon request. The IIMS provides information (both spatial and tabular) on ongoing and planned irrigation projects. It gives users of irrigation data a platform to discover what is available, querry the system for specific datasets and download data in different formats, such as .pdf documents, maps, spreadsheets, etc.
bridge
barrage
somalia
rivers
EPSG:4326
CRS:84
42.2814521789551
45.5968475341797
0.748955070972443
4.73541688919067
other
other
text/xml
other
other
other
other
geonode:SOM_River_Culvert_FAOSWALIM
Culverts along Juba and Shabelle
Locations of culverts along Juba and Shabelle rivers in Somalia. Data is collected and compiled by FAO Water and Land Information Management (SWALIM).
culverts
SOM_Rivers_Juba_shabelle_line_FAOSWALIM
juba
EPSG:4326
CRS:84
44.2314300537109
44.3730621337891
1.60074150562286
1.66698217391968
other
other
text/xml
other
other
other
other
geonode:SOM_River_Gauging_Stations_FAOSWALIM3
Somalia River Gauging Stations
River gauging stations along Shabelle and Juba Rivers to measure river water levels. Data is collected and compiled by FAO Somalia Water and Land Information Management (SWALIM).
SOM_River_Gauging_Stations_FAOSWALIM
features
EPSG:4326
CRS:84
42.22314453125
45.5672721862793
1.24476993083954
4.73598003387451
other
other
text/xml
other
other
other
other
geonode:SOM_River_Shabelle_Juba_Poly_FAOSWALIM
River Shabelle & Juba (Polygon)
A polygon layer data of Shabelle and Juba Rivers. Digitized and compiled from VHR satellite images by FAO Somalia Water and Land Information Management (SWALIM).
SOM_Rivers_Juba_shabelle_line_FAOSWALIM
juba
somalia
EPSG:4326
CRS:84
42.1221389770508
45.6786079406738
-0.253669440746307
4.97289752960205
other
other
text/xml
other
other
other
other
geonode:SOM_Rivers_FAOSWALIM
Somalia Rivers
The Shuttle Radar Topography Mission (SRTM) obtained 90-meter (3 arc-second) resolution data on a near-global scale (between 56 degrees South and 60 degrees North latitude) and 30 meter (1 arc-second) resolution over United States, providing a valuable global topographic dataset. The SRTM data were collected during an 11-day mission in February of 2000 from a radar system onboard the Space Shuttle Endeavor.
somalia
rivers
EPSG:4326
CRS:84
40.9090690612793
51.3552742004395
-1.32950973510742
11.9788703918457
other
other
text/xml
other
other
other
other
geonode:SOM_Rivers_Juba_shabelle_line_FAOSWALIM
River Juba and Shabelle (line)
The Shuttle Radar Topography Mission (SRTM) obtained 90-meter (3 arc-second) resolution data on a near-global scale (between 56 degrees South and 60 degrees North latitude) and 30 meter (1 arc-second) resolution over United States, providing a valuable global topographic dataset. The SRTM data were collected during an 11-day mission in February of 2000 from a radar system onboard the Space Shuttle Endeavor.
SOM_Rivers_Juba_shabelle_line_FAOSWALIM
juba
rivers
EPSG:4326
CRS:84
42.1221313476562
45.6782379150391
-0.251115590333939
4.95320701599121
other
other
text/xml
other
other
other
other
geonode:SOM_SRTM_Contours_100_USGS
SRTM_Contours_100m_USGS
100m resolution contours obtained from the U.S. Geological Survey
100m
usgs
resolution
contours
srtm
EPSG:4326
CRS:84
41.1106605529785
51.4435157775879
-1.66656935214996
11.9631805419922
other
other
text/xml
other
other
other
other
geonode:SOM_SRTM_Contours_25m_USGS
SRTM_Contours_25m_USGS
25m resolution contours obtained from the U.S. Geological Survey
usgs
resolution
contours
SOM_SRTM_Contours_25m_USGS
srtm
EPSG:4326
CRS:84
39.9995803833008
54.5273056030273
-3.00041675567627
13.0004167556763
other
other
text/xml
other
other
other
other
geonode:SOM_SRTM_Contours_5m_USGS
SRTM_Contours_5m_USGS
5m resolution contours obtained from the U.S. Geological Survey
usgs
resolution
contours
SOM_SRTM_Contours_25m_USGS
srtm
EPSG:4326
CRS:84
40.9993743896484
51.5001983642578
-1.66667854785919
11.9999170303345
other
other
text/xml
other
other
other
other
geonode:SOM_SRTM_Spot_Heights
SRTM Spot Heights
No abstract provided
spot
srtm
heights
EPSG:4326
CRS:84
41.0020790100098
51.4123001098633
-1.61666142940521
11.9665012359619
other
other
text/xml
other
other
other
other
geonode:SOM_Secondary_Roads_ADC2010
SOM_Secondary_Roads_ADC2010
This dataset is an extraction of roads from OpenStreetMap data made by WFP following UNSDI-T standards. The data is updated in near-real time from OSM servers and include all latest updates. NOTE: this dataset doesn't include streets and pathways that have been published on a separate dataset (streets and pathways).
roads
SOM_Functioning_Boreholes_2010
somalia
secondary
EPSG:4326
CRS:84
40.9903373718262
51.2599143981934
-1.64603495597839
11.9586000442505
other
other
text/xml
other
other
other
other
geonode:SOM_Sentinel2_ImageIndex_Granule
Sentinel2_ImageIndex_Granule_Somalia
Image Index: it contains feature classes that contain information about the path/row, scene boundaries and the geographic coordinates of the imageries acquired by satellites for Somalia. These indices help in identifying the numbers of images covering Somalia and are easy guides for identifying searching for the required images.
index
image
sentinel2
granule
EPSG:4326
CRS:84
40.7966918945312
52.0109519958496
-1.89681804180145
12.6645498275757
other
other
text/xml
other
other
other
other
geonode:SOM_Settlements_UNOCHA2011_P_Coded
Somalia Settlements_UNOCHA_P_Coded_2011
Data is updated by Somalia Information and mapping coordination (SIMAC) working Group in 2016. Up to present in 2019 this is the settlement layer endorsed by UNOCHA and used by UN in Somalia.This file was updated on May 2018 to incorporate the P-codes. Some settlements fall outside the disputed territory due to positional error probably.For settlements classification use the field named: DEFINITION
Charcoalsites_2018_2019_Antony_20112019
Charcoalsites_2018_2019_pascal
Mogadishu_Adminbnda_Districts_UNOCHA
SOM_settlements_ASSORTED
coded
somalia
EPSG:4326
CRS:84
40.9920768737793
51.2826614379883
-1.637540102005
11.9603900909424
other
other
text/xml
other
other
other
other
geonode:SOM_Settlements_UNSOS_OCT2016
Somalia Settlements_UNSOS_2016
These settlements were updated by UNSOS in october 2016
SOM_Land_Degradation_ADESO_FAOSWALIM2016
SOM_settlements_ASSORTED
october
SOM_Airfields_UNSOS
somalia
EPSG:4326
CRS:84
40.9922294616699
51.2826614379883
-1.637540102005
11.9603900909424
other
other
text/xml
other
other
other
other
geonode:SOM_Shabelle_Juba_River_Contours_50cm
Shabelle_Juba_River_Contours_50cm
50 cm resolution contours spanning around Rivers Juba and Shabelle
juba
rivers
SOM_Shabelle_Juba_River_Contours_50cm
contours
SOM_Rivers_Juba_shabelle_line_FAOSWALIM
resolution
EPSG:4326
CRS:84
42.1024322509766
45.7032928466797
-0.268356293439865
4.98001670837402
other
other
text/xml
other
other
other
other
geonode:SOM_Soil_250k
Somalia Soil Map - Scale of 1:250,000
To achieve the preliminary land unit classification, a set of updated multispectral satellite images was acquired . A set of Sentinel-2 (from January to February 2020 interval time) images covering integrally the complexes of the project area with a spatial resolution of 10m per pixel has been processed using Near infrared, green and blue spectral band combination) and analyzed using a GIS software. The analysis of the images helps to classify the areas at fourth levels:
- Soil Region (1:3M scale)
- System (1:1M scale)
- Subsystems (1:500k scale)
- Land Unit (1:250k scale)
Further based on the photointerpretation, the morphologic and physiographic analysis the homogenous land units are defined. First, a general overview of the study area done based on bibliographic material concerning existing soil surveys, geography, geology, climate, agriculture, vegetation and soil characteristics.
Particularly, in this case a mixed descending and ascending building and interpretation methodology was applied. As shown in the following picture, the descending methodology has used where existing soil data are lacking, starting from morphology, lithology and land use. Homogeneous polygons with same morphology, lithology and land use/cover may have same soils. For large part of Somalia country this methodology was applied.
The ascending methodology has applied where existing soil data was available, in Somalia some 1.50.000/1:100.000 soil maps was available in north and in south part of the country. Of course due the different scale of the final soil map, the details are different, so some existing Land Units at semidetailed scale has merged to each one relating to the extensions and soil typology.
The photointerpretation has done mainly with the aid of the following:
- Sentinel-2 satellite images (Jan-Feb 2020) 10m resolution (NIR-GB spectral bands)
- SRTM Digital Elevation model 30x30 and 90x90 m resolution
- “Sm-landform” SWALIM shapefile
somalia
data_soils
250000
scale
EPSG:32638
CRS:84
40.90291677458611
51.44900497025844
-1.6586870110756073
12.047507674989797
other
other
text/xml
other
other
other
other
geonode:SOM_Somalis_Clan_Distribution_Abikar1999
Somalis_Clan_Distribution_Abikar_1999
Clans are mainly self-governing bodies ruled by a council of elders. Most people respect this system way more than any modern-style central government.
clans
SOM_KE_ET_DJ_Somalis_Clan_Distribution_Abikar1999
distribution
abikar
somalia
EPSG:4326
CRS:84
40.9943161010742
51.4130363464355
-1.66441881656647
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:SOM_Strategic_Water_Sources_Feb2018
Strategic Water Sources - February 2018
The dataset contains the most common water source types in Somalia classified into five categories namely: boreholes, shallow wells, dams, springs and berkads. Other source types which do not fit in any of these five categories are classified as “Other” The data is periodically updated from field surveys carried out by FAO/SWALIM through government and NGO partners, or received from WASH Cluster partners.Modern technology has been adopted for field data collection, using mobile phones, building on the conventional paper based questionnaire first developed and adopted by WASH partners in Somalia back in 2006
features
SOM_Strategic_Water_Sources_Feb2018
EPSG:4326
CRS:84
40.9912528991699
51.2828636169434
-1.05600273609161
11.9533405303955
other
other
text/xml
other
other
other
other
geonode:SOM_Surface_Water_Sources
SOM_Surface_Water_Sources
No abstract provided
SOM_Surface_Water_Sources
features
EPSG:4326
CRS:84
-3.40282E38
51.2077903747559
-3.40282E38
11.7885608673096
other
other
text/xml
other
other
other
other
geonode:SOM_TopomapsIndex
Topographic Maps Index _Somalia
The Topographic Maps are from varying sources and dates, but the legends are very similar and the index numbers have remained the same. Scanned legends can be found in the same folder as the topomaps in jpeg format. All versions have been supplied. Scale is 1:100,000 and contour interval is 20 metres. Maps have been scanned and georeferenced to Geographic WGS84. Supplied to SWALIM by UNDP Somalia and RCMRD (Regional Centre for Mapping of Resources for Development). 'British Maps' These were produced by stereo photography using aerial photography in the early 1970's. Originally printed by Bureau of Cartography, Ministry of Defense, Somalia Democratic Republic in the late 1970's. Reprinted by Canadian Forces Mapping and Charting Establishment from reprographic material of native Somalia mapping by the British Director General of Military Survey. Revisions were made by the Defense Mapping Agency Hydrographic/Topographic Centre, Bethesda, MD, in cooperation with the Canadian Canadian Forces Mapping and Charting Establishment in the early 1990's. 'Russian Maps' are also included but no information is provided in the metadata as there is no translation available at present. They are in the minority in the SWALIM archive and have only been included where there are gaps in the British/Canadian maps. The legend is similar to the British/Canadian maps.
SOM_TopomapsIndex
index
topographic
somalia
EPSG:4326
CRS:84
40.9990997314453
51.4992141723633
-1.66626739501953
12.0005664825439
other
other
text/xml
other
other
other
other
geonode:SOM_VeryHighResolution_ImageIndex
Very High Resolution Image Index_Somalia
Image Index: it contains feature classes that contain information about the path/row, scene boundaries and the geographic coordinates of the imageries acquired by satellites for Somalia. These indices help in identifying the numbers of images covering Somalia and are easy guides for identifying searching for the required images.
index
very
image
high
somalia
resolution
EPSG:4326
CRS:84
-2.62777781486511
51.3320121765137
-2.33475852012634
36.7050018310547
other
other
text/xml
other
other
other
other
geonode:SOM_WAT_BASINS_FAOSWALIM_USGS
Somalia Water Basins
This dataset contains polygon shapefile of Drainage Basins of Somalia.
water basins
somalia
EPSG:4326
CRS:84
36.2761077880859
51.330753326416
-1.81083428859711
11.9967555999756
other
other
text/xml
other
other
other
other
geonode:SOM_Water_Sources_Ground_FAOSWALIM
Somalia Surface Water Sources
The the dataset contains Somali Water Catchments, and their rehabilitation status, for the period between 2013 and 2017 divided into phases 2a, 2b, 2c, 3, 4, 5, 6a, 6b, 6c and 7a.The status depicted could either be 'rehabilitated', 'not rehabilitated', 'partially rehabilitated', 'not clear' or 'not assessed'.The water catchments are managed by different Non-Governmental Organizations in Somali.This dataset was generated by taking photographs of the catchments using GPS enabled digital cameras. The geographical X and Y coordinates of the catchments were extracted from the photographs and plotted to generate a shapefile layer. The developed shapefile was overlaid on high resolution satellite images captured just before and immediately after the rehabilitation of the water catchments. The satellite images were then observed for rehabilitation status of different water catchments.
water
water sources
ground
EPSG:4326
CRS:84
40.9941253662109
51.2828636169434
-1.05600273609161
11.9533405303955
other
other
text/xml
other
other
other
other
geonode:SOM_regional_capitals
Somalia Regional Capitals
DATA AND INFORMATION MANAGEMENT UNIT (DIMU) ORGANIZATION: UNITED NATIONS DEVELOPMENT PROGRAMME (UNDP SOMALIA) MODIFIED BY: DATA AND INFORMATION MANAGEMENT UNIT UNDP SOMALIA MODIFICATION DATES: 22/03/2001 The settlements where digitized from Somalia topographic maps at a scale of 1: 100,000. The maps were produced by stereo photography using aerial photography 1974.Printed 1976 by Bureau of cartography, Ministry of Defence, Somalia Democratic government. Reprinted by Canadian Forces Mapping and Charting Establishment from reprographic material of native Somalia mapping by the British Director of Military Survey. Other settlements were collated from gazetteer data ( Defense Mapping Agency USA) and UNDOS, populations surveys 1997/98 of N.W Somalia- Middle and lower Shabelle. STATUS COMPLETE TABLES SOURCE: DATA AND INFORMATION MANAGEMENT UNIT UNDP SOMALIA SOURCE PUBLICATION DATE: 2000 ATTRIBUTE RELIABILITY: Good STATUS: COMPLETE FIELDS DESCRIPTION SETT_ID Internal Feature Number HUTS Number of huts in a settlement HOUSES Number of houses in a settlement NAME Name of the settlement PCODE Alphanumeric coding system for coding the settlements based on the region and district they belong to. CODE Code assigned to the settlements to classify them. Code definition 1 = Settlement 5 = Other town 10 = District town 100 = Regional Capital 1000 = Capital town REG_CODE Codes assigned to the regions. From 1 to 17 REGION Name of the region DISTRICT Name of the district SOURCE Source of the settlement either gazetteer, topomap or survey. PPRWARES Population pre-war estimated using number of nomadic huts. PPRWANS Population pre-war estimated using number of houses. TOP_POP Population from the topomaps. SUR_POP Field Survey populations. X COOD X coordinate of the settlement or the easting Y COOD Y coordinate of the settlement or the northing.
somalia
SOM_regional_capitals
EPSG:4326
CRS:84
42.2198753356934
49.1806297302246
-0.360290020704269
11.2836608886719
other
other
text/xml
other
other
other
other
geonode:SOM_settlements_ASSORTED
Somalia Assorted Settlements
This dataset contains districts and regional capitals, towns, settlements, and IDP camps. Below is a description of the fields in the attribute table: OBJECTID - unique ID for the dataset KEMRI_TEAM - team number assigned for the collection of the KEMRI settlements KEMRI_NO_H - number of houses identified by KEMRI surveys KEMRI_POP - population of settlement as identified by KEMRI surveys GTZ_LOCATO - GTZ unique ID for settlements identified in the GTZ survey GTZ_TYPE - type of settlement as identified by GTZ survey GTZ_ECONOM - type of economic activity in settlement as identified by GTZ survey GTZ_POP - population of settlement as identified by GTZ survey GTZ_MINE_A - settlement affected by mines according to GTZ survey DP97_HUTS - number of huts in settlement as identified by UNDP 1997 survey DP97_HOUSE - number of houses in settlement as identified by UNDP 1997 survey DP97_PCODE - place code as allocated by UNDP 1997 survey DP97_CODE - settlement code, identifying type of settlement. 1 = Settlement, 5 = Other town, 10 = District town, 100 = Regional Capital, 1000 = Capital town DP97_SOURC - source of the settlement for the UNDP 1997 survey DP97_PPRES - pre-war population estimate using number of huts for UNDP 1997 survey DP97_PPNRS - pre-war population estimate using number of houses for UNDP 1997 survey DP97_POP - population from topomaps for UNDP 1997 survey DP97_SPOP - surveyed population for UNDP 1997 survey ALT_NAME - Alternative names and spellings for settlement
SOM_settlements_ASSORTED
somalia
assorted
EPSG:4326
CRS:84
40.9931488037109
51.2826614379883
-1.60050010681152
11.9603900909424
other
other
text/xml
other
other
other
other
geonode:SWALIM_River_Breakages_August_2022
Status of River Breakages along Juba and Shabelle Rivers - Issued August 2022
Four consecutive poor rainy seasons in most parts of the Horn of Africa region have resulted to current serious hydrological drought conditions in Somalia and neighboring countries.. Juba and Shabelle Rivers, with the headwaters in the Ethiopian highlands have remained below the long term average since the beginning of 2022, negatively impacting agriculture production, domestic and livestock water use for the riverine communities. The low river levels however provide an opportunity to fix the river breakages and weak embankments, ahead of the next rainy season.
SWALIM has updated the status of the river breakages along the Juba and Shabelle Rivers using available Very High Resolution (VHR) satellite imagery and a Digital Elevation Model (DEM).
Five types of breakages have been identified, namely; open, overflow, potential overflows, potential breakages and closed with sandbags. The open breakages are those that are currently open as observed on the latest VHR image available. All the observations reported refers to the latest suitable VHR satellite image available, which is indicated in the online database.
100 Open breakage points have been identified, 70 on the Shabelle River and 30 on the Juba River which require immediate action. 13 Overflows were also identified during this season. Jowhar district was worst affected along the Shabelle while Bu’aale and Jilib districts are worst affected along the Juba.
Users are advised that the methodology is biased towards Remote Sensing (RS) interpretation with only limited “ground truthing” due to access constraints. Open breakages might have been omitted in some cases where satellite images may not have been very clear (e.g. heavy cloud cover) or were not available.
august
rivers
Agrimask_Galgaduud_29th_Aug_2022
somalia
Shabellebreakages_Aug2019
Growing_Period_Length_FAOSWALIM
EPSG:4326
CRS:84
42.0680885314941
45.6786727905273
-0.070970006287098
4.89294004440308
other
other
text/xml
other
other
other
other
geonode:Salty_Ground_Water_Sources
Salty Ground Water Sources
This data contains areas with Salty ground water
water
salty
somalia
water sources
ground
EPSG:4326
CRS:84
45.9045639038086
48.6622734069824
3.30184578895569
6.60507440567017
other
other
text/xml
other
other
other
other
geonode:Second_shape_file
Second_shape_file
No abstract provided
Second_shape_file
features
EPSG:32638
CRS:84
46.84082951534041
47.52401022428145
5.359791739780833
5.767011401396769
other
other
text/xml
other
other
other
other
geonode:Second_shape_file0
Second_shape_file0
No abstract provided
Second_shape_file
features
EPSG:32638
CRS:84
46.84082951534041
47.52401022428145
5.359791739780833
5.767011401396769
other
other
text/xml
other
other
other
other
geonode:Shabelle_Hagaa_Floods_11092020
Shabelle River Floods - Hagaa 2020
This data was produced through visual interpretation of Sentinel 1 and Sentinel 2 satellite images acquired between August and September
09-03-2020
floods
SOM_Rivers_Juba_shabelle_line_FAOSWALIM
hagaa
EPSG:32638
CRS:84
44.585234079524966
45.71594272775314
1.7542504849571587
5.010025530383798
other
other
text/xml
other
other
other
other
geonode:Shabelle_Juba_Aug_2020FRRMIS_
Status of River Breakages Along Juba and Shabelle Rivers - Issued August 2020
FAO SWALIM has finalized the analysis and mapping of the river breakages along the Juba and Shabelle rivers using very high resolution satellite imagery. Breakages identified in the map have been classified into five different categories; Open, Overflow, Potential Overflows, Potential breakages and Closed with sandbags. A total of 154 Open points have been identified, 109 on the Shabelle River and 45 on the Juba River which require immediate attention.
Revised_middlejuba_Mohamed_Sufi1
august
rivers
Shabelle_Juba_Aug_2020FRRMIS_
Shabellebreakages_Aug2019
09-03-2020
SOM_Rivers_Juba_shabelle_line_FAOSWALIM
features
EPSG:4326
CRS:84
42.0681838989258
45.6786727905273
-0.070970006287098
4.89294004440308
other
other
text/xml
other
other
other
other
geonode:Shabelle_Juba_Breakages_Feb_2022
Status of River Breakages Along Juba and Shabelle Rivers - Issued February 2022
Three consecutive poor rainy seasons within the Juba and Shabelle River basins inside Somalia and the Ethiopian highlands have led to the current serious hydrological drought along the two rivers. The river levels in the upper sections are currently at their historical minimum, while the mid and lower sections of the Shabelle River having run dry. With no rains expected in February and most of March, the river flow will continue to decline. The reduced river flow along the two rivers has negatively impacted agriculture production, domestic and livestock water use. This has also led to an increase of new river breakages as the riverine communities attempt to extract the limited resource to support livelihood activities.
SWALIM has completed analysis and mapping of the river breakages along the two rivers using very high resolution satellite images acquired thanks to the kind contribution of the Embassy of France. The study has identified 101 open points along the Shabelle, out of which 24 points are new and the rest have remained open since the last survey in August 2021. Along the Juba River, 35 open points were identified out of which 5 are new points. During this drought period, it is expected that the riverine communities will continue to extract water from the rivers by breaching the banks and this will only see an increase of the open river bank points.
Several other weak points which are not necessarily open but have the potential to overflow or break were identified during the analysis. The summary of the different types of points mapped. This information and data set is also available in the SWALIM spatial database (https://spatial.faoswalim.org/layers/geonode:Shabelle_Juba_Breakages_Feb_2022).
Revised_middlejuba_Mohamed_Sufi1
february
Shabelle_Juba_Breakages_Feb_2022
rivers
somalia
Shabellebreakages_Aug2019
Middal_shabelle_LYR2
EPSG:4326
CRS:84
42.0680885314941
45.6786727905273
-0.070970006287098
4.89294004440308
other
other
text/xml
other
other
other
other
geonode:Shabelle_Juba_Feb_2020FRRMIS
Status of River Breakages Along Juba and Shabelle Rivers - Issued March 2020
FAO SWALIM has finalized the analysis and mapping of the river breakages along the Juba and Shabelle rivers using very high resolution satellite imagery.
Breakages identified in the map have been classified into five different categories; Open, Overflow, Potential Overflows, Potential breakages and Closed with sandbags.
A total of 152 Open points have been identified, 100 on the Shabelle River and 52 on the Juba River which require immediate attention.
Shabelle_Juba_Feb_2020FRRMIS
features
EPSG:4326
CRS:84
42.0681838989258
45.6786727905273
-0.070970006287098
4.86415719985962
other
other
text/xml
other
other
other
other
geonode:Shabelle_Juba_River_Breakages_Aug2019
Status of River Breakages Along Juba and Shabelle Rivers - Issued August 2019
Breakages identified in the map have been classified into six different categories; Open, Overflow, Potential Overflows, Potential breakages, Closed with sandbags and Closed. A total of 84 Open points were identified, 39 on the Shabelle River and 45 on the Juba River which require immediate action in advance of the strong possibility of normal to above normal Deyr (October-December) 2019 season rainfall.
Floods
Jubba_Breakages2019
Juba River
Somalia
Shabelle River
EPSG:4326
CRS:84
42.0681838989258
45.6786727905273
-0.070970006287098
4.86415719985962
other
other
text/xml
other
other
other
other
geonode:Shabelle_Juba_River_Breakages_Feb2017
Status of River Breakages Along Juba and Shabelle Rivers - Issued February 2017
A total of 37 Open points were identified, 19 along Juba River and 18 along Shabelle River. Several other points, which are either potential ; 359 or temporarily closed with sandbags ;122 were also identified.
feature
EPSG:4326
CRS:84
42.0681838989258
45.6786727905273
-0.070970006287098
4.86415719985962
other
other
text/xml
other
other
other
other
geonode:Shabelle_Juba_River_Breakages_Feb2019
Status of River Breakages Along Juba and Shabelle Rivers - Issued February 2019
Four types of breakages have been identified, namely; open, potential, closed with sandbags and closed. The open breakages are those that are still open as observed on the latest VHR image available, therefore a field verification is needed before planning any repair.
feature
EPSG:4326
CRS:84
42.0681838989258
45.6786727905273
-0.070970006287098
4.86415719985962
other
other
text/xml
other
other
other
other
geonode:Shabelle_Juba_River_Breakages_Feb_2018
Status of River Breakages Along Juba and Shabelle Rivers - Issued February 2018
A total of 68 Open points were identified, 41 along Juba River and 27 along Shabelle River. Several other points, which are either potential ; 401 or temporarily closed with sandbags ;64 were also identified.
feature
EPSG:4326
CRS:84
42.0681838989258
45.677562713623
-0.070970006287098
4.85454416275024
other
other
text/xml
other
other
other
other
geonode:Shabelle_Juba_River_Breakages_July2018
Status of River Breakages Along Juba and Shabelle Rivers - Issued July 2018
A total of 101 Open points were identified, 54 along Juba River and 47 along Shabelle River.The Gu 2018 heavy rains led to increased river levels and subsequent additional river breakages. 205 Overflow (Overbank river spillage) points were also been identified. The estimated cumulative length of the Overflow sections along the river bank is approximately 82.6 km whose calculation is derived from visible spillages and fresh sand deposits during the flooding period. Several other points, which are either potential or temporarily closed with sandbags, have also been identified.
feature
EPSG:4326
CRS:84
42.0681838989258
45.6786727905273
-0.070970006287098
4.86415719985962
other
other
text/xml
other
other
other
other
geonode:Shabelle_Juba_River_Breakages_Sept2017
Status of River Breakages Along Juba and Shabelle Rivers - Issued September 2017
A total of 48 Open points were identified, 12 along Juba River and 36 along Shabelle River. Several other points, which are either potential or temporarily closed with sandbags, have also been identified.
feature
EPSG:4326
CRS:84
42.0681838989258
45.6786727905273
-0.070970006287098
4.86415719985962
other
other
text/xml
other
other
other
other
geonode:Shabelle_River_Flooding_30th_Oct_2019
Shabelle River Flooding (As at 30th Oct 2019)
No abstract provided
feature
EPSG:32638
CRS:84
44.99717216202905
45.71956366716023
2.3352735010548153
5.019690670981253
other
other
text/xml
other
other
other
other
geonode:SoM_AgriMask_AoI
SoM_AgriMask_AoI
No abstract provided
feature
EPSG:4326
CRS:84
42.4009056091309
44.6909523010254
1.46171414852142
3.92149138450623
other
other
text/xml
other
other
other
other
geonode:Soil_100K_N_FAOSWALIM
Soil_100K_N_FAOSWALIM
This dataset is about a semi detailed soil map at a scale of 1:100,000 for Dur Dur and Gibeley areas in north Somalia
soil
scale
100000
Northern
somalia
EPSG:4326
CRS:84
43.0110130310059
44.4621047973633
9.16652679443359
10.6897344589233
other
other
text/xml
other
other
other
other
geonode:Soil_100K_S_FAOSWALIM
Soil_100K_S_FAOSWALIM
This datasets shows a semi detailed soil map at a scale of 1:100,000 for for Juba and Shabelle Riverine Areas in south Somalia.
soil
scale
southern
100000
somalia
EPSG:4326
CRS:84
41.8916931152344
46.1500358581543
-0.266810089349747
5.06004571914673
other
other
text/xml
other
other
other
other
geonode:Soil_50K_N_FAOSWALIM
Soil_50K_N_FAOSWALIM
This dataset is about a detailed soil map at a scale of 1:50,000 for north western part of Somaliland in Somalia.
50000
soil
scale
somalia
Northern
EPSG:4326
CRS:84
43.0110092163086
44.4621047973633
9.16651344299316
10.1207056045532
other
other
text/xml
other
other
other
other
geonode:Soil_Jowhar_FAOSWALIM
Soil_Jowhar_100K_FAOSWALIM
This dataset is about a semi detailed soil map at a scale of 1:100,000 for Jowhar areain Somalia. Data is produced by FAO SWALIM (Somalia Water and Land Information Management Project)
soil
jowhar
100000
scale
EPSG:4326
CRS:84
45.4991073608398
45.6022148132324
2.6888473033905
2.80313301086426
other
other
text/xml
other
other
other
other
geonode:Soil_Simplified_FAOSWALIM2012
Soil_Simplified_FAOSWALIM2012
SWALIM has conducted numerous surveys of the soil in different parts of Somalia which is resulted in compiling a national soil database for Somalia
soil
simplified
SOM_Soil_Simplified_FAOSWALIM2012
EPSG:4326
CRS:84
40.9886322021484
51.4130325317383
-1.66205298900604
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:SomDotGrid2014
Somalia Dot Grid (2014)
The Dot Grid was produced by applying the USGS Rapid Land Cover Mapper (RLCM) methodology that uses regularly spaced (in this case 500m spacing) dot grid statistical approach to generate land cover data. The principal aim was to produce an updated statistical assessment of Somalia's cultivable area. USGS EROS Data Center provided with ASTER satellite imagery of 15m resolution , acquired mainly in 2013. The average visual interpretation scale is 1:50,000. The joint Research Center(JRC) of the European Commission provided with additional additional LANDSAT ETM (2009-2011) satellite images and contributed to the definition and accuracy assessment of the methodology . LANDSAT imagery complemented the baseline images in areas where either cloud cover was present on ASTER images or when gaps occurred. Very high resolution satellite images were used to refine the interpretation of cultivable areas and to verify/validate the natural vegetation cover.
Standardized FAO Land Cover Meta Language (LCML) methodology was utilized to create the legend for the analysis. The area covered by each land cover class was derived from the classified dots
grid
somalia
dot
EPSG:4326
CRS:84
40.9591751098633
51.4134140014648
-1.65042173862457
11.9870004653931
other
other
text/xml
other
other
other
other
geonode:Som_Agrimask_Hiraan_Region_UTM_17102022
Som_Agrimask_Hiraan_Region_UTM_17102022
No abstract provided
AgriMask_Ahmed_AoI
EPSG:32638
CRS:84
44.75205235760529
46.40267952611883
3.2035522724073267
5.412625812062329
other
other
text/xml
other
other
other
other
geonode:Som_Agrimask_Mudug_17102022
Som_Agrimask_Mudug_17102022
No abstract provided
AgriMask_Ahmed_AoI
EPSG:32638
CRS:84
46.564882154051595
47.82907678855699
4.4894528318750435
7.291528273447662
other
other
text/xml
other
other
other
other
geonode:Som_Agrimask_WorkingAOI0
Som_Agrimask_WorkingAOI
No abstract provided
feature
EPSG:4326
CRS:84
42.4009094238281
44.6909523010254
1.46171414852142
3.92149114608765
other
other
text/xml
other
other
other
other
geonode:Som_Baidoa_Town_15Km_buffer_Dams_24102022
Som_Baidoa_Town_15Km_buffer_Dams_24102022
This Layer was created by updating the Strategic Water sources Survey data through visual interpretation and digitization of water surface features at a scale of 1000 from Very High Resolution Satellite Data acquired from Digital Globe. The length and width measurements for all the surface water sources was taken considering the largest extent of the dataset. Areas for the dams was computed in Square Metres while the distance to the nearest settlement has been captured in Kilometres.
Water Sources
EPSG:4326
CRS:84
43.5105895996094
43.7739639282227
2.995845079422
3.25321793556213
other
other
text/xml
other
other
other
other
geonode:Som_Baidoa_Town_15Km_buffer_Dams_points_24102
Som_Baidoa_Town_15Km_buffer_Dams_points_24102
No abstract provided
Water Sources
EPSG:4326
CRS:84
43.511043548584
43.7736625671387
2.99613356590271
3.25300288200378
other
other
text/xml
other
other
other
other
geonode:Som_Bakool_Agrimask_31052020_WGS
Som_Bakool_Agrimask_31052020_WGS
No abstract provided
feature
EPSG:4326
CRS:84
42.8894309997559
44.7852439880371
3.33841156959534
4.96077060699463
other
other
text/xml
other
other
other
other
geonode:Som_DotGrid_20144
Som_DotGrid_2014
The Dot Grid was produced by applying the USGS Rapid Land Cover Mapper (RLCM) methodology that uses regularly spaced (in this case 500m spacing) dot grid statistical approach to generate land cover data. The principal aim was to produce an updated statistical assessment of Somalia's cultivable area. USGS EROS Data Center provided with ASTER satellite imagery of 15m resolution , acquired mainly in 2013. The average visual interpretation scale is 1:50,000. The joint Research Center(JRC) of the European Commission provided with additional additional LANDSAT ETM (2009-2011) satellite images and contributed to the definition and accuracy assessment of the methodology . LANDSAT imagery complemented the baseline images in areas where either cloud cover was present on ASTER images or when gaps occurred. Very high resolution satellite images were used to refine the interpretation of cultivable areas and to verify/validate the natural vegetation cover.
Standardized FAO Land Cover Meta Language (LCML) methodology was utilized to create the legend for the analysis. The area covered by each land cover class was derived from the classified dots
features
Som_DotGrid_2014
EPSG:3857
CRS:84
40.95917496125942
51.413413403305185
-1.6393792178323987
11.909056990883878
other
other
text/xml
other
other
other
other
geonode:Som_DotGrid_2014_
Som_DotGrid_2014_
features
Som_DotGrid_2014_
EPSG:4326
CRS:84
4559554.5
5723315.0
-182519.765625
1335360.25
geonode:Som_Drought_Conditions_April_2017
Somalia Drought Conditions - April 2017
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
drought
somalia
EPSG:4326
CRS:84
40.9896812438965
51.4143142700195
-1.65970516204834
11.9867792129517
other
other
text/xml
other
other
other
other
geonode:Som_Drought_Conditions_January_2017
Somalia Drought Conditions - January 2017
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factores in infromation from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
feature
EPSG:4326
CRS:84
40.9896812438965
51.4143142700195
-1.65970516204834
11.9867792129517
other
other
text/xml
other
other
other
other
geonode:Som_Drought_Conditions_September_2021
Somalia Drought Conditions - September 2021
The drought condition map was produced by factoring in the Combined Drought Index (CDI) - which takes into account the rainfall, Land Surface Temperature and Soil Moisture/NDVI.
Additionally, it factors in information from the field such as livestock and vegetation condition to verify the severity of the drought.
Data source:
Rainfall - CHIRPS
Land Surface Temperature - MODIS
NDVI - MODIS
Soil Moisture - USGS
drought
somalia
EPSG:4326
CRS:84
40.9943161010742
51.4130325317383
-1.66491448879242
11.9852027893066
other
other
text/xml
other
other
other
other
geonode:Som_Flood_Prone_areas_20200
Flood prone areas 2020
This layer shows flood prone areas derived from radar images (Sentinel 1) and optical images (Sentinel 2, Landsat, and Maxar VHR images) acquired during the period 2018-2020. The analysis was performed by FAO Somalia Water and Land Information Management (SWALIM).
Floods
EPSG:4326
CRS:84
42.0785179138184
45.7423629760742
-0.267397582530975
5.01968812942505
other
other
text/xml
other
other
other
other
geonode:Som_Flood_Prone_areas_Merge_of_Historical0
SOM Flood Prone Areas - Merge of Flood Extent 2013-2023
This is a merge of historical flood extents. See "SOM Juba Shabelle River Floods" layers: Deyr 2023, Gu 2021, Gu 2020, Hagaa 2020, Gu 2019, Deyr 2019, Gu 2018, Deyr 2018, Gu 2016, Gu 2015, Deyr 2013
floods
prone
somalia
historical
areas
EPSG:4326
CRS:84
40.9956474304199
48.3404655456543
-1.62330758571625
6.01075601577759
other
other
text/xml
other
other
other
other
geonode:Som_Gedo_Region_Agrimask_21122020WGS_
Som Gedo Region Agrimask 21122020WGS
This classification was done using LCCS3 legend as a framework for classifying agriculture. LCCS3 is the last version of the Land Cover Classification System (LCCS) developed by FAO and UNEP in 1998 to facilitate the understanding of theclasses of land cover regardless of the scale of mapping, the type of coverage,method of data collection, or geographic location.Sentinel 2 images were downloaded and segmented using ecognition software.The segments were then classified using LCCS legend.During the classification, photo keys of different agricultural classes created on google earth was used as visual verification of class types before classification, given that sentinel 2 images has a resolution of 30m and therefore not clear enough to visually detect differences between classes.In additon , High Resolution Google and Esri satellite basemaps were used for verification.The scale of editing and digitization of polygons was 1:10000
feature
EPSG:4326
CRS:84
41.7590560913086
43.1246871948242
1.94245088100433
4.18697452545166
other
other
text/xml
other
other
other
other
geonode:Som_Juba_River_Flooding_As_at_10th_Nov_2019
Juba River Flooding (As at 10th Nov 2019)
No abstract provided
Som_Juba_River_Flooding_As_at_10th_Nov_2019
juba
rivers
Charcoalsites_2018_2019_Antony
AgriMask_Ahmed_AoI
Climate_Zones_in_Somalia
flooding
november
EPSG:32638
CRS:84
42.11526662062061
42.80374675034536
-0.26743428843423267
4.165473178416574
other
other
text/xml
other
other
other
other
geonode:Som_Juba_Shabelle_Flooding_Gu_2020
Juba Shabelle River Floods - Gu 2020
This data was produced through visual interpretation of Sentinel 1 and Sentinel 2 satellite images acquired between April and June 2020
Floods
Juba River
EPSG:32638
CRS:84
42.13419971781773
45.58338106682103
-0.03518486339282422
5.010417273794963
other
other
text/xml
other
other
other
other
geonode:Som_Juba_Shabelle_River_Floods_Gu_2021
Juba Shabelle River Floods - Gu 2021
This data was produced through visual interpretation of Sentinel 2 satellite images acquired between 1st May - 31st August 2021.
Additionally, we complemented the data with Kompsat 5 satellite images images for the month of May 2021
Revised_middlejuba_Mohamed_Sufi1
satellite
floods
sentinel2
Jubba_Breakages_August2019
rivers
images
somalia
season
Middal_shabelle_LYR2
kompsat5
EPSG:4326
CRS:84
42.6860618591309
45.6706352233887
0.105532951653004
4.80852699279785
other
other
text/xml
other
other
other
other
geonode:Som_Livestock_Trade_Routes_Aug2022
Som_Livestock_Trade_Routes_Aug2022
The Livestock Trade Routes were prepared by combining field Livestock reports on livestock trade routes from the villages to the markets, with the Somalia Roads datasets retrieved from Open Street Maps and the Secondary roads authored by WFP and ADA 2010. The dataset contains a name column that indicates the route from and the the destination market e.g Tallex-Garoowe. In places where a primary road exist, the route was selected however in areas where it is non-existent, the secondary routes was selected instead. The routes are updated as new field data becomes available.
Livestock Routes
Livestock
EPSG:4326
CRS:84
40.9945983886719
50.8119888305664
-1.13716804981232
11.5744161605835
other
other
text/xml
other
other
other
other
geonode:Som_Lower_Juba_Agrimask_29062021WGS_
Som Lower Juba Agrimask 29062021WGS
This classification was done using LCCS3 legend as a framework for classifying agriculture. LCCS3 is the last version of the Land Cover Classification System (LCCS) developed by FAO and UNEP in 1998 to facilitate the understanding of theclasses of land cover regardless of the scale of mapping, the type of coverage,method of data collection, or geographic location.Sentinel 2 images were downloaded and segmented using ecognition software.The segments were then classified using LCCS legend.During the classification, photo keys of different agricultural classes created on google earth was used as visual verification of class types before classification, given that sentinel 2 images has a resolution of 30m and therefore not clear enough to visually detect differences between classes.In additon , High Resolution Google and Esri satellite basemaps were used for verification.The scale of editing and digitization of polygons was 1:10000
feature
EPSG:4326
CRS:84
41.4376640319824
42.9008522033691
-1.66186499595642
0.892129123210907
other
other
text/xml
other
other
other
other
geonode:Som_Mog_Urban_Sites
Mogadishu Urban Sites
This dataset shows key institutions and buildings in Mogadishu .The data was extracted from topographic map of scale 1:25000
urban
Charcoalsites_2018_2019_Antony
mogadishu
somalia
EPSG:4326
CRS:84
45.2350883483887
45.3798522949219
1.98064994812012
2.09048008918762
other
other
text/xml
other
other
other
other
geonode:Som_North_Aquifers_2012
Somalia North Aquifers 2012
No abstract provided
features
Som_North_Aquifers_2012
EPSG:4326
CRS:84
42.6608543395996
51.4098434448242
6.72866678237915
11.9814615249634
other
other
text/xml
other
other
other
other
geonode:Som_Puntland_CeelDoofar_Agrimask_17102022
Som_Puntland_CeelDoofar_Agrimask_17102022
No abstract provided
AgriMask_Ahmed_AoI
EPSG:32639
CRS:84
49.006029949685946
49.09036751087946
10.613270545722358
10.659217557537104
other
other
text/xml
other
other
other
other
geonode:Som_Security_Projected_Risk_2018_FAOSWALIM
Projected Security Risk in Somalia (2018)
This dataset contains the administrative bundaries of Somalia (district, regions, zones).
The boundaries were first created by USAID/FEWS and modified by UNDP - Data & Information Management Unit (DIMU) in 21/03/2001
This layer contains the first level (International) boundary at a 1:1,000,000 scale. The coverage is part of the Famine Early Warning System (FEWS)/Associates in Rural Development (ARD), African series.
The coastline was digitized from LANDSAT TM images at a scale of 1:50000 by DIMU. This coastline was then added to the boundaries and the original coastline amended.
Acknowledgement of USAID, FEWS, EDC-International Program, ADDS (Africa Data Dissemination Service), UNDP and the U.S Geological Survey would be appreciated in products derived from these data.
The source of the second (region) and third (district) level boundaries are from the Digital Chart of the World (DCW).
The names of the regions and districts have been agreed upon by the Statistical Working Group at very high levels by statistical focal points from UN agencies, and Ministries from within the Transitional Federal government of Somalia. The names have also been endorsed by the Somalia Interagency Mapping and Coordination group (SIMaC).
This layer was compiled using the pre-war boundaries and the names adopted by this statistical working group. Additional columns hold "alternate" spellings such as anglicized names.
somalia
Agricultural_Flooded_Areas_25May_3June2018
security
projected
risk
EPSG:4326
CRS:84
40.9895057678223
51.4141311645508
-1.66006135940552
11.9859437942505
other
other
text/xml
other
other
other
other
geonode:Somalia_Coastal_Mask
Somalia Coastal Mask
Somalia coast mask scale 1:250, 000
Som_Agrimask_WorkingAOI
somalia
coastal
EPSG:4326
CRS:84
40.4722175598145
52.4714508056641
-4.98693943023682
13.5211839675903
other
other
text/xml
other
other
other
other
geonode:Somalia_HOTOSM_data_planet_osm_line_lines2
Somalia_HOTOSM_data_lines
OpenStreetMap Data downloaded with the HOT Export Tool, an open service that creates customized extracts of up-to-date OSM data in various file formats.
Use the data simply by crediting the OpenStreetMap contributors.
Data is copyright OpenStreetMap Contributors, ODbL 1.0 licensed.
features
EPSG:4326
CRS:84
40.9899978637695
51.4150695800781
-1.66000008583069
11.9900007247925
other
other
text/xml
other
other
other
other
geonode:Somalia_HOTOSM_data_planet_osm_point_points0
Somalia_HOTOSM_data_points
OpenStreetMap Data downloaded with the HOT Export Tool, an open service that creates customized extracts of up-to-date OSM data in various file formats.
Use the data simply by crediting the OpenStreetMap contributors.
Data is copyright OpenStreetMap Contributors, ODbL 1.0 licensed.
features
EPSG:4326
CRS:84
40.9920768737793
51.4115753173828
-1.65984714031219
11.98743724823
other
other
text/xml
other
other
other
other
geonode:Somalia_HOTOSM_data_planet_osm_polygon_polygo
Somalia_HOTOSM_data_polygons
OpenStreetMap Data downloaded with the HOT Export Tool, an open service that creates customized extracts of up-to-date OSM data in various file formats.
Use the data simply by crediting the OpenStreetMap contributors.
Data is copyright OpenStreetMap Contributors, ODbL 1.0 licensed.
features
EPSG:4326
CRS:84
40.9899978637695
51.4200019836426
-1.66000008583069
11.9900007247925
other
other
text/xml
other
other
other
other
geonode:Somaliland_Agrimask_17102022
Somaliland_Agrimask_17102022
No abstract provided
AgriMask_Ahmed_AoI
EPSG:32638
CRS:84
43.46838784535685
43.50599400497368
9.579635887340276
9.616508480700396
other
other
text/xml
other
other
other
other
geonode:Strategic_Water_Sources_20225
Strategic_Water_Sources_December_2022
No abstract provided
features
Strategic_Water_Sources_2022
EPSG:4326
CRS:84
40.9956665039062
51.2722625732422
-1.14006161689758
11.42396068573
other
other
text/xml
other
other
other
other
geonode:Strategic_Water_Sources_All_Sept2020
Strategic Water Sources - September 2020
The dataset contains the most common water source types in Somalia classified into five categories namely: boreholes, shallow wells, dams, springs and berkads. Other source types which do not fit in any of these five categories are classified as “Other” The data is periodically updated from field surveys carried out by FAO/SWALIM through government and NGO partners, or received from WASH Cluster partners.Modern technology has been adopted for field data collection, using mobile phones, building on the conventional paper based questionnaire first developed and adopted by WASH partners in Somalia back in 2006
Strategic_Water_Sources_All_Sept2020
features
EPSG:4326
CRS:84
41.0186996459961
51.282901763916
-0.366900026798248
11.9533004760742
other
other
text/xml
other
other
other
other
geonode:Surface_Water_Sources
Surface_Water_Sources
No abstract provided
features
Surface_Water_Sources
EPSG:4326
CRS:84
-3.40282E38
51.2077903747559
-3.40282E38
11.7885608673096
other
other
text/xml
other
other
other
other
geonode:T37MGU_20180113T072239_B0stack_raster_Liban_F1
T37MGU_20180113T072239_B0stack_raster_Liban_F1
No abstract provided
features
T37MGU_20180113T072239_B0stack_raster_Liban_F0
EPSG:32737
CRS:84
41.4373217799386
41.90658237984302
-1.6620277888112112
-0.8255374932991194
other
other
text/xml
other
other
other
other
geonode:T37MHV_20180103T072259_B0stack_raster_Liban_P0
T37MHV_20180103T072259_B0stack_raster_Liban_P0
No abstract provided
T37MHV_20180103T072259_B0stack_raster_Liban_P
features
EPSG:32737
CRS:84
41.70021476736851
42.52702392790747
-0.9895814587748176
-0.3439642185820984
other
other
text/xml
other
other
other
other
geonode:T37MHV_20180103T072259_B0stack_raster_Liban_P1
T37MHV_20180103T072259_B0stack_raster_Liban_P1
No abstract provided
T37MHV_20180103T072259_B0stack_raster_Liban_P0
features
EPSG:32737
CRS:84
41.70021454378477
42.52702392790747
-0.9895814587748176
-0.3431695730595315
other
other
text/xml
other
other
other
other
geonode:T37NHA_20180222T071909_B0stack_raster2
T37NHA_20180222T071909_B0stack_raster
features
T37NHA_20180222T071909_B0stack_raster
EPSG:32637
CRS:84
41.694843721224785
42.682938586212146
-0.0883842151787315
0.30156154233857274
geonode:T37NHA_20180222T071909_B0stack_raster_SUFI
T37NHA_20180222T071909_B0stack_raster_SUFI
No abstract provided
T37NHA_20180222T071909_B0stack_raster_SUFI
features
EPSG:32637
CRS:84
41.694095143226164
42.695883231719705
0.13767485596186957
0.9064335041307702
other
other
text/xml
other
other
other
other
geonode:T37NHA_20180222T071909_B0stack_raster_Sufi_An
T37NHA_20180222T071909_B0stack_raster_Sufi_An
No abstract provided
T37NHA_20180222T071909_B0stack_raster_Sufi_Antony2
features
EPSG:32637
CRS:84
41.80697968251682
42.901311764038724
0.10122597998028421
0.8922400921612212
other
other
text/xml
other
other
other
other
geonode:T37NHB_20180113T072239_B0stack_raster_Sufi
T37NHB_20180113T072239_B0stack_raster_Sufi
No abstract provided
T37NHB_20180113T072239_B0stack_raster_Sufi
features
EPSG:32637
CRS:84
41.71606171991782
42.593119976655956
0.8144416687994732
1.3157917300405324
other
other
text/xml
other
other
other
other
geonode:T37NHB_20180113T072239_B0stack_raster_Sufi_An
T37NHB_20180113T072239_B0stack_raster_Sufi_An
No abstract provided
features
T37NHB_20180113T072239_B0stack_raster_Sufi_Antony1
EPSG:32637
CRS:84
41.95610310528032
42.58709067648457
0.8148194744794854
1.315516835053021
other
other
text/xml
other
other
other
other
geonode:T38NKF_20180113T072239_B0stack_ISSE_raster1
T38NKF_20180113T072239_B0stack_ISSE_raster1
No abstract provided
feature
EPSG:32638
CRS:84
42.3347351665486
42.89311508901658
-0.0707136821222415
0.140924783161121
other
other
text/xml
other
other
other
other
geonode:T38NKJ_20180920T071609_B0stack_raster_30
T38NKJ_20180920T071609_B0stack_raster_3
features
T38NKJ_20180920T071609_B0stack_raster_3
EPSG:32638
CRS:84
42.9365899357142
43.200930158088376
2.712314785496814
2.896547471498747
geonode:T38NLH_20181020T071919_B0stack_raster00
T38NLH_20181020T071919_B0stack_raster00
No abstract provided
feature
EPSG:32638
CRS:84
43.779620297842996
44.18963916679777
1.7430173053735405
2.013878062075586
other
other
text/xml
other
other
other
other
geonode:T38NMG_20180130T071129_B0stack_raster10
T38NMG_20180130T071129_B0stack_raster10
No abstract provided
feature
EPSG:32638
CRS:84
44.097967145704374
44.90878439611829
1.623567179983431
1.81869088811981
other
other
text/xml
other
other
other
other
geonode:T38NMH_20180130T071129_B0stack_raster00
T38NMH_20180130T071129_B0stack_raster00
No abstract provided
feature
EPSG:32638
CRS:84
44.11042928445042
44.93500039934062
1.7195194239137956
2.176824904218532
other
other
text/xml
other
other
other
other
geonode:T38NMH_20180130T071129_B0stack_raster_Isse01
T38NMH_20180130T071129_B0stack_raster_Isse01
No abstract provided
feature
EPSG:32638
CRS:84
44.68113266312375
45.094306555145224
2.3793856686471737
2.716456113540246
other
other
text/xml
other
other
other
other
geonode:T38NMH_20180130T071129_B0stack_raster_Isse011
T38NMH_20180130T071129_B0stack_raster_Isse011
No abstract provided
feature
EPSG:32638
CRS:84
44.664246542374244
45.09435316664969
2.3793816829564687
3.2622449097444326
other
other
text/xml
other
other
other
other
geonode:T38NMH_20180130T071129_B0stack_raster_Isse1
T38NMH_20180130T071129_B0stack_raster_Isse1
No abstract provided
feature
EPSG:32638
CRS:84
44.68113266312375
45.094306555145224
2.3793856686471737
2.716456113540246
other
other
text/xml
other
other
other
other
geonode:T38NMH_20180130T071129_SUFI_B0stack_raster20
T38NMH_20180130T071129_SUFI_B0stack_raster20
No abstract provided
feature
EPSG:32638
CRS:84
44.569603151971386
45.38745683699538
1.8681965132706415
2.5267838718173468
other
other
text/xml
other
other
other
other
geonode:T38NMH_20180130T071129_SUFI_B0stack_raster200
T38NMH_20180130T071129_SUFI_B0stack_raster200
No abstract provided
feature
EPSG:32638
CRS:84
44.569603151971386
45.38745683699538
1.8681965132706415
2.5267838718173468
other
other
text/xml
other
other
other
other
geonode:T38NMH_T38NMG_T38NLH_2018_B0stack_Antony
T38NMH_T38NMG_T38NLH_2018_B0stack_Antony
No abstract provided
feature
EPSG:32638
CRS:84
44.26684565756331
44.9315936925941
1.623635940231231
2.1777354072527357
other
other
text/xml
other
other
other
other
geonode:T38NMH_T38NMG_T38NLH_2018_B0stack_Antony_v2
T38NMH_T38NMG_T38NLH_2018_B0stack_Antony_v2
No abstract provided
feature
EPSG:32638
CRS:84
44.26684565756331
44.9315936925941
1.623635940231231
2.1777354072527357
other
other
text/xml
other
other
other
other
geonode:T38NMJ_20180902T070621_B0stack_raster_Isse1
T38NMJ_20180902T070621_B0stack_raster_Isse1
No abstract provided
feature
EPSG:32638
CRS:84
44.664246542374244
45.09188992535095
2.6283617503375982
3.2622448837454847
other
other
text/xml
other
other
other
other
geonode:T38NMK_20180902T070621_B0stack_raster
T38NMK_20180902T070621_B0stack_raster
No abstract provided
feature
EPSG:32638
CRS:84
44.7524873907325
44.77550376096248
4.191628893804316
4.209151217997784
other
other
text/xml
other
other
other
other
geonode:T38NMK_20180902T070621_B0stack_raster1
T38NMK_20180902T070621_B0stack_raster1
No abstract provided
feature
EPSG:32638
CRS:84
44.7524873907325
44.77550376096248
4.191628893804316
4.209151217997784
other
other
text/xml
other
other
other
other
geonode:T38NMK_20180902T070621_B0stack_raster10
T38NMK_20180902T070621_B0stack_raster10
No abstract provided
feature
EPSG:32638
CRS:84
44.7524873907325
44.77550376096248
4.191628893804316
4.209151217997784
other
other
text/xml
other
other
other
other
geonode:T38NMK_20180902T070621_B0stack_raster5
T38NMK_20180902T070621_B0stack_raster5
No abstract provided
feature
EPSG:32638
CRS:84
44.7524873907325
44.77550376096248
4.191628893804316
4.209151217997784
other
other
text/xml
other
other
other
other
geonode:T38NMK_20180902T070621_B0stack_raster6
T38NMK_20180902T070621_B0stack_raster6
No abstract provided
feature
EPSG:32638
CRS:84
44.7524873907325
44.77550376096248
4.191628893804316
4.209151217997784
other
other
text/xml
other
other
other
other
geonode:T38NNH_20180130T071129_B0stack_Julie_16Nov0
T38NNH_20180130T071129_B0stack_Julie_16Nov0
No abstract provided
agrimask_middlejuba_Mohamed_Sufi_1
EPSG:4326
CRS:84
45.1012496948242
45.7297782897949
2.15210723876953
3.22041273117065
other
other
text/xml
other
other
other
other
geonode:T38NNH_20180130T071129_B0stack_julie_13th_Oct
T38NNH_20180130T071129_B0stack_julie_13th_Oct
No abstract provided
AgriMask_Ahmed_AoI
EPSG:4326
CRS:84
45.1012496948242
45.7332801818848
2.15166926383972
3.22041273117065
other
other
text/xml
other
other
other
other
geonode:T38NNH_20180130T071129_B0stack_raster_Isse1
T38NNH_20180130T071129_B0stack_raster_Isse1
No abstract provided
feature
EPSG:32638
CRS:84
45.07831544356092
45.33909686602549
2.297241095193584
2.6273802954402568
other
other
text/xml
other
other
other
other
geonode:T38NNH_20180130T071129_B0stack_raster_Isse1_R
T38NNH_20180130T071129_B0stack_raster_Isse1_R
No abstract provided
feature
EPSG:32638
CRS:84
45.078315330197164
45.33909686602549
2.295158869659987
2.6273802954402568
other
other
text/xml
other
other
other
other
geonode:T38NNH_20180130T071129_B0stack_raster_Isse4
T38NNH_20180130T071129_B0stack_raster_Isse4
No abstract provided
feature
EPSG:32638
CRS:84
45.175922735287834
45.719270564814764
2.153328838267268
3.001292006575537
other
other
text/xml
other
other
other
other
geonode:T38NNH_20180130T071129_B0stack_raster_Liban0
T38NNH_20180130T071129_B0stack_raster_Liban0
No abstract provided
feature
EPSG:32638
CRS:84
45.59315446438569
45.95959852939548
2.2063218040057633
2.7157304443210273
other
other
text/xml
other
other
other
other
geonode:T38NNJ_20180130T071129_B0stack_raster_Isse0
T38NNJ_20180130T071129_B0stack_raster_Isse0
No abstract provided
feature
EPSG:32638
CRS:84
45.07781621116902
45.3410831816962
2.621079470419963
3.2142023187665227
other
other
text/xml
other
other
other
other
geonode:T38NNJ_20180130T071129_B0stack_raster_Isse0_R
T38NNJ_20180130T071129_B0stack_raster_Isse0_R
No abstract provided
feature
EPSG:32638
CRS:84
44.99983714155408
45.3410831816962
2.621071837045361
3.214205299946244
other
other
text/xml
other
other
other
other
geonode:T38NNJ_20180130T071129_B0stack_raster_Isse2
T38NNJ_20180130T071129_B0stack_raster_Isse2
No abstract provided
feature
EPSG:32638
CRS:84
45.14636804479731
45.59231896228861
2.713951174077554
3.2126406495109054
other
other
text/xml
other
other
other
other
geonode:T38NNJ_20180130T071129_B0stack_raster_Liban0
T38NNJ_20180130T071129_B0stack_raster_Liban0
No abstract provided
feature
EPSG:32638
CRS:84
45.425053761688666
45.89061575948774
2.681771222686142
3.2253968425963477
other
other
text/xml
other
other
other
other
geonode:T38NNJ_20180130T071129_B0stack_raster_Sufi0
T38NNJ_20180130T071129_B0stack_raster_Sufi0
No abstract provided
feature
EPSG:32638
CRS:84
45.71057689447547
46.14635819149765
2.514404808884296
3.252300085898469
other
other
text/xml
other
other
other
other
geonode:T38NNJ_T38NNH_20180130T071129_14102022
T38NNJ_T38NNH_20180130T071129_14102022
No abstract provided
AgriMask_Ahmed_AoI
EPSG:32638
CRS:84
45.42463012608957
46.14635819149765
2.3207740798878533
3.2524618072273066
other
other
text/xml
other
other
other
other
geonode:T38NNK_20180902T070621_B0stack_raste1
T38NNK_20180902T070621_B0stack_raste1
No abstract provided
feature
EPSG:32638
CRS:84
45.19473113769076
45.89900522241364
4.245822916943599
4.508913798576967
other
other
text/xml
other
other
other
other
geonode:T38NNK_20180902T070621_B0stack_raster
T38NNK_20180902T070621_B0stack_raster
No abstract provided
feature
EPSG:32638
CRS:84
45.19473113769076
45.89900522241364
4.245822916943599
4.508913798576967
other
other
text/xml
other
other
other
other
geonode:T38NNK_20180902T070621_B0stack_raster2
T38NNK_20180902T070621_B0stack_raster2
No abstract provided
feature
EPSG:32638
CRS:84
45.19473113769076
45.89900522241364
4.245822916943599
4.508913798576967
other
other
text/xml
other
other
other
other
geonode:T38NNK_20180902T070621_B0stack_raster6
T38NNK_20180902T070621_B0stack_raster6
No abstract provided
feature
EPSG:32638
CRS:84
45.19473113769076
45.89900522241364
4.245822916943599
4.508913798576967
other
other
text/xml
other
other
other
other
geonode:T38NNL_20180912T070621_B0stack_raster
T38NNL_20180912T070621_B0stack_raster
No abstract provided
feature
EPSG:32638
CRS:84
44.99981918334971
45.981896741372694
4.434176668315336
5.412626421480122
other
other
text/xml
other
other
other
other
geonode:T38NNL_20180912T070621_B0stack_raster0
T38NNL_20180912T070621_B0stack_raster0
No abstract provided
feature
EPSG:32638
CRS:84
45.013228348057005
45.981896741372694
4.295781218164039
5.41262627667127
other
other
text/xml
other
other
other
other
geonode:T38NNL_20180912T070621_B0stack_raster1
T38NNL_20180912T070621_B0stack_raster1
No abstract provided
feature
EPSG:32638
CRS:84
45.013228348057005
45.981896741372694
4.295781218164039
5.41262627667127
other
other
text/xml
other
other
other
other
geonode:T38NNL_20180912T070621_B0stack_raster2
T38NNL_20180912T070621_B0stack_raster2
No abstract provided
feature
EPSG:32638
CRS:84
45.013228348057005
45.981896741372694
4.295781218164039
5.41262627667127
other
other
text/xml
other
other
other
other
geonode:T38NNL_20180912T070621_B0stack_raster3
T38NNL_20180912T070621_B0stack_raster3
No abstract provided
feature
EPSG:32638
CRS:84
45.013228348057005
45.981896741372694
4.295781218164039
5.41262627667127
other
other
text/xml
other
other
other
other
geonode:T38NNL_20180912T070621_B0stack_raster5
T38NNL_20180912T070621_B0stack_raster5
No abstract provided
feature
EPSG:32638
CRS:84
45.012079048738855
45.981896741372694
4.295781218164039
5.412626300745111
other
other
text/xml
other
other
other
other
geonode:T38NNL_20180912T070621_B0stack_raster6
T38NNL_20180912T070621_B0stack_raster6
No abstract provided
feature
EPSG:32638
CRS:84
45.012079048738855
45.981896741372694
4.295781218164039
5.412626300745111
other
other
text/xml
other
other
other
other
geonode:T38NPJ_20180130T071129_B0stack_raster
T38NPJ_20180130T071129_B0stack_raster
No abstract provided
feature
EPSG:32638
CRS:84
45.96173115527991
46.380121346988794
2.8545335140289123
3.4838558695750144
other
other
text/xml
other
other
other
other
geonode:T38NPJ_20180130T071129_B0stack_raster0
T38NPJ_20180130T071129_B0stack_raster0
No abstract provided
feature
EPSG:32638
CRS:84
45.96173115527991
46.380121346988794
2.8545335140289123
3.4838558695750144
other
other
text/xml
other
other
other
other
geonode:T38NPJ_20180130T071129_B0stack_raster1
T38NPJ_20180130T071129_B0stack_raster1
No abstract provided
feature
EPSG:32638
CRS:84
45.96173115527991
46.380121346988794
2.8545335140289123
3.4838558695750144
other
other
text/xml
other
other
other
other
geonode:T38NPJ_20180130T071129_B0stack_raster2
T38NPJ_20180130T071129_B0stack_raster2
No abstract provided
feature
EPSG:32638
CRS:84
45.96158805938622
46.40456102894306
2.676906321238847
3.491232349512627
other
other
text/xml
other
other
other
other
geonode:T38NPJ_20180130T071129_B0stack_raster_Aways1
T38NPJ_20180130T071129_B0stack_raster_Aways1
No abstract provided
feature
EPSG:32638
CRS:84
46.329998588916304
46.88075231469621
3.1863129795379637
3.63522470955443
other
other
text/xml
other
other
other
other
geonode:T38NPK_20180902T070621_B0stack_raster
T38NPK_20180902T070621_B0stack_raster
No abstract provided
feature
EPSG:32638
CRS:84
46.198670992275396
46.8929002515082
3.9098338511931394
4.522930711421049
other
other
text/xml
other
other
other
other
geonode:T38NPK_20180902T070621_B0stack_raster0
T38NPK_20180902T070621_B0stack_raster0
No abstract provided
feature
EPSG:32638
CRS:84
46.198670992275396
46.8929002515082
3.9098338511931394
4.522930711421049
other
other
text/xml
other
other
other
other
geonode:T38NPK_20180902T070621_B0stack_raster_Aways0
T38NPK_20180902T070621_B0stack_raster_Aways0
No abstract provided
feature
EPSG:32638
CRS:84
46.19271022124793
46.81191345650505
3.371857738156653
3.8658310635923256
other
other
text/xml
other
other
other
other
geonode:T38NPK_20180902T070621_B0stack_raster_Hersi
T38NPK_20180902T070621_B0stack_raster_Hersi
No abstract provided
feature
EPSG:32638
CRS:84
46.065705434991585
46.39710875401863
3.527366930769008
3.738734786322742
other
other
text/xml
other
other
other
other
geonode:T38NPK_20180902T070621_B0stack_raster_Hersi0
T38NPK_20180902T070621_B0stack_raster_Hersi0
No abstract provided
feature
EPSG:32638
CRS:84
46.065705434991585
46.39710875401863
3.527366930769008
3.738734786322742
other
other
text/xml
other
other
other
other
geonode:T38NPL_20180902T070621_B0stack_raster_Isse
T38NPL_20180902T070621_B0stack_raster_Isse
No abstract provided
feature
EPSG:32638
CRS:84
45.816275127214396
46.774783638725026
5.07573911467815
5.434943382817884
other
other
text/xml
other
other
other
other
geonode:T38NPL_20180902T070621_B0stack_raster_Isse2
T38NPL_20180902T070621_B0stack_raster_Isse2
No abstract provided
feature
EPSG:32638
CRS:84
45.59228031653105
46.774783638725026
5.07573911467815
5.435205045982862
other
other
text/xml
other
other
other
other
geonode:T38NQL_20180731T065621_B0stack_raster_Aways
T38NQL_20180731T065621_B0stack_raster_Aways
No abstract provided
feature
EPSG:32638
CRS:84
46.957138615959515
47.63125922982542
4.017528667161916
4.799121849260863
other
other
text/xml
other
other
other
other
geonode:T38NQL_20180731T065621_B0stack_raster_Aways2
T38NQL_20180731T065621_B0stack_raster_Aways2
No abstract provided
feature
EPSG:32638
CRS:84
46.449533930670306
47.63125922982542
3.7187714874744437
4.800390469145841
other
other
text/xml
other
other
other
other
geonode:T38NQL_20180731T065621_B0stack_raster_Aways20
T38NQL_20180731T065621_B0stack_raster_Aways20
No abstract provided
feature
EPSG:32638
CRS:84
46.449533930670306
47.63125922982542
3.7187714874744437
4.800390469145841
other
other
text/xml
other
other
other
other
geonode:T38NQL_20180907T070609_B0stack_raster
T38NQL_20180907T070609_B0stack_raster
No abstract provided
feature
EPSG:32638
CRS:84
46.26200213008165
46.89642585459268
3.909825887981688
4.524776523068787
other
other
text/xml
other
other
other
other
geonode:T38NQM_20180808T070619_B0stack_raster_Isse
T38NQM_20180808T070619_B0stack_raster_Isse
No abstract provided
feature
EPSG:32638
CRS:84
46.375935168830175
47.50030860248065
5.200304279092893
6.305900395517046
other
other
text/xml
other
other
other
other
geonode:T38NQM_20180808T070619_B0stack_raster_Isse2
T38NQM_20180808T070619_B0stack_raster_Isse2
No abstract provided
feature
EPSG:32638
CRS:84
46.375935168830175
47.50030860248065
5.200304279092893
6.305900395517046
other
other
text/xml
other
other
other
other
geonode:T38NQM_20180808T070619_B0stack_raster_last
T38NQM_20180808T070619_B0stack_raster_last
No abstract provided
feature
EPSG:32638
CRS:84
46.375935168830175
47.50030860248065
5.200304279092893
6.305900395517046
other
other
text/xml
other
other
other
other
geonode:T38NQM_20180808T070619_B0stack_raster_last0
T38NQM_20180808T070619_B0stack_raster_last0
No abstract provided
feature
EPSG:32638
CRS:84
46.375935168830175
47.50030860248065
5.200304279092893
6.305900395517046
other
other
text/xml
other
other
other
other
geonode:T38NQN_20180729T070619_SUFI_B0stack_raster0
T38NQN_20180729T070619_SUFI_B0stack_raster0
No abstract provided
feature
EPSG:32638
CRS:84
46.56921147345657
47.807918864398594
6.190897484127275
7.291528273447662
other
other
text/xml
other
other
other
other
geonode:T38NQN_20180729T070619_SUFI_B0stack_raster1
T38NQN_20180729T070619_SUFI_B0stack_raster1
No abstract provided
feature
EPSG:32638
CRS:84
46.56921147345657
47.807918864398594
6.190897484127275
7.291528273447662
other
other
text/xml
other
other
other
other
geonode:T38NQN_20180729T070619_SUFI_B0stack_raster2
T38NQN_20180729T070619_SUFI_B0stack_raster2
No abstract provided
feature
EPSG:32638
CRS:84
46.564882154051595
47.807918864398594
4.489533849154713
7.291528273447662
other
other
text/xml
other
other
other
other
geonode:T38NQN_20180729T070619_SUFI_B0stack_raster3
T38NQN_20180729T070619_SUFI_B0stack_raster3
No abstract provided
feature
EPSG:32638
CRS:84
46.564882154051595
47.82907678855699
4.4894528318750435
7.291528273447662
other
other
text/xml
other
other
other
other
geonode:Water_Cathments_Phases_2Ato7B_SWS
Water Cathments
Somalia surface water resources infrastructures rehabilitated by FAO Somalia under Cash for Work Programme.
Water Sources
EPSG:4326
CRS:84
40.9912528991699
50.5030174255371
-0.496943026781082
11.2295351028442
other
other
text/xml
other
other
other
other
geonode:Water_Sourse_Data_2022v3
Water_Sourse_Data_2022v3
No abstract provided
features
Water_Sourse_Data_2022v3
EPSG:4326
CRS:84
-3.40282E38
4.5288132E7
-3.40282E38
11.42396068573
other
other
text/xml
other
other
other
other
geonode:Water_Sourse_Data_2022v30
Water_Sourse_Data_2022v30
No abstract provided
features
Water_Sourse_Data_2022v3
EPSG:4326
CRS:84
-3.40282E38
4.5288132E7
-3.40282E38
11.42396068573
other
other
text/xml
other
other
other
other
geonode:ch_2011_20130
Charcoal Sites (2011-2013)
The dataset shows the dynamics of charcoal production in Somalia from 2011 to 2013.Multi-temporal dataset of very high-resolution remote sensing images such as WorldView-1, 2 and 3 were used to map kiln locations in the study area. The acquisition dates of the images range from 2011 to 2013. Charcoal production sites can be seen on satellite images as dark round/ almost round patches. Many small tracks/ paths are a common feature at these sites. They are used for access and transport. The average radius of the kilns is 3.3m but some are as big as 6m in radius.Multi temporal analysis of vhr images reveals a tremendous increase of charcoal sites over the years.
somalia
proscal
2011/2018
SOM_Flooded_Areas_Dayr_2013
charcoalsites_2018_2019_Benard
EPSG:4326
CRS:84
41.177375793457
43.1520118713379
-1.29592263698578
1.37009334564209
other
other
text/xml
other
other
other
other
geonode:ch_2014_20160
Charcoal Sites (2014-2016)
The dataset shows the dynamics of charcoal production in Somalia from 2014 to 2016.Multi-temporal dataset of very high-resolution remote sensing images such as WorldView-1, 2 and 3 were used to map kiln locations in the study area. The acquisition dates of the images range from 2014 to 2016. Charcoal production sites can be seen on satellite images as dark round/ almost round patches. Many small tracks/ paths are a common feature at these sites. They are used for access and transport. The average radius of the kilns is 3.3m but some are as big as 6m in radius.Multi temporal analysis of vhr images reveals a tremendous increase of charcoal sites over the years.
somalia
Flood_Mapping_Juba_May2016
proscal
charcoalsites_2018_2019_Benard
DotGrid2014
EPSG:4326
CRS:84
41.1956329345703
43.1609954833984
-1.57558763027191
1.20752143859863
other
other
text/xml
other
other
other
other
geonode:ch_2017_20190
Charcoal Sites (2017-2019)
The dataset shows the dynamics of charcoal production in Somalia from 2017 to 2019.Multi-temporal dataset of very high-resolution remote sensing images such as WorldView-1, 2 and 3 were used to map kiln locations in the study area. The acquisition dates of the images range from 2017 to 2019. Charcoal production sites can be seen on satellite images as dark round/ almost round patches. Many small tracks/ paths are a common feature at these sites. They are used for access and transport. The average radius of the kilns is 3.3m but some are as big as 6m in radius.Multi temporal analysis of vhr images reveals a tremendous increase of charcoal sites over the years.
proscal
charcoalsites_2018_2019_Benard
somalia
EPSG:4326
CRS:84
40.9957885742188
43.0554618835449
-1.61712372303009
1.29524850845337
other
other
text/xml
other
other
other
other
geonode:first_shape_file_in_Galgadud
first_shape_file_in_Galgadud
No abstract provided
features
first_shape_file_in_Galgadud
EPSG:32638
CRS:84
46.87338915619378
47.58451972809551
4.9484951685975025
5.425018839045708
other
other
text/xml
other
other
other
other
geonode:harmonised_settlement_14112022
Somalia Harmonised Settlements - November 2022
OCHA Somalia
Harmonised settlement 14 11 2022
features
harmonised_settlement_14112022
EPSG:4326
CRS:84
40.9920768737793
51.2826614379883
-1.637540102005
11.9603900909424
other
other
text/xml
other
other
other
other
geonode:labSoil
Somalia North & South Soil Survey Samples Distribution
Data Shows soils field samples collected in the Northern and southern parts of Somalia by SWALIM during soil surveys in 2006 and 2008 respectively.For field photos attached to some of the sample points see the link https://www.dropbox.com/sh/n60nk7i00g76r1e/AAAYlbJUDYgR_MmWKcvkKyVMa?dl=0
labSoil
features
EPSG:4326
CRS:84
42.257999420166
45.791202545166
0.039444398134947
10.6282005310059
other
other
text/xml
other
other
other
other
geonode:lshabelle_Hersi_23_2_2021
lshabelle_Hersi_23_2_2021
No abstract provided
lshabelle_Hersi_23_2_2021
features
EPSG:32638
CRS:84
44.087478001265076
44.64504225655606
1.2445686439330668
1.7148800445512444
other
other
text/xml
other
other
other
other
geonode:merge
T38NNL_20180912T070621_B0stack_raster_Abdimajid
No abstract provided
feature
EPSG:4326
CRS:84
44.7524909973145
45.9813766479492
4.19162893295288
5.41233968734741
other
other
text/xml
other
other
other
other
geonode:merge2
merge
features
merge
EPSG:4326
CRS:84
44.7524909973145
45.9813766479492
4.19162893295288
5.41233968734741
geonode:mogadishu_city_roads
Mogadishu City Roads
Digital city map of Mogadishu based on local citymaps, NIMA citymaps, Landsat ETM+ and QB-images
mogadishu_city_roads
mogadishu
EPSG:4326
CRS:84
45.2217102050781
45.4724617004395
1.9974570274353
2.13535952568054
other
other
text/xml
other
other
other
other