GeoNode Local GeoServer

Africa_Cities (geonode:Africa_Cities)

The layer of all cities in Africa from the Department of Survey 2019

Chitipa_forests (geonode:Chitipa_forests)

No abstract providedMinistry of Forest is running a project on forest regeneration in all the districts of Malawi. this data was digitized by the department of surveys with the help of the forest department at both National and District levels. the layer depicts all the the sites for forest regeneration in Chitipa Districts

Malawi_Sentinel2_LULC20160 (geonode:Malawi_Sentinel2_LULC20160)

No abstract provided

Phalombe_Forest (geonode:Phalombe_Forest)

Ministry of Forest is running a project on forest regeneration in all the districts of Malawi. this data was digitized by the department of surveys with the help of the forest department at both National and District levels. the layer depicts all the the sites for forest regeneration in Phalombe Districts.

Thyolo_Forest (geonode:Thyolo_Forest)

Ministry of Forest is running a project on forest regeneration in all the districts of Malawi. this data was digitized by the department of surveys with the help of the forest department at both National and District levels. the layer depicts all the the sites for forest regeneration in Thyolo Districts

Forest Landscape Restoration: Agricultural technologies opportunity (geonode:agro_opp_shp)

This dataset represents the opportunity area for implementing the agricultural technologies restoration intervention as modeled for the National Forest Landscape Restoration Assessment. Agricultural technologies refer to any type of intercropping of trees with crops and include conservation agriculture (CA), farmer-managed natural regeneration (FMNR), and agroforestry (AF).

Basemap 8 (geonode:basemap_8)

No abstract provided

SRBMP- Locations Surveyed for Birds (geonode:bird_locations)

SIBNHES- Locations Surveyed for Birds Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project (Year 2)

blantyre_boundary (geonode:blantyre_boundary)

No abstract provided

Borrow pit location (geonode:bpc2_4)

No abstract provided

OSM Building (as points) (geonode:building_poly_pt_as_points)

Data generate from OSM using the HOT export (http://export.hotosm.org/en/jobs/8398). Buildings as point and polygons were combined to a single point layer using QGIS2.

Proposed Shire Valley Canal (geonode:canal)

The layer depict the proposed shire valley Canal being constructed in chikwawa. The program will address the agriculture, water, energy nexus in a landscape approach, aiming at irrigating about 43,370 ha of land in Chikwawa and Nsanje Districts, in order to increase their economic prospects and food security. It involves a water intake from the Shire River located within the Majete Wildlife Reserve (MWR) and three Main Canals of a total length of about 133 km.

Catchment Intervention (geonode:catchmentintervention)

Catchment level intervention in four priority catchments of SRBMP

Chiradzulu District Boundary (geonode:chiradzulu)

Creative

chiradzulu_district (geonode:chiradzulu_district)

Its a district found in the Southern part of Malawi, it shares boundary with Thyolo, Zomba and Blantyre. The languages used there are Lomwe and Yao

chitipa (geonode:chitipa)

chitipa district boundary, extracted 17 in salima

chitipa_10 (geonode:chitipa_10)

No abstract provided

chitipa_12 (geonode:chitipa_12)

No abstract provided

chitipa_3 (geonode:chitipa_3)

No abstract provided

chitipa_9 (geonode:chitipa_9)

No abstract provided

Forest Landscape Restoration: Community forest opportunity (geonode:commforest_opp_shp)

This dataset represents the opportunity area for implementing community forest and woolot interventions as modeled for the National Forest Landscape Restoration Assessment. Community forests (such as graveyards and village forest areas (VFAs)) and woodlots are areas of customary or private land set aside and managed for wood and range of provisioning, regulating and non-wood-cultural services including, non-timber products, medicinal plants and burial. They may be managed by a Traditional Authority, a community, a family or an individual. Community non-cultural forests and woodlots, if planned and managed properly, can provide a regular supply of products (e.g., poles, timber, fuel wood, fruit, etc.) for household consumption and/or for sale. Both through provision of wood products and income, community forests can reduce pressure on forest reserves and other protected areas. Credits: Ministry of Natural Resources, Energy and Mining - Malawi, International Union for Conservation of Nature (IUCN) and World Resources Institute (WRI) in support of the Forest Landscape Restoration Opportunities Assessment for Malawi, 2017.

Biodiversity and Degradation, Restoration Priority (geonode:degradation_biodiversity)

Forest landscape restoration opportunity for biodiversity, this analysis multiplies the magnitude of coincident biodiversity criteria in the Biodiversity Opportunity Areas map (as defined in the National Forest Landscape Restoration Assessment, 2017) with the magnitude of coincident criteria in the Functional Degradation map. The result demonstrates a level of priority for FLR intervention activities that would conceivably support both biodiversity priorities and the restoration of degraded land.

Forest Landscape Restoration Priority (geonode:degradation_fs_res_bio_add)

Data shows the sum of the three priority landscape restoration scenarios that formed the objectives of FLR in Malawi. These data demonstrate priority areas for addressing all of the FLR themes in Malawi, as described in the National Assessment and are underpinned by an assessment of functional degradation . These data indicate the highest priority areas based on an additive treatment of the product of three multi-criteria analyses (food security*degradation + resilience*degradation + and biodiversity*degradation). Based on the input data, restoration in the highest priority areas would most benefit all of the input criteria, including drivers of degradation.

Elephant Marsh (geonode:em_1)

No abstract provided

SRBMP existing_hydropower_stations (geonode:existing_hydropower_stations)

No abstract provided

features (geonode:features)

No abstract provided

Jan2015-flood_TerraSARX_by_DLR (geonode:flood_terrasarx_worldbank)

GLIDE#: FL-2015-000006-MWI. DLR derived flood extent using TerraSARX data for Malawi floods Jan 2015. Source: TerraSAR-X Acquired: TerraSAR-X: 10/01/2015 Copyright: TerraSAR-X © German Aerospace Center (DLR), 2015 Airbus Defence and Space / Infoterra GmbH

SRBMP- Large Mammal Focus Group Discussions (geonode:focal_group_discussions)

SIBNHES- Large Mammal Focus Group Discussions Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project (Year 2)

Forest Landscape Restoration: Forest management opportunity (geonode:forestmgmt_opp_shp)

This dataset represents the opportunity area for implementing the forest management restoration intervention as modeled for the National Forest Landscape Restoration Assessment. Forest management interventions include three types: 1) Regeneration of recently degraded or deforested areas through managed natural regeneration and plantings; 2) Improved management of existing plantations; and 3) Protection of existing natural forests inside and outside forest reserves and other protected areas.

Malawi - Admin boundaries (GAUL) (geonode:gaul_malawi_poly)

The Global Administrative Unit Layers (GAUL) is an initiative implemented by FAO within the CountrySTAT and Agricultural Market Information System (AMIS) projects. The GAUL compiles and disseminates the best available information on administrative units for all the countries in the world, providing a contribution to the standardization of the spatial dataset representing administrative units. The GAUL always maintains global layers with a unified coding system at country, first (e.g. departments) and second administrative levels (e.g. districts). Where data is available, it provides layers on a country by country basis down to third, fourth and lowers levels. The overall methodology consists in a) collecting the best available data from most reliable sources, b) establishing validation periods of the geographic features (when possible), c) adding selected data to the global layer based on the last country boundaries map provided by the UN Cartographic Unit (UNCS), d) generating codes using GAUL Coding System and e) distribute data to the users (see TechnicalArticleG2014.pdf). Because GAUL works at global level, unsettled territories are reported. The approach of GAUL is to deal with these areas in such a way to preserve national integrity for all disputing countries (see TechnicalArticleG2014.pdf and G2014_DisputedAreas.dbf). GAUL is released once a year and the target beneficiary of GAUL data is the UN community and other authorized international and national partners. Data might not be officially validated by authoritative national sources and cannot be distributed to the general public. A disclaimer should always accompany any use of GAUL data.

BRL Intervention (geonode:inrbrl_1)

REQUIRED: A brief narrative summary of the data set.

licenseexport (geonode:licenseexport)

No abstract provided

likoma Island (geonode:likoma_1)

This is Likoma Island, the smallest District in Malawi

lilongwe_4 (geonode:lilongwe_4)

No abstract provided

Upper Lisungwi Intervention (geonode:lswint1)

No abstract provided

machinga_district (geonode:machinga_district)

This is Machinga District.

Malawi Cities (geonode:malawi_cities)

This point layer represents Malawi Cities: Lilongwe, Blantyre and Mzuzu.

Administration - District Boundaries (geonode:malawi_district_boundaries)

District Boundary data provided by Department of Surveys, Lilongwe

malawi_government_buildings (geonode:malawi_government_buildings)

This is the data collected during the E Government project. where the department of surveys with the help of NSO and The Department of Land Resource collected all government offices. The data will be used to calculate distance for the connectivity of Internet.

Karonga fluvial flood risk ( 20 years return period) (geonode:malawi_karonga_flo_wd_fluvial_12m_t0020)

Karonga District fluvial flood risk 20 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of 20 years; otherwise expressed as its probability of occurring being 1/20, or 5% in any one year. This means that, in any given year, there is a 5% chance that it will happen, regardless of when the last similar event was.

Karonga district fluvial flood risk (500 years return period) (geonode:malawi_karonga_flo_wd_fluvial_12m_t0500)

Karonga fluvial flood risk 500 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of a flood might be 500 years; otherwise expressed as its probability of occurring being 1/500, or 0.2% in any one year. This means that, in any given year, there is a 0.2% chance that it will happen, regardless of when the last similar event was.

Malawi Landcover 1990 Scheme II (geonode:malawi_landcover_1990_schema_2)

Land Cover maps were developed for Green Houses gases Inventories to provide baseline data for Land use, land-use change and forestry (LULUCF)sector. The coverage for the Land Cover maps is six Eastern and Southern Africa (ESA) countries: Malawi, Rwanda, Zambia, Namibia, Botswana and Tanzania. The Land Cover maps have been developed from Landsat Imagery (30m by 30m) resolution using supervised classification. Image interpretation was done per scene. Images used for classification were selected based on seasonality, dry season images preferred. Land Cover maps are developed for two epochs: 2010 and 2000. For each year flexibility in image selection is allowed from previous and next year in each epoch. An epoch for 1990 is available in some of the project countries: Malawi and Rwanda. Classification scheme used is based on Intergovernmental Panel on Climate Change (IPCC) 6 land over categories for Schema I: Forestland, Grassland, Wetland, Cropland, Settlement and Other land. Classification Scheme II is informed by country specific interest, definitions, descriptions, mapping goals and policy statements and documents with guidance from IPCC Good Practice guidelines. Scheme II is such that it meets the country specific mapping standards and can be rolled back to the IPCC categories. Final map is taken through a 3pixel by 3 pixel filter to eliminate salt and pepper effect and remove isolated pixels.

Malawi Landcover 2000 Scheme II (geonode:malawi_landcover_2000_schema_2)

Land Cover maps were developed for Green Houses gases Inventories to provide baseline data for Land use, land-use change and forestry (LULUCF)sector. The coverage for the Land Cover maps is six Eastern and Southern Africa (ESA) countries: Malawi, Rwanda, Zambia, Namibia, Botswana and Tanzania. The Land Cover maps have been developed from Landsat Imagery (30m by 30m) resolution using supervised classification. Image interpretation was done per scene. Images used for classification were selected based on seasonality, dry season images preferred. Land Cover maps are developed for two epochs: 2010 and 2000. For each year flexibility in image selection is allowed from previous and next year in each epoch. An epoch for 1990 is available in some of the project countries: Malawi and Rwanda. Classification scheme used is based on Intergovernmental Panel on Climate Change (IPCC) 6 land over categories for Schema I: Forestland, Grassland, Wetland, Cropland, Settlement and Other land. Classification Scheme II is informed by country specific interest, definitions, descriptions, mapping goals and policy statements and documents with guidance from IPCC Good Practice guidelines. Scheme II is such that it meets the country specific mapping standards and can be rolled back to the IPCC categories. Final map is taken through a 3pixel by 3pixel filter to eliminate salt and pepper effect and remove isolated pixels.

Malawi Landcover 2010 Scheme II (geonode:malawi_landcover_2010_schema_2)

Land Cover maps were developed for Green Houses gases Inventories to provide baseline data for Land use, land-use change and forestry (LULUCF)sector. The coverage for the Land Cover maps is six Eastern and Southern Africa (ESA) countries: Malawi, Rwanda, Zambia, Namibia, Botswana and Tanzania. The Land Cover maps have been developed from Landsat Imagery (30m by 30m) resolution using supervised classification. Image interpretation was done per scene. Images used for classification were selected based on seasonality, dry season images preferred. Land Cover maps are developed for two epochs: 2010 and 2000. For each year flexibility in image selection is allowed from previous and next year in each epoch. An epoch for 1990 is available in some of the project countries: Malawi and Rwanda. Classification scheme used is based on Intergovernmental Panel on Climate Change (IPCC) 6 land over categories for Scheme I: Forestland, Grassland, Wetland, Cropland, Settlement and Other land. Classification Scheme II is informed by country specific interest, definitions, descriptions, mapping goals and policy statements and documents with guidance from IPCC Good Practice guidelines. Scheme II is such that it meets the country specific mapping standards and can be rolled back to the IPCC categories. Final map is taken through a 3pixel by 3pixel filter to eliminate salt and pepper effect and remove isolated pixels.

Mangochi pluvial flood risk (2 years return period) (geonode:malawi_mangochi_flo_wd_pluvial_12m_t0002)

Mangochi District pluvial flood risk 2 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of 2 years; otherwise expressed as its probability of occurring being 1/2, or 50% in any one year. This means that, in any given year, there is a 50% chance that it will happen, regardless of when the last similar event was

Mangochi pluvial flood risk (20 years return period) (geonode:malawi_mangochi_flo_wd_pluvial_12m_t0020)

Mangochi District pluvial flood risk 20 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of 20 years; otherwise expressed as its probability of occurring being 1/20, or 5% in any one year. This means that, in any given year, there is a 5% chance that it will happen, regardless of when the last similar event was

malawi_national_building_material (geonode:malawi_national_building_material)

Poorly constructed dwellings/houses are more sensitive to climate hazards. Houses whose walls are made of mud or unburnt bricks have a higher probability of damage incase of hazards like floods than for instance those made of burnt bricks, and concrete. This data has been developed by RCMRD and Malawi Department of Disaster Management Affairs (DoDMA). SERVIR is a joint USAID-NASA project. For more information on SERVIR

malawi_national_poverty_levels (geonode:malawi_national_poverty_levels)

No abstract provided

malawi_national_soil_organic_carbon (geonode:malawi_national_soil_organic_carbon)

High Soil organic matter is generally associated with higher crop yields and greater soil moisture retention. In Malawi, areas with high productivity are more sensitive to disruptions as a result of climate change and climate variability. This data has been developed by RCMRD and Malawi Department of Disaster Management Affairs (DoDMA). o abstract provided

malawi_natural (geonode:malawi_natural)

No abstract provided

Resilience, Opportunity Areas (geonode:malawi_resiliencemca)

Data represent a multi-criteria analysis of resilience as described on pp38 and pp122 (Annex 18), Table A12 of Malawi's National Forest Landscape Restoration Assessment (2017). https://portals.iucn.org/library/node/46837 Call number: IUCN-2017-029ed

Salima district fluvial flood risk (10 years return period) (geonode:malawi_salima_flo_wd_fluvial_12m_t0010)

Salima District fluvial flood risk 10 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of a flood might be 10 years; otherwise expressed as its probability of occurring being 1/10, or 10% in any one year. This means that, in any given year, there is a 10% chance that it will happen, regardless of when the last similar event was.

Salima fluvial flood risk ( 100 years return period) (geonode:malawi_salima_flo_wd_fluvial_12m_t0100)

Salima fluvial flood risk 100 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of a flood might be 100 years; otherwise expressed as its probability of occurring being 1/100, or 1% in any one year. This means that, in any given year, there is a 1% chance that it will happen, regardless of when the last similar event was. Or, put differently, it is 10 times less likely to occur than a flood with a return period of 10 years (or a probability of 10%)

Functional Landscape Degradation (geonode:malawidegradation_mca)

Data represent a multi-criteria analysis of functional landscape degradation as described on pp33 and pp122 (Annex 18), Table A10 of Malawi's National Forest Landscape Restoration Assessment (2017). https://portals.iucn.org/library/node/46837 Call number: IUCN-2017-029ed

mangochi_rivers_pgis_2018 (geonode:mangochi_rivers_pgis_2018)

No abstract provided

mlw_airports (geonode:mlw_airports)

No abstract provided

mw_railways_50kv1 (geonode:mw_railways_50kv1)

No abstract provided

Malawi City and Towns - Urban (geonode:mw_urban_city_towns)

Cities and towns boundaries to help delineate urban rural areas

mwi_trs_roadaccessconstraints_wfp (geonode:mwi_trs_roadaccessconstraints_wfp)

This dataset is based on an extraction of OpenStreetMap. In addition, it contains access constraints status in the column "status" and is edited regularly with information received by partners.

mzimba_district_boundary (geonode:mzimba_district_boundary)

Mzimba district boundary clipped from the Malawi district boundary

NkhataBay District (geonode:nkhata_bay_district)

NkhataBay is a lakeshore district in the northern region of Malawi. It is bordered by Rumphi to the northt, Mzimba to the northeast and southeast and Nkhotakota the south.

No Regret Intervention for Flood BRL (geonode:nrifld_1)

No abstract provided

nsanje (geonode:nsanje)

No abstract provided

nsanje_2 (geonode:nsanje_2)

No abstract provided

ntcheu_district_health_facilities (geonode:ntcheu_district_health_facilities)

Data for most of Healthy facilities in Ntcheu District

OSM Waterways (geonode:osm_waterway_lines)

Data derived from OpenStreetMap (export.hotosm.org). Selected features were extracted using QGIS. License under ODbL V1.

point_ft (geonode:point_ft)

No abstract provided

Rumphi Flight Plan (geonode:pp_blk8)

1995 Aerial Imagery Flight Plan

Primary Schools (geonode:primary_schools_ll)

Location of Primary Schools (2013)

SRBMP proposed_hydropower_station (geonode:proposed_hydropower_station)

No abstract provided

SRBMP- Locations of Regeneration Potential Survey Plots (geonode:regeneration_potential_survey_plots)

SIBNHES- Locations of Regeneration Potential Survey Plots Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project (Year 2)

Forest Landscape Restoration: River and stream-bank restoration opportunity (geonode:river_stream_opp_shp)

This dataset represents the opportunity area for implementing river- and stream-bank interventions as modeled for the National Forest Landscape Restoration Assessment. River- and stream-bank restoration focuses on establishing buffers of trees along streams and rivers courses to stabilize the soil, either through active planting or natural regeneration. The benefits of these protective buffers include decreased erosion and sedimentation into waterways, which improves water quality and quantity. This practice is particularly important in watersheds with downstream hydropower and reservoir infrastructure, where sedimentation is a major impediment to their efficiency and sustainability.

Salima District Administrative Boundary (geonode:salima)

Map of Salima District where you find Livingstonia Beach Hotel, Mpatsa Lodge, Blue Waters Lodge

Secondary Schools (geonode:secondary_schools_ll)

Secondary Schools (2013)

Sediment Retention, Ecosystem Service (geonode:sed_retention_malawi_aoi_lakes)

Utilising the InVEST sediment delivery ratio model, developed by The Natural Capital Project, these data demonstrate sediment retention to stream networks in Malawi. Model documentation can be found here: http://data.naturalcapitalproject.org/nightly-build/invest-users-guide/html/sdr.html

SRBMP shire_areas_highly_prone_to_floods_dd (geonode:shire_areas_highly_prone_to_floods_dd)

No abstract provided

SRBMP shire_interventions (geonode:shire_interventions)

Interventions currently under Shire River Basin Management Program.

SRBMP shire_streamflow_gauges_dd (geonode:shire_streamflow_gauges_dd)

No abstract provided

SRBMP- Interpolated GHI Values (geonode:sibnhes_interpolated_ghi_values)

SIBNHES- Interpolated Genetic Heat Index Values Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project

SRBMP- Rapid Botanical Surveys (geonode:sibnhes_rapid_botanical_surveys)

SIBNHES- Rapid Botanical Surveys Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project

SRBMP- Shire Forested Areas 2015 (geonode:sibnhes_shire_forested_areas_2015)

SIBNHES- Shire Forested Areas 2015 Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project

SRBMP- Year 1 Natural Habitat Survey Plots (geonode:sibnhes_year_1_natural_habitat_survey_plots)

SIBNHES- Year 1 Natural Habitat Survey Plots Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project

OSM Sports Facilities (geonode:sports_fields)

Data downloaded from OSM, Features extracted in QGIS, Licence under ODbL version1

Village GRM C2 (geonode:villagegrmc2)

Villages selected for implementation of Grievance Redressal Mechanism around Kamuzu barrage under SRBMP