Lebanon GIS data

geoBoundaries ADM1 boundaries for Lebanon: 9 units, representing 2017. Source: geoBoundaries, Wikimedia Commons.

geoBoundaries ADM2 boundaries for Lebanon: 26 units, representing 2021. Source: geoBoundaries, Wikipedia. We also attribute credit to Wikimedia Commons user Rarelibra

geoBoundaries ADM0 boundaries for Lebanon: 1 units, representing 2010. Source: geoBoundaries, Wikimedia Commons.

uav imagery by Omran NAJJAR, acquired 2025-12-24 to 2025-12-24, 2.9 cm per pixel, published on OpenAerialMap.

This dataset that contains information about cultural places, such as museums, theaters, historic sites, and other entertainment venues, and describes the structure and availability of that data.

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2. Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Lebanon **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0517 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0099 million km (19.1793%) - Unpaved roads: 0.002 million km (3.8679%) About 0.0398 million km (76.9528%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0001 million km (0.2769%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

Lebanon administrative level 0-3 boundaries (COD-AB) dataset version 02. This dataset is structured into 3 levels: - Admin 1: 8 Governorate (Mouhafaza) - Admin 2: 26 District (Kaza) - Admin 3: 1627 Cadaster (Manatek Ikaria) Dates associated with this dataset: - 30 October 2025: dataset reviewed for accuracy and completeness - 13 November 2024: valid for use by the humanitarian community - 02 May 2014: last edit to the dataset before publishing - 02 May 2014: boundaries created by the source Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

This dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.

Historical baseline for OSM buildings that includes buildings not modified or digitized after March 20 2025 Building footprints across the region from OSM. Includes every feature tagged as a building (building=*), from residential homes through hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban planning. OpenStreetMap is a free, collaborative world map built by volunteers. Completeness varies by region: urban areas are usually well-mapped, remote areas may be incomplete. Cross-check critical decisions with local field knowledge. Source: OpenStreetMap OSM tags: Key:building Part of many exports grid on HDX by the HOT

Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: place, landuse - Part of the country exports on HDX by HOT

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: highway - Part of the country exports on HDX by HOT

Financial services: banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via `amenity`. Useful for financial-inclusion mapping, cash-distribution planning, and economic analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity - Part of the country exports on HDX by HOT

Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT

Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, building - Part of the country exports on HDX by HOT

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: building - Part of the country exports on HDX by HOT

Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: waterway, water, natural - Part of the country exports on HDX by HOT

Points of interest: public-facing amenities, shops, tourist attractions, and landmarks tagged via `amenity`, `shop`, `tourism`, or `man_made`. Useful for general POI reference, urban analysis, and basemap detail. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, shop, tourism, man_made - Part of the country exports on HDX by HOT

Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: aeroway, building, emergency - Part of the country exports on HDX by HOT

Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

A geospatial impact assessment was conducted to detect the significant negative changes of vegetation cover for evergreen forest during the conflict of 2023-2024 in Lebanon. The assessment used the baseline period of October 2019 to 7th October 2023 compared to October 2024 without field validation. Land cover and forest categories data are critical resources for analysing the status of forests. Sentinel-2 satellite imagery (10m resolution) was utilized for establishing baseline land cover for 2023. This map depicts the severity of damage in localized areas for evergreen forest. Each hexagon shows the total evergreen forest area with negative change. **Data creation:** 2025-09-15 **Supplemental Information:** Datasets for negative vegetation cover changes in forest categories are also available: [Negative vegetation cover changes in deciduous forest (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/dataset/699c121e-3209-4eeb-983e-224f1a23c5ea) More information on the damage to forest categories can be found in the following publications: - Fact-sheet: Forest categories with negative vegetation cover changes during the conflict in Lebanon as of October 2024 - [Report: Assessing vegetation changes in agriculture and forestry during the conflict in Lebanon – October 2023 to October 2024]( https://openknowledge.fao.org/handle/20.500.14283/cd5364en) **Contact points:** Metadata Contact: [Geospatial Unit](mailto:gis-manager@fao.org) Resource Contact: [Matieu Henry](mailto:Matieu.Henry@fao.org) **Data lineage:** Landsat 7 and 8 imageries (30m resolution) were used to analyse negative vegetation changes. The Normalized Difference Vegetation Index (NDVI) was used to compare forest categories conditions between the baseline and the October 2024 periods. Additionally, very high-resolution Maxar satellite imagery (<0.5 m) was used to calibrate the impact assessment model for different forest categories (evergreen and deciduous). **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** Fact-sheet: Forest categories with negative vegetation cover changes during the conflict in Lebanon as of October 2024

A geospatial impact assessment was conducted to detect the significant negative changes of vegetation cover for deciduous forest during the conflict of 2023-2024 in Lebanon. The assessment used the baseline period of October 2019 to 7th October 2023 compared to October 2024 without field validation. Land cover and forest categories data are critical resources for analysing the status of forests. Sentinel-2 satellite imagery (10m resolution) was utilized for establishing baseline land cover for 2023. This map depicts the severity of damage in localized areas for deciduous forest. Each hexagon shows the total deciduous forest area with negative change. **Data creation:** 2025-09-15 **Supplemental Information:** Datasets for negative vegetation cover changes in forest categories are also available: [Negative vegetation cover changes in evergreen forest (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/iso/2855517e-dcac-4d1c-a332-8711aae0912a) More information on the damage to forest categories can be found in the following publications: - Fact-sheet: Forest categories with negative vegetation cover changes during the conflict in Lebanon as of October 2024 - [Report: Assessing vegetation changes in agriculture and forestry during the conflict in Lebanon – October 2023 to October 2024]( https://openknowledge.fao.org/handle/20.500.14283/cd5364en) **Contact points:** Resource Contact: [Matieu Henry](mailto:Matieu.Henry@fao.org) Metadata Contact: [Geospatial Unit](mailto:gis-manager@fao.org) **Data lineage:** Landsat 7 and 8 imageries (30m resolution) were used to analyse negative vegetation changes. The Normalized Difference Vegetation Index (NDVI) was used to compare forest categories conditions between the baseline and the October 2024 periods. Additionally, very high-resolution Maxar satellite imagery (<0.5 m) was used to calibrate the impact assessment model for different forest categories (evergreen and deciduous). **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** Fact-sheet: Forest categories with negative vegetation cover changes during the conflict in Lebanon as of October 2024

A geospatial impact assessment was conducted to detect the significant negative changes of vegetation cover for fruit tree orchard during the conflict of 2023-2024 in Lebanon. The assessment used the baseline period of October 2019 to 7th October 2023 compared to October 2024 without field validation. Land cover and cropland data are critical resources for analysing the status of agricultural land. Sentinel-2 satellite imagery (10m resolution) was utilized for establishing baseline land cover for 2023. This map depicts the severity of damage in localized areas for fruit tree orchard. Each hexagon shows the total fruit tree orchard area with negative change. **Data creation:** 2025-09-15 **Supplemental Information:** This assessment was conducted in collaboration between FAO-NSL and CNRS, where the cropland/crop category datasets are derived from previous LULC maps produced by CNRS. Datasets for negative vegetation cover changes in other crop categories are also available, along with the aggregated dataset on cropland: [Negative vegetation cover changes in olive plantation (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/dataset/d9dcfdd8-7b7e-466b-b481-d18f482beda7) [Negative vegetation cover changes in field crops & grape field (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/iso/8e66aef8-db2d-463a-bbc4-1e1e8f3678f7) [Negative vegetation cover changes in cereals (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/dataset/92eb5e26-7117-4e2f-9ee7-c0ef882ebe0e) Negative vegetation cover changes in cropland (Lebanon, October 2024) More information on the damage to cropland and cropland categories can be found in the following fact-sheets: - Cropland with negative vegetation cover changes during the conflict in Lebanon as of October 2024 - Crop categories with negative vegetation cover changes during the conflict in Lebanon as of October 2024 **Contact points:** Resource Contact: [Matieu Henry](mailto:Matieu.Henry@fao.org) Metadata Contact: [Geospatial Unit](mailto:gis-manager@fao.org) **Data lineage:** Landsat 7 and 8 imageries (30m resolution) were used to analyse negative vegetation changes. The Normalized Difference Vegetation Index (NDVI) was used to compare cropland conditions between the baseline and the October 2024 periods. Additionally, very high-resolution Maxar satellite imagery (<0.5 m) was used to calibrate the impact assessment model for cropland. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** Fact-sheet: Crop categories with negative vegetation cover changes during the conflict in Lebanon as of October 2024

A geospatial impact assessment was conducted to detect the significant negative changes of vegetation cover for field crops & grape field during the conflict of 2023-2024 in Lebanon. The assessment used the baseline period of October 2019 to 7th October 2023 compared to October 2024 without field validation. Land cover and cropland data are critical resources for analysing the status of agricultural land. Sentinel-2 satellite imagery (10m resolution) was utilized for establishing baseline land cover for 2023. This map depicts the severity of damage in localized areas for field crops and grape field. Each hexagon shows the total field crops & grape field area with negative change. **Data creation:** 2025-09-15 **Supplemental Information:** This assessment was conducted in collaboration between FAO-NSL and CNRS, where the cropland/crop category datasets are derived from previous LULC maps produced by CNRS. Datasets for negative vegetation cover changes in other crop categories are also available, along with the aggregated dataset on cropland: [Negative vegetation cover changes in olive plantation (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/dataset/d9dcfdd8-7b7e-466b-b481-d18f482beda7) [Negative vegetation cover changes in cereals (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/dataset/92eb5e26-7117-4e2f-9ee7-c0ef882ebe0e) [Negative vegetation cover changes in fruit tree orchard (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/dataset/e344d2d0-20f3-4773-a60d-9e3280139170) Negative vegetation cover changes in cropland (Lebanon, October 2024) More information on the damage to cropland and cropland categories can be found in the following fact-sheets: - Cropland with negative vegetation cover changes during the conflict in Lebanon as of October 2024 - Crop categories with negative vegetation cover changes during the conflict in Lebanon as of October 2024 **Contact points:** Resource Contact: [Matieu Henry](mailto:Matieu.Henry@fao.org) Metadata Contact: [Geospatial Unit](mailto:gis-manager@fao.org) **Data lineage:** Landsat 7 and 8 imageries (30m resolution) were used to analyse negative vegetation changes. The Normalized Difference Vegetation Index (NDVI) was used to compare cropland conditions between the baseline and the October 2024 periods. Additionally, very high-resolution Maxar satellite imagery (<0.5 m) was used to calibrate the impact assessment model for cropland. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** Fact-sheet: Crop categories with negative vegetation cover changes during the conflict in Lebanon as of October 2024

A geospatial impact assessment was conducted to detect the significant negative changes of vegetation cover for cropland during the conflict of 2023-2024 in Lebanon. The assessment used the baseline period of October 2019 to 7th October 2023 compared to October 2024 without field validation. Land cover and cropland data are critical resources for analysing the status of agricultural land. Sentinel-2 satellite imagery (10m resolution) was utilized for establishing baseline land cover for 2023. This map depicts the severity of damage in localized areas for cropland. Each hexagon shows the total cropland area with negative change. **Data creation:** 2025-09-15 **Supplemental Information:** This assessment was conducted in collaboration between FAO-NSL and CNRS, where the cropland/crop category datasets are derived from previous LULC maps produced by CNRS. Datasets for negative vegetation cover changes in other crop categories are also available: [Negative vegetation cover changes in olive plantation (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/dataset/d9dcfdd8-7b7e-466b-b481-d18f482beda7) [Negative vegetation cover changes in field crops & grape field (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/iso/8e66aef8-db2d-463a-bbc4-1e1e8f3678f7) [Negative vegetation cover changes in cereals (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/dataset/92eb5e26-7117-4e2f-9ee7-c0ef882ebe0e) [Negative vegetation cover changes in fruit tree orchard (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/dataset/e344d2d0-20f3-4773-a60d-9e3280139170) More information on the damage to cropland and cropland categories can be found in the following fact-sheets: - Cropland with negative vegetation cover changes during the conflict in Lebanon as of October 2024 - Crop categories with negative vegetation cover changes during the conflict in Lebanon as of October 2024 **Citation:** FAO. [YYYY (year of last update)]. [Name of database: Name of dataset OR Name of database]. [Accessed on [DD Month YYYY]]. [URL] Licence: CC-BY-4.0 **Contact points:** Resource Contact: [Matieu Henry](mailto:Matieu.Henry@fao.org) Metadata Contact: [Geospatial Unit](mailto:gis-manager@fao.org) **Data lineage:** Landsat 7 and 8 imageries (30m resolution) were used to analyse negative vegetation changes. The Normalized Difference Vegetation Index (NDVI) was used to compare cropland conditions between the baseline and the October 2024 periods. Additionally, very high-resolution Maxar satellite imagery (<0.5 m) was used to calibrate the impact assessment model for cropland. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** Fact-sheet: Cropland with negative vegetation cover changes during the conflict in Lebanon as of October 2024

A geospatial damage assessment was carried out to evaluate the impact of the 2023–2024 conflict on greenhouses in Lebanon. In addition to the remote sensing analysis, visual interpretation using very high-resolution (VHR) satellite imagery from WorldView-2 (50 cm) and WorldView-3 (30 cm), was undertaken comparing images from before October 2023 and 2024 in four of the most conflict-affected governorates. The map depicts the distribution of damaged greenhouse areas using a hexagonal grid for the two visual interpretations. Overall, a 3.4 percent difference of damaged greenhouses was observed between visually interpreted points and delineated greenhouses. Two independent assessments were conducted with less than 3 percent difference. **Data creation:** 2024-12-31 **Supplemental Information:** This greenhouse assessment was conducted as part of a collaborative effort between FAO-NSL, CNRS, and UNOSAT/UNITAR, with CNRS providing essential data to update the assessment. More information can be found at https://openknowledge.fao.org/handle/20.500.14283/cd5246en **Contact points:** Resource Contact: [Matieu Henry](mailto:Matieu.Henry@fao.org) Metadata Contact: [Geospatial Unit](mailto:gis-manager@fao.org) **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** Shapefile, QGIS project, and SLD file for Greenhouse damage assessment (Lebanon, 2024) Fact-sheet: Greenhouse damage assessment during the conflict across Lebanon in 2024

This greenhouse baseline in Lebanon before the 2023-2024 conflict was generated by using Sentinel-2 satellite imagery with a resolution of 10 meters. The baseline map is part of a geospatial damage assessment carried out to evaluate the impact of the 2023–2024 conflict across Lebanon on the greenhouses in the country. The overall accuracy of remote sensing analysis was 94 percent. In addition to the remote sensing analysis, visual interpretation using VHR was undertaken in four governorates. The map represents the distribution of greenhouse areas in a hexagonal grid. **Data creation:** 2024-12-31 **Supplemental Information:** This greenhouse assessment was conducted as part of a collaborative effort between FAO-NSL, CNRS, and UNOSAT/UNITAR, with CNRS providing essential data to update the assessment. More information can be found at https://openknowledge.fao.org/handle/20.500.14283/cd5071en **Contact points:** Resource Contact: [Matieu Henry](mailto:Matieu.Henry@fao.org) Metadata Contact: [Geospatial Unit](mailto:gis-manager@fao.org) **Data lineage:** Nearly 3000 training data were collected using very high-resolution (VHR) satellite imagery (WorldView-2 [50 cm], WorldView-3 [30cm] and Google Earth), and available greenhouse area delineations for four governorates. Separability metrics for various indices were calculated for seasonal composites, including but not limited to the Plastic Greenhouse Index (PGHI), the Advanced Plastic Greenhouse Index (APGI) and the Plastic-Mulched Land Cover Index (PMLI). **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** Shapefile for Greenhouse baseline assessment (Lebanon, 2024) Fact-sheet: Greenhouse baseline assessment during the conflict across Lebanon in 2024

A geospatial impact assessment was conducted to detect the significant negative changes of vegetation cover for olive plantation during the conflict of 2023-2024 in Lebanon. The assessment used the baseline period of October 2019 to 7th October 2023 compared to October 2024 without field validation. Land cover and cropland data are critical resources for analysing the status of agricultural land. Sentinel-2 satellite imagery (10m resolution) was utilized for establishing baseline land cover for 2023. This map depicts the severity of damage in localized areas for olive plantation. Each hexagon shows the total olive plantation area with negative change. Changed **Data creation:** 2025-09-15 **Supplemental Information:** This assessment was conducted in collaboration between FAO-NSL and CNRS, where the cropland/crop category datasets are derived from previous LULC maps produced by CNRS. Datasets for negative vegetation cover changes in other crop categories are also available, along with the aggregated dataset on cropland: [Negative vegetation cover changes in field crops & grape field (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/iso/8e66aef8-db2d-463a-bbc4-1e1e8f3678f7) [Negative vegetation cover changes in cereals (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/dataset/92eb5e26-7117-4e2f-9ee7-c0ef882ebe0e) [Negative vegetation cover changes in fruit tree orchard (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/dataset/e344d2d0-20f3-4773-a60d-9e3280139170) Negative vegetation cover changes in cropland (Lebanon, October 2024) More information on the damage to cropland and cropland categories can be found in the following fact-sheets: - Cropland with negative vegetation cover changes during the conflict in Lebanon as of October 2024 - Crop categories with negative vegetation cover changes during the conflict in Lebanon as of October 2024 **Contact points:** Resource Contact: [Matieu Henry](mailto:Matieu.Henry@fao.org) Metadata Contact: [Geospatial Unit](mailto:gis-manager@fao.org) **Data lineage:** Landsat 7 and 8 imageries (30m resolution) were used to analyse negative vegetation changes. The Normalized Difference Vegetation Index (NDVI) was used to compare cropland conditions between the baseline and the October 2024 periods. Additionally, very high-resolution Maxar satellite imagery (<0.5 m) was used to calibrate the impact assessment model for cropland. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** Fact-sheet: Crop categories with negative vegetation cover changes during the conflict in Lebanon as of October 2024

Boundary of the Litani River Basin obtained from HydroBASINS (Lerner and Grill, 2013). Litani River Basin is derived from the HydroRIVERS product, which was obtained by delineating river networks from hydrologically corrected elevation data (WWF HydroSHEDS, Lehner et al. 2008; Lehner and Grill 2013). The Litani river, as well as any other river within the HydroRIVERS database, is co-registered to the sub-basin of the HydroBASINS database in which it resides (via a shared ID). Source: The HydroRIVERS product has been developed on behalf of World Wildlife Fund US (WWF), Washington DC, USA, in collaboration with McGill University, Montreal, Canada. Major funding for the underpinning HydroSHEDS project was provided to WWF by Sealed Air Corporation. Citations and acknowledgements of the HydroRIVERS data should be made as follows: Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org. **Data publication:** 2019-07-01 **Contact points:** Metadata Contact: [Bich Tran](mailto:b.tran@un-ihe.org) **Data lineage:** Contact WWF Hydrosheds for more information at https://www.hydrosheds.org/page/hydrobasins **Resource constraints:** HydroRIVERS data are covered by the same License Agreement as the HydroSHEDS database, which is available at https://www.hydrosheds.org/products/hydrorivers. HydroRIVERS data are free for non-commercial and commercial use. For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement. By downloading and using the data the user agrees to the terms and conditions of the License Agreement. **Online resources:** WWF Hydrosheds official website

Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027.  A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Lebanon Country Boundary provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. These were published in December 2022. Other administrative boundaries for this country are also available: Muhafazat

Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027.  A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Lebanon Muhafazat Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. These were published in December 2022. Other administrative boundaries for this country are also available: Country

Open and free data for assessing the human presence on the planet. The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.