This dataset provides a two-tier annual Land Use (LU) and Urban Land Cover (LC) product suite over three African countries, Ethiopia, Nigeria, and South Africa, across a 5-year period of 2016-2020. Remote sensing data sources were used to create 30-m resolution LU maps (Tier-1), which were then utilized to delineate urban boundaries for 10-m resolution LC classes (Tier-2). Random Forest machine learning classifier models were trained on reference data for each tier and country (but one model was trained across all years); models were validated using a separate reference data set for each tier and country. Tier-1 LU maps were based on the 30-m Landsat time series, and Tier-2 urban LC maps were based on the 10-m Sentinel-2 time series. Additional data sources included climate, topography, night-time light, and soils. The overall map accuracy was 65-80% for Tier-1 maps and 60-80% for Tier-2 maps, depending on the year and country. The data are provided in cloud optimized GeoTIFF (COG) format.

This series of three-period land use land cover (LULC) datasets (1975, 2000, and 2013) aids in monitoring change in West Africa’s land resources (exception is Tchad at 4 kilometers). To monitor and map these changes, a 26 general LULC class system was used. The classification system that was developed was primarily inspired by the “Yangambi Classification” (Trochain, 1957). This fairly broad class system for LULC was used because the classes can be readily identified on Landsat satellite imagery. A visual photo-interpretation approach was used to identify and map the LULC classes represented on Landsat images. The Rapid Land Cover Mapper (RLCM) was used to facilitate the photo-interpretation using Esri’s ArcGIS Desktop ArcMap software. Citation: Trochain, J.-L., 1957, Accord interafricain sur la définition des types de végétation de l’Afrique tropicale: Institut d’études centrafricaines.

Nigeria Land Use: Land Area data was reported at 910,770.000 sq km in 2022. This stayed constant from the previous number of 910,770.000 sq km for 2021. Nigeria Land Use: Land Area data is updated yearly, averaging 910,770.000 sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 910,770.000 sq km in 2022 and a record low of 910,770.000 sq km in 2022. Nigeria Land Use: Land Area data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Nigeria – Table NG.OECD.ESG: Environmental: Land Use: Non OECD Member: Annual.

Nigeria NG: Urban Land Area Where Elevation is Below 5 Meters: % of Total Land Area data was reported at 0.041 % in 2010. This stayed constant from the previous number of 0.041 % for 2000. Nigeria NG: Urban Land Area Where Elevation is Below 5 Meters: % of Total Land Area data is updated yearly, averaging 0.041 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 0.041 % in 2010 and a record low of 0.041 % in 2010. Nigeria NG: Urban Land Area Where Elevation is Below 5 Meters: % of Total Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank: Land Use, Protected Areas and National Wealth. Urban land area below 5m is the percentage of total land where the urban land elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

Nigeria Real Estate – Land Use Zoning

  Dataset Description

Synthetic Land Administration & Titles data for Nigeria real estate sector. Category: Land Administration & TitlesRows: 30,000Format: CSV, ParquetLicense: MITSynthetic: Yes (generated using reference data from PropertyPro, Knight Frank, NBS, CBN, FMBN)

  Dataset Structure





  Schema

id: string date: string city: string value: float category: string

  Sample Data

| id | date… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/nigerian_realestate_land_use_zoning.

Nigeria Land Use: Land Area: Arable Land and Permanent Crops data was reported at 434,720.000 sq km in 2021. This stayed constant from the previous number of 434,720.000 sq km for 2020. Nigeria Land Use: Land Area: Arable Land and Permanent Crops data is updated yearly, averaging 333,000.000 sq km from Dec 1961 (Median) to 2021, with 61 observations. The data reached an all-time high of 434,720.000 sq km in 2021 and a record low of 271,760.000 sq km in 1961. Nigeria Land Use: Land Area: Arable Land and Permanent Crops data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Nigeria – Table NG.OECD.ESG: Environmental: Land Use: Non OECD Member: Annual.

The land use and urban data were derived from Landsat satellite imagery. The overall accuracy (OA) of the classification for specific dates was 98.5% (1984), 80.7% (2000), 85.4% (2006), 87.5% (2013) and 94.5% (2020). Vector datasets for the period of study were derived from OpenStreetMap datasets and comparisons with historical satellite imagery. All data have been reprojected to UTM Zone 31, WGS 1984 to allow for uniformity and measurements in metric units. Calibration was initially carried out using data spanning the entire study period. Subsequently, data up to 2013 was used to predict 2020 actual land use. Calibration validation was then carried out using pixel-based accuracy metrics. Finally, optimum calibration values were used for forecasting growth up to year 2040 at 30m resolution.

Nigeria Land Use: % of Land Area: Forest data was reported at 23.387 % in 2022. This records a decrease from the previous number of 23.566 % for 2021. Nigeria Land Use: % of Land Area: Forest data is updated yearly, averaging 26.256 % from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 29.125 % in 1990 and a record low of 23.387 % in 2022. Nigeria Land Use: % of Land Area: Forest data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Nigeria – Table NG.OECD.ESG: Environmental: Land Use: Non OECD Member: Annual.

Rapid urbanization in most African countries is increasing impervious surfaces, including building roofs, glass, concrete, asphalt, and paved roads. However, regionally consistent urban data are lacking to support large-scale research and assessment of impervious surface expansion and its impacts on urban heat islands, hydrology and flood risk, infectious disease risk, cropland and biodiversity loss, habitat fragmentation, and carbon sequestration. The West Africa Dataset of Impervious Surface Change (WADISC) uses all available Landsat data to map urban change in Ghana, Togo, Benin, and Nigeria. The approach combines machine learning algorithm with LandTrendr time series analysis to generate annual maps of urban impervious surface cover from 2001 - 2020. The overall mean absolute error was less than 6% cover and the root mean squared error was less than 10% cover, giving us confidence that the predictions can effectively distinguish areas with high versus low impervious cover. We further classified the impervious cover into developed (pixel value is greater than 20%) and undeveloped (pixel value is less than or equal to 20%) with 93% overall accuracy and approximately similar producer (79%) and user (80%) accuracies in developed areas. WADISC is available in two forms: 1) continuous impervious cover with values ranging from 0% – 100% and 2) Developed area classification with pixel values of 1 and 0, representing the presence and absence of developed area. These data can support consistent city, national and regional assessments and research on urbanization and its impacts.WADISC_Impervious.zip contains GeoTIFF files with continuous impervious cover data from 2001-2020.WADISC_Developed.zip contains GeoTIFF files with classified developed area data from 2001-2020.

Nigeria NG: Urban Land Area data was reported at 17,196.234 sq km in 2010. This stayed constant from the previous number of 17,196.234 sq km for 2000. Nigeria NG: Urban Land Area data is updated yearly, averaging 17,196.234 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 17,196.234 sq km in 2010 and a record low of 17,196.234 sq km in 2010. Nigeria NG: Urban Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank: Land Use, Protected Areas and National Wealth. Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

Nigeria Agriculture – Land Tenure

  Dataset Description

Synthetic Farm Management & Mechanization data for Nigeria agriculture sector. Category: Farm Management & MechanizationRows: 90,000Format: CSV, ParquetLicense: MITSynthetic: Yes (generated using reference data from FAO, NBS, NiMet, FMARD)

  Dataset Structure





  Schema

id: string date: string state: string value: float category: string

  Sample Data

| id | date | state |… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/nigerian_agriculture_land_tenure.

Nigeria NG: Rural Land Area data was reported at 880,103.313 sq km in 2010. This stayed constant from the previous number of 880,103.313 sq km for 2000. Nigeria NG: Rural Land Area data is updated yearly, averaging 880,103.313 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 880,103.313 sq km in 2010 and a record low of 880,103.313 sq km in 2010. Nigeria NG: Rural Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Land Use, Protected Areas and National Wealth. Rural land area in square kilometers, derived from urban extent grids which distinguish urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

Nigeria Land Use: Land Area: Permanent Meadows and Pastures data was reported at 251,720.000 sq km in 2022. This stayed constant from the previous number of 251,720.000 sq km for 2021. Nigeria Land Use: Land Area: Permanent Meadows and Pastures data is updated yearly, averaging 261,155.000 sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 270,000.000 sq km in 1976 and a record low of 234,710.000 sq km in 2009. Nigeria Land Use: Land Area: Permanent Meadows and Pastures data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Nigeria – Table NG.OECD.ESG: Environmental: Land Use: Non OECD Member: Annual.

There are so many problems confronting most contemporary cities in the recent time particularly among the less developed countries around the world. These problems have been recognized to be the product of lack of urban planning by the authority in-charge as well as individual members of the society. However, the negative relationship between urban population and urban development has been identified using different methodologies. The prime objective is to apply the technique of Remote Sensing and GIS technology to examine the trend, pattern, the relationship between sprawl and population as well as the socio-economic implications of urban sprawl in Ibadan. However, the population is estimated to increase by 68.5% between year 2000 and 2020 (2,207,829 – 3,223,429) while the corresponding projected land consumption is also expected to rise by 58.5% (52,220.3 – 89, 192.3 ha) which implies that both would have doubled but the population is likely to double itself much faster than the land mass. Similarly, there was a significant change in the land use of land cover between 1986 and 2000 and a good example was the farmland which had decreased by 67.9% between this periods. The implication of this growth on the socioeconomic well being of the population is that urban development would have encroached on the urban fringe where urban and periurban agriculture is being practiced leading to acute shortage of fresh food supply to the urban populace, while similarly the sprawl is likely to result in slums development.

The dataset arises from the "Forest Disturbance Detection Using Remote Sensing and Artificial Intelligence in Africa" (EO4Forest) project, a collaboration financed by the European Space Agency involving Wrocław University of Environmental and Life Sciences in Poland and Lagos State University in Nigeria. Aimed at forest monitoring in the Ogun and Lagos states, it incorporates detailed maps for 2015, 2019, 2022, and 2023, with a 20-meter per pixel resolution to ensure precise land cover representation. A legend file elucidating established land classes accompanies the m

The data includes an extensive set of training and validation samples, which are crucial for the accurate use and evaluation of the Random Forest classification technique used to analyze the data. The data segments also offer maps of land cover and forest gain and loss, with a focus on recent updates for specific subareas in 2022 and 2023, which can be found in the NTR_ForestUpdate folder.

Derived from Sentinel-2 and Landsat-8 satellite imagery, the dataset uses the Random Forest algorithm to determine land cover types, highlighting its importance for environmental studies, particularly those related to deforestation, reforestation and afforestation. It also includes maps of forest change to indicate areas of forest loss and gain, shedding light on the dynamics of Nigeria's forest cover along with the geographical boundaries of the study area.

The methodology behind the dataset, including data acquisition, processing, classification and analysis, is detailed in Python scripts available in a companion GitHub repository. This approach ensures transparency and reproducibility, providing users with access to both the processed data and the methodologies generating those results, thus offering a robust framework for advanced forest monitoring.

This web map is designed to provide an enriched geospatial platform to ascertain the flood potential status of our local place of residence and other land-use activities. Information on the flood risk distribution can be extracted by 5 major magnitudes (very high, high, moderate, low, and very low). The buildings, roads, and rail tracks that are susceptible to flooding based on the identified magnitudes are also included in the web map. In addition, the historical or flood inventory layer, which contains information on the previous flooding disasters that have occurred within the river basin, is included.

This web map is the result of extensive research using available data, open source and custom datasets that are extremely reliable.The collaborative study was done by Dr. Felix Ndidi Nkeki (GIS-Unit, BEDC Electricity PLC, 5, Akpakpava Road, Benin City, Nigeria and Department of Geography and Regional Planning, University of Benin, Nigeria), Dr. Ehiaguina Innocent Bello (National Space Research and Development Agency, Obasanjo Space Centre, FCT-Abuja, Nigeria) and Dr. Ishola Ganiy Agbaje (Centre for Space Science Technology Education, Obafemi Awolowo University, Ile-Ife, Nigeria). The study results are published in a reputable leading world-class journal known as the International Journal of Disaster Risk Reduction. The methodology, datasets, and full results of the study can be found in the paper.

The major sources of data are: ALOS PALSAR DEM; soil data from Harmonised World Soil Database-Food and Agriculture Organisation of the United Nations (FAO); land-use and surface geologic datasets from CSSTE, OAU Campus, Ile-Ife, Nigeria and Ibadan Urban Flood Management Project (IUFMP), Oyo State, Nigeria; transport network data was extracted from Open Street Map; building footprint data was mined from Google open building; and finally, rainfall grid data was downloaded from the Centre for Hydrometeorology and Remote Sensing (CHRS).

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems.

These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty.

Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis.

Hazards:

• coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)
• extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)
• tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)

Exposure:

• population (Schiavina et al, 2023)
• built-up area (Pesaresi et al, 2023)
• roads (OpenStreetMap, 2023)
• railways (OpenStreetMap, 2023)
• power plants (Global Energy Observatory et al, 2018)
• power transmission lines (Arderne et al, 2020)

Contextual information:

• elevation (European Union and ESA, 2021)
• land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)
• administrative boundaries from geoBoundaries (Runfola et al., 2020)

The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people.

To learn more about related concepts, there is a free short course available through the Open University on Infrastructure and Climate Resilience. This overview of the course has more details.

These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow:

• snkit helps clean network data
• nismod-snail is designed to help implement infrastructure exposure, damage and risk calculations

The open-gira repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis.

For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at https://east-africa.infrastructureresilience.org/ and is described in detail in Hickford et al (2023).

References

• Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: 10.5281/zenodo.3628142
• Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif
• Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/12705164.v3
• Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/14510817.v3
• Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.006f2c9a (Accessed on 09-AUG-2024)
• Copernicus DEM - Global Digital Elevation Model (2021) DOI: 10.5270/ESA-c5d3d65 (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved)
• Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; resourcewatch.org/
• Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries
• Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: 10.1029/2020EF001616
• Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details
• OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at global.infrastructureresilience.org
• Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA
• Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: 10.1371/journal.pone.0231866.
• Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: 10.5281/zenodo.8147088
• Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE
• Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: www.wri.org/publication/aqueduct-floods-methodology.

This web app is designed to provide an enriched geospatial platform to ascertain the flood potential status of our local place of residence and other land-use activities. Information on the flood risk distribution can be extracted by 5 major magnitudes (very high, high, moderate, low, and very low). The individual flood magnitude can be extracted by applying the query or filter tool on the top right-hand corner of the web app, and using search by GPS coordinate or address, the individual location can be checked whether or which flood magnitude it falls into. The buildings, roads, and rail tracks that are susceptible to flooding based on the identified magnitudes are also included in the web app. In addition, the historical or flood inventory layer, which contains information on the previous flooding disasters that have occurred within the river basin, is included.

This web app is the result of extensive research using available data, open source and custom datasets that are extremely reliable.The collaborative study was done by Dr. Felix Ndidi Nkeki (GIS and Enumeration Department, BEDC Electricity PLC, 5, Akpakpava Road, Benin City, Nigeria and Department of Geography and Regional Planning, University of Benin, Nigeria), Dr. Ehiaguina Innocent Bello (National Space Research and Development Agency, Obasanjo Space Centre, FCT-Abuja, Nigeria) and Dr. Ishola Ganiy Agbaje (Centre for Space Science Technology Education, Obafemi Awolowo University, Ile-Ife, Nigeria). The study results are published in a reputable leading world-class journal known as the International Journal of Disaster Risk Reduction. The methodology, datasets, and full results of the study can be found in the paper.

The major sources of data are: ALOS PALSAR DEM; soil data from Harmonised World Soil Database-Food and Agriculture Organisation of the United Nations (FAO); land-use and surface geologic datasets from CSSTE, OAU Campus, Ile-Ife, Nigeria and Ibadan Urban Flood Management Project (IUFMP), Oyo State, Nigeria; transport network data was extracted from Open Street Map; building footprint data was mined from Google open building; and finally, rainfall grid data was downloaded from the Centre for Hydrometeorology and Remote Sensing (CHRS). Contact Info: Phone: +23408063131159Email: nkekifndidi@gmail.com Phone: +2348117643525

Multi-year landuse classifications of Kano and Lagos, Nigeria; each in multiple years. The landuse classification is based on SPOT5 imagery, using the open-source MapPy libraries, developed for the World Bank Group by Jordan Graesser. Each classification is 4 category:
1) Neighborhoods with Small Regular Planned Buildings
2) Neighborhoods with Small Irregular Buildings
3) Neighborhoods with Large Buildings
4) Other (non-built up) Areas

Nigerians depend on fish for maintaining diverse and healthy diets. Fish are a key source of protein and micronutrients, both of which are important for healthy diets. Some research has shown that forests provide important ecosystem functions that support the productive capacity and sustainability of inland fisheries. Our study aims to empirically assess the relationship between forest cover around rivers and fish consumption. We use data from the Living Standards Measurement Survey (LSMS) and spatially merge household and village data with forest cover and river maps. We estimate the relationship between forest cover around rivers and average village fresh fish consumption, while also accounting for other socio-economic and geographical determinants. We find that that the density of forest cover around rivers is positively and significantly correlated with village consumption of fresh fish. Our results suggest that forests influence the consumption of fresh fish by improving the productivity of inland fisheries and increasing the availability of fish. Aquatic habitats tend to be overlooked in debates on land use and food production, and yet can be critically important sources of nutrient-rich foods that are limited in rural diets in developing countries, particularly for the poor. Clearing forests for agriculture in order to produce more agricultural crops might have the unintended consequence of reducing another important food source.