Ph.D. Student in Surveying and Mapping Science and Technology, Research Center for Spatial Planning and Human-Environment System Simulation, School of Geography and Information Engineering, China University of Geosciences, Wuhan,
INTEGRATED MAPPING OF AGRICULTURAL PRODUCE MARKETS, LAND USE, LAND COVER, AND RURAL ACCESS ROADS IN BORNO STATE, NIGERIA
This integrated multi-thematic map was created to support ongoing efforts toward achieving Sustainable Development Goal (SDG) 1: No Poverty and SDG 2: Zero Hunger by addressing various aspects of food security and sustainable agriculture, particularly in the rural areas of Borno State, Nigeria, which have faced serious economic and security challenges for over a decade. With a total area of approximately 70,898 km², the state is a strategic and potential economic zone, boasting an international border with three countries and Lake Chad.
This map serves as a valuable resource for a diverse audience, including laymen, professionals, businesspeople, investors, and decision-makers. It provides crucial information for accessing food markets, as well as the existing and proposed roads linking farmlands and markets.
The spatial and attribute data of the seven agricultural produce markets and their thirty-two commodities were collected on-site by visiting all the markets, utilizing a handheld Garmin GPS, and administering questionnaires. Additionally, a total of thirty-four feeder roads linking agricultural produce from farmlands to markets were tracked using a handheld GPS. These roads were categorized into three senatorial zones of the state, each assigned a different color code (North zone in Yogo blue; Central zone in Flame red; and South zone in Spruce Green). Each road was assigned a unique ID, and its total length was recorded.
The existing road network layer obtained from DIVA-GIS was classified into Federal, State, and Local Government Roads and overlaid on the map. Simultaneously, the Land Use Land Cover (LULC) single-epoch map was processed to display the LULC classes of the area, especially the farmlands class, and their relationship to market produce. To create the LULC map, Landsat 8 imagery from 2019 was downloaded from the USGS website, pre-processed, and classified using the Maximum Likelihood Algorithm. Six classes were produced: Vegetation (Mantis color), Farmland (Light yellow), Forest (Fir green), Bareland (Light brown), Settlements (Mars red), Waterbody (Medium blue), and Lake Chad (Light blue), with their percentage cover calculated.
The map, produced on A0 paper size at a scale of 1:750,000, uses the WGS 84 Geographic Coordinate System, incorporating all essential map elements.