Adopting Land Cover Standards for Sustainable Development in Ghana: Challenges and Opportunities

Elisha Njomaba (), Fatima Mushtaq, Raymond Kwame Nagbija, Silas Yakalim, Ben Emunah Aikins and Peter Surovy
Additional contact information
Elisha Njomaba: Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 165 00 Prague, Czech Republic
Fatima Mushtaq: Food and Agriculture Organization of the United Nations (FAO), Viale delle Terme di Caracalla, 00153 Rome, Italy
Raymond Kwame Nagbija: Faculty of Geoinformation Science & Earth Observation, Department of Natural Resource Management, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
Silas Yakalim: Touton Ghana, Monitoring and Evaluation Department, 20-36 N Airport Road, Accra, Ghana
Ben Emunah Aikins: School of Public Health, College of Health Sciences, University of Ghana, Accra P.O. Box LG 13, Ghana
Peter Surovy: Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 165 00 Prague, Czech Republic

Land, 2025, vol. 14, issue 3, 1-40

Abstract: The adoption of land cover standards is essential for resolving inconsistencies in global, regional, and national land cover datasets. This study examines the challenges associated with integrating existing datasets, including variations in land cover class definitions, classification methodologies, limited interoperability, and reduced comparability across scales. Focusing on Ghana as a case study, this research aims to develop a land cover legend and land cover map aligned with International Organization for Standardization (ISO) 19144-2 standards, evaluate the effectiveness of improving land cover classification and accuracy of data, and finally, assess the challenges and opportunities for the adoption of land cover standards. This study uses a multi-sensor remote sensing approach, integrating Sentinel-1 and Sentinel-2 optical imagery with ancillary data (elevation, slope, and aspect), to produce a national land cover dataset for 2023. Using the random forest (RF) algorithm, the land cover map was developed based on a land cover legend derived from the West African land cover reference system (WALCRS). The study also collaborates with national and international organizations to ensure the dataset meets global reporting standards for Sustainable Development Goals (SDGs), including those for land degradation neutrality. Using a survey form, stakeholders in the land cover domain were engaged globally (world), regionally (Africa), and nationally (Ghana), to assess the challenges to and opportunities for the adoption of land cover standards. The key findings reveal a diverse range of land cover types across Ghana, with cultivated rainfed areas (28.3%), closed/open forest areas (19.6%), and savanna areas (15.9%) being the most dominant classes. The classification achieved an overall accuracy of 90%, showing the robustness of the RF model for land cover mapping in a heterogeneous landscape such as Ghana. This study identified a limited familiarity with land cover standards, lack of documentation, cost implication, and complexity of standards as challenges to the adoption of land cover standards. Despite the challenges, this study highlights opportunities for adopting land cover standards, including improved data accuracy, support for decision-making, and enhanced capacity for monitoring sustainable land cover changes. The findings highlight the importance of integrating land cover standards to meet international reporting requirements and contribute to effective environmental monitoring and sustainable development initiatives.

Keywords: standards; interoperability; sustainable development goals; geospatial; land cover; legend; Ghana; harmonization (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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