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A Geoinformation Approach for Assessing Urban Growth Impact on Farmlands Using Earth Observation Data: A Case Study of Owerri in Nigeria
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Resumo(s)
This study investigates the impact of rapid urban expansion on agricultural land in the southeastern part of Nigeria, leveraging Cloud computing, machine learning, remote sensing, GIS, and community engagement techniques. The unprecedented pace of urbanization poses significant challenges to sustainable development, particularly threatening food security by converting fertile agricultural lands into urban areas. Utilizing earth observation data, the study maps urban growth patterns and assesses their effects on the contraction of farmlands. Through a combination of supervised machine learning models—Random Forest, Support Vector Machine, and Classification and Regression Trees—applied on Landsat images from 1986, 2000, and 2022, the study identifies significant land cover changes, quantifying the transition from farmland to built-up areas. The findings reveal a notable increase in built-up land, from 8.2% in 1986 to 26.6% in 2022, alongside a variable trend in farmland coverage, initially decreasing from 37.3% in 1986 to 27.7% in 2000, then partially recovering to33.2% in 2022, yet not to its original extent. This transformation underscores the tension between urban development and agricultural sustainability, with implications for local food production and ecosystem services. Community engagement through surveys with local farmers further enriches the analysis, providing insights into the socio-economic impacts of urban sprawl, including altered farming practices and adaptive strategies. The study underscores the need for integrated urban planning and policy interventions to balance urban growth with agricultural preservation, contributing to the broader discourse on sustainable development and food security. Recommendations include fostering sustainable land-use practices, enhancing community adaptive capacities, and advocating for policy reforms to support affected farmers and ensure long-term sustainability of both urban and rural landscapes.
Descrição
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Palavras-chave
Earth Observation Geographical Information Systems Machine Learning Remote Sensing Spatial Analysis Sustainable Development Goals SDG 2 - Zero hunger SDG 11 - Sustainable cities and communities