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<p>Land cover change and its consequences such as environmental degradation and biodiversity loss pose significant global challenges, including in Nigeria’s Anambra River Basin. This study focuses on monitoring, predicting and understanding land cover changes in the basin from 1987 to 2018, with projections up to 2030. It explores the intricate relationship between population growth and land cover dynamics, aiming to contribute to sustainable land management practices and align with the Sustainable Development Goals (SDGs) for 2030. Using a combination of neural network classification and the CA-Markov model, the study analyses historical land cover data to identify significant transformations. Between 1987 and 2018, bare lands increased by 29%, vegetation increased by 14%, built-up areas increased by 128% and waterbodies increased by 10%, whereas there was a 58% decline in the extent of wetlands. The most significant transformation occurred in the wetlands, with a total of 1819.46 km<sup>2</sup> being converted to various land cover types. The results demonstrate remarkable shifts characterised by rapid urbanisation, substantial wetland loss and a decline in vegetation cover. Expectedly, population growth is found to be closely linked to the expansion of built-up areas while negatively impacting other land cover types. These findings underscore the urgent need for sustainable land management strategies that balance the demands of growing populations with the preservation of natural ecosystems and biodiversity. Furthermore, the study provides future projections that offer crucial insights for decision-makers involved in land use planning, biodiversity conservation and sustainable development.</p>
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Land cover change and its consequences such as environmental degradation and biodiversity loss pose significant global challenges, including in Nigeria’s Anambra River Basin. This study focuses on monitoring, predicting and understanding land cover changes in the basin from 1987 to 2018, with projections up to 2030. It explores the intricate relationship between population growth and land cover dynamics, aiming to contribute to sustainable land management practices and align with the Sustainable Development Goals (SDGs) for 2030. Using a combination of neural network classification and the CA-Markov model, the study analyses historical land cover data to identify significant transformations. Between 1987 and 2018, bare lands increased by 29%, vegetation increased by 14%, built-up areas increased by 128% and waterbodies increased by 10%, whereas there was a 58% decline in the extent of wetlands. The most significant transformation occurred in the wetlands, with a total of 1819.46 km2 being converted to various land cover types. The results demonstrate remarkable shifts characterised by rapid urbanisation, substantial wetland loss and a decline in vegetation cover. Expectedly, population growth is found to be closely linked to the expansion of built-up areas while negatively impacting other land cover types. These findings underscore the urgent need for sustainable land management strategies that balance the demands of growing populations with the preservation of natural ecosystems and biodiversity. Furthermore, the study provides future projections that offer crucial insights for decision-makers involved in land use planning, biodiversity conservation and sustainable development.

Dynamics of Land Cover Change in the Anambra River Basin of Nigeria and Implications for Sustainable Land Management

Quaestiones Geographicae

This work is licensed under the Creative Commons Attribution 4.0 International License.

Land cover change and its consequences such as environmental degradation and biodiversity loss pose significant global challenges, including in Nigeria’s...

Dynamics of Land Cover Change in the Anambra River Basin of Nigeria and Implications for Sustainable Land Management

Publicado en línea: 20 mar 2024

Páginas: 179 - 195

Recibido: 25 jun 2023

DOI: https://doi.org/10.14746/quageo-2024-0011

© 2024 Nnanjar G. Njar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.