Developing a localized vegetation classification system for sustainable land use management in Kebbi State, Nigeria
Year: 2024
Type: article
Abstract: Accurate vegetation classification is crucial for environmental monitoring, natural resource management, and climate change modelling. This study develops a localized vegetation classification system using the Normalized Difference Vegetation Index (NDVI) and machine learning algorithms for Kebbi State, Nigeria. Landsat 8 imagery and field observations were used to train a Random Forest model, achieving an overall accuracy of 88.2%. The results show significant differences in NDVI values across vegetation types, effectively distinguishing between grasslands, shrubs, and barren lands. The classification system demonstrates the potential of NDVI for vegetation classification in Kebbi State, supporting sustainable land use management practices such as reforestation, crop selection, and land degradation monitoring. This study contributes to developing localized vegetation classification systems, addressing regional specificities in vegetation characteristics and promoting informed decision-making for environmental conservation.
Authors: Abubakar Abubakar, Usman Lawal Gulma
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: hybrid
OpenAlex ID: W4404727485