@ARTICLE{Isiaka_Ibrahim_Opeyemi_Land_2024,
 author={Isiaka, Ibrahim Opeyemi and Herbert, Tata and Akande, Samuel Olumide},
 volume={vol. 73},
 number={No. 1},
 pages={article no. e52},
 journal={Advances in Geodesy and Geoinformation},
 howpublished={online},
 year={2024},
 publisher={Polska Akademia Nauk/ Komitet Geodezji Polskiej Akademii Nauk; Polish Academy of Sciences / Commitee on Geodesy Polish Academy of Sciences},
 abstract={Land use dynamics describes the changes occurring in an area which can be approximated from a land use and land cover map (LULC). This research aims to assess how the mangrove forest in the Niger Delta Region has degraded. Landsat imagery of 1987, 2002, and 2022 were used to create LULC maps from which change detection of the area derived from supervised classification technique, were investigated. The classification accuracy was determined and the result shows that for 1987, 2002, and 2022 the accuracy score are 82.16%, 82.16%, and 83.97%, respectively and the corresponding kappa coefficients for the epochs are 75.63%, 75.63%, and 78.43%. The producer and user accuracy are high for all the classes except grassland and urban areas. The change detection result showed a net loss in mangrove, grassland, and water bodies between 1987 and 2002 with significant gains for urban and woodlands however significant losses were revealed in the mangrove, woodlands, water bodies, and grassland with gains in only the urban settlement between the 2002 and 2022 study period. The result also revealed that the mangrove forest cover has seen a consistent loss in the two study periods. The Niger Delta Region is a key component of the global climate system and our research provides an accurate assessment of the land use dynamics and the mangrove degradation in the region.},
 type={Article},
 title={Land use dynamics and mangrove degradation in the Niger delta region},
 URL={http://ochroma.man.poznan.pl/Content/131760/PDF-MASTER/e52.pdf},
 doi={10.24425/agg.2023.146164},
 keywords={GIS, Niger delta, Mangrove, remote sensing, land cover},
}