Adagbasa, Efosa GbengaMukwada, Geofrey2022-08-102022-08-102022Adagbasa, E.G., & Mukwada, G. (2022). Mapping vegetation species succession in a mountainous grassland ecosystem using Landsat, ASTER MI, and Sentinel-2 data. PLOS ONE, 17(1), e0256672. https://doi.org/10.1371/journal.pone.02566721932-6203http://hdl.handle.net/11660/11820Publisher's versionVegetation species succession and composition are significant factors determining the rate of ecosystem biodiversity recovery after being disturbed and subsequently vital for sustainable and effective natural resource management and biodiversity. The succession and composition of grasslands ecosystems worldwide have significantly been affected by accelerated environmental changes due to natural and anthropogenic activities. Therefore, understanding spatial data on the succession of grassland vegetation species and communities through mapping and monitoring is essential to gain knowledge on the ecosystem and other ecosystem services. This study used a random forest machine learning classifier on the Google Earth Engine platform to classify grass vegetation species with Landsat 7 ETM+ and ASTER multispectral imager (MI) data resampled with the current Sentinel-2 MSI data to map and estimate the changes in vegetation species succession. The results indicate that ASTER MI has the least accuracy of 72%, Landsat 7 ETM+ 84%, and Sentinel-2 had the highest of 87%. The result also shows that other species had replaced four dominant grass species totaling about 49 km2 throughout the study.enEcosystemsInvasive speciesBiodiversityGrasslandsMachine learningWildfiresGrassesMachine learning algorithmsArticleAuthor(s)http://creativecommons.org/licenses/by/4.0/