Sentinel 2 High-Resolution Land Cover Mapping in Sub-Saharan Africa with Google Earth Engine

Elena Belcore, Marco Piras

2023

Abstract

This work aims to develop an efficient methodology for high-resolution spatial and thematic land cover maps of sub-Saharan areas based on Sentinel-2 data. LC mapping in these areas is complicated due to their land morphology, climatic conditions and homogeneity of surface spectral responses. Two pixel-based supervised classification approaches are compared in Google Earth Engine. The aggregated method classifies each image and then aggregates the results on frequency bases at pixel level. The stacked method classifies all the images together in a single stacked database. Additionally, the influence of linear atmospheric correction models on the overall accuracy (OA) is assessed, and the best-performing approach is compared to existing Land Cover (LC) maps of the area. 16 Sentinel-2 images (level 1C) from 2017 and 2019 were atmospheric and topographically corrected and classified into nine classes. The results show similar performances for the analysed approaches, with a slightly high OA for the aggregated classification (0.97). The atmospheric correction has little impact on the results.

Paper Citation

in Harvard Style

Belcore E. and Piras M. (2023). Sentinel 2 High-Resolution Land Cover Mapping in Sub-Saharan Africa with Google Earth Engine. In Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-649-1, SciTePress, pages 27-36. DOI: 10.5220/0011746500003473

in Bibtex Style

@conference{gistam23,
author={Elena Belcore and Marco Piras},
title={Sentinel 2 High-Resolution Land Cover Mapping in Sub-Saharan Africa with Google Earth Engine},
booktitle={Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2023},
pages={27-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011746500003473},
isbn={978-989-758-649-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Sentinel 2 High-Resolution Land Cover Mapping in Sub-Saharan Africa with Google Earth Engine
SN - 978-989-758-649-1
AU - Belcore E.
AU - Piras M.
PY - 2023
SP - 27
EP - 36
DO - 10.5220/0011746500003473
PB - SciTePress