DSM and forest disturbances derived from UAV acquisitions acquired in 2018, 2019 and 2020 in the Republic of the Congo

This is a dataset repository attached to the manuscript: <br>
Mercier, A., Betbeder, J., Mortier, F., Barbier, N., Ploton, P., Cornu, G., Couteron, P., 2026. Mapping small-sized logging disturbances in tropical forests using Sentinel-1 time series and an extensive ground truth dataset. Front. Remote Sens. 7. https://doi.org/10.3389/frsen.2026.1659305
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This dataset includes raw digital surface models derived from UAV acquisitions in the Republic of Congo in 2018, 2019 and 2020. The DSM are georeferenced based on the dsm_2020.tif.<br>
It also includes two binary small-sized forest disturbance/non disturbance maps derived from the three resulting DSM.
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For details of the processing steps, see the "Data" section of the manuscript above.

The author(s) declared that financial support was received for this work and/or its publication. This research was funded through the PROFEAAC project funded by the French Development Agency (AFD) (AGREEMENT AFD No. CZZ 2123.01V) and the European Union – NextGenerationEU as part of the ARTISDIG research council of Finland project (decision number 348152).

We thank Vincent Istace, Eric Forni and the consultants of the CIB SYLVAFRICA funded by the project DYNAFOR for providing the GPS points of felled trees and the UAV acquisitions over the FSC study areas. We also acknowledge support from the AfriCam project, funded by the French Development Agency (AFD) as part of PREACTS (PREZODE in Action in the Global south) program.