UAV-Derived Digital Surface Models and Forest Disturbance Maps in the Congo Basin

A collection of raw Digital Surface Models (DSMs) derived from UAV acquisitions in the Republic of the Congo from 2018 to 2020, along with binary forest disturbance maps. It was created to support research on mapping small-sized logging disturbances using Sentinel-1 time series.

Use Cases

• Analyze changes in forest canopy height by comparing DSM layers from 2018, 2019, and 2020.
• Validate binary forest disturbance/non-disturbance maps against the underlying DSM data.
• Study the spatial patterns of small-sized logging disturbances in tropical forests using the provided disturbance maps.
• Integrate UAV-derived DSMs with Sentinel-1 SAR time series for improved forest degradation monitoring.

Strengths

• Provides multi-temporal DSMs from three consecutive years (2018, 2019, 2020).
• Includes derived binary maps specifically for small-sized forest disturbance detection.
• Dataset is directly attached to a peer-reviewed manuscript detailing its creation and purpose.

Limitations

• The specific spatial resolution, coverage area, and file sizes of the DSMs and maps are not provided.
• The dataset is focused on a specific region (Republic of the Congo) and may not be generalizable to other forest types.
• The raw DSMs may require geospatial expertise and specialized software for processing and analysis.

Provenance

Source

Mercier, A., et al., attached to the manuscript 'Mapping small-sized logging disturbances in tropical forests using Sentinel-1 time series and an extensive ground truth dataset'.

Collection Method

Derived from UAV acquisitions.

Time Range

2018, 2019, 2020

Freshness

Data was last updated in February 2026.

Geography

Republic of the Congo, Congo Basin, Central Africa