EGU23-11391, updated on 26 Feb 2023
https://doi.org/10.5194/egusphere-egu23-11391
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Rapid monitoring of Congo Basin logging roads with Sentinel-1 and Sentinel-2 data

Bart Slagter1, Kurt Fesenmyer2, and Johannes Reiche1
Bart Slagter et al.
  • 1Wageningen University, Laboratory of Geoinformation Science & Remote Sensing, Wageningen, Netherlands (bart.slagter@wur.nl)
  • 2The Nature Conservancy, Boise, ID, United States of America (kurt.fesenmyer@TNC.ORG)

The construction of logging roads has a major ecological impact on tropical forests and leads to large carbon emissions (Kleinschroth & Healy, 2017, Umunay et al. 2019). Negative impacts and emissions of logging roads could potentially be drastically lowered with the adoption of reduced-impact logging practices (Umunay et al. 2019). Accurate, timely, and dynamic logging road maps would help quantify and prioritize opportunities for improved road management and forest conservation across the globe. However, to-date, the limited mapping of logging roads has required time-consuming field data collection or manual digitization from satellite images. The open availability of Sentinel-1 radar and Sentinel-2 optical satellite imagery at high spatiotemporal resolutions now offers a unique opportunity for better automated logging road monitoring in the tropics.

In this study, we employ Sentinel-1 and Sentinel-2 data for near real-time mapping of logging roads in the Congo Basin tropical forests. We monitor newly constructed roads based on Sentinel-1 change ratio composites and cloud-masked Sentinel-2 composites. We acquired an extensive reference dataset of manually digitized logging roads to train and test a convolutional neural network for road/non-road classifications.

First results indicate promising capacities of Sentinel-1 and -2 data to monitor logging roads especially in forest types in the Republic of Congo and the Democratic Republic of Congo. Forest landscapes in Gabon, Equatorial Guinea and Cameroon appeared to be more challenging for logging road monitoring due to effects of cloud-cover and elevation. Near-future work includes model refinements, the acquisition of more reference data, and a Google Earth Engine-based wall-to-wall application of our model to produce a dynamic Congo Basin logging road dataset.

 

References:

Kleinschroth, Fritz, Healy, John R. (2017), Impacts of logging roads on tropical forests, Biotropica 49(5): 620–635 2017

Umunay, Peter M., Gregoire, Timothy G., Gopalakrishna, Trisha, Ellis, Peter W., Putz, Francis E. (2019) Selective logging emissions and potential emission reductions from reduced-impact logging in the Congo Basin, Forest Ecology and Management 437 360-371

How to cite: Slagter, B., Fesenmyer, K., and Reiche, J.: Rapid monitoring of Congo Basin logging roads with Sentinel-1 and Sentinel-2 data, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11391, https://doi.org/10.5194/egusphere-egu23-11391, 2023.