A multi-sensor remote sensing approach to monitor illegal charcoal production sites in Somalia’s forests

Illegal charcoal production, by means of indiscriminate logging activities, poses significant threats to the stability of the drylands’ ecosystem in the Somali territory. In addition, the revenues from the charcoal trade often serve further illegal activities, exacerbating the already complex socio-political context of the country. In this work, we investigated the application of freely available multi-sensor remote sensing products (Sentinel-1 and Sentinel-2) and machine learning techniques to detect the presence of charcoal production sites (i.e., kilns) over large areas. Exploiting Google Earth Engine and open-source tools, we were able to develop a binary classification of kilns’ presence-absence for the years 2019, 2020, and 2021 in a remote area (approximately 32000 km²) north-west of Mogadishu, Somalia. Concerning the workflow, we first computed median images, spanning the first three months of each year, composed of numerous optical, SAR (Synthetic Aperture Radar), and combined vegetation indices. Images were then subtracted between consecutive years and a Support Vector Classification (SVC) algorithm was trained and validated on the indices’ values extracted from those. As a reference dataset, we employed known kilns’ locations from a preceding study by FAO-SWALIM, where photointerpretation of very high resolution images was used to individuate the appearance of illegal charcoal kilns. The evaluation of the classifications showed that our approach has great capabilities for the automatic individuation and the monitoring of illegal charcoal production sites, with R² values and accuracy metrics ranging between 0.80-0.88 for the three considered years (2019, 2020, 2021). Moreover, mappings of the predicted presence-absence of kilns (at 10 m spatial resolution) were produced starting from the trained SVC model, giving a spatial representation of the phenomenon and allowing an assessment of the most impacted areas. In conclusion, our results represent a significant advancement in monitoring illegal charcoal production activities in Somalia, offering a reliable and transferable methodology based on accessible satellite imagery and tools.