Multi-sensor satellite data fusion and machine learning for Eucalyptus mapping in Meket district, Ethiopia.

Eucalyptus supports Ethiopia's economy and its zero-carbon strategy, yet its rapid expansion in the highlands of Ethiopia creates ecological concerns. For better management, accurate mapping is needed, but it is challenged by cloud contamination, spectral mimicry, and the requirement for high-resolution commercial imagery. Therefore, this study used an integrated multi-sensor satellite data, such as Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multi-Spectral Imagery (MSI), for reliable and cost-effective mapping of Eucalyptus trees in Meket district, Ethiopia. Sentinel-2 data is endowed with multi-spectral bands that are sensitive to chlorophyll and leaf water content, whereas Sentinel-1 SAR also functions in all weather conditions and can capture moisture and structural information of trees. Eighteen features from spectral bands, radar backscatter, and vegetation indices were fused using a feature-level fusion strategy and classified using Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Trees (CART). Results show that using freely available satellite data, RF achieved the highest performance, with an Overall Accuracy (OA) of 90% and a kappa coefficient of 0.81 in detecting Eucalyptus trees. SVM also achieved nearly the same performance with a 1% difference from RF. The study concludes that using publicly available Sentinel-1/2 fusion data with an appropriate classifier provides cost-effective, reliable, and accurate results for mapping of Eucalyptus, and supports Ethiopia's zero-carbon strategy. It also helps policymakers and planners by providing geospatial technology-based land use planning for monitoring and sustainable management of Eucalyptus trees.