Mapping tropical dry Miombo woodlands into functional forest classes using Sentinel-1 and Sentinel-2 imagery and machine learning

Tropical dry forests, such as the Miombo woodlands, play crucial roles both as an effective global carbon sink, and as the source of livelihood for a vast number of local communities. However, mapping Miombo woodlands accurately into definable classes is a great challenge due to their sparse and heterogeneous nature and their alteration due to anthropogenic impacts. Nevertheless, such mapping is important to underpin management and conservation efforts. We explored the potential of Sentinel (S-1) and Sentinel-2 (S-2) seasonal and multi-seasonal images for two tasks: (i) mapping Land Use Land Cover (LULC) such as to identify the Miombo woodlands and (ii) mapping three specific forest classes (reference, degraded and regrowth forests) within the Miombo woodlands of Zambia. The Random Forest (RF) algorithm within Google Earth Engine (GEE) was selected for the LULC classification while a U-Net convolutional neural network (CNN) was applied to classify the different types of forest. Models were trained, validated, and tested using ground validation data. The RF model achieved an overall accuracy of 93% for LULC classification, with the forest class F1-scores ranging from 93% to 96% across different seasons. The U-Net CNN effectively delineated the Miombo woodlands into reference, degraded and regrowth forests, with respective F1-scores of 85%, 73% and 72%. Combining multi-seasonal S-1 and S-2 images and their derivatives yielded the greatest accuracy for LULC mapping, while combining the year-round S-1 and S-2 bands produced the highest F1-scores for the forest type classification. The hierarchical approach employed was, thus, demonstrated to be effective, providing more nuanced functional forest information. The approach holds great promise for mapping and monitoring programs aiming to manage and conserve the Miombo woodlands sustainably. • Hierarchical methodology combining RF and U-Net to map and discriminate woodlands • Assessed optimal combination of optical and SAR data for mapping woodlands • Multi-season images produced the greatest accuracy for LULC classification with RF • Year-round images outperformed seasonal images for forest type mapping with U-Net