Trees sustain livelihoods and mitigate climate change but a predominance of trees outside forests and limited resources make it difficult for many tropical countries to conduct automated nation-wide inventories. Here, we propose an approach to map the carbon stock of each individual overstory tree at the national scale of Rwanda using aerial imagery from 2008 and deep learning. We show that 72% of the mapped trees are located in farmlands and savannas and 17% in plantations, accounting for 48.6% of the national aboveground carbon stocks. Natural forests cover 11% of the total tree count and 51.4% of the national carbon stocks, with an overall carbon stock uncertainty of 16.9%. The mapping of all trees allows partitioning to any landscapes classification and is urgently needed for effective planning and monitoring of restoration activities as well as for optimization of carbon sequestration, biodiversity and economic benefits of trees.

Eastern Rwanda consists of a mosaic of different land cover types, with agroforestry, forest patches, and shrubland all containing tree cover. Mapping and monitoring the landscape is costly and time-intensive, creating a need for automated methods using openly available satellite imagery. Google Earth Engine and the random forests algorithm offer the potential to use such imagery to map tree cover types in the study area. Sentinel-2 satellite imagery, along with vegetation indices, texture metrics, principal components, and non-spectral layers were combined over the dry and rainy seasons. Different combinations of input bands were used to classify land cover types in the study area. Recursive feature elimination was used to select the most important input features for accurate classification, with three final models selected for classification. The highest classification accuracies were obtained for the forest class (85–92%) followed by shrubland (77–81%) and agroforestry (68–77%). Agroforestry cover was predicted for 36% of the study area, forest cover was predicted for 14% of the study area, and shrubland cover was predicted for 18% of the study area. Non-spectral layers and texture metrics were among the most important features for accurate classification. Mixed pixels and fragmented tree patches presented challenges for the accurate delineation of some tree cover types, resulting in some discrepancies with other studies. Nonetheless, the methods used in this study were capable of delivering accurate results across the study area using freely available satellite imagery and methods that are not costly and are easy to apply in future studies.

Trees sustain livelihoods and mitigate climate change, but a predominance of trees outside forests and limited resources make it difficult for many developing countries to conduct frequent nation-wide inventories. Here, we propose a rapid and accurate approach to map the carbon stock of each individual tree and shrub at the national scale of Rwanda using aerial imagery and deep learning. We show that 72% of the mapped trees are located in farmlands and savannas, and 15% in plantations. These non-forest trees account for 41% of the national carbon stocks. Natural forests cover 5% of the country and 11% of the total tree count, but comprise 59% of the national carbon stocks. The mapping of all trees facilitates any landscape stratification and is urgently needed for effective planning and monitoring of landscape restoration activities as well as for optimization of carbon sequestration, biodiversity and economic benefits of trees.