Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda
Mugabowindekwe, Maurice; Brandt, Martin; Chave, Jerome; Reiner, Florian; Skole, David; Kariryaa, Ankit; Igel, Christian; Hiernaux, Pierre; Ciais, Philippe; Mertz, Ole; Tong, Xiaoye; Li, Sizhuo; Rwanyiziri, Gaspard; Dushimiyimana, Thaulin; Ndoli, Alain; Valens, Uwizeyimana; Lillesø, Jens-Peter; Gieseke, Fabian; Tucker, Compton; Saatchi, Sassan S; Fensholt, Rasmus
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 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.
tree maps; carbon stocks; machine learning; deep learning