ArticleNature climate change2023
Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda.
Article in Nature climate change, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Net carbon losses in Central African forests revealed by high-resolution biomass change maps.Nature communications · 2026Article
- BCAR-Net: A Bidirectional Cross-Attention Network with Auxiliary Reconstruction for Tree Counting in Complex Forest Scenes Using Airborne RGB and LiDAR Data.Plants (Basel, Switzerland) · 2026Article
- A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science.Eco-Environment & Health · 2024Article
- Forest demography and biomass accumulation rates are associated with transient mean tree size vs. density scaling relations.PNAS nexus · 2024Article
- Scattered tree death contributes to substantial forest loss in California.Nature communications · 2024Article
- The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe.Science advances · 2023Article
- More than one quarter of Africa's tree cover is found outside areas previously classified as forest.Nature communications · 2023Article
- Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale.PNAS nexus · 2023Article
- Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda.Nature climate change · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.