Multi-scale lidar measurements suggest miombo woodlands contain substantially more carbon than thought

communications earth & environment Article https://doi.org/10.1038/s43247-024-01448-x Multi-scale lidar measurements suggest miombo woodlands contain substantially more carbon than thought Check for updates 1234567890():,; 1234567890():,; 1 2 1 3,4 Miro Demol , Naikoa Aguilar-Amuchastegui , Gabija Bernotaite , Mathias Disney , Laura Duncanson 5, Elise Elmendorp 1, Andres Espejo 2, Allister Furey1, Steven Hancock 6, Johannes Hansen 1, Harold Horsley 1, Sara Langa7, Mengyu Liang 5, Annabel Locke 1, Virgílio Manjate8, Francisco Mapanga9, Hamidreza Omidvar 1, Ashleigh Parsons1, Elitsa Peneva-Reed 2, Thomas Perry 1, Beisit L. Puma Vilca 1, Pedro Rodríguez-Veiga 1,10, Chloe Sutcliffe1, Robin Upham 1, Benoît de Walque 1 & Andrew Burt 1 Miombo woodlands are integral to livelihoods across southern Africa, biodiversity in the region, and the global carbon cycle, making accurate and precise monitoring of their state and change essential. Here, we assembled a terrestrial and airborne lidar dataset covering 50 kha of intact and degraded miombo woodlands, and generated aboveground biomass estimates with low uncertainty via direct 3D measurements of forest structure. We found 1.71 ± 0.09 TgC was stored in aboveground biomass across this landscape, between 1.5 and 2.2 times more than the 0.79–1.14 TgC estimated by conventional methods. This difference is in part owing to the systematic underestimation of large trees by allometry. If these results were extrapolated across Africa’s miombo woodlands, their carbon stock would potentially require an upward revision of approximately 3.7 PgC, implying we currently underestimate their carbon sequestration and emissions potential, and disincentivise their protection and restoration. Miombo woodlands, the dry tropical forests spanning large areas of southern Africa, directly support many millions of livelihoods in various ways including supply of plant-based materials, fertile soils for agriculture, and grazing lands1. These ecosystems also hold cultural and spiritual significance, provide habitat for substantial plant and animal biodiversity, and regulate both the climate and water resources2. These landscapes, however, are changing because of human activities, with cover reducing from approximately 2.7 to 1.9 million km2 between 1980 and 20203. Owing to both their importance and dynamic nature, it is therefore crucial to monitor how the world’s miombo woodlands are changing. One essential climate variable that requires accurate and precise monitoring is the aboveground biomass (AGB) and carbon stored in these woodlands4. Any uncertainty that exists in the quantification of these stocks has consequences, particularly regarding misinformed policy and decision making towards them, as well as the misallocation of funding and resources5,6. Carbon markets for example, through programmes such as Reducing Emissions from Deforestation and Degradation (REDD+)7, require low uncertainty in estimates of carbon stocks if they are to properly incentivise direct climate benefits, and cobenefits including biodiversity and ecosystem services, by safeguarding these woodlands. Further, intended outcomes from international climate agreements towards greenhouse gas emissions reductions, such as the Paris Agreement including individual countries’ Nationally Determined Contributions, are premised on forest carbon accounting with low uncertainty8. That is, both high accuracy and precision, quantitatively expressed as a bias and variance, respectively, are usually important for any estimate of forest AGB stocks in these contexts. Whilst accuracy is the principal concern in accounting (systematic over- or under-estimation commensurately misleads understanding of forest carbon sequestration and emissions potential)9, precise estimates are also important, including from the requirement to detect change over time (it can be problematic to interpret differences between observations with low precision)10. This is 1 Sylvera Ltd, London, UK. 2The World Bank Group, Washington, DC, USA. 3Department of Geography, University College London, London, UK. 4NERC National Centre for Earth Observation (NCEO), Leicester, UK. 5Department of Geographical Sciences, University of Maryland, College Park, USA. 6School of GeoSciences, University of Edinburgh, Edinburgh, UK. 7Independent researcher, Maputo, Mozambique. 8Independent researcher, Boane, Mozambique. 9Independent e-mail: researcher, Inhaca, Mozambique. 10School of Geography, Geology and the Environment, University of Leicester, Leicester, UK. Communications Earth & Environment | (2024)5:366 1 Article https://doi.org/10.1038/s43247-024-01448-x particularly the case for miombo woodlands given the aforementioned pace of their anthropogenic change. The conventional approach to quantifying region-scale forest AGB stocks across miombo woodlands, and forests generally, within the context of UNFCCC- and IPCC-compliant greenhouse gas inventories, sees the combination of activity data and emissions factors (EF): remotely sensed estimates of forest area are multiplied by values of expected AGB per unit area of forest11. These expected values, based on in-situ measurements, might be generated from National Forest Inventories (NFI), or alternatively, where such data are unavailable, taken from the literature, such as IPCC defaults12. While this overall approach can be readily implemented it does have limitations, including: (i) restricted ability to describe AGB variations within forest types; (ii) EFs not being representative of the forest in question; and (iii) failing to detect change beyond binary transition between forest and non-forest (e.g. degradation). For example, when focusing solely on the EF, and ignoring immediate questions surrounding the representativeness of applying a single value to any particular region of miombo woodland, uncertainties arise from the methods used to gather the in-situ data from the forest plots underlying the EFs themselves13. A ubiquitous feature of such measurements is the application of allometric models to estimate individual tree AGB. These models characterise the correlations that exist between tree shape and mass, enabling AGB estimation from more readily-measurable predictor variables such as stem diameter and tree height14. Such allometrics are themselves calibrated using hard-won destructive weighing measurements collected from a limited number of harvested trees that then must represent the entire variability of the specific taxa or region where that model is subsequently applied. Uncertainties in allometric-derived AGB predictions therefore arise from the selection, measurement and modelling of these calibration trees, and the measurement of the predictor variables of any out-of-sample tree15. Several studies have explored the precision of allometric predictions of tropical and subtropical forests, where the expectation is that uncertainties range from 10 to 40% of the estimate (...truncated)