Restoring natural ecosystems has the potential to remove billions of tons of CO2 annually through the end of the century, but rigorously measuring the climate impacts of restoration activities on the ground remains elusive. Ecosystem restoration interventions across hundreds or thousands of smallholder properties require robust above-ground biomass (AGB) products at high spatial (deca-metric: 10–30 m) resolution for annual monitoring, reporting, and verification (MRV). In addition to ongoing monitoring, historical AGB time series across the region are also necessary. Historical maps are for example needed for eligibility checks and the selection of appropriate counterfactuals, i.e., to establish a dynamic performance benchmark. We present a novel AGB product based on a recently developed foundation model leveraging progress in self-supervised learning (SSL) techniques from multi-spectral Earth Observation (EO) time series. The foundation model is non-contrastive and condenses all available spectral observations acquired within a year into a few, orthogonal and highly informative representations at 10 m (for Sentinel-2) and 30 m (for Landsat 7/8). Combined with spatially sparse Global Ecosystem Dynamics Investigation (GEDI) full-waveform measurements at two relative heights (RH95 and RH10), but otherwise without any further fine-tuning, we are able to estimate forest biomass with an RMSE of <25 Mg/ha, when validated against 38 in-situ AGB measurement sites across a range of agroforestry (cacao and oil palm) and restoration age classes. Compared to five openly available datasets – most of them not available at annual time steps – our approach reduces the RMSE by 15–55%. We demonstrate the scalability of our approach, by producing annual AGB maps covering the entire state of Para, Brazil, for the years 2013 to 2024. The approach is computationally efficient, fully self-supervised without relying on contrastive samples, and can therefore be scaled to global coverage, even under conditions of high cloudiness.
Tag: Aboveground biomass
Séquestration du carbone et provision d’autres services écosystémiques des parcs agroforestiers à karité au Burkina Faso [Carbon sequestration and other ecosystem services provided by shea-tree agroforestry parks in Burkina Faso]
Shea-tree agroforestry parks play a crucial role in climate mitigation and ecosystem services in West Africa, yet their carbon sequestration potential remains poorly documented. This study assesses the contribution of shea-tree (Vitellaria paradoxa) agroforestry parks in Burkina Faso, using household surveys and field inventories across three phytogeographical sectors. Findings indicate that over 89% of surveyed households have shea trees in their fields, which provide fuelwood, timber, improve soil fertility, and help control erosion. Tree density varies from 32 to 45 individuals per hectare, with an average height of 7.93 meters and a diameter at breast height (DBH) of 31.9 cm. Above-ground biomass estimates range from 15.5 to 42.8 Mg/ha, averaging 25.7 Mg/ha or 12.8 Mg of carbon per hectare. The study highlights the significant carbon sequestration potential of these agroforestry systems, though benefits vary based on tree DBH and regional factors. Notably, all surveyed trees had a DBH greater than 24 cm, indicating a lack of young trees and an urgent need for regeneration through assisted natural regeneration, planting, or a combination of both.
Recovery of aboveground biomass, soil carbon stocks and species diversity in tropical montane secondary forests of East Africa
Tropical montane forests are fragile ecosystems that provide a wide range of ecosystem services including hydrological services, biodiversity protection and storing carbon in the above and belowground and soils contributing to climate change mitigation. The world’s tropical montane forests are increasingly exposed to degradation and their recovery after disturbance has not been adequately quantified. Here, using information from 47 plots in three blocks of the Mau Forest Complex of Kenya, we assessed the changes in aboveground biomass (AGB), tree species diversity, soil carbon and nitrogen stocks following forest clearance. AGB recovered at an annual rate of 6.42 Mg ha−1 yr−1 in the first 20 years, the rate then slowed down to 4.46–4.67 Mg ha−1 yr−1 at around 25–30 years. Around 25 years after disturbance, AGB in recovering forests was 70 % (198.32 ± 78.11 Mg ha−1) of the AGB in the old secondary forest (OSF) (282.86 ± 71.64 Mg ha−1) and was statistically indistinguishable. Stem density, species diversity and richness indices did not show significant differences across recovery classes. There was no significant difference in soil carbon and nitrogen stocks across classes with the soil carbon (184.1 ± 41 Mg ha−1) of the young secondary forest (<10 years) being 84.5 % of that of the OSF (217.9 ± 51.8 Mg ha−1). This study reports a rapid rate of AGB and carbon accumulation within 20 years of disturbance, and high levels of species richness in these previously disturbed fragments of tropical montane forests of East Africa.
Soil carbon stocks in planted woodlots and Ngitili systems in Shinyanga, Tanzania
Early-spring soil warming partially offsets the enhancement of alpine grassland aboveground productivity induced by warmer growing seasons on the Qinghai-Tibetan Plateau
Allometric equations for predicting above-ground biomass of selected woody species to estimate carbon in East African rangelands
Sensitivity of Above-Ground Biomass Estimates to Height-Diameter Modelling in Mixed-Species West African Woodlands
It has been suggested that above-ground biomass (AGB) inventories should include tree height (H), in addition to diameter (D). As H is a difficult variable to measure, H-D models are commonly used to predict H. We tested a number of approaches for H-D modelling, including additive terms which increased the complexity of the model, and observed how differences in tree-level predictions of H propagated to plot-level AGB estimations. We were especially interested in detecting whether the choice of method can lead to bias. The compared approaches listed in the order of increasing complexity were: (B0) AGB estimations from D-only; (B1) involving also H obtained from a fixed-effects H-D model; (B2) involving also species; (B3) including also between-plot variability as random effects; and (B4) involving multilevel nested random effects for grouping plots in clusters. In light of the results, the modelling approach affected the AGB estimation significantly in some cases, although differences were negligible for some of the alternatives. The most important differences were found between including H or not in the AGB estimation. We observed that AGB predictions without H information were very sensitive to the environmental stress parameter (E), which can induce a critical bias. Regarding the H-D modelling, the most relevant effect was found when species was included as an additive term. We presented a two-step methodology, which succeeded in identifying the species for which the general H-D relation was relevant to modify. Based on the results, our final choice was the single-level mixed-effects model (B3), which accounts for the species but also for the plot random effects reflecting site-specific factors such as soil properties and degree of disturbance.
Aboveground carbon stocks in oil palm plantations and the threshold for carbon-neutral vegetation conversion on mineral soils
The carbon (C) footprint of palm-oil production is needed to judge emissions from potential biofuel use. Relevance includes wider sustainable palm oil debates. Within life cycle analysis, aboveground C debt is incurred if the vegetation replaced had a higher C stock than oil palm plantations. Our study included 25 plantations across Indonesia, in a stratified study design representing the range of conditions in which oil palm is grown. From allometric equations for palm biomass and observed growth rates, we estimated the time-averaged aboveground C stock for 25-year rotations and 95%-confidence intervals to be 42.07 (42.04-42.10) Mg C ha-1 for plantations managed by company on mineral soil, 40.03 (39.75-40.30) Mg C ha-1 for plantations managed by company on peat, and 37.76 (37.42-38.09) Mg C ha-1 for smallholder oil palm on mineral soils. Oil palm can be established C debt-free on mineral soils with aboveground C stocks below these values; neutrality of mineral soil C pools was documented in a parallel study. Acknowledging variation in shoot:root ratios, the types of vegetation that can be converted debt-free to oil palm include grasslands and shrub, but not monocultural rubber plantations, rubber agroforest, and similar secondary or logged-over forests of higher C stock.
Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests
Estimates of forest biomass are needed for tracking changes in C stocks, as well as for other purposes. A common method for estimating forest biomass is through use of allometric equations which relate the biomass of individual trees to easily obtainable non-destructive measurements, such as diameter. A common form is B=aDb for biomass B, diameter D and parameters a and b. Field data collected in Sumatra and compared with previously published data show that the values of a and b vary between sites. This variation is likely to be the major source of uncertainty if biomass estimates are produced using equations that are not calibrated for individual sites. However, calibration by collection of B and D data for each site is unrealistic, requiring destructive measures. Methods of choosing values for a and b are, therefore, proposed that do not require destructive measurements. The parameter b can be estimated from the site-specific relationship between height (H) and diameter, H=kDc as b=2+c. The parameter a can be estimated from the average wood density (ρ) at the site as a=rρ, where r is expected to be relatively stable across sites. The allometric equation proposed is therefore B=rρD2+c.
Allometric equations based on a fractal branching model for estimating aboveground biomass of four native tree species in the Philippines
Fractal branching models can provide a non-destructive and generic tool for estimating tree shoot and root length and biomass, but field validation is rarely described in the literature. We compared estimates of above ground tree biomass for four indigenous tree used on farm in the Philippines based on the WanFBA model tree architecture with data from destructive sampling. Allometric equations for the four species varied in the constant (biomass at virtual stem diameter 1) and power of the scaling rule (b in Y = aDb), deviating from the value of 8/3 that is claimed to be universal. Allometric equations for aboveground biomass were 0.035 D2.87 for Shorea contorta, 0.133 D2.36 for Vitex parviflora, 0.063 D2.54 for Pterocarpus indicus and 0.065 D2.28 for Artocarpus heterophyllus, respectively. Allometric equations for branch biomass had a higher b factor than those for total biomass (except in Artocarpus); allometric equations for the leave twig fraction a lower b. The performance of the WanFBA model was significantly improved by introduction of a tapering factor”s” for decrease of branch diameter within a single link. All statistical tests performed on measured biomass versus biomass predicted from the WanFBA results confirm the viability of the WanFBA model as a non-destructive tool for predicting above-ground biomass equations for total biomass, branch biomass and the leaf twig fraction.