Aboveground carbon (AGC) fluxes from deforestation and subsequent regrowth in tropical moist forest (TMF) are increasingly well characterized, but carbon losses and gains following partial disturbance are uncertain. We synthesized 146 studies quantifying postdisturbance AGC changes relative to undisturbed forests across TMF. Immediate AGC losses (mean ± 1 SD; 2.5 ± 2.3 years after disturbance) following partial anthropogenic disturbances were greatest for forest fires (49 ± 26%), selective logging (34 ± 20%), and edge effects (31 ± 19%). Higher-frequency and -intensity disturbances significantly increased carbon loss. After 20 years of regeneration, AGC stock was higher in recovering degraded forests (41 to 117%) compared to secondary regrowth forests after complete deforestation (1 to 74%), indicating greater regeneration potential when forest structure is preserved. Our compiled database and associated meta-analysis improve accuracy and completeness for carbon inventory reporting and modeling. Substantial AGC losses and gains from distinct degradation and recovery processes are now better characterized, serving as an evidence base for policies to halt degradation and foster recovery for climate mitigation.

Tropical forests are dynamic ecosystems shaped by deforestation, degradation, and recovery processes, with consequences for the carbon cycle. While emissions from deforestation have been well understood and quantified, information on emissions from degradation such as fire, logging, windrow and drought remain relatively poorly quantified, reflecting the complexity of these processes in space and time. Similarly, the carbon recovery potential of degraded forests is understudied compared to secondary forests regrowing after deforestation. Closing these knowledge gaps is crucial to reduce uncertainties in estimates of the tropical carbon budget and for addressing the priorities of international climate policies, which increasingly emphasize the value of protecting and restoring forests, without which we cannot constrain global warming to critical limits.In recent years, research on carbon emissions and removals in tropical forests has surged, driven in part by advancements in Earth Observation. Here we synthesize these approaches with the aim to bring clarity and advance our understanding on aboveground carbon (AGC) emission and removal factors applicable for tropical moist forests. We contextualise the current studies, highlighting where there are sufficient data estimates to quantify emissions and removals post-disturbance, and where specific kinds of estimates are lacking.Our synthesis of 66 studies of AGC loss due to disturbance shows emission estimates vary widely across disturbance types: average AGC losses are 3% (range 1–4%) for extreme drought, 27% (range 3–75%) for selective logging, and 52% (range 9–83%) for fire, relative to nearby und previously undisturbed forest. Our analysis underscores the need to account for disturbance severity, frequency and the cumulative effects of interacting disturbances to reduce variability between emissions estimates.For AGC recovery, our synthesis of 68 studies indicates that degraded forests regained 41–117% of AGC within 20 years relative to undisturbed forests; significantly higher than forests regrowing from deforestation, which regained between 1% and 74% of undisturbed forest AGC. Younger recovering forests ( 20 years). In the Amazon region, where we have the greatest number of field site and region-specific remote sensing data, we see good agreement between field- and satellite- derived regrowth rate estimates. Remote sensing data therefore has the potential to fill the gaps in our spatial knowledge where field data is limited.Our results also highlight some of the major gaps that still exist to provide long-lasting and relevant information into the policy and wider carbon budget science domain. Key research needs include: (i) reducing the variability of emission factors within disturbance types by further stratifying according to disturbance severity, frequency and co-occuring disturbances, (ii) addressing the research bias towards the Americas, particularly the Amazon, by expanding studies to areas where there are currently fewer estimates. Finally, we call for a more integrated approach between research focusing on deforestation, degradation and regrowth, recovery and consider these processes as interconnected, co-occurring and influencing each other in space and time.

Forest age transitions are critical in shaping the global carbon balance, yet their influence on carbon stocks and fluxes remains poorly quantified. Here we analyse global forest age dynamics from 2010 to 2020 using the Global Age Mapping Integration v2.0 dataset, alongside satellite-derived aboveground carbon (AGC) and atmospheric inversion-derived net CO2 flux data. We reveal widespread declines in forest age across the Amazon, Congo Basin, Southeast Asia and parts of Siberia, primarily driven by stand-replacing disturbances such as fire and harvest, leading to the replacement of older forests by younger stands. Meanwhile, forests in China, Europe and North America experienced net ageing. Globally, stand replacement resulted in substantial AGC losses, with old forests (>200 years, ~98.0 MgC ha-1) transitioning to younger, carbon-poor stands (<20 years, ~43.5 MgC ha-1), leading to a net AGC loss of ~0.14 PgC per year. Despite this, regions with high rates of young stands replacing old forests exhibited a temporary strengthening of the carbon sink, driven by the rapid regrowth of these young stands. Crucially, these young forests do not compensate for the long-term carbon storage of old forests. Our findings underscore the importance of protecting old forests while optimizing forest management strategies to maximize carbon gains and enhance climate mitigation.

Earth Observation (EO) data can provide added value to nations' assessments of vegetation aboveground biomass density (AGBD) with minimal additional costs. Yet, neither open access to global-scale EO datasets of vegetation heights or biomass, nor the availability of computational power, has proven sufficient for their wide uptake in climate policy-related assessments. Using Mexico as an example, one of the primary obstacles to enhancing their National Forest Inventory (NFI) with such global EO datasets is the lack of statistically defensible methodologies that do so, while addressing the nation's existing reporting needs and gaps. In collaboration with the Comisi & oacute;n Nacional Forestal (CONAFOR), this study develops a geostatistical model that integrates vegetation height and AGBD estimates from NASA's Global Ecosystem Dynamics Investigation (GEDI) and ESA's Climate Change Initiative (CCI) with Mexico's NFI to attain sub-national and geographically-explicit biomass predictions. The posited model includes spatially varying parameters, allowing flexibility to capture non-stationary relations between the EO-based covariates and NFI-estimated AGBD. Inference is conducted with Bayesian methods, allowing the computation of summary statistics, such as the standard deviations for single-location and area-wide predictions of AGBD. This enables the transparent disclosure and traceability of sources of uncertainty throughout the prediction approach. Results indicate strong model performance; the EO-based covariates explain 79% of the variance in NFI-estimated AGBD in a randomly withheld sample of 10% of observations and a heuristic root mean squared error (RMSE) of 21.55 Mg/ha. Approximately 96% of the observations falling within the 95% credible intervals of our predictions, with some systematic under-prediction observed at AGBD ranges of >100 Mg/ha. To ease the operational uptake of the model for policy purposes, source code based in the 'R' language with the optional use of urban and (non)forest masks for AGBD predictions is released. It includes demonstrations for predicting AGBD in Mexico's Natural Protected Areas, terrestrial ecological strata, and community forest management or payment for environmental services projects, which are commonly used delineations in its climate policy reports. For other nations considering the presented approach for policy purposes, the study discusses challenges concerning the use of EO-based covariates and the limitations of the model. It concludes with a broader call toward ensuring consistency in EO data streams, and prioritizing the co-development of EO-NFI integration approaches with nations in the future, thereby directly addressing their long-term climate policy needs.

Understanding the impact of forest age transitions on the global net carbon balance is critical for advancing forest management and climate change mitigation strategies. We analysed changes in the global forest age (2010-2020) using the Global Age Mapping Integration (GAMI) v2.0 dataset alongside satellite-derived aboveground carbon (AGC) and atmospheric inversion-derived net CO2 flux data. We observe decreasing forest age in the Amazon, Congo Basin, and Southeast Asia, primarily in old-growth forests due to stand-replacing disturbances like clear-cutting followed by regrowth. Large patches of older Siberian forests, ranging from 80 to 200 years, transitioned to younger ages due to increased fire5 and harvest6. Despite stand-replacements, forests in China, Europe, and North America experienced a net ageing of nearly ten years on average. A substantial portion of the gradually ageing forests is located in South America Tropical (0.19 of total gradually ageing forest fraction, 0.64 billion hectares), Eurasia boreal (a fraction of 0.17, 0.56 billion hectares), Europe (a fraction of 0.10, 0.35 billion hectares), and North America temperate (a fraction of 0.094, 0.31 billion hectares). We find a significant correlation between stand-replaced forest fraction and the inversely derived 2010-2020 trend in carbon sink strength at global scales (R2 = 0.33, slope = +109.19 gC m-2 year-2, p-val < 0.001, N=60). This is partly due to the transition from carbon-rich old-growth forests (approximately 98.0 MgC ha⁻¹) to young stand-replaced forests (approximately 43.5 MgC ha⁻¹), resulting in a net AGC loss of +0.15 (+ denotes loss of AGC) PgC year⁻¹. When accounting for all stand-replaced forests, this loss increases to +0.43 PgC year⁻¹, representing approximately 1.6% of the total forest biomass (around 270 PgC in 2020) over ten years. Our findings highlight that shifts in forest age are crucial to understanding global carbon losses and gains. Understanding these dynamics is essential for developing forest management strategies that optimise harvesting methods and sequester more anthropogenic CO2.

Aboveground biomass density (AGBD) estimates from Earth Observation (EO) can be presented with the consistency standards mandated by United Nations Framework Convention on Climate Change (UNFCCC). This article delivers AGBD estimates, in the format of Intergovernmental Panel on Climate Change (IPCC) Tier 1 values for natural forests, sourced from National Aeronautics and Space Administration's (NASA's) Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud and land Elevation Satellite (ICESat-2), and European Space Agency's (ESA's) Climate Change Initiative (CCI). It also provides the underlying classification used by the IPCC as geospatial layers, delineating global forests by ecozones, continents and status (primary, young (≤20 years) and old secondary (>20 years)). The approaches leverage complementary strengths of various EO-derived datasets that are compiled in an open-science framework through the Multi-mission Algorithm and Analysis Platform (MAAP). This transparency and flexibility enables the adoption of any new incoming datasets in the framework in the future. The EO-based AGBD estimates are expected to be an independent contribution to the IPCC Emission Factors Database in support of UNFCCC processes, and the forest classification expected to support the generation of other policy-relevant datasets while reflecting ongoing shifts in global forests with climate change.

Monitoring forest aboveground biomass (AGB) is essential for quantifying the carbon cycle and mitigating climate change. Tropical secondary forests are significant carbon sinks that sequester large amounts of carbon dioxide. While recent studies have attempted to estimate the AGB recovery rates in tropical forests, considerable uncertainty remains in the estimation of AGB recovery of secondary forests and the spatial variability of the effects that different environmental conditions and degrees of human use may have on AGB recovery. These knowledge gaps hinder further understanding of climate change mitigation potential of secondary forests. Remote sensing products provide spatially and temporally explicit information for understanding and monitoring secondary forest dynamics. To explore the local effects of different factors on AGB of secondary forests in Brazil, we used geographically weighted regression (GWR) models that account for spatial heterogeneity in geospatial data to estimate the AGB of secondary forests in Brazil. Secondary forest areas (29142 polygons) were extracted from Brazil's forest age maps between 1984 and 2019. The AGB of these areas was derived from the Climate Change Initiative Biomass maps. The effects of selected predictors such as forest age, climatic water deficit, the cation exchange capacity of soil and surrounding tree cover were analyzed. The two most influential factors, forest age and surrounding tree cover were utilized to estimate the AGB and the recovery rates per year. Our results show the high spatial variation of different predictors' effects on the AGB of secondary forests. Also, the GWR model (with an adjusted R2 of 0.74) showed considerable improvements regarding "goodness of fit" of models compared with the Ordinary Least Squares (with an adjusted R2 of 0.53). Our estimated average AGB recovery rate across all Brazil's biomes is 7.5 Mg ha -1 yr- 1 (using forest age) for the first 20 years. We presented the map of the spatial variation of AGB recovery rates in Brazil. The estimated AGB recovery rates range using forest age is 28.9 Mg ha -1 yr- 1. Our estimated mean AGB recovery rates of different biomes are 17.7 % on average higher than IPCC default rates. Our results provide baseline information for reducing uncertainties related to carbon sink estimation of secondary forests in Brazil, hence assisting in developing sustainable forest management and ecosystem restoration strategies.

Amazonian forests function as biomass and biodiversity reservoirs, contributing to climate change mitigation. While they continuously experience disturbance, the effect that disturbances have on biomass and biodiversity over time has not yet been assessed at a large scale. Here, we evaluate the degree of recent forest disturbance in Peruvian Amazonia and the effects that disturbance, environmental conditions and human use have on biomass and biodiversity in disturbed forests. We integrate tree-level data on aboveground biomass (AGB) and species richness from 1840 forest plots from Peru's National Forest Inventory with remotely sensed monitoring of forest change dynamics, based on disturbances detected from Landsat-derived Normalized Difference Moisture Index time series. Our results show a clear negative effect of disturbance intensity tree species richness. This effect was also observed on AGB and species richness recovery values towards undisturbed levels, as well as on the recovery of species composition towards undisturbed levels. Time since disturbance had a larger effect on AGB than on species richness. While time since disturbance has a positive effect on AGB, unexpectedly we found a small negative effect of time since disturbance on species richness. We estimate that roughly 15% of Peruvian Amazonian forests have experienced disturbance at least once since 1984, and that, following disturbance, have been increasing in AGB at a rate of 4.7 Mg ha(-1) year(-1) during the first 20 years. Furthermore, the positive effect of surrounding forest cover was evident for both AGB and its recovery towards undisturbed levels, as well as for species richness. There was a negative effect of forest accessibility on the recovery of species composition towards undisturbed levels. Moving forward, we recommend that forest-based climate change mitigation endeavours consider forest disturbance through the integration of forest inventory data with remote sensing methods.

Illegal logging is an important driver of tropical forest loss. A wide range of organizations and interested parties wish to track selective logging activities and verify logging intensities as reported by timber companies. Recently, free availability of 10 m scale optical and radar Sentinel data has resulted in several satellite-based alert systems that can detect increasingly small-scale forest disturbances in near-real time. This paper provides insight in the usability of satellite-based forest disturbance alerts to track selective logging in tropical forests. We derive the area of tree cover loss from expert interpretations of monthly PlanetScope mosaics and assess the relationship with the RAdar for Detecting Deforestation (RADD) alerts across 50 logging sites in the Congo Basin. We do this separately for various aggregation levels, and for tree cover loss from felling and skidding, and logging roads. A strong linear relationship between the alerts and visually identified tree cover loss indicates that with dense time series satellite data at 10 m scale, the area of tree cover loss in logging concessions can be accurately estimated. We demonstrate how the observed relationship can be used to improve near-real time tree cover loss estimates based on the RADD alerts. However, users should be aware that the reliability of estimations is relatively low in areas with few disturbances. In addition, a trade-off between aggregation level and accuracy requires careful consideration. An important challenge regarding remote verification of logging activities remains: as opposed to tree cover loss area, logging volumes cannot yet be directly observed by satellites. We discuss ways forward towards satellite-based assessment of logging volumes at high spatial and temporal detail, which would allow for better remote sensing based verification of reported logging intensities and tracking of illegal activities.

Abstract Characterization of regrowing forests is vital for understanding forest dynamics to assess the impacts on carbon stocks and to support sustainable forest management. Although remote sensing is a key tool for understanding and monitoring forest dynamics, the use of exclusively remotely sensed data to explore the effects of different variables on regrowing forests across all biomes in Brazil has rarely been investigated. Here, we analyzed how environmental and human factors affect regrowing forests. Based on Brazil's secondary forest age map, 3060 locations disturbed between 1984 and 2018 were sampled, interpreted and analyzed in different biomes. We interpreted the time since disturbance for the sampled pixels in Google Earth Engine. Elevation, slope, climatic water deficit (CWD), the total Nitrogen of soil, cation exchange capacity (CEC) of soil, surrounding tree cover, distance to roads, distance to settlements and fire frequency were analyzed in their importance for predicting aboveground biomass (AGB) and tree cover derived from global forest aboveground biomass map and tree cover map, respectively. Results show that time since disturbance interpreted from satellite time series is the most important predictor for characterizing AGB and tree cover of regrowing forests. AGB increased with increasing time since disturbance, surrounding tree cover, soil total N, slope, distance to roads, distance to settlements and decreased with larger fire frequency, CWD and CEC of soil. Tree cover increased with larger time since disturbance, soil total N, surrounding tree cover, distance to roads, distance to settlements, slope and decreased with increasing elevation and CWD. These results emphasize the importance of remotely sensing products as key opportunities to improve the characterization of forest regrowth and to reduce data gaps and uncertainties related to forest carbon sink estimation. Our results provide a better understanding of regional forest dynamics, toward developing and assessing effective forest‐related restoration and climatic mitigation strategies.

National forest inventories (NFI) provide essential forest-related biomass and carbon information for country greenhouse gas (GHG) accounting systems. Several tropical countries struggle to execute their NFIs while the extent to which space-based global information on aboveground biomass (AGB) can support national GHG accounting is under investigation. We assess whether the use of a global AGB map as auxiliary information produces a gain in precision of subnational AGB estimates for the Peruvian Amazonia. We used model-assisted estimators with data from the country's NFI and explored hybrid inferential techniques to account for the sources of uncertainty associated with the integration of remote sensing-based products and NFI plot data. Our results show that the selected global biomass map tends to overestimate AGB values across the Peruvian Amazonia. For most strata, directly using the map in its published form did not reduce the precision of AGB estimates. However, after calibrating the map using the NFI data, the precision of our map-assisted AGB estimates increased by up to 50% at stratum level and 20% at Amazonia level. We further demonstrate how different sources of uncertainties can be incorporated in the map-NFI integrated estimates. With the hybrid inferential analysis, we found that the small spatial support of the NFI plots compared to the remote sensing-based sample units of aggregated pixels (within block variability) contributed the most to the total uncertainty associated with the AGB estimates from our map-NFI integration. Uncertainties caused by measurement variability and allometric model prediction uncertainty were the second largest contributors. When these uncertainties were incorporated, the increase in precision of our calibrated map-assisted AGB estimates was negligible, probably hindered by the great contribution of the within block variability to our map-plot assessment. We developed a reproducible method that countries can build upon and further improve while the global biomass products continue to evolve and better characterize the AGB distribution under large biomass conditions. We encourage further cross-country case studies that reflect a wider range of AGB distributions, especially within humid tropical forests, to further assess the contribution of global biomass maps to (sub)national AGB estimates and finally GHG monitoring and reporting.

For monitoring and reporting forest carbon stocks and fluxes, many countries in the tropics and subtropics rely on default values of forest aboveground biomass (AGB) from the Intergovernmental Panel on Climate Change (IPCC) guidelines for National Greenhouse Gas (GHG) Inventories. Default IPCC forest AGB values originated from 2006, and are relatively crude estimates of average values per continent and ecological zone. The 2006 default values were based on limited plot data available at the time, methods for their derivation were not fully clear, and no distinction between successional stages was made. As part of the 2019 Refinement to the 2006 IPCC Guidelines for GHG Inventories, we updated the default AGB values for tropical and subtropical forests based on AGB data from >25 000 plots in natural forests and a global AGB map where no plot data were available. We calculated refined AGB default values per continent, ecological zone, and successional stage, and provided a measure of uncertainty. AGB in tropical and subtropical forests varies by an order of magnitude across continents, ecological zones, and successional stage. Our refined default values generally reflect the climatic gradients in the tropics, with more AGB in wetter areas. AGB is generally higher in old-growth than in secondary forests, and higher in older secondary (regrowth >20 years old and degraded/logged forests) than in young secondary forests (⩽20 years old). While refined default values for tropical old-growth forest are largely similar to the previous 2006 default values, the new default values are 4.0–7.7-fold lower for young secondary forests. Thus, the refined values will strongly alter estimated carbon stocks and fluxes, and emphasize the critical importance of old-growth forest conservation. We provide a reproducible approach to facilitate future refinements and encourage targeted efforts to establish permanent plots in areas with data gaps.

Disturbed African tropical forests and woodlands have the potential to contribute to climate change mitigation. Therefore, there is a need to understand how carbon stocks of disturbed and recovering tropical forests are determined by environmental conditions and human use. In this case study, we explore how gradients in environmental conditions and human use determine aboveground biomass (AGB) in 1958 national forest inventory (NFI) plots located in forests and woodlands in mainland Tanzania. Plots were divided into recovering forests (areas recovering from deforestation for <25years) and established forests (areas consistently defined as forests for ⩾25 years). This division, as well as the detection of year of forest establishment, was obtained through the use of dense satellite time series of forest cover probability. In decreasing order of importance, AGB in recovering forests unexpectedly decreased with water availability, increased with surrounding tree cover and time since establishment, and decreased with elevation, distance to roads, and soil phosphorus content. AGB in established forests unexpectedly decreased with water availability, increased with surrounding tree cover, and soil nitrogen content, and decreased with elevation. AGB in recovering forests increased by 0.4 Mg ha −1 yr −1 during the first 20 years following establishment. Our results can serve as the basis of carbon sink estimates in African recovering tropical forests and woodlands, and aid in forest landscape restoration planning.

Managing forests for climate change mitigation requires action by diverse stakeholders undertaking different activities with overlapping objectives and spatial impacts. To date, several forest carbon monitoring systems have been developed for different regions using various data, methods and assumptions, making it difficult to evaluate mitigation performance consistently across scales. Here, we integrate ground and Earth observation data to map annual forest-related greenhouse gas emissions and removals globally at a spatial resolution of 30 m over the years 2001–2019. We estimate that global forests were a net carbon sink of −7.6 ± 49 GtCO 2 e yr −1 , reflecting a balance between gross carbon removals (−15.6 ± 49 GtCO 2 e yr −1 ) and gross emissions from deforestation and other disturbances (8.1 ± 2.5 GtCO 2 e yr −1 ). The geospatial monitoring framework introduced here supports climate policy development by promoting alignment and transparency in setting priorities and tracking collective progress towards forest-specific climate mitigation goals with both local detail and global consistency.

As countries advance in greenhouse gas (GHG) accounting for climate change mitigation, consistent estimates of aboveground net biomass change (∆AGB) are needed. Countries with limited forest monitoring capabilities in the tropics and subtropics rely on IPCC 2006 default ∆AGB rates, which are values per ecological zone, per continent. Similarly, research into forest biomass change at a large scale also makes use of these rates. IPCC 2006 default rates come from a handful of studies, provide no uncertainty indications and do not distinguish between older secondary forests and old‐growth forests. As part of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, we incorporate ∆AGB data available from 2006 onwards, comprising 176 chronosequences in secondary forests and 536 permanent plots in old‐growth and managed/logged forests located in 42 countries in Africa, North and South America and Asia. We generated ∆AGB rate estimates for younger secondary forests (≤20 years), older secondary forests (>20 years and up to 100 years) and old‐growth forests, and accounted for uncertainties in our estimates. In tropical rainforests, for which data availability was the highest, our ∆AGB rate estimates ranged from 3.4 (Asia) to 7.6 (Africa) Mg ha −1 year −1 in younger secondary forests, from 2.3 (North and South America) to 3.5 (Africa) Mg ha −1 year −1 in older secondary forests, and 0.7 (Asia) to 1.3 (Africa) Mg ha −1 year −1 in old‐growth forests. We provide a rigorous and traceable refinement of the IPCC 2006 default rates in tropical and subtropical ecological zones, and identify which areas require more research on ∆AGB. In this respect, this study should be considered as an important step towards quantifying the role of tropical and subtropical forests as carbon sinks with higher accuracy; our new rates can be used for large‐scale GHG accounting by governmental bodies, nongovernmental organizations and in scientific research.

El Onceavo Foro de las Naciones Unidas sobre Bosques se llevo a cabo del 04 al 15 de mayo del 2015 en la Sede Principal de las Naciones Unidas en Nueva York, Estados Unidos. El tema general del evento fue “Los bosques: progresos, desafios y perspectivas futuras del acuerdo internacional sobre los bosques”. El principal resultado de este evento fue la elaboracion de un Proyecto de declaracion ministerial2 y un Proyecto de resolucion3 los cuales fueron aprobados por unanimidad el 14 y el 15 de mayo, respectivamente. Durante este evento, una delegacion de integrantes de la Asociacion Internacional de Estudiantes Forestales (conocido como IFSA por sus siglas en ingles) tuvieron la oportunidad de intervenir como parte del Grupo Principal Infancia y Juventud (MGCY), participando en las reuniones y observando como se tomaban decisiones a escala internacional con respecto al manejo sostenible de los bosques.