The 2015/16 El Niño brought severe drought and record-breaking temperatures in the tropics. Here, using satellite-based L-band microwave vegetation optical depth, we mapped changes of above-ground biomass (AGB) during the drought and in subsequent years up to 2019. Over more than 60% of drought-affected intact forests, AGB reduced during the drought, except in the wettest part of the central Amazon, where it declined 1 y later. By the end of 2019, only 40% of AGB reduced intact forests had fully recovered to the predrought level. Using random-forest models, we found that the magnitude of AGB losses during the drought was mainly associated with regionally distinct patterns of soil water deficits and soil clay content. For the AGB recovery, we found strong influences of AGB losses during the drought and of γ . γ is a parameter related to canopy structure and is defined as the ratio of two relative height (RH) metrics of Geoscience Laser Altimeter System (GLAS) waveform data—RH25 (25% energy return height) and RH100 (100% energy return height; i.e., top canopy height). A high γ may reflect forests with a tall understory, thick and closed canopy, and/or without degradation. Such forests with a high γ ( γ ≥ 0.3) appear to have a stronger capacity to recover than low- γ ones. Our results highlight the importance of forest structure when predicting the consequences of future drought stress in the tropics.

A globally relevant and standardized taxonomy and framework for consistently describing land cover change based on evidence is presented, which makes use of structured land cover taxonomies and is underpinned by the Driver‐Pressure‐State‐Impact‐Response (DPSIR) framework. The Global Change Taxonomy currently lists 246 classes based on the notation ‘impact (pressure)’, with this encompassing the consequence of observed change and associated reason(s), and uses scale‐independent terms that factor in time. Evidence for different impacts is gathered through temporal comparison (e.g., days, decades apart) of land cover classes constructed and described from Environmental Descriptors (EDs; state indicators) with pre‐defined measurement units (e.g., m, %) or categories (e.g., species type). Evidence for pressures, whether abiotic, biotic or human‐influenced, is similarly accumulated, but EDs often differ from those used to determine impacts. Each impact and pressure term is defined separately, allowing flexible combination into ‘impact (pressure)’ categories, and all are listed in an openly accessible glossary to ensure consistent use and common understanding. The taxonomy and framework are globally relevant and can reference EDs quantified on the ground, retrieved/classified remotely (from ground‐based, airborne or spaceborne sensors) or predicted through modelling. By providing capacity to more consistently describe change processes—including land degradation, desertification and ecosystem restoration—the overall framework addresses a wide and diverse range of local to international needs including those relevant to policy, socioeconomics and land management. Actions in response to impacts and pressures and monitoring towards targets are also supported to assist future planning, including impact mitigation actions.

Forest structure is a useful proxy for carbon stocks, ecosystem function and species diversity, but it is not well characterised globally. However, Earth observing sensors, operating in various modes, can provide information on different components of forests enabling improved understanding of their structure and variations thereof. The Ice, Cloud and Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS), providing LiDAR footprints from 2003 to 2009 with close to global coverage, can be used to capture elements of forest structure. Here, we evaluate a simple allometric model that relates global forest canopy height (RH100) and canopy density measurements to explain spatial patterns of forest structural properties. The GLA14 data product (version 34) was applied across subdivisions of the World Wildlife Federation ecoregions and their statistical properties were investigated. The allometric model was found to correspond to the ICESat GLAS metrics (median mean squared error, MSE: 0.028; inter-quartile range of MSE: 0.022–0.035). The relationship between canopy height and density was found to vary across biomes, realms and ecoregions, with denser forest regions displaying a greater increase in canopy density values with canopy height, compared to sparser or temperate forests. Furthermore, the single parameter of the allometric model corresponded with the maximum canopy density and maximum height values across the globe. The combination of the single parameter of the allometric model, maximum canopy density and maximum canopy height values have potential application in frameworks that target the retrieval of above-ground biomass and can inform on both species and niche diversity, highlighting areas for conservation, and potentially enabling the characterisation of biophysical drivers of forest structure.

The above ground biomass (AGB) of woody vegetation is proportional to the amount of carbon stored primarily in the trunks and branches, with changes over time indicating sources or sinks of carbon. Accurate quantification of AGB is indispensable for climate studies and policy development, yet significant gaps persist due to limitations in current observational and modeling approaches. Satellite-based Earth Observation (EO) provides a promising avenue for global biomass estimation, particularly when a diversity of data sources and advanced algorithms are used.Recent initiatives, such as the European Space Agency’s (ESA) Climate Change Initiative (CCI) Biomass and BiomAP projects, have pioneered methodologies for generating time series of global maps of woody AGB at varying spatial resolutions. These efforts utilize multiple predictors derived from active and passive microwave data sources, including Sentinel-1, ALOS-2, SMOS, SMAP and ASCAT as well as LiDAR-based vegetation structural metrics. However, the absence of globally and evenly distributed AGB measurements acting as reference constrains retrievals to use fully physical models. These models are then calibrated using spatially explicit datasets from other satellite data (e.g., optical imagery) and AGB statistics. Evaluations of these maps with independent reference measurements not used in the retrieval process highlight the critical balance between data precision and algorithm design. The complexity of accurately mapping biomass at global scales is compounded by uncertainties in LiDAR sampling, satellite data uncertainty, and the dependence on high-quality reference data. Additionally, biases arise from the simplistic assumptions often required for model fitting, which can affect the reliability of AGB estimates. Temporal assessments of biomass change face additional hurdles, including uncertainties in AGB trends and a scarcity of reference data for validation.Despite these challenges, EO-driven biomass mapping continues to advance, supported by improvements in sensor technologies and retrieval algorithms. Long-term maintenance of satellite missions suitable for AGB mapping is however essential as is the promotion of space-based LiDAR observations. Enhanced understanding of satellite signal characteristics will enable more accurate AGB retrievals, fostering the development of sophisticated retrieval models that may identify complex interactions not described by the physical models currently in use. Crucially, this progress must be complemented by spatially dense and continuous AGB measurements from local ground-based or airborne surveys.The scope of this presentation is to emphasize the transformative potential of satellite EO in quantifying and monitoring AGB and detail efforts at quantifying and reducing uncertainties in retrieval. By reviewing existing data products and illustrating strategies to address data gaps and methodological challenges, this work aims to inform and guide future global biomass estimation efforts from existing, recently launched (e.g., ALOS-4 PALSAR, Sentinel-1C), and forthcoming (NASA/ISRO NISAR and ESA BIOMASS) missions.

Wildfires are an integral part of boreal forest dynamics. Understanding the carbon loss/recovery associated with fires is crucial to assess the stability of these slow-growing forests. Yet, the carbon balance from fires and post-fire forest recovery remain uncertain at the biome scale due to the lack of spatial details about rates of forest regrowth. Here, we quantify carbon losses from fire emissions and gains from post-fire regrowth using high spatial-resolution satellite data and a bookkeeping model. We combined a 35-year long record of burned area from the Landsat satellites since 1985 with local biomass-age regrowth curves derived from high-resolution satellite-based above ground biomass (AGB) datasets. We found that forests in Eurasia tend to recover faster and reach higher biomass levels than those in North America. Young forests recovering from post-1985 wildfires produced a carbon sink of 652±200 TgC during the period 1985 to 2020. The additional recovery of older secondary forests that burned before 1985 further adds a cumulative sink of 1659±346 TgC. Comparatively, old-growth forests that did not burn accumulated 930±233 TgC during the period 1985-2020. This result shows 71% of the contemporary carbon sink in AGB is contributed by recovery from fires. After accounting for fire emissions each year and for the slow decay of coarse woody debris after burning, the net AGB carbon sink in boreal forests is 2108±234 TgC during 1985-2020. This study provides the first spatially explicit aboveground observation-based carbon budget of boreal forests and provides insights on the key factors that will control its future evolution.