The understanding of forest growth is crucial for the preservation of forests in the future. Factors such as tree species, age, forest management and environmental conditions influence this growth.Tree height and age data can be combined to describe forest growth and infer known environmental effects. In this study, we constructed height-age growth curves for 14 monospecific and mixed-species stands using ground measurements and satellite data in northern France. A random forest height model was constructed using tree species and age, area of disturbance, and 125 environmental parameters (climate, altitude, soil composition, geology, stand ownership, and proximity to road and urban areas). Through feature elimination and SHAP analysis, six key features were identified that explain forest growth and their effect on height was investigated.The agreement between satellite and ground data justifies their simultaneous exploitation. Age and tree species emerged as the primary predictors of tree height, accounting for 49% and 10% of the variation, respectively. Post-disturbance growth is influenced by the disturbed patch area, which reveals the regeneration method, accounting for 19% of the variation. Soil pH, altitude, and summer climatic water budget have varying effects on tree height depending on age and tree species.The agreement between satellite and ground data justifies their simultaneous exploitation. Age and tree species are the primary predictors of tree height, accounting for 49% and 10% of the variation, respectively. Post-disturbance growth is influenced by the disturbed patch area, which reveals the regeneration method, accounting for 19% of the variation. Soil pH, altitude, and summer climatic water budget have varying effects on tree height depending on age and tree species. The integration of satellite and field data shows potential for analyzing future forest evolution.

Tropical moist forests have been severely affected by natural and anthropogenic disturbances, leading to substantial changes in global carbon cycle and climate. These effects have received great attention in scientific research and debates. Here we review recent progress on drivers and ecological impacts of tropical moist forest disturbances, and their monitoring and modeling methods. Disturbances in tropical moist forests are primarily driven by clearcutting, selective logging, fire, extreme drought, and edge effects. Compound disturbances such as fire and edge effects aggravate degradation in the edge forests. Drought can result in terrestrial carbon loss via physiological impacts. These disturbances lead to direct carbon loss, biophysical warming and microclimate change. Remote sensing observations are promising for monitoring forest disturbances and revealing mechanisms, which will be useful for implementing disturbance processes in dynamic vegetation models. Yet, constrained spatiotemporal coverages and resolutions limit the application of these data in process-based models. It is also challenging to represent physical processes derived from fine-resolution remote sensing data in coarse-resolution models. We highlight the need to continuously integrate new datasets and physical processes in forest disturbance modeling to advance understanding of disturbance patterns and impacts. Interactions and impacts of climate change and anthropogenic activities should also be considered for modeling and assessing feedbacks of tropical moist forest disturbances.

Non-technical summary Oil palm has been criticized for being an environmentally unfriendly oil crop. In recent decades, oil palm plantations have extended into conservation landscapes, causing severe environmental damage and harming biodiversity. Nevertheless, oil palm remains a highly productive oil crop from which most of the world's vegetable oil is produced. Therefore, measuring the environmental impact of oil palm plantations and identifying suitable land to support its sustainable development is crucial.Technical summary To meet the rising global palm oil demand sustainably, we tracked annual land cover changes in oil palm plantation and mapped areas worldwide suitable for sustainable oil palm cultivation. From 1982 to 2019, 3.6 Mha of forests were converted to oil palm plantations. Despite a recent decline in overall conversion, the shift from forest to oil palm plantations has become increasingly more common over the last decade, rising from 14.1 to 34.5% between 2009 and 2019. During 1982-2019, 2.23 Mha of peatland and 0.1 Mha of protected areas were converted for oil palm plantations. The potential sustainable land amounts to 103.5-317.9 Mha (Asia: 44.6-105.1 Mha, Africa: 34.7-96.4 Mha, and Latin America: 35.2-116.5 Mha). Future oil palm expansion is anticipated to take place in countries like Brazil, Nigeria, Colombia, Indonesia, Ivory Coast, the Democratic Republic of the Congo, and Ghana, where more sustainable land is available for cultivation. Malaysia, on the other hand, is about to exceed the area of sustainable cultivation, and further expansion is not recommended. These findings can advance our understanding of the environmentally damaging impacts of oil palm and enhance the feasibility of sustainable oil palm development.Social media summary How should suitable land be chosen for the establishment of oil palm plantations to support the sustainable development of the oil palm plantation industry?

Abstract Many land surface models (LSMs) assume a steady‐state assumption (SS) for forest growth, leading to an overestimation of biomass in young forests. Parameters inversion under SS will potentially result in biased carbon fluxes and stocks in a transient simulation. Incorporating age‐dependent biomass into LSMs can simulate real disequilibrium states, enabling the model to simulate forest growth from planting to its current age, and improving the biased post‐calibration parameters. In this study, we developed a stepwise optimization framework that first calibrates “fast” light‐controlled CO2 fluxes (gross primary productivity, GPP), then leaf area index (LAI), and finally “slow” growth‐controlled biomass using the Global LAnd Surface Satellite (GLASS) GPP and LAI products, and age‐dependent biomass curves for the 25 forests. To reduce the computation time, we used a machine learning‐based model to surrogate the complex integrated biosphere simulator LSM during calibration. Our calibrated model led to an error reduction in GPP, LAI, and biomass by 28.5%, 35.3% and 74.6%, respectively. When compared with net biome productivity (NBP) using no‐age‐calibrated parameters, our age‐calibrated parameters increased NBP by an average of 50 gC m−2 yr−1 across all forests, especially in the boreal needleleaf evergreen forests, the NBP increased by 118 gC m−2 yr−1 on average, increasing the estimate of the carbon sink in young forests. Our work highlights the importance of including forest age in LSMs, and provides a novel framework for better calibrating LSMs using constraints from multiple satellite products at a global scale.

Management practices that increase the surface albedo of cultivated land could mitigate climate change, with similar effectiveness to practices that reduce greenhouse gas emissions or favor natural CO 2 sequestration. Yet, the efficiency of such practices is barely quantified. In this study, we quantified the impacts of seven different management practices on the surface albedo of winter wheat fields (nitrogen fertilizer, herbicide, fungicide, sowing, harvest, tillage, and crop residues) by analyzing observed daily albedo dynamics from eight European flux-tower sites with interpretable machine learning. We found that management practices have significant influences on surface albedo dynamics compared with climate and soil conditions. The nitrogen fertilizer application has the largest effect among the seven practices as it increases surface albedo by 0.015 ± 0.004 during the first two months after application, corresponding to a radiative forcing of −4.39 ± 1.22 W m −2 . Herbicide induces a modest albedo decrease of 0.005 ± 0.002 over 150 d after application by killing weeds in the fallow period only, resulting in a magnitude of radiative forcing of 1.33 ± 1.06 W m −2 which is higher than radiative forcing of other practices in the same period. The substantial temporal evolution of the albedo impacts of management practices increases uncertainties in the estimated albedo-mediated climate impacts of management practices. Although these albedo effects are smaller than published estimates of the greenhouse gas-mediated biogeochemical practices, they are nevertheless significant and should thus be accounted for in climate impact assessments.

Introduction: The knowledge about forest growth, influenced by factors such as tree species, tree age, and environmental conditions, is a key for future forest preservation. Height and age data can be combined to describe forest growth and used to infer known environmental effects.Methods: In this study, we built 14 height growth curves for stands composed of monospecific or mixed species using ground measurements and satellite data. We built a random forest height model from tree species, age, area of disturbance, and 125 environmental parameters (climate, altitude, soil composition, geology, stand ownership, and proximity to road and urban areas). Using feature elimination and SHapley Additive exPlanations (SHAP) analysis, we identified six key features explaining the forest growth and investigated how they affect the height.Results: The agreement between satellite and ground data justifies their simultaneous exploitation. Age and tree species are the main predictors of tree height (49% and 10%, respectively). The disturbed patch area, revealing the regeneration method, impacts post-disturbance growth at 19%. The soil pH, altitude, and climatic water budget in summer impact tree height differently depending on the age and tree species.Discussion: Methods integrating satellite and field data show promise for analyzing future forest evolution.

Tropical terrestrial ecosystems play an important role in modulating the global carbon balance. However, the complex dynamics and factors controlling tropical aboveground live biomass carbon (AGC) are not fully understood. Here, using remotely sensed observations, we find a moderate net AGC sink of 0.21 +/- 0.06 PgC yr(-1) throughout the global tropics from 2010 to 2020. This arises from a gross loss of -1.79 PgC yr(-1) offset by a marked gain of 2.01 +/- 0.06 PgC yr(-1). Fire emissions in non-forested African shrubland/savanna biomes, coupled with post-fire carbon recovery, substantially dominated the interannual variability of tropical AGC. Fire radiative power was identified as the primary determinant of the spatial variability in AGC gains, with soil moisture also playing a crucial role in shaping trends. We highlight the dominant roles of anthropogenic and hydroclimatic determinants in orchestrating tropical land carbon dynamics and advocate for land management to conserve indispensable ecosystem services worldwide.

Disturbance, vegetation productivity, and recovery are crucial for aboveground biomass carbon (AGC) dynamics. Here, we use multiple satellite-based datasets to analyze the drivers of AGC dynamics in Central Africa. During 2010-2019, deforestation induced a gross AGC loss of 102.2 +/- 17.1 Tg C year- 1 , which was counterbalanced by an AGC increase of 116.9 +/- 41.1 Tg C year- 1 , leading to a net gain of 14.6 +/- 3.8 Tg C year- 1 . Compared to anthropogenic and soil factors, changes in climate-related factors (e.g., radiation) are more important for the non-deforestation AGC changes. A large AGC increase was found in the northern savannas. In moist forests, strong biomass recovery and growth largely compensated the carbon loss from deforestation and degradation. Considering the increasing resource demand due to rapid population growth, reconciling natural conservation and economic development in Central Africa remains challenging and depends on climate changes and country-specific social-economic conditions.

In 2023, the CO2 growth rate was 3.37 ± 0.11 ppm at Mauna Loa, which was 86% above that of the previous year and hit a record high since observations began in 1958, while global fossil fuel CO2 emissions only increased by 0.6% ± 0.5%. This implies an unprecedented weakening of land and ocean sinks, and raises the question of where and why this reduction happened. Here, we show a global net land CO2 sink of 0.44 ± 0.21 GtC yr-1, which is the weakest since 2003. We used dynamic global vegetation models, satellite fire emissions, an atmospheric inversion based on OCO-2 measurements and emulators of ocean biogeochemical and data-driven models to deliver a fast-track carbon budget in 2023. Those models ensured consistency with previous carbon budgets. Regional flux anomalies from 2015 to 2022 are consistent between top-down and bottom-up approaches, with the largest abnormal carbon loss in the Amazon during the drought in the second half of 2023 (0.31 ± 0.19 GtC yr-1), extreme fire emissions of 0.58 ± 0.10 GtC yr-1 in Canada and a loss in Southeast Asia (0.13 ± 0.12 GtC yr-1). Since 2015, land CO2 uptake north of 20°N had declined by half to 1.13 ± 0.24 GtC yr-1 in 2023. Meanwhile, the tropics recovered from the 2015-2016 El Niño carbon loss, gained carbon during the La Niña years (2020-2023), then switched to a carbon loss during the 2023 El Niño (0.56 ± 0.23 GtC yr-1). The ocean sink was stronger than normal in the equatorial eastern Pacific due to reduced upwelling from La Niña's retreat in early 2023 and the development of El Niño later. Land regions exposed to extreme heat in 2023 contributed a gross carbon loss of 1.73 GtC yr-1, indicating that record warming in 2023 had a strong negative impact on the capacity of terrestrial ecosystems to mitigate climate change.

Meeting global climate commitments and avoiding severe global warming requires drastic reductions in emissions and large-scale removals of atmospheric carbon dioxide1. Forest regrowth offers scalable and cost-effective carbon removal,2,3 but rates vary substantially by location and age of forest stand4,5. Here we generate pixel level (~1-km2) Chapman-Richards growth curves for aboveground carbon accumulation in naturally regrowing forests, based on 109,708 field estimates and 66 environmental covariates. Across the globe and over the first 100 years of growth, maximum carbon removal rates varied 160-fold demonstrating the need for spatially and temporally refined estimates. Tropical and subtropical moist broadleaf forests, and some temperate forest areas, exhibited the highest rates at young stand ages, suggesting that newly regrowing forests in these areas can provide the largest and most timely carbon removal. However, overall removal rates were greatest in ~20 to 40 year old forests. Thus. during the next climate critical decades, protecting secondary forests can provide greater carbon removal per hectare than new regrowth (up to 8-fold more). These data help optimize natural forest regrowth as a carbon removal strategy and improve estimates of secondary forests’ contribution to global carbon cycling.

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.

ABSTRACTRemote sensing and land resource surveys have been used in recent decades for land use/land cover (LULC) mapping; however, keeping the developed LULC up-to-date and consistent with land survey statistics remains challenging. This study developed a practical and effective framework to automatically update existing LULC products and bridge the gap between remote sensing classification results and land survey data. This study employed Landsat imagery time series, change detection algorithms, sample migration, and random forests to develop a framework for updating existing LULC products in China from 1980–2015 to 1980–2022. The updated LULC maps reflect the post-2015 LULC changes well and maintain continuity with the pre-2015 products. Additionally, a statistical space allocation method based on the minimum cross-entropy strategy was proposed to optimize the LULC maps, increasing the correlation coefficient (r) with China’s second and third national land survey statistics from 0.41–0.89 to 0.86–0.99. Thus, the framework and products developed in this study provide valuable tools for sustainable land use and policy planning.

The frequency of heatwaves, droughts and their co-occurrence vary greatly in simulations of different climate models. Since these extremes are expected to become more frequent with climate change, it is important to understand how vegetation models respond to different climatologies in heatwave and drought occurrence. In previous work, six climate scenarios featuring different drought-heat signatures have been developed to investigate how single versus compound extremes affect vegetation and carbon dynamics. Here, we use these scenarios to force six dynamic global vegetation models to investigate model agreement in vegetation and carbon cycle response to these scenarios. We find that global responses to different drought-heat signatures vary considerably across models. Models agree that frequent compound hot-dry events lead to a reduction in tree cover and vegetation carbon stocks. However, models show opposite responses in vegetation changes for the scenario with no extremes. We find a strong relationship between the frequency of concurrent hot-dry conditions and the total carbon pool, suggesting a reduction of the natural land carbon sink for increasing occurrence of hot-dry events. The effect of frequent compound hot and dry extremes is larger than the sum of the effects when only one extreme occurs, highlighting the importance of studying compound events. Our results demonstrate that uncertainties in the representation of compound hot-dry event occurrence in climate models propagate to uncertainties in the simulation of vegetation distribution and carbon pools. Therefore, to reduce uncertainties in future carbon cycle projections, the representation of compound events in climate models needs to be improved.

The year 2022 saw record breaking temperatures in Europe during both summer and fall. Similar to the recent 2018 drought, close to 30% (3.0 million km2) of the European continent was under severe summer drought. In 2022, the drought was located in central and southeastern Europe, contrasting the Northern-centered 2018 drought. We show, using multiple sets of observations, a reduction of net biospheric carbon uptake in summer (56-62 TgC) over the drought area. Specific sites in France even showed a widespread summertime carbon release by forests, additional to wildfires. Partial compensation (32%) for the decreased carbon uptake due to drought was offered by a warm autumn with prolonged biospheric carbon uptake. The severity of this second drought event in 5 years suggests drought-induced reduced carbon uptake to no longer be exceptional, and important to factor into Europe's developing plans for net-zero greenhouse gas emissions that rely on carbon uptake by forests.

As the most biodiversity-rich part of the protected areas system, habitats within the pilot national parks have long been threatened by drastic human-induced land use and land cover changes. The growing concern about habitat loss has spurred China's national park project to shift from pilot to construction phase with the official establishment of China's first group of national parks (CFGNPs) in October 2021. But far too little attention has been paid to the synergistic work concerning the habitat quality (HQ) dynamics of all five national parks. Here, the InVEST model, combined with a satellite-derived land use and land cover product and a hot spot analysis (HSA) method, was used to investigate the HQ dynamics at the park- and pixel-scale within the CFGNPs. Our results demonstrate that the past ecological conservation practices within national parks have been unpromising, especially in Giant Panda National Park, Northeast China Tiger and Leopard National Park (NCTL), and Wuyi Mountain National Park (WYM), where HQ as a whole showed a significant decline. Furthermore, more than half of Hainan Tropical Rainforest National Park (87.2%), WYM (77.4%), and NCTL (52.9%) showed significant HQ degradation from 1980 to 2019. Besides, increasing trends in the area shares of HQ degraded pixels were observed in all five national parks from 1980-1999 to 2000-2019. The HSA implied that the hot spots of high HQ degradation rates tend to occur in areas closer to urban settlements or on the edge of national parks, where human activities are intensive. Despite these disappointing findings, we highlighted from the observed local successes and the HQ plateau that the construction of CFGNPs is expected to reverse the deteriorating HQ trends. Thus, we concluded our paper by proposing an HSA-based regulatory zoning scheme that includes five subzones to guide the future construction of China's national park system.

Tropical ecosystems play an important role in regulating the global carbon balance. Existing studies have extensively analyzed the carbon dynamics of tropical forests, the largest terrestrial component of the global carbon budget, showing a likely neutral contribution of tropical forests to the global carbon cycle. However, high-resolution dynamics of aboveground carbon (AGC) change of the whole tropical terrestrial ecosystem and its processes remain rarely investigated. In this study, we first used low-frequency L-band passive microwave observations to derive wall-to-wall maps of annual AGC stocks over the tropics at 25-km spatial resolution. Using high-resolution satellite observations of land-cover change and biomass maps and random forest models, we separated the AGC stock into various ecosystems, including forest, shrub, and short-vegetation (grass and crop), and attributed the change to different degradation processes such as fires and deforestation at 100-m resolution. Our preliminary results show that total AGC stocks in tropical ecosystems increased by +2.25 [+1.19,+3.29] PgC (the range represents the minimum and maximums of the multiple estimates) from 2010 to 2020. The coast of Brazilian Mata Atlantica, Central African Republic, and east Tanzania are the hotspots of net increase, while the Arc of Deforestation in the Amazon basin and the Congo Basin show substantial net losses. Gross losses from non-fire deforestation and fire totaled -1.62 [-1.38,-1.86] PgC yr-1. We also observed strong recovery in African burned regions, possibly due to post-fire regrowth and additional recovery resulting from declining fires in the region. Our results highlight the importance of explicit temporal and spatial mapping of tropical carbon dynamics at high resolution, which can help us better understand the role of tropical terrestrial ecosystems in the global carbon cycle.

Tropical forests have undergone extensive deforestation and degradation during the past few decades, but the area and the carbon loss due to degradation could be larger than the losses from deforestation. Degraded forests also induce biophysical feedback on climate, as they sustain less cooling from evapotranspiration. Here we estimate the biophysical and biogeochemical temperature changes caused by tropical moist forest degradation using high-resolution remote sensing data from 2010. Degraded forests, including burned, isolated, edge and other degraded forests, account for 24.1% of the total tropical moist forest area. The land surface temperature of degraded tropical moist forests is higher than that of nearby intact forests, leading to a warming effect of 0.022 ± 0.014 °C over the tropics. The cumulative carbon deficit of degraded forests reaches 6.1 ± 2.0 PgC, equivalent to a biogeochemical warming effect of 0.026 ± 0.013 °C. Forest degradation caused by anthropogenic disturbances from 1990 to 2010 induces a daytime warming effect of 0.018 ± 0.008 °C and a carbon deficit of 2.3 ± 0.8 PgC. These values are of the same order of magnitude as those due to deforestation. Our results emphasize the importance of accounting for the combined biophysical and biogeochemical effects in mitigation pledges related to reducing forest degradation and the restoration of tropical forest. Biophysical and biogeochemical effects of forest degradation cause comparable temperature increases in tropical rainforests, according to analyses of high-resolution satellite observations.

Sustainable oil palm production is a key issue in global biodiversity conservation and sustainable development. As one of the world’s major vegetable oil crops, oil palm has expanded exponentially to meet increased demand over the past decades. However, previous monitoring and assessments of oil palm plantations were hampered because of the lack of high-resolution annual maps at the global scale. We produced annual oil palm plantation maps in 4 major producer countries (Indonesia, Malaysia, Thailand and Papua New Guinea) in Asia-Pacific from 2007 to 2018 at 100-m resolution using advanced remote sensing techniques with Phased Array L-band Synthetic Aperture Radar (PALSAR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data. We uncover the global patterns of oil palm expansion and find that global oil palm expansion has a very high degree of potential conflict with local biodiversity. Globally, 99.9% of oil palm plantations overlapped with Conservation Priority Zones (CPZs) and oil palm plantations encroached on 231 protected areas. We suggest to incorporate the related issues into the Post-2020 Global Biodiversity Framework.

China is prone to broad land degradation and thus has been implementing ecological restoration projects (ERPs) since the reform and opening up. The extent of ERPs, as well as the varied planting efforts including tree gain projects (TGPs), grass gain projects (GGPs), and shrub gain projects (SGPs), have remained largely unknown. In addition, the mixed success of ERPs on preventing soil erosion and improving biodiversity is not well known. Based on a land use and land cover (LULC) product and a trajectory-based change detection approach, we successfully generated the first national map of ERPs associated with land use and land cover change (LUCC) and its three associated subcategories. Then, we applied the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to evaluate the dynamics of sediment retention and habitat quality. In addition, we explored the heterogeneous patterns for the ecological impacts of ERPs. Our results suggested that during the past 40 years, a total ERP area of 9.54 × 106 hm2 was observed nationwide, mainly in the northwestern provinces of China. Of the three ERP subcategories, TGPs accounted for the largest area (48.55%), followed by GGPs (47.50%) and SGPs (3.96%). The national average sediment retention experienced a significant increase, whereas the national average habitat quality experienced a significant decline. ERP-driven increases in habitat quality were offset partly by the LUCCs induced by economic development policies in some regions, especially in northeast China. The simultaneous effect of construction land expansion and ERP implementation on sediment retention made the synchronization between ERP implementation and sediment retention improvement insignificant. We also suggested the optimal direction for ERP implementation.

Plantation is an important land use type that differs from natural forests and affects the economy and the environment. Tree age is one of the key factors used to quantify the impact of plantations. However, there is a lack of datasets explicitly documenting the planting years of global plantations. Here we used time-series Landsat archive from 1982 to 2020 and the LandTrendr algorithm to generate global maps of planting years based on the global plantation extent products in Google Earth Engine (GEE) platform. The datasets developed in this study are in a GeoTIFF format with 30-meter spatial resolution by recording gridded specie types and planting years of global plantations. The derived dataset could be used for yield prediction of tree crops and social and ecological cost-benefit analysis of plantations.