Issam Boukhris

Protected areas (PAs) are the cornerstone of global terrestrial biodiversity conservation, yet systematic, scalable assessment of their vegetation structural condition remains limited. NASA’s Global Ecosystem Dynamics Investigation (GEDI), a spaceborne full-waveform lidar mission, offers a unique opportunity to consistently quantify vegetation structure within PA boundaries at near-global scales. This review synthesises 45 peer-reviewed studies published between January 2018 and May 2026 that use GEDI to study PAs within 26 countries on five continents. Estimates of canopy height, canopy cover, and vertical structure from GEDI’s L2A and L2B products were most widely used. Aboveground biomass density was also frequently derived, often through L4A or multi-sensor modelling. Studies of vegetation structure, carbon storage, methodological integration and multi-sensor fusion dominate the evidence base, while analyses of disturbance, biodiversity proxies, and PA effectiveness remain more limited, with only 16 studies addressing these topics. Most studies combine GEDI with optical imagery, synthetic aperture radar (SAR), airborne lidar and spaceborne lidar, field plots, or terrestrial laser scanning to support spatial mapping and validation. GEDI-derived structural metrics can support monitoring under the Kunming–Montreal Global Biodiversity Framework, the Paris Agreement, and biodiversity assessments led by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), including reporting aligned with the Essential Biodiversity Variables (EBV) framework. However, this review shows that their operational use still requires stronger biodiversity linkages, clearer validation protocols, and better testing across ecosystems. Future research could further strengthen PA monitoring by integrating GEDI with new SAR and forthcoming lidar missions, and analyses using geospatial foundation models.

Predicting forest attributes and dynamics has long been a central objective of forestry, yet robust and transferable prediction remains challenging. Forest ecosystems are intrinsically heterogeneous, only partially observable, and shaped by interacting processes operating across multiple spatial and temporal scales. Over the past decades, predictive modelling has expanded across methodological paradigms that differ fundamentally in where predictive information is encoded and how it is learned. This perspective traces this evolution from equation-based models to machine learning, deep learning, and most recently, geospatial foundation models (GeoFMs). We do not view these paradigms as a succession of increasingly superior approaches, we examine them through the lens of environmental representation learning, with particular attention to how information about forest ecosystems is represented, learned, and transferred across tasks and contexts. GeoFMs extend this trajectory through large-scale self-supervised pretraining on Earth observation data, offering the potential to reduce dependence on task-specific labelled datasets and transfer learned representations across contexts and prediction tasks. However, much of this potential remains unrealized in forestry. The ecological meaning of learned representations remains poorly understood, their robustness across space and time is insufficiently established, and current applications largely focus on forest state inference rather than explicitly learning and predicting forest state evolution. We identify key research directions for addressing these limitations and propose a framework for guiding model choice across predictive paradigms. We ultimately position GeoFMs as complementary to, rather than replacements for, existing modelling approaches and argue that future advances in forest prediction may increasingly depend on how their respective strengths are combined.

Forests are central to the EU’s climate neutrality strategy, currently offsetting ∼9% of total greenhouse gas emissions and offering further mitigation potential through harvested wood products and the substitution effect. However, the climate benefit of the forest sector is influenced by multiple interacting factors, including forest management, climate change, wood-use strategies, and assumptions about substitution benefits, as well as the timing and fate of carbon across the forest ecosystem and technosphere. To evaluate these drivers, we used a coupled forest growth and a wood products model to simulate five different silvicultural strategies under three climate change scenarios, four wood use schemes, and five displacement factor decay pathways over a 285-year period (2015–2300), applied to a Pinus nigra forest in Italy, questioning the impact of these factors on climate mitigation potential of the forest sector. We assessed forest sector balance (FSB, net carbon exchange between forest system and atmosphere), radiative forcing from biogenic CO ₂ (RF _bio ), and mitigation efficiency (ME) – the proportion of sequestered carbon contributing to net climate benefit. Results showed that FSB and RF _bio were “broadly” aligned, but ME varied with the magnitude and duration of biogenic emissions. The scenarios BIOE (bioenergy) and TM (modular cutting) achieved high FSB but showed lower ME due to concentrated or sustained emissions. WOOD (promotion of long-lived wood) and ADAPT (adaptation management) yielded higher ME under SSP1-2.6, while several strategies (WOOD, ADAPT, TRANS) became net sources under SSP5-8.5 after 2200. Substitution benefits declined under degressive assumptions, reducing mitigation by up to 53% especially for high-harvest scenarios. FSB was primarily shaped by climate and management, secondly by substitution, however; wood-use strategies had no significant long-term effect provided they did not impact resource availability. Together, these findings underscore that effective forest-sector mitigation requires not only maximizing cumulative carbon stocks, but also minimizing the magnitude, timing, and atmospheric residence time of emissions while carefully considering the role of substitution benefits. Highlights: The long-term forest sector carbon balance is mainly governed by active management, climate conditions, and wood substitution pathways. Forest Sector Balance as a metric is “broadly” aligned with the radiative forcing from biogenic emissions The mitigation efficiency of forest sector options depends on emissions timing, duration, and amplitude Substitution factors also known as displacement factors need to be considered with greater caution

Harvested wood products (HWPs) have a pivotal role in climate change mitigation, a recognition solidified in many Nationally Determined Contributions (NDCs) under the Paris Agreement. Integrating HWPs' greenhouse gas (GHG) emissions and removals into accounting requirements relies on typical decision-oriented tools known as wood product models (WPMs). The study introduces the TimberTracer (TT) framework, designed to simulate HWP carbon stock, substitution effects, and emissions from wood decay and bioenergy. Coupled with the 3D-CMCC-FEM forest growth model, TimberTracer was applied to Laricio Pine (Pinus nigra subsp. laricio) in Italy’s Bonis watershed, evaluating three forest management practices (clearcut, selective thinning, and shelterwood) and four wood-use scenarios (business as usual, increased recycling rate, extended average lifespan, and a simultaneous increase in both the recycling rate and the average lifespan) over a 140 year planning horizon, to assess the overall carbon balance of HWPs. Furthermore, this study evaluates the consequences of disregarding landfill methane emissions and relying on static substitution factors, assessing their impact on the mitigation potential of various options. This investigation, covering HWPs stock, carbon (C) emissions, and the substitution effect, revealed that selective thinning emerged as the optimal forest management scenario. In addition, a simultaneous 10% increase in both the recycling rate and half-life, under the so-called “sustainability” scenario, proved to be the optimal wood-use strategy. Finally, the analysis shows that failing to account for landfill methane emissions and the use of dynamic substitution can significantly overestimate the mitigation potential of various forest management and wood-use options, which underscores the critical importance of a comprehensive accounting in climate mitigation strategies involving HWPs. Our study highlights the critical role of harvested wood products (HWPs) in climate change mitigation, as endorsed by multiple Nationally Determined Contributions (NDCs) under the Paris Agreement. Utilizing the TimberTracer framework coupled with the 3D-CMCC-FEM forest growth model, we identified selective thinning as the optimal forest management practice. Additionally, enhancing recycling rates and extending product lifespan effectively bolstered the carbon balance. Moreover, this study emphasizes the necessity of accounting for landfill methane emissions and dynamic product substitution, as failing to do so may significantly overestimate the mitigation potential of implemented projects. These findings offer actionable insights to optimize forest management strategies and advance climate change mitigation efforts.

Purpose of Review: Forests are integral to global ecological stability, climate regulation, and economic resilience. They function as major carbon sinks, act as biodiversity reservoirs, and provide ecosystem services. Accurately modeling forest growth is essential to predict ecosystem responses to climate change and optimize ecosystem services. However, predicting forest growth remains challenging due to complex interactions between ecological processes, external drivers like climate change, and intrinsic dynamics, such as legacy effects and emergent properties, that influence forest responses over time. This work provides a systematic in-depth analysis of both established and emerging theories as found in the literature, exploring their integration into modern forest growth modeling with a special focus on new approaches, as implemented in 18 forest growth models which vary in their structure, objectives, and overarching goals. Recent Findings: Forest modeling requires a deep understanding of forest growth theories driven by multiple interacting processes. Over time, numerous eco-physiological theories have been developed to predict forest growth under both current and future climatic conditions via dynamic vegetation models. While some were established in the past, new approaches continue to emerge, refining the complexity, predictive accuracy, and practical applicability of models. This ongoing evolution has resulted in models that are theoretically diversified but also increasingly relevant for real-world case studies dealing with both anthropogenic and natural disturbances. Machine learning, trained on increasingly large datasets, is emerging as a powerful complement to traditional forest models. Rather than replacing process-based approaches, it can be combined with them in hybrid frameworks that integrate mechanistic understanding with data-driven flexibility. This combination improves predictive performance, extends model applicability, and supports more robust decision-making in forest management. Summary: Amid the ongoing’chicken-and-egg’ debate on whether photosynthesis drives growth or growth drives photosynthesis, our review synthesizes key interconnected theories, including Functional Balance, Local Determination of Growth, and Optimality Principles of forest growth. By integrating these perspectives, we offer a clear and comprehensive overview of the main frameworks governing growth and resource allocation in plants. As multiple studies emphasize the importance of integrating different and recent theories to better capture growth dynamics, we build on a state-of-the-art multi-modelling comparison to discuss what the implications of different theories might be at different temporal and spatial resolutions. Finally, we explore how emerging technologies, such as machine learning, can enhance predictive accuracy and help address current modeling limitations.

Purpose of Review Forests are integral to global ecological stability, climate regulation, and economic resilience. They function as major carbon sinks, act as biodiversity reservoirs, and provide ecosystem services. Accurately modeling forest growth is essential to predict ecosystem responses to climate change and optimize ecosystem services. However, predicting forest growth remains challenging due to complex interactions between ecological processes, external drivers like climate change, and intrinsic dynamics, such as legacy effects and emergent properties, that influence forest responses over time. This work provides a systematic in-depth analysis of both established and emerging theories as found in the literature, exploring their integration into modern forest growth modeling with a special focus on new approaches, as implemented in 18 forest growth models which vary in their structure, objectives, and overarching goals. Recent Findings Forest modeling requires a deep understanding of forest growth theories driven by multiple interacting processes. Over time, numerous eco-physiological theories have been developed to predict forest growth under both current and future climatic conditions via dynamic vegetation models. While some were established in the past, new approaches continue to emerge, refining the complexity, predictive accuracy, and practical applicability of models. This ongoing evolution has resulted in models that are theoretically diversified but also increasingly relevant for real-world case studies dealing with both anthropogenic and natural disturbances. Machine learning, trained on increasingly large datasets, is emerging as a powerful complement to traditional forest models. Rather than replacing process-based approaches, it can be combined with them in hybrid frameworks that integrate mechanistic understanding with data-driven flexibility. This combination improves predictive performance, extends model applicability, and supports more robust decision-making in forest management. Summary Amid the ongoing ’chicken-and-egg’ debate on whether photosynthesis drives growth or growth drives photosynthesis, our review synthesizes key interconnected theories, including Functional Balance, Local Determination of Growth, and Optimality Principles of forest growth. By integrating these perspectives, we offer a clear and comprehensive overview of the main frameworks governing growth and resource allocation in plants As multiple studies emphasize the importance of integrating different and recent theories to better capture growth dynamics, we build on a state-of-the-art multi-modelling comparison to discuss what the implications of different theories might be at different temporal and spatial resolutions. Finally, we explore how emerging technologies, such as machine learning, can enhance predictive accuracy and help address current modeling limitations.

Maintaining or increasing forest carbon sinks is considered essential to mitigate the rise of atmospheric CO₂ concentrations. Harvesting trees is perceived as having negative consequences on both the standing biomass stocks and the carbon sink strength. However, harvesting needs to be examined from a forest stand canopy perspective since carbon assimilation occurs in the canopy. Here we show that a threshold of leaf area exists beyond which additional leaves do not contribute to ecosystem fluxes. The associated biomass can be harvested without affecting the forest carbon fluxes. Based on eddy covariance measurements we show that CO₂ uptake (GPP) and net ecosystem exchange (NEP) in temperate forests are of similar magnitude in both unmanaged and sustainably managed forests, in the order of 1500–1600 gC m^-2 y^-1 for GPP and 542 – 483 gC m⁻² y⁻¹ for NEP. A threshold of about 4 m² m^-2 LAI (leaf area index) can be used as a definition of sustainable harvesting with regard to CO₂ uptake. Simulations based on the LPJ-GUESS model reproduce the saturation of GPP and NEP and convergence on the LAI threshold range. Accordingly, in managed forests, trees can be harvested while maintaining a high tree biomass and carbon sink of the remaining stand. In this case competition between neighbour trees in unmanaged forests is replaced by harvest management. In unmanaged forests, competition for light, nutrient and water cause self-thinning, thereby limiting the carbon sink strength.

Maintaining or increasing forest carbon sinks is considered essential to mitigate the rise of atmospheric CO₂ concentrations. Harvesting trees is perceived as having negative consequences on both the standing biomass stocks and the carbon sink strength. However, harvesting needs to be examined from a forest stand canopy perspective since carbon assimilation occurs in the canopy. Here we show that a threshold of leaf area exists beyond which additional leaves do not contribute to ecosystem fluxes. The associated biomass can be harvested without affecting the forest carbon fluxes. Based on eddy covariance measurements we show that CO₂ uptake (GPP) and net ecosystem exchange (NEP) in temperate forests are of similar magnitude in both unmanaged and sustainably managed forests, in the order of 1500–1600 gC m^-2 y^-1 for GPP and 542 – 483 gC m⁻² y⁻¹ for NEP. A threshold of about 4 m² m^-2 LAI (leaf area index) can be used as a definition of sustainable harvesting with regard to CO₂ uptake. Simulations based on the LPJ-GUESS model reproduce the saturation of GPP and NEP and convergence on the LAI threshold range. Accordingly, in managed forests, trees can be harvested while maintaining a high tree biomass and carbon sink of the remaining stand. In this case competition between neighbour trees in unmanaged forests is replaced by harvest management. In unmanaged forests, competition for light, nutrient and water cause self-thinning, thereby limiting the carbon sink strength.