Regional estimates of gross primary production applying the Process-Based Model 3D-CMCC-FEM vs. Remote-Sensing multiple datasets

European Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: 57(1)

Published: Jan. 9, 2024

Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of carbon fluxes stocks in forests. Yet, their predictive capacity should be demonstrated not only at stand-level but also context broad heterogeneity. We apply a stand scale PBFM (3D-CMCC-FEM) spatially explicit manner 1 km resolution southern Italy. developed methodology initialize model that includes information derived from integration Remote Sensing (RS) National Inventory (NFI) data regional forest maps characterize structural features main species. Gross primary production (GPP) is simulated over 2005–2019 period capability simulating GPP evaluated both aggregated as species-level through multiple independent sources based on different nature RS-based products. show able reproduce most (~2800 km2) (32 years total) observed seasonal, annual interannual time scales, even species-level. These promising results open confindently applying 3D-CMCC-FEM investigate forests' behaviour under climate environmental variability large areas across highly variable ecological bio-geographical heterogeneity Mediterranean region.

Language: Английский

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Projected effects of climate change and forest management on carbon fluxes and biomass of a boreal forest

Agricultural and Forest Meteorology, Journal Year: 2024, Volume and Issue: 349, P. 109959 - 109959

Published: March 7, 2024

Boreal forests are key to global carbon (C) sequestration and storage. However, the potential impacts of climate change on these could be profound. Nearly 70 % European boreal intensively managed, but our understanding combined effects forest management forest's integral role as a C sink is still limited. In this study, we aim fill gap with simulations process-based dynamic vegetation model LPJ-GUESS. We evaluated four options under two different scenarios (RCP 4.5 RCP 8.5), at southern stand in Sweden. These were compared against baseline without clear-cut or interventions. found that projected increase temperatures (+2 +4 °C) during latter part 21st century will reduce net strength, particularly unmanaged forest. The standing biomass for reforestations was 57–67 lower 2100 than old 2022. study also revealed replanted pine may surpass 200-years far future (2076–2100). did not detect statistically significant differences overall exchange between subsequent reforestation baseline, even though specific strategies, such plantations, enhanced by 7–20 relative 2022–2100. findings underscore profound influence budget, surpassing alone. By adopting pertinent uptake augmented, concurrently improved productivity, resulting favourable outcomes critical storage amidst changing climate.

Language: Английский

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Adaptive forest management improves stand-level resilience of temperate forests under multiple stressors

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 948, P. 174168 - 174168

Published: June 26, 2024

Language: Английский

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Safeguarding the Aspromonte Forests: Random Forests and Markov Chains as Forecasting Models for Predicting Land Transformations

Forests, Journal Year: 2025, Volume and Issue: 16(2), P. 290 - 290

Published: Feb. 8, 2025

Forests are crucial for human well-being and the health of our planet, particularly due to their role in carbon storage climate mitigation. Mediterranean forests, particular, a vital natural resource region. They help absorb anthropogenic emissions, reduce erosion, provide essential habitats various species, which turn increases genetic diversity species richness. This study combines Random Forest Markov chain models propose highly accurate method predicting land use. approach offers substantial scientific support sustainable management policies. The methods used demonstrated excellent classification performance over time, allowing an examination evolution forests Aspromonte also provides foundation estimating stored above below ground using remote sensing images. model achieved impressive accuracy 98.88%, making it reliable tool dynamics forests. results this have significant implications urban planning change mitigation efforts.

Language: Английский

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Predicted Future Changes in the Mean Seasonal Carbon Cycle Due to Climate Change

Forests, Journal Year: 2024, Volume and Issue: 15(7), P. 1124 - 1124

Published: June 28, 2024

Through photosynthesis, forests absorb annually large amounts of atmospheric CO2. However, they also release CO2 back through respiration. These two, opposite in sign, fluxes determine how much the carbon is stored or released into atmosphere. The mean seasonal cycle (MSC) an interesting metric that associates phenology and (C) partitioning/allocation analysis within forest stands. Here, we applied 3D-CMCC-FEM model analyzed its capability to represent main C-fluxes, by validating against observed data, questioning if sink/source seasonality influenced under two scenarios climate change, five contrasting European sites. We found has, current conditions, robust predictive abilities estimating NEE. Model results predict a consistent reduction forest’s capabilities act as C-sink change stand-aging at all Such predicted despite number annual days evergreen increasing over years, indicating downward trend. Similarly, deciduous forests, maintaining relatively stable throughout year century, show their overall capacity. Overall, both types sites future mitigating potential.

Language: Английский

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Altitudinal shifting of major forest tree species in Italian mountains under climate change

Frontiers in Forests and Global Change, Journal Year: 2023, Volume and Issue: 6

Published: Sept. 8, 2023

Climate change has profound implications for global ecosystems, particularly in mountainous regions where species distribution and composition are highly sensitive to changing environmental conditions. Understanding the potential impacts of climate on native forest is crucial effective conservation management strategies. Despite numerous studies impacts, there remains a need investigate future dynamics suitability key species, especially specific sections. This study aims address this knowledge gap by examining shifts altitudinal range Italy's regions. By using models, through MaxEnt we show divergent among scenarios, with most experiencing contraction their whereas others extend beyond current tree line. The Northern North-Eastern Apennines exhibit greatest widespread all emphasizing vulnerability. Our findings highlight complex dynamic nature Italy. While projected experience range, European larch Alpine region Turkey oak gains could play significant roles maintaining wooded populations. line generally expected shift upward, impacting beech—a keystone Italian mountain environment—negatively arc Apennines, while showing good above 1,500 meters Central Southern Apennines. Instead, Maritime pine emerges as promising candidate biodiversity, terms population composition, suggest comprehensive emphasizes importance high-resolution data considering multiple factors scenarios when assessing have at local, regional, national levels, continued efforts producing reliable datasets forecasts inform targeted adaptive strategies face change.

Language: Английский

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Integrating climate and soil factors enhances biomass estimation for natural white birch (Betula platyphylla Sukaczev)

Frontiers in Forests and Global Change, Journal Year: 2025, Volume and Issue: 8

Published: March 14, 2025

Introduction Accurate biomass estimation is crucial for quantifying forest carbon storage and guiding sustainable management. In this study, we developed four modeling systems natural white birch ( Betula platyphylla Sukaczev) in northeastern China using field data from 148 trees. Methods The included diameter at breast height (DBH), tree (H), crown dimensions, components (stem, branch, foliage, root biomass), as well soil climate variables. We employed Seemingly Unrelated Regression (SUR) mixed-effects models (SURM) to account component correlations spatial variability. Results base model (SUR ba ), only the DBH variable, explained 89-96% of variance (RMSE%: 1.34-19.94%). second bio ) incorporated H stem/branch length (CL) improving predictions stem, foliage (R 2 increased by 1.69–4.86%; RMSE% decreased 10.76-59.04%). Next, SUR ba-abio bio-abio integrated abiotic factors, including organic content (SOC), mean annual precipitation (MAP), degree-days above 18°C (DD18), bulk density (BD). Both showed improvement, with factor performing similarly biotic (ΔR < 4.36%), while performed best. Subsequently, random effects were introduced sampling point (Forestry Bureau) level, developing seemingly unrelated (SURM , SURM which improved fitting prediction accuracy. gap between (with factors) (including both was minimal 2.80%). stabilized when calibrated aboveground measurements Discussion conclusion, these provide an effective approach estimating China. absence serve reliable alternatives, emphasizing importance factors offering a practical solution predicting biomass.

Language: Английский

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Drought alters aboveground biomass production efficiency: Insights from two European beech forests

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 919, P. 170726 - 170726

Published: Feb. 6, 2024

Language: Английский

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Changes in Mean Seasonal Carbon Cycle Due to Climate Change

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 21, 2024

Abstract Through photosynthesis, forests absorb annually large amounts of atmospheric CO 2 . However, they also release back through respiration. These two, opposite in sign, fluxes determine, much the carbon that is stored or released to atmosphere. The mean seasonal cycle (MSC) an interesting metric associates phenology and (C) partitioning-allocation analysis within forest stands. Here we applied 3D-CMCC-FEM model analyzed its capability represent main C-fluxes, by validating against observed data, questioning if sink/source seasonality influenced under two scenarios climate change, five contrasting European sites. We found has, current conditions, robust predictive abilities estimating NEE. Model results predict a consistent reduction forest’s capabilities act as C-sink change stand-ageing at all Such predicted despite number annual days evergreen increasing over years, indicating downward trend. Similarly, deciduous forests, maintaining relatively stable throughout year century, show their overall capacity. Overall, both types sites future mitigating potential.

Language: Английский

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Carbon sequestration potential of plantation forests in New Zealand - no single tree species is universally best

Carbon Balance and Management, Journal Year: 2024, Volume and Issue: 19(1)

Published: April 5, 2024

Abstract Background Plantation forests are a nature-based solution to sequester atmospheric carbon and, therefore, mitigate anthropogenic climate change. The choice of tree species for afforestation is subject debate within New Zealand. Two key issues whether use (1) exotic plantation versus indigenous forest and (2) fast growing short-rotation slower species. In addition, there lack scientific knowledge about the sequestration capabilities different species, which hinders optimal sequestration. We contribute this discussion by simulating five Pinus radiata , Pseudotsuga menziesii Eucalyptus fastigata Sequoia sempervirens Podocarpus totara across three sites two silvicultural regimes using 3-PG an ecophysiological model. Results model simulations showed that potential varies among regimes. Indigenous or can provide plausible options long-term contrast, short term rapid be obtained planting radiata, . Conclusion No single was universally better at sequestering on all we tested. general, results study suggest robust framework ranking testing candidate with regard given site. Hence, could help towards more efficient decision-making forestry.

Language: Английский

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