Mapping global forest age from forest inventories, biomass and climate data

The forest age can be defined as the time since the last stand-replacement event. Within the local context, it determines the forest successional stage, a fundamental variable for diagnosing the net carbon fluxes in terrestrial ecosystems. To accurately quantify the carbon sink-source strength in these ecosystems and inform adaptation-mitigation strategies, it is essential to be able to infer forest age at high resolution.This study presents an updated version of the MPI-BGC forest age product, featuring global distributions of forest age for 2010, 2017, 2018, and 2020 at 100m spatial resolution. We employed two machine learning approaches, XGBoost and a multi-layer perceptron model, to create data-driven estimates of forest age based on over 40,000 forest inventory plots, biomass, remote-sensing, and climate data. One key innovation of our approach is the incorporation of Landsat-based disturbance history metrics as input variables. Our updated estimates show better precision in identifying old-growth forests and reduce overestimation biases in young forests and underestimation biases in old forests, but not completely. Additionally, we found substantial regional variations related to changes in covariate strength and improvement in the model. Also, we discussed the uncertainty layers, created using model ensembles, that materialize the quantification of methodological uncertainty in the forest age estimates.An analysis of the global distribution of forest age reveals significant variations across the years studied. We also quantified the changes in forest age in regions with high deforestation or forest degradation rates, where younger stands are becoming more prevalent. We discuss the challenges and limitations of using regression-based mapping approaches, including the choice of machine learning algorithm, spatial cross-validation techniques, and the caveats of extrapolation, given data limitations. Our research highlights the complementary biomass-based approaches for determining forest age and underscores the importance of detailed global estimates at high spatial resolutions. Overall, this study advances our understanding of forest age, a key variable for understanding the carbon cycle in terrestrial ecosystems.

This study analysed age‐related water‐use dynamics across three temperate conifer forest plantations (aged 11, 39 and 74 years, as of 2013, henceforth referred to as TP02, TP74 and TP39, respectively, where the last two digits represent the year of planting) in southern Ontario, Canada, from 2008 to 2013. Eddy covariance‐measured mean evapotranspiration over the growing season (April–October) was 438 ± 19, 392 ± 19 and 323 ± 25 mm at TP39, TP74 and TP02, respectively. Daytime bulk surface conductance was highest and most variable at the TP39 site (8.5 ± 4.0 mm s−1), followed by the TP74 (7.0 ± 2.8 mm s−1) and TP02 (5.4 ± 2.5 mm s−1) sites. Evapotranspiration at all the forests was sensitive to air temperature and tended to decrease with increasing atmospheric dryness. The youngest forest's evapotranspiration was most conservative, which led to an increase in water‐use efficiency throughout the study period, in particular during drought events. The oldest forest was the least restrictive in its water use during drought, which led to lower water‐use efficiency during such events as compared with the younger forests. The oldest forest was thinned in early 2012, when about one third of trees were commercially harvested. No significant change in evapotranspiration or water‐use efficiency was observed at this site following thinning; however, daytime bulk surface conductance declined. Our results suggested that changes in stand structure with forest ageing, such as reduction in stem density and increase in sapwood area, were responsible for differences in soil water demand during drought and non‐drought periods, leading to differences in forest water use. Hence, forest age, because of its structural implications, is an important control on stand‐level water‐use efficiency and forests' response to drought events. Our study suggested that younger forests may be best suited to maximize growth and carbon uptake efficiency under rising air temperatures and increasing precipitation variability as predicted by climate models for eastern North America. Copyright © 2015 John Wiley & Sons, Ltd.

Objective: To discuss the effects of Complementary Law 140, of December 8, 2011, on the environmental jurisdiction of Brazilian municipalities. Theoretical Framework: The study is based on Federalism and Complementary Law 140. The former deals with the division of power among federated entities; the latter offers greater legal certainty to society, seeks greater speed in the analysis of environmental impacts and in the issuance of the respective licenses. Method: Literature review and documentary research. Results and Discussion: The interpretation of the law suggests that the jurisdiction for environmental inspection should not be limited to federated entities, especially municipalities. This jurisdiction is common to all entities, and there should be cooperation between them. Regarding environmental licensing, the law provided greater legal certainty by allowing this to also be carried out by municipalities in cases of local impacts. However, despite the advances of the law, it is questionable whether the municipalities are able to carry out their environmental responsibilities given their deficiencies in organizational, financial, legislative and personnel structures. Research Implications: We suggest empirical research in municipalities in the Legal Amazon, particularly those located in the so-called “deforestation arc”, where the highest rates of deforestation in the Amazon are found. Originality/Value: The paper addressed the effects of Complementary Law 140 on the environmental responsibilities of municipalities. The analysis of the bibliography and documents allowed us to determine the advances and setbacks in the environmental responsibilities of municipalities.

Biomass change of the world's forests is critical to the global carbon cycle. Despite storing nearly half of global forest carbon, the boreal biome of diverse forest types and ages is a poorly understood component of the carbon cycle. Using data from 871 permanent plots in the western boreal forest of Canada, we examined net annual aboveground biomass change (ΔAGB) of four major forest types between 1958 and 2011. We found that ΔAGB was higher for deciduous broadleaf (DEC) (1.44Mgha(-1) year(-1) , 95% Bayesian confidence interval (CI), 1.22-1.68) and early-successional coniferous forests (ESC) (1.42, CI, 1.30-1.56) than mixed forests (MIX) (0.80, CI, 0.50-1.11) and late-successional coniferous (LSC) forests (0.62, CI, 0.39-0.88). ΔAGB declined with forest age as well as calendar year. After accounting for the effects of forest age, ΔAGB declined by 0.035, 0.021, 0.032 and 0.069Mgha(-1) year(-1) per calendar year in DEC, ESC, MIX and LSC forests, respectively. The ΔAGB declines resulted from increased tree mortality and reduced growth in all forest types except DEC, in which a large biomass loss from mortality was accompanied with a small increase in growth. With every degree of annual temperature increase, ΔAGB decreased by 1.00, 0.20, 0.55 and 1.07 Mgha(-1) year(-1) in DEC, ESC, MIX and LSC forests, respectively. With every cm decrease of annual climatic moisture availability, ΔAGB decreased 0.030, 0.045 and 0.17Mgha(-1) year(-1) in ESC, MIX and LSC forests, but changed little in DEC forests. Our results suggest that persistent warming and decreasing water availability have profound negative effects on forest biomass in the boreal forests of western Canada. Furthermore, our results indicate that forest responses to climate change are strongly dependent on forest composition with late-successional coniferous forests being most vulnerable to climate changes in terms of aboveground biomass.

Influence of forest age, tree size, and climate factors on biomass and carbon storage allocation in Chinese fir forests

The rate of deforestation in the Amazon is increasing. Predictive models estimate that as a result of agricultural expansion 40% of these forests will be lost by 2050. As a consequence the habitat of forest-dwelling species such as the Endangered black-faced black spider monkey Ateles chamek is being lost, particularly along the arc of deforestation in the Brazilian Amazon. We used species distribution modelling to (1) define the distribution of this spider monkey, using environmental predictors, (2) calculate the area of this distribution covered by the protected area network, and (3) calculate the expected loss of the species’ habitat under future scenarios of deforestation. We found that the species occupies only c. 28% of its extent of occurrence. Only 32% of the species’ area of occupancy is legally protected, and the modelling suggests that 31–40% of the species’ habitat will be lost by 2050. We highlight three unprotected regions with extensive forest cover that are predicted to become severely deforested by 2050 as priority regions for expanding the protected area network. We also propose landscape management and restoration in three human-modified regions. Our study provides an example of how species distribution modelling can be applied to assess threats to species and support decision makers in implementing conservation actions.

<strong class="journal-contentHeaderColor">Abstract.</strong> Young forest age mapping at a fine spatial resolution is important for increasing the accuracy of estimating land-atmosphere carbon fluxes and guiding forest management practices. In recent decades, China has actively conducted afforestation and forest protection projects, thereby, laying the foundation for the realization of carbon neutrality. However, very few studies have been conducted which map the ages of young forests for the whole of China at a fine spatial resolution. In this research, a continuous change detection and classification (CCDC)-based method suitable for large-scale forest age mapping is proposed, and used to estimate young forest ages across China in 2020 at a spatial resolution of 30 m. First, a 10 m spatial resolution land cover dataset (WorldCover2020) from the European Space Agency (ESA) was used to determine the forest cover areas in 2020. Then, the CCDC algorithm was used to identify stand-replacing disturbances to determine the stand age based on 436,967 Landsat tiles across China from 1990 to 2020. A validation sample set composed of multiple land use/land cover (LULC) products was used to calculate the overall accuracy (OA) of the 2020 young forest age (1&ndash;31 years) map of China, and the OA was 90.28 %. The reliability and applicability of the proposed CCDC-based forest age mapping method was validated by comparing the forest age map with Hansen&rsquo;s forest change dataset, Max Planck Institute for Biogeochemistry (MPI-BGC) 1 km global forest age datasets and field measurements. The CCDC-based method has strong application potential in real-time mapping of the age of young forests at the global scale. The produced forest age map provides a basic dataset for research on the forest carbon cycle and forest ecosystem services, and important guidance for government departments, such as the National Forestry and Grassland Administration and National Development and Reform Commission in China.

<strong class="journal-contentHeaderColor">Abstract.</strong> Young forest age mapping at a fine spatial resolution is important for increasing the accuracy of estimating land-atmosphere carbon fluxes and guiding forest management practices. In recent decades, China has actively conducted afforestation and forest protection projects, thereby, laying the foundation for the realization of carbon neutrality. However, very few studies have been conducted which map the ages of young forests for the whole of China at a fine spatial resolution. In this research, a continuous change detection and classification (CCDC)-based method suitable for large-scale forest age mapping is proposed, and used to estimate young forest ages across China in 2020 at a spatial resolution of 30 m. First, a 10 m spatial resolution land cover dataset (WorldCover2020) from the European Space Agency (ESA) was used to determine the forest cover areas in 2020. Then, the CCDC algorithm was used to identify stand-replacing disturbances to determine the stand age based on 436,967 Landsat tiles across China from 1990 to 2020. A validation sample set composed of multiple land use/land cover (LULC) products was used to calculate the overall accuracy (OA) of the 2020 young forest age (1&ndash;31 years) map of China, and the OA was 90.28 %. The reliability and applicability of the proposed CCDC-based forest age mapping method was validated by comparing the forest age map with Hansen&rsquo;s forest change dataset, Max Planck Institute for Biogeochemistry (MPI-BGC) 1 km global forest age datasets and field measurements. The CCDC-based method has strong application potential in real-time mapping of the age of young forests at the global scale. The produced forest age map provides a basic dataset for research on the forest carbon cycle and forest ecosystem services, and important guidance for government departments, such as the National Forestry and Grassland Administration and National Development and Reform Commission in China.

ABSTRACT: The shift from rural to urban living has resulted in natural revegetation of abandoned rural areas located in mountainous regions. These land-use changes, combined with recent temperature and precipitation trends, affect water resources and sediment yields. Consequently, surface runoff, water infiltration, and sediment production/transport are impacted. Studies of this impact in mountainous areas are limited and partial, restricted to small basins. Therefore, this study examines the changes in both water and sediment fluxes in the northern draining region of the Ebro Basin. For the study, the SWAT+ hydrological model was employed. SWAT+ is a semi-distributed, deterministic, continuous basin model that operates on a daily time step. The model requires several inputs: a Digital Elevation Model (DEM), reservoir locations, land use map and index, soil type map and properties, as well as climatic data. The Ebro basin was divided into eight sub-basins and a hydrological model was developed for each. Calibration and validation processes were conducted in two steps: firstly, hydrological calibration and validation was performed and afterwards the process was repeated for the sediments. Firstly, hydrological calibration and validation were conducted at available and relevant gauging stations within the sub-basins. In total, over 30 stations were used as control points. The precision of the calibration and validation was evaluated using the Nash-Sutcliffe Efficiency (NSE). Secondly, sediment calibration was conducted using available reservoir bathymetry data or sediment yield estimates from scientific literature. The sediment calibration aimed to reproduce values of the same order of magnitude as those derived from bathymetry or literature data. The resulting hydrologic and sedimentologic model was re-run and NSE, maintaining or improving the NSE values obtained from the hydrological calibration. NSE values, thus, ranged from 0.53 to 0.95 depending on the gauging station. The mean NSE for the entire basin under study was 0.75, indicating that a well-established hydrologic and sedimentologic model was achieved. Future work will involve, firstly, applying climate change scenarios from CMIP6 and comparing the current results to observe the response to climate change. Secondly, generating downscaled land-use change maps to accurately represent the evolution of land-use change in each sub-basin. This approach will allow for the analysis of the impacts of climate change alone and in combination with land-use change.&amp;#160;ACKNOWLEDGMENTS: This work is funded by the European Research Council (ERC) through the Horizon Europe 2021 Starting Grant program under REA grant agreement number 101039181 - SEDAHEAD.

Objective: Analyze the dynamics of land use and coverage in the Arc of Deforestation between 1985 and 2023, quantifying the conversion of forest into agricultural areas and identifying the main drivers of this transformation. Theoretical Framework: The research is based on the application of data science and geoprocessing to analyze the complex relationships between the expansion of the agricultural frontier and deforestation in the Amazon. Method: A quantitative temporal analysis was performed using data from MapBiomas (Collection 9). The cross-tabulation technique via map algebra was used to detect changes, and the R and Python languages ​​were used for data processing and visualization, including the generation of Sankey Diagrams. Results and Discussion: The results indicate a net loss of 397,685 km² of forest (-25.54%), while agriculture expanded by 399,052 km² (+410.95%), demonstrating a perfect negative correlation (-1). The conversion of "Forest Formation" to "Pasture" was the dominant flow of change, pointing to extensive livestock farming as the main driver of deforestation, followed by the expansion of soybeans. Research Implications: The findings provide quantitative evidence that can support the development of more effective public policies for environmental conservation and sustainable soil management in the Amazon region. Originality/Value: The study offers a precise and long-term (38 years) quantification of forest conversion in the region, consolidating, through robust data analysis, extensive livestock farming as the main historical driver of deforestation in the Arc of Deforestation.

Forest-fragmentation-related edge effects are one of the major causes of forest degradationin Amazonia and their spatio-temporal dynamics are highly influenced by annualdeforestation patterns. Rapid biomass collapse due to edge effects in forest fragments hasbeen reported in the Brazilian Amazon; however the collective impacts of this process onAmazonian carbon fluxes are poorly understood. We estimated biomass loss and carbonemissions from deforestation and forest fragmentation related to edge effects on the basis ofthe INPE (Brazilian National Space Research Institute) PRODES deforestation data andforest biomass volume data. The areas and ages of edge forests were calculated annuallyand the corresponding biomass loss and carbon emissions from these forest edges wereestimated using published rates of biomass decay and decomposition corresponding to theareas and ages of edge forests. Our analysis estimated carbon fluxes from deforestation(4195 Tg C) and edge forest (126–221 Tg C) for 2001–10 in the Brazilian Amazon.The impacts of varying rates of deforestation on regional forest fragmentationand carbon fluxes were also investigated, with the focus on two periods: 2001–5(high deforestation rates) and 2006–10 (low deforestation rates). Edge-releasedcarbon accounted for 2.6–4.5% of deforestation-related carbon emissions. However,the relative importance of carbon emissions from forest fragmentation increasedfrom 1.7–3.0% to 3.3–5.6% of the respective deforestation emissions between thetwo contrasting deforestation rates. Edge-related carbon fluxes are of increasingimportance for basin-wide carbon accounting, especially as regards ongoing reducingemissions from deforestation and forest degradation (REDD) efforts in BrazilianAmazonia.

Distribution characteristics of mercury concentration and estimation of mercury pools in different age groups of Larix gmelinii forests of Daxing'an Mountain

Effects of forest continuity and tree age on epiphytic lichen biota in coniferous forests in Estonia

Forest fragmentation, structure, and age characteristics as a legacy of forest management

Deforestation and forest degradation are major drivers of global environmental change and tropical forests are subjected to unprecedented pressures from both. For most tropical zones, deforestation rates are averaged across entire countries, often without highlighting regional differentiation. There are also very few estimates of forest degradation, either averaged or localized for the tropics. We quantified regional and country‐wide changes in deforestation and forest degradation rates for Madagascar from Landsat temporal data (in two intervals, 1994–2002 and 2002–2014). To our knowledge, this is the first country‐wide estimate of forest degradation for Madagascar. We also performed an intensity analysis to categorize the magnitude and speed of transitions between forest, vegetation matrix, cultivated land and exposed surface. We found significant regional heterogeneity in deforestation and forest degradation. Deforestation rates decreased annually in lowland evergreen moist forest by −0.24% and in all other vegetation zones. Forest degradation rates had annual increases in the same period in lowland evergreen moist forest (0.09%), littoral forest (0.06%) but decreased in medium altitude moist evergreen forest (−0.25%), dry deciduous forest (−0.23%) and scelrophyllous woodland (−0.61%) in the same period. Despite these regional differences, higher rates of deforestation and forest degradation were consistently driven by rapid and large‐sized conversions of largely intact forest to cultivated lands and exposed surfaces, most of which occurred between 1994 and 2002. These results suggest that while targeted conservation projects may have reduced forest degradation rates in some areas (e.g. medium altitude moist evergreen forest), the drivers of land cover change remain intense in relatively neglected regions. We advocate a more balanced approach to future conservation initiatives, one recognizing that deforestation and forest degradation, particularly in tropical Africa, are often driven by region‐specific conditions and therefore require conservation policies tailored for local conditions.