Mapping Forest Age at High-Resolution Using Inventory Data

Abstract. Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. However, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. This study provides a new global distribution of forest age circa 2010, estimated using a machine learning approach trained with more than 40 000 plots using forest inventory, biomass and climate data. First, an evaluation against the plot-level measurements of forest age reveals that the data-driven method has a relatively good predictive capacity of classifying old-growth vs. non-old-growth (precision = 0.81 and 0.99 for old-growth and non-old-growth, respectively) forests and estimating corresponding forest age estimates (NSE = 0.6 – Nash–Sutcliffe efficiency – and RMSE = 50 years – root-mean-square error). However, there are systematic biases of overestimation in young- and underestimation in old-forest stands, respectively. Globally, we find a large variability in forest age with the old-growth forests in the tropical regions of Amazon and Congo, young forests in China, and intermediate stands in Europe. Furthermore, we find that the regions with high rates of deforestation or forest degradation (e.g. the arc of deforestation in the Amazon) are composed mainly of younger stands. Assessment of forest age in the climate space shows that the old forests are either in cold and dry regions or warm and wet regions, while young–intermediate forests span a large climatic gradient. Finally, comparing the presented forest age estimates with a series of regional products reveals differences rooted in different approaches and different in situ observations and global-scale products. Despite showing robustness in cross-validation results, additional methodological insights on further developments should as much as possible harmonize data across the different approaches. The forest age dataset presented here provides additional insights into the global distribution of forest age to better understand the global dynamics in the forest water and carbon cycles. The forest age datasets are openly available at https://doi.org/10.17871/ForestAgeBGI.2021 (Besnard et al., 2021).

Including leaf trait information helps empirical estimation of jmax from vcmax in cool-temperate deciduous forests

Abstract. A simulation model based on satellite observations of monthly vegetation greenness from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate monthly carbon fluxes in terrestrial ecosystems of Brazilian Amazon and Cerrado regions over the period 2000–2002. The NASA-CASA (Carnegie Ames Stanford Approach) model estimates of annual forest production were used for the first time as the basis to generate a prediction for the standing pool of carbon in above-ground biomass (AGB; g C m−2) for forested areas of the Brazilian Amazon region. Plot-level measurements of the residence time of carbon in wood in Amazon forest from Malhi et al. (2006) were interpolated by inverse distance weighting algorithms and used with CASA to generate a new regional map of AGB. Data from the Brazilian PRODES (Estimativa do Desflorestamento da Amazônia) project were used to map deforested areas. Results show that net primary production (NPP) sinks for carbon varied between 4.25 Pg C yr−1 (1 Pg=1015 g) and 4.34 Pg C for the region and were highest across the eastern and northern Amazon areas, whereas deforestation sources of CO2 flux from decomposition of residual woody debris were higher and less seasonal in the central Amazon than in the eastern and southern areas. Increased woody debris from past deforestation events was predicted to alter the net ecosystem carbon balance of the Amazon region to generate annual CO2 source fluxes at least two times higher than previously predicted by CASA modeling studies. Variations in climate, land cover, and forest burning were predicted to release carbon at rates of 0.5 to 1 Pg C yr−1 from the Brazilian Amazon. When direct deforestation emissions of CO2 from forest burning of between 0.2 and 0.6 Pg C yr−1 in the Legal Amazon are overlooked in regional budgets, the year-to-year variations in this net biome flux may appear to be large, whereas our model results implies net biome fluxes had actually been relatively consistent from year to year during the period 2000–2002. This is the first study to use MODIS data to model all carbon pools (wood, leaf, root) dynamically in simulations of Amazon forest deforestation from clearing and burning of all kinds.

Spatio-temporal drivers of soil and ecosystem carbon fluxes at field scale in an upland grassland in Germany

Understanding aerosol–climate–ecosystem interactions and the implications for terrestrial carbon sink using the Community Earth System Model

In view of the unsolved questions in the researches of the spatial pattern of carbon flux, this study aims to synthetically compile the observation data of carbon fluxes, climate, soil and vegetation properties of the Northern Hemisphere terrestrial ecosystems to: (1) Reveal the spatial pattern of carbon fluxes of terrestrial ecosystems in the Northern Hemisphere; (2) Reveal the main influence factors of the spatial pattern of carbon fluxes in the Northern Hemisphere terrestrial ecosystems; (3) Clarify the controlling mechanisms for the spatial pattern of carbon fluxes in the Northern Hemisphere terrestrial ecosystems.

<p>Correspondence: Xiaolu Tang (lxtt2010@163.com)</p><p>As a natural disaster, earthquake could cause remarkable impacts on terrestrial ecosystems, e.g. vegetation cover loss. Previous studies evaluating the impact of earthquake mainly focused on vegetation recover using normalized difference vegetation index or enhanced vegetation index, however, very limited studies assessed the impact of earthquake on carbon sequestration capability. Therefore, in current study, we quantitively assessed the carbon sequestration loss (indicated by aboveground net primary production (ANPP)) after the 7.0-magnitude earthquake in Jiuzhaigou National Nature Reserve (JNNR) in the Eastern Tibet Plateau combining Landsat 8, Sentinel 2 and field observations. Annual ANPP was estimated based on 50 fixed inventory plots set in 2018 and measured in 2019, 2020 and 2021. Mean ANPP of 2019-2020 and 2020-2021 was used in modelling to reduce its inter-annual variabilities. Three approaches - linear regression (LR) and two machine learning approaches - random forest (RF) and extreme gradient boosting (XGBoost) were used to predict ANPP across the whole JNNR. Results showed that observed forest ANPP of the JNNR varied from 0.8 to 11.5 Mg ha<sup>-1</sup> year<sup>-1</sup> with an average of 4.07 Mg ha<sup>-1</sup> year<sup>-1</sup>. A total of 5.75% forest area was lost after the earthquake estimated from Sentinel-2 images. Both Landsat 8 and Sentinel-2 images successfully estimated ANPP using LR, RF and XGBoost respectively, however, the model performance varied greatly. Regardless of the modeling approaches, the integration of Landsat 8 and Sentinel-2 images significantly improved model efficiency. The results highlight a potential way to improve the prediction accuracy of forest ANPP in mountainous areas by integrating the Sentinel-2 and Landsat 8 images. Finally, XGBoost model performed the best with a model efficiency (<em>R<sup>2</sup></em>) of 0.67 and root mean square error (<em>RMSE</em>) of 1.23 Mg ha<sup>-1</sup> year<sup>-1</sup> and then it was used for spatial modelling. Modelled forest ANPP showed a strong spatial variability across the study area, where the pre-earthquake forest ANPP was 2.1 × 10<sup>5</sup> Mg year<sup>-1</sup>, and the post-seismic value was 1.65 × 10<sup>5</sup> Mg year<sup>-1</sup>, indicating a total loss of 0.45 × 10<sup>5</sup> Mg year<sup>-1</sup>, accounting for about 21.43% of total ANPP. This study proposed a potential approach to assess the loss of carbon sequestration caused by natural disaster in regional scales. Our findings also suggested a remarkable carbon loss after the earthquake and the natural disaster should be considered in regional carbon sequestration estimate and biogeochemical models to accurately predict carbon cycling in terrestrial ecosystems.</p><p><strong>Keywords: </strong>earthquake,<strong> </strong>carbon sequestration capacity,<strong> </strong>aboveground net primary production; Landsat 8; Sentinel-2</p><p> </p><p> </p><p>Acknowledgement: The study was supported by the Specialized Fund for the Post-Disaster Reconstruction and Heritage Protection in Sichuan Province (No. 5132202019000128).</p>

Terrestrial ecosystem carbon cycle plays an important role in global carbon cycle because of its complexity and uncertainty. Study on terrestrial ecosystem carbon cycle relates to global climate change and potential ways or means to address. This paper is attempting to provide an overview of mechanism and research method of terrestrial ecosystem carbon cycle and perspective of emphases and hotspots on the research terrestrial ecosystem carbon cycle. We conclude that it prospective to be researched in the application of science complexity to terrestrial ecosystem carbon cycle. Terrestrial ecosystem is an complex adaptive system. The inner interaction of ecosystem is the main driving force for the self-organization, complexity and order in the ecosystem. This paper discusses mechanism and development of ecosystem complexity, by applying the principle and methods of complexity science which is a new approach for understanding ecological and evolutionary problems.

Temperature sensitivity (Q10) of ecosystem respiration (ER) is a crucial parameter for predicting the fate of CO2 in terrestrial e cosystems under global warming. Most studies focus their attention in the variation of Q10 in one or two components of ER, but not in the integration or comparison among Q10 in major components of ER. Vertical and seasonal variations in individual components, including leaf respiration, stem respiration and soil respiration, of ER were observed synchronously along the gradient of leaf–stem–soil over a 2 year period in three forest stands dominated by masson pine, loblolly pine and oak, respectively, in a subtropical forest ecosystem of central China. We found that Q10 in individual components of ER increased along the vertical gradient of leaf–stem–soil. The vertical pattern of Q10 in individual components of ER was ascribed to variations of diurnal temperature range (DTR) and activation energy (ΔHa). These results suggest that a vertical pattern of Q10 in individual components of ER along the gradient of leaf–stem–soil should be taken into consideration in process-based models that simulate respiratory carbon flux in terrestrial ecosystems.

Abstract. Soil respiration (Rs) is an important carbon flux in terrestrial ecosystems, and knowledge about this CO2 release process and the drivers involved is a key topic in the context of global change. However, temporal and spatial variability has not been studied extensively in semi-arid systems such as olive groves. In this study, we show a full year of continuous measurements of Rs with six automatic chambers in a fertigated olive grove with bare soil in the Mediterranean accompanied by modeled ecosystem respiration (Reco) estimated by decomposing net ecosystem exchange (NEE) measured using the eddy covariance (EC) technique. To study spatial variability, the automatic chambers were distributed equally under the canopy (Rs Under-Tree) and in the center of the alley (Rs Alley), and the gradient of Rs between both locations was measured in several manual campaigns in addition to angular changes about the olive trees. The results indicate that Rs Under-Tree was 3 times higher than Rs Alley in the annual computations. Higher Rs was found on the southern face, and an exponential decay of Rs was observed until the alley's center was reached. These spatial changes were used to weigh and project Rs onto the ecosystem scale, whose annual balance was 1.6–2.3 times higher than the Reco estimated using EC-derived models. Rs Under-Tree represented 39 % of the Rs of the olive grove. We found values of Q10<1 in the vicinity of the olive tree in the warm period. Outbursts of CO2 emissions associated with precipitation events were detected, especially in the alley, during dry periods and after extended periods without rain, but they were not accurately detected by EC-derived respiration models. We point out an interaction between several effects that vary in time, that are different under the canopy than in the alleys, and that the accepted models for estimating Q10 and Reco do not consider. These results show high spatial and temporal heterogeneity in soil respiration and the factors involved, which must be considered in future works in semi-arid agroecosystems.

Abstract. Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, https://doi.org/10.3334/ORNLDAAC/1934).

<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.

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

A better carbon-water flux simulation in multiple vegetation types by data assimilation