Understanding Of Carbon Dynamics Research Articles

Assessing and mapping of soil organic carbon at multiple depths in the semi-arid Trans-Ural steppe zone

The quantification of soil organic carbon (SOC) and its vertical distribution is crucial for understanding carbon dynamics in terrestrial ecosystems. This study aimed to 2.5D digital mapping of SOC content in the Trans-Ural steppe zone (Russia) using a quantile regression forest (QRF) approach. The study utilized a dataset comprising 2495 SOC measurements collected from 1316 locations across three soil depths: 0–20 cm, 20–40 cm, and 40–60 cm. Environmental covariates were incorporated into the SOC modeling process, capturing major soil formation factors, and the uncertainty of the generated maps was estimated. The results revealed that SOC content ranged from 0.59 to 9.05 % in the topsoil, from 0.5 to 6.61 % in the subsurface layer and from 0.06 to 4.64 % in the subsoil. Based on the error metrics, including root mean square error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe efficiency coefficient (NSE), we found a decrease in prediction accuracy with increasing soil depth. Furthermore, climate and vegetation variables, as well as elevation, emerged as key factors influencing the prediction of SOC concentrations at all depths. We also made an attempt to assess the future change of SOC under the influence of climate and anthropogenic impact. We anticipate that climate aridization and plowing will lead to a decline in SOC content in the Trans-Ural steppe region. Our findings contribute to the existing knowledge of SOC dynamics in steppe ecosystems at several depths, supporting informed decision-making for sustainable land use and climate change mitigation strategies.

Quantifying the CO2 sink intensity of large and small saline lakes on the Tibetan Plateau

This study quantitatively evaluates the carbon dioxide (CO2) sink intensity of a large saline lake (area > 2000 km2) and a small saline lake (area 1.4 km2) on the Tibetan Plateau (TP), alongside an alpine meadow, by analysing their net ecosystem exchange (NEE) figures obtained by eddy covariance (EC) measurements. Specifically, the “large lake” exhibits an NEE value of −122.51 g C m−2 yr−1, whereas the small lake has an NEE value of −47.17 g C m−2 yr−1. The alpine meadow, in contrast, demonstrates an NEE value of −128.18 g C m−2 yr−1. Through standardization of the eddy flux data processing and accounting for site-specific conditions with a wind direction filter and footprint analysis, the study provides robust estimates of CO2 sink intensity. The “large lake” was found to absorb CO2 primarily during non-icing cold periods with minimal exchange occurring during ice-covered season, whereas the “small lake” showed no significant CO2 exchange throughout the year. On the other hand, alpine meadows engaged in CO2 uptake during the vegetative growth season but showed weak CO2 release in winter. CO2 uptake in lakes is mainly controlled by ice barrier and chemical processes, while biological processes dominate the alpine meadow. The carbon sink intensity of the TP's saline lakes is estimated to be 1.87–3.01 Tg C yr−1, smaller than the previous reported estimations. By evaluating the CO2 sink intensity of different lakes, the study highlights the importance of saline lakes in regional carbon balance assessments. It specifically points out the differential roles lakes of various sizes play in the carbon cycle, thereby enriching our understanding of carbon dynamics in high-altitude lacustrine ecosystems.

Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation phenology, climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research demonstrates an efficient method of employing various open-source remote sensing and GIS datasets to comprehend the spatial variability and soil respiration mechanisms in coastal TAI. There is no one-size-fits-all approach to modeling carbon fluxes released by soil respiration in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

From plant litter to soil organic matter: a game to understand carbon dynamics

Managing ecosystems to sequester soil carbon requires a thorough understanding of complex soil processes. Here, we integrate these soil processes through the metaphor of a game—one that moves through multiple dimensions (from macro‐aggregates to micropores and clay particles) and scales (from centimeters to nanometers) of the soil. The rules of the game are based on current understanding of soil carbon persistence, which differs from the classic humus concept of molecular complexity. The game's objective is to win points, by keeping “tokens” (plant‐derived organic compounds) within the soil organic matter for as long as possible. The game begins when tokens enter different “pool‐levels” (plant litter, particulate organic matter, dissolved organic matter, and mineral‐associated organic matter) of the soil, either directly or after metabolic transformation by soil biota. Points are lost through either respiration by soil biota or leaching. We invite readers to play this game and explore different natural ecosystems and land‐use scenarios to better comprehend complex soil processes.

Environmental and Management Drivers of Carbon Dioxide and Methane Emissions From Actively‐Extracted Peatlands in Alberta, Canada

AbstractThe installation of drainage ditches and removal of vegetation in preparation for vacuum harvesting alters the carbon dynamics of peatlands. However, we lack the measurements to understand the spatial distribution and environmental and substrate quality controls of carbon dioxide (CO2) and methane (CH4) emissions, as well as how these factors change over the 20–30 year extraction period. For three summers, we measured CO2 and CH4 emissions using the closed chamber method at three actively extracted peatlands near Drayton Valley, Alberta, ranging from 2 to 28 years since the start of extraction. Measurements were made in the ditches, and on segments of peat (fields) between adjacent ditches. Field emissions did not change with distance from ditches, likely due to the observed homogeneity of volumetric water content (VWC) and temperature across the fields. Understanding carbon dynamics in the ditches will be important, as they emitted on average two and 10 times, respectively, the amount of CO2 and CH4 per square meter of the fields. We found moderate to weak relationships between carbon emissions and soil temperature, VWC and ditch water level, though ditch emissions were significantly reduced when there was standing water present. Altering conventional site management, such as increasing ditch spacing, could substantially reduce CH4 emissions from the managed area. Emissions did not decrease with time since start of extraction. We suggest that Canadian emission factor calculations for land‐based emissions consider both peat quality variations among sites, and a site's extraction duration, which has been important in other studies.

Seasonal particulate organic carbon dynamics of the Kolyma River tributaries, Siberia

Abstract. Arctic warming is causing permafrost thaw and release of organic carbon (OC) to fluvial systems. Permafrost-derived OC can be transported downstream and degraded into greenhouse gases that may enhance climate warming. Susceptibility of OC to decomposition depends largely upon its source and composition, which vary throughout the seasonally distinct hydrograph. Most studies on carbon dynamics to date have focused on larger Arctic rivers, yet little is known about carbon cycling in lower-order rivers and streams. Here, we characterize the composition and sources of OC, focusing on less studied particulate OC (POC), in smaller waterways within the Kolyma River watershed. Additionally, we examine how watershed characteristics control carbon concentrations. In lower-order systems, we find rapid initiation of primary production in response to warm water temperatures during spring freshet, shown by decreasing δ13C-POC, in contrast to larger rivers. This results in CO2 uptake by primary producers and microbial degradation of mainly autochthonous OC. However, if terrestrially derived inorganic carbon is assimilated by primary producers, part of it is returned via CO2 emissions if the autochthonous OC pool is simultaneously degraded. As Arctic warming and hydrologic changes may increase OC transfer from smaller waterways to larger river networks, understanding carbon dynamics in smaller waterways is crucial.

Estimated mangrove carbon stocks and fluxes to inform MRV for REDD+ using a process-based model

Mangrove forests are important due to their strong capability for carbon storage, especially in soils. Understanding carbon dynamics in these forests is fundamental to estimate their roles in carbon storage and mitigating climate change. This study used a process-based model, MCAT-DNDC, to assess mangrove carbon sequestration and fluxes at a 30-m spatial resolution in three African countries, Gabon, Mozambique and Tanzania. The simulated above- and below-ground biomass at inventory plots in each country was approximate to actual observations with mean errors <5% for aboveground biomass and <8% for belowground biomass, indicating that the MCAT-DNDC model can be a useful tool for assessing mangrove carbon storage and fluxes. The results from assessing mangrove carbon storage and fluxes for the three countries showed that the mangroves in these countries are large carbon pools, they export large amounts of dissolved and particulate carbon components to riverine and oceanic ecosystems, and they bury a large amount of carbon in soils. However, soil-borne greenhouse gases CO2, CH4 and N2O fluxes from mangrove forest lands were low. There were large differences in all mangrove carbon components among the mangroves in the three countries, indicating that regional or global mangrove carbon stocks estimated using a process-based model may be better than the extrapolations using limited inventories.

Soil Respiration and Related Abiotic and Remotely Sensed Variables in Different Overstories and Understories in a High-Elevation Southern Appalachian Forest

Accurately predicting soil respiration (Rs) has received considerable attention recently due to its importance as a significant carbon flux back to the atmosphere. Even small changes in Rs can have a significant impact on the net ecosystem productivity of forests. Variations in Rs have been related to both spatial and temporal variation due to changes in both abiotic and biotic factors. This study focused on soil temperature and moisture and changes in the species composition of the overstory and understory and how these variables impact Rs. Sample plots consisted of four vegetation types: eastern hemlock (Tsuga canadensis L. Carriere)-dominated overstory, mountain laurel (Kalmia latifolia L.)-dominated understory, hardwood-dominated overstory, and cinnamon fern (Osmundastrum cinnamomeum (L.) C. Presl)-dominated understory, with four replications of each. Remotely sensed data collected for each plot, light detection and ranging, and hyperspectral data, were compiled from the National Ecological Observatory Network (NEON) to determine if they could improve predictions of Rs. Soil temperature and soil moisture explained 82% of the variation in Rs. There were no statistically significant differences between the average annual Rs rates among the vegetation types. However, when looking at monthly Rs, cinnamon fern plots had statistically higher rates in the summer when it was abundant and hemlock had significantly higher rates in the dormant months. At the same soil temperature, the vegetation types’ Rs rates were not statistically different. However, the cinnamon fern plots showed the most sensitivity to soil moisture changes and were the wettest sites. Normalized Difference Lignin Index (NDLI) was the only vegetation index (VI) to vary between the vegetation types. It also correlated with Rs for the months of August and September. Photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), and normalized difference nitrogen index (NDNI) also correlated with September’s Rs. In the future, further research into the accuracy and the spatial scale of VIs could provide us with more information on the capability of VIs to estimate Rs at these fine scales. The differences we found in monthly Rs rates among the vegetation types might have been driven by varying litter quality and quantity, litter decomposition rates, and root respiration rates. Future efforts to understand carbon dynamics on a broader scale should consider the temporal and finer-scale differences we observed.

Trend for Soil CO2 Efflux in Grassland and Forest Land in Relation with Meteorological Conditions and Root Parameters

The key process in understanding carbon dynamics under different ecosystems is quantifying soil CO2 efflux. However, this process can change annually as it depends on environmental variables. The results of this paper present the effects of root network, soil temperature, and volumetric water content on soil CO2 efflux, which were investigated on Retisol of two types of land uses in Western Lithuania in 2017–2019: forest and grassland. It was determined that the average soil CO2 efflux in the grassland was 32% higher than in the forest land. The CO2 efflux, average across land uses, tended to increase in the following order: 2017 &lt; 2018 &lt; 2019. Dry weather conditions with high temperatures during the vegetation period governed the soil CO2 efflux increase by 14%. Soil temperature (up to 20 °C) and volumetric water content (up to 23–25%) had a positive effect on the soil CO2 efflux increase on Retisol. We established that the root’s activity plays one of the main roles in the CO2 production rate—in both land uses, the soil CO2 efflux was influenced by the root length density and the root volume.

Comparison of Model-Assisted Endogenous Poststratification Methods for Estimation of Above-Ground Biomass Change in Oregon, USA

Quantifying above-ground biomass changes, ΔAGB, is key for understanding carbon dynamics. National Forest Inventories, NFIs, aims at providing precise estimates of ΔAGB relying on model-assisted estimators that incorporate auxiliary information to reduce uncertainty. Poststratification estimators, PS, are commonly used for this task. Recently proposed endogenous poststratification, EPS, methods have the potential to improve the precision of PS estimates of ΔAGB. Using the state of Oregon, USA, as a testing area, we developed a formal comparison between three EPS methods, traditional PS estimators used in the region, and the Horvitz-Thompson, HT, estimator. Results showed that gains in performance with respect to the HT estimator were 9.71% to 19.22% larger for EPS than for PS. Furthermore, EPS methods easily accommodated a large number of auxiliary variables, and the inclusion of independent predictions of ΔAGB as an additional auxiliary variable resulted in further gains in performance.

Multi-source remote sensing data reveals complex topsoil organic carbon dynamics in coastal wetlands

• UAV data accurately predicted SOC concentrations (RMSE < 2.44 %) • Radar data characterized spatial patterns of flood frequency in coastal areas. • The effect of flood frequency on SOC was highest at pioneer and seaward communities. • AGB showed a larger explanatory power than flood frequency in SOC predictions. • UAV-SAR fusion unveils site-specific fine-scale patterns of SOC in coastal meadows. Coastal wetlands are considered important stores of blue carbon, containing some of the largest stores of pedologic and biotic carbon per unit area on the planet. These ecosystems are however highly sensitive to Climate Change and changes in the management practices. It is of utmost importance to address relevant ecosystem scales in order to fully understand carbon dynamics in coastal wetlands. In this regard, Unoccupied Aerial Vehicles (UAVs) can provide spatial scales detailed enough to address the fine scale patters of topsoil organic carbon accumulation in coastal wetlands. This study demonstrates the use of multispectral and photogrammetric data derived from UAVs to accurately map plant communities and topsoil organic carbon concentration in coastal wetlands. The overall accuracies from the classification of plant communities ranged from 88% to 97% whereas RMSE for topsoil organic carbon concentration ranged from 2.44% to 0.74%. By combining both models, site-specific variations in topsoil carbon concentrations among plant communities were unveiled. Open pioneer communities consistently showed the lowest topsoil organic carbon concentration, while the concentrations vary considerably across plant communities characterized by denser vegetation coverage. Furthermore, Sentinel-1 radar data was used to assess the spatial patterns of flood frequency. GAMs were used to combine flood frequency with the plant communities and topsoil organic carbon models, as well as an aboveground biomass (AGB) model from a previous study. GAMs revealed a stronger effect than flood frequency on topsoil organic carbon. Regarding flooding, increased flood frequency generally led decreased topsoil organic concentrations across communities and sites. However, the relative contribution of flood frequency to topsoil organic carbon concentration in Baltic coastal wetlands depends strongly on the location of the wetland and the nature of the floods. Higher flood frequencies could lead to increased topsoil organic carbon in wetlands subject to the input of estuarine sediments. Lastly, the integration of remote sensing platforms constitutes an effective tool for revealing spatial heterogeneity of carbon storage in coastal wetlands.

Estimating the impact of climate change on the carbon exchange of a temperate meadow steppe in China

Temperate meadow steppe is a representative ecosystem in northeast China. A process-oriented biogeochemistry model, denitrification–decomposition (DNDC) biogeochemistry model was employed and adapted to interpret and integrate field observations to determine whether temperate meadow steppe ecosystem was a carbon dioxide (CO2) sink during the growing seasons from 2010 to 2011. Then, we applied the model to predict the long-term impacts of climate change on the carbon dynamics in the ecosystem. Daily weather data for 2010–2011 in conjunction with the soil properties data and management practices data for this site were utilized as inputs to simulate the grass growth and soil carbon dynamics. The modeled carbon fluxes were compared with eddy tower data. The observed and modeled CO2 fluxes data were consistent, with both showing that the temperate meadow steppe is a stable carbon sink. Prediction tests were conducted with a baseline and eight alternative climate scenarios for the period 2017–2050. Simulations for the years 2017–2050 found that (1) the temperate meadow steppe will be a carbon sink under the baseline climate conditions; (2) the decelerated warming and drier climate scenario produced the lowest grass production and lowest carbon sequestration; and (3) the accelerated warming and wetter climate scenario produced the highest biomass and highest carbon sequestration. By using DNDC model we can understand carbon dynamics in temperate meadow steppe. The ecosystem production is limited by precipitation, a wetter future climate would substantially elevate the carbon sequestration capacity of the ecosystem. However, the carbon sequestration potential could significantly decrease if the climate is more arid from 2017 to 2050.

Temporal and Spatial Dynamics of Carbon Storage in Qinghai Grasslands

Accurate quantification of ecosystem carbon storage dynamics is very important in regional ecological management. However, the dynamics of grassland carbon storage in Qinghai, China, are still unexplored. We investigated the temporal and spatial dynamics of carbon storage in the Qinghai grasslands from 1979 to 2018, using the spatially explicit Biome-BGCMuSo model. The average annual value of vegetation carbon density (VCD) was 52.71 gC·m−2. After 2000, VCD showed an overall increasing trend, with an average rate of 2.14 gC·m−2. The VCD was relatively high in the eastern and southeastern regions of Qinghai compared with that in the western and central areas. The increasing trend in VCD was mainly observed in the eastern and southeastern regions, while a decreasing trend was evident in western and central Qinghai. Annual soil organic carbon density (SOCD) in Qinghai grasslands generally increased from 1979 to 2018. After 2001, the SOCD increased by an average rate of 7.07 gC·m−2. The SOCD was relatively high in eastern and southeastern Qinghai compared with that in western and central Qinghai. The pronounced increasing trend of SOCD was mainly distributed in the southeast and northeast parts of Qinghai, while the decreasing trend was mainly distributed in the area between southeast and northeast Qinghai, and in the central and western regions. This study deepened our understanding of carbon dynamics in the Qinghai grasslands and provided data for guiding the ecological restoration and carbon management of local grasslands.

Biochemical components ofSphagnumand persistence in peat soil

The amounts and arrangements of polysaccharides (cellulose and hemicellulose), proteins, phenolic lignin, and pectin that make up plant tissue, in part, determine its decay rate. Lignin-rich and/or nitrogen-poor tissue has been described as biochemically recalcitrant causing a slow decay rate. Although a controversial mechanism for organic matter storage in soils with mineral particles, biochemical recalcitrance is still poorly understood in organic peat soil (Histosols). To investigate the role of Sphagnum in formation of peat soil, we characterize biochemical components for 10 species and examine persistence of the components in soil to 150 cm depth in three peatland ecosystems. We hypothesize that species from hummock microforms have more biochemical structural components and cohesion than species from hollows. Relative proportions of biochemical components changed markedly between plant material and the top 10 cm of peat soil, suggesting that decomposition occurred at the peat soil surface, but thereafter relative proportions of biochemical components did not vary significantly to 150 cm deep. A few differences in biochemical components that distinguished hummock species from hollow species persisted to the deepest depth sampled. Although persistence of the lignin-like component was expected, persistence of soluble and ionically bound pectin compounds was surprising as these biopolymers are thought to be readily decomposable. Our findings indicate that structural components of Sphagnum, specifically polysaccharides and pectin in addition to oft-cited phenolic lignin-like components, persist in peat soil and should not be overlooked in trying to understand carbon dynamics in Sphagnum-dominated ecosystems.

Photosynthetic Carbon Assimilation and Electron Transport Rates in Two Symbiont-Bearing Planktonic Foraminifera

Photosymbiosis is one of the key features characterizing planktonic foraminifera; the number of symbiont cells within a single host has been reported to be well over thousands, meaning that photosynthesis by photosymbiosis may be a “hot spot” for primary production, especially in oligotrophic oceans. As microenvironmental conditions around foraminifera are greatly affected by rapid biological activities—such as photosynthesis and respiration—information on the photosynthetic activities of symbionts is essential to interpret the geochemical proxies recorded in foraminiferal tests (e.g., δ13C and δ18O). Recently, active chlorophyll fluorometry has been increasingly employed as a useful tool for immediate estimation of photosynthesis. However, carbon assimilation rates are the only direct indicator of the photosynthetic carbon flux. Therefore, before utilizing active fluorescence methods to understand carbon dynamics in foraminiferal symbiosis, it is necessary to confirm the relationship between the fluorescence-based photosynthetic rate [electron transport rate (ETR)] and carbon assimilation rate (P). Here, these two rates were compared for two species, Trilobatus sacculifer and Globigerinella siphonifera Type II, using 14C-tracer experiments and active fluorometric measurements by fast repetition rate fluorometry. The results showed a significant positive correlation between the P and ETR of the two species, indicating that carbon assimilation can be estimated by the fluorometric method. However, the regression slopes, which represent the apparent electron requirement for carbon assimilation (e–/C), were significantly different in the two species, and were estimated at 26.2 for T. sacculifer and 96.5 for G. siphonifera. These are strikingly high, considering the theoretically and empirically realistic e–/C values. We hypothesized that the high e–/C observed may be due in part to the use of unlabeled respiratory carbon (underestimation of P). A simple mass balance calculation suggests that a significant amount of carbon should derive from the host’s respired CO2, whose contribution is higher in G. siphonifera than in T. sacculifer. Within the context of using test geochemical parameters, such as δ13C, as paleoceanographic proxies, it is important to note that the potential magnitude of the photosynthetic effect varies among species. This attempt to couple ETR and P could comprehensively reveal an interesting perspective on the intimate interactions existing within photosymbiotic systems.

Global evidence on the asymmetric response of gross primary productivity to interannual precipitation changes

Understanding gross primary productivity (GPP) response to precipitation (PPT) changes is essential for predicting land carbon uptake under increasing PPT variability and extremes. Previous studies found that ecosystem GPP may have an asymmetric response to PPT changes, leading to the inconsistency of GPP gains in wet years compared to GPP declines in dry years. However, it is unclear how the asymmetric responses vary among vegetation types and under different PPT variabilities. This study evaluated the global patterns of asymmetries of GPP response to different PPT changes using two state-of-science global GPP datasets. The result shows that under mild PPT changes (|ΔPPT| ≤ 25%), grasslands, savannas, shrublands, and tundra show positive asymmetric responses (i.e., larger GPP gains in wet years than GPP losses in dry years), while other vegetation types show negative asymmetric responses (i.e., larger GPP losses in dry years than GPP gains in wet years). Conversely, all vegetation types show negative GPP asymmetric responses to moderate (25% < |ΔPPT| ≤ 50%) and extreme (|ΔPPT| > 50%) PPT changes. Thus, we propose a new non-linear asymmetric GPP-PPT model that incorporates three modes with regards to vegetation types. Meanwhile, we found that the spatial patterns of asymmetry were mainly driven by PPT amount and variability. Stronger and negative asymmetries were found in areas with smaller PPT amount and variability, while positive asymmetries were found in areas with higher PPT variability. These findings promote our understanding of carbon dynamics under increased PPT variability and extremes and provide new insights for land models to better predict future carbon uptake and its feedback to climate change.

Training a new generation of researchers for effective forest management strategies under the effect of global warming: the ETN Skill-For.Action

Training a new generation of researchers for effective forest management strategies under the effect of global warming: the ETN Skill-For.Action Innovative, adaptive and integrative forest management plays a key role in driving forests to face environmental changes, maintain high forest carbon sequestration potential, and guarantee economic efficiency and more ecologically sound forest operations. The ETN Skill-For.Action integrates the fundamental research in forest ecology and applied science of forest engineering to comprehensibly understand carbon dynamics in forests.

Effects of Spatial Resolution on Burned Forest Classification With ICESat-2 Photon Counting Data

Accurately monitoring forest fire activities is critical to understanding carbon dynamics and climate change. Three-dimensional (3D) canopy structure changes caused by fire make it possible to adopt Light Detection and Ranging (LiDAR) in burned forest classification. This study focuses on the effects of spatial resolution when using LiDAR data to differentiate burned and unburned forests. The National Aeronautics and Space Administration’s (NASA) Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission provides LiDAR datasets such as the geolocated photon data (ATL03) and the land vegetation height product (ATL08), which were used in this study. The ATL03 data were filtered by two algorithms: the ATL08 algorithm (ILV) and the adaptive ground and canopy height retrieval algorithm (AGCH), producing classified canopy points and ground points. Six typical spatial resolutions: 10, 30, 60, 100, 200, and 250 m were employed to divide the classified photon points into separate segments along the track. Twenty-six canopy related metrics were derived from each segment. Sentinel-2 images were used to provide reference land cover maps. The Random Forest classification method was employed to classify burned and unburned segments in the temperate forest in California and the boreal forest in Alberta, respectively. Both weak beams and strong beams of ICESat-2 data were included in comparisons. Experiment results show that spatial resolution can significantly influence the canopy structures we detected. Classification accuracies increase along with coarser spatial resolutions and saturate at 100 m segment length, with overall accuracies being 79.43 and 92.13% in the temperate forest and the boreal forest, respectively. Classification accuracies based on strong beams are higher than those of using weak beams due to a larger point density in strong beams. The two filtering algorithms present comparable accuracies in burned forest classification. This study demonstrates that spatial resolution is a critical factor to consider when using spaceborne LiDAR for canopy structure characterization and classification, opening an avenue for improved measurement of forest structures and evaluation of terrestrial vegetation responses to climate change.

Abiotic contribution to phenol oxidase activity across a manganese gradient in tropical forest soils

Phenol oxidases are a group of extracellular enzymes that catalyze the oxidation of lignin and phenolic compounds in soil organic matter. Despite their importance, little is known about these enzymes in tropical soils, particularly the extent to which minerals like manganese (Mn) oxides contribute to the measured activity. We assayed phenol oxidase activity in surface and subsurface (A and B) horizons from 15 soil profiles representing a steep Mn gradient under a lowland tropical forest in Panama. Fresh and sterilized (autoclaved) soils were assayed to determine the abiotic contribution to activity. Assays were conducted using 5 mM l-DOPA substrate in bicarbonate buffer at pH 8.5. We also determined amorphous metal oxide concentrations, soil pH, organic matter, and effective cation exchange capacity (CEC). We found little difference in phenol oxidase activity between fresh and autoclaved soils, indicating a considerable abiotic contribution to substrate oxidation. Furthermore, phenol oxidase activity was similar in surface and subsurface soils, despite markedly lower organic matter at depth. Phenol oxidase activity was correlated positively with amorphous Mn oxide concentrations (extractable Mn determined by acidic ammonium oxalate extraction), suggesting a potential mechanism of abiotic oxidation. These results have important implications for understanding carbon dynamics in tropical soils, because they suggest that (1) a portion of organic matter turnover is mediated by abiotic oxidation via Mn oxides, and (2) conventional measures of phenol oxidase activity reflect abiotic oxidation rather than the activity of enzymes synthesized by the microbial community.

Managing agricultural grazing to enhance the carbon sequestration capacity of freshwater wetlands

Freshwater wetlands are important carbon sinks with an estimated ~ 450 gigatons of carbon stored in their sediments. However, when disturbed they can become significant sources of carbon emissions. Understanding carbon dynamics in freshwater wetlands is a research priority to maximise carbon drawdown opportunities through effective management. Grazing can have considerable impacts on the health of freshwater wetland functionality. Fencing to exclude livestock from freshwater wetlands is one approach used to reduce grazer impacts; however, this is not always feasible. Managing grazing intensity is one option that is amenable to most farmers and resource managers; however, to date there have been no studies to evaluate its efficacy. The impacts of seasonal grazing intensity (by sheep) on carbon sequestration by rain-filled freshwater wetlands were investigated in south eastern Australia (Wimmera region). Over a 1-year study duration, this research compared: a) the effects of continuous grazing (sheep present across all seasons); b) crash grazing (sheep present within the spring); and c) exclusion plots (controls; no sheep access) on above-ground plant biomass, soil carbon concentration, and fluxes across seasons. Results suggest that above-ground biomass was 6.4% higher within exclusion plots compared to grazed plots in both continuous and crash grazing regimes. Exclusion plots had 30% higher soil carbon concentration compared to grazed plots. Open-continuously grazed plots had higher fluxes (8.69 ± 10.61 g CO2 m−2 day−1) compared with exclusion plots (6.92 ± 15.86 g CO2-e m−2 day−1). This study shows that exclusion plots had higher soil carbon concentration and lower carbon emissions. Further, excluding grazing from wetlands within the Wimmera region would increase above-ground biomass and soil carbon stock by 545,300 tons of carbon while reducing CO2 emissions by 782,412 tons of CO2 year−1.