Carbon Density Research Articles

Variations in microscopic properties of biomass char: Implications for biochar characterization

Biochar, a carbon-rich material produced during the thermochemical conversion of biomass, is considered to be an important material for environmental applications and sustainable agriculture. With interest in its use growing, it is essential to understand how pyrolysis parameters control biochar properties and, subsequently, which analytical methods can be applied effectively in characterizing biochar. While various methods are already in use, the potential of reflected light microscopy analysis has not yet been widely explored.This paper focuses on examining the microscopic properties of various biochars by implementing reflected light microscopy and assessing whether this technique has the potential to deliver quick, reliable, and essential information for biochar characterization. To support microscopic observations, selected physical (density and surface area) and chemical parameters (carbon and sulfur content, functional group distribution) of biochar samples were also determined.Megascopically and microscopically there is a considerable difference among biochars of different feeds including their size and shape, porosity, ratio of the abundance of cellular to non-cellular pores, inorganic matter content, the ratio of thick-walled to thin-walled inertinite, or the ratio between high-reflectance inertinite to low-reflectance inertinite and their reactivity. The biochars studied also showed variations in the oxygenated groups contribution, a wide range of carbon content (14.7 to 98.8%), density (1.45 to 2.47 g/cm3), and surface areas (from <1 to 363.6 m2/g).Our results demonstrate that microscopic characteristics and reflectance of biochar can provide crucial diagnostic value for predicting biochar properties and, in combination with other physical and chemical properties, help to enhance information about the biomass conversion process and potential practical use of biochar.

Carbon Sequestration, Biomass and Soil Carbon Pool Estimation in Oak- Dominated Forests of Hindu-Kush Ranges of Pakistan

The present study aimed to determine the vegetation biomass, soil carbon stocks and carbon sequestration potential of Oak-dominated forests. Thirty forest stands having 10 quadrats of 20×20 were randomly sampled. Out of eighteen tree species, twelve species were associated with Group II followed by Groups I and III, with eight species each. Quercus dilatata was the only Oak species recorded in all three groups having maximum density in Group II (56.14). Quercus semecarpifolia accounted the highest proportion of carbon (235 MgC/ha) in Group II. Quercus baloot, being the dominant species of Group I, is found to accommodate the highest quantities of BMC (335±43 Mg/ha) for all size classes. The highest nitrogen content, total nitrogen and carbon-nitrogen ratio were 0.185%, 8.23 and 59.64, respectively, in Group II. The mean bulk density was 1.519 g/cm3 in group III. The highest soil organic carbon (SOC) was recorded in Group II (2.69%, 119.82 tons/ha). Because of their large aerial scale and high carbon density, Oak-dominated forests in Group II store most of the carbon. These results suggest that organic carbon is a major source of forest carbon with significant climate change mitigation potential that must be conserved and improved by sustainable forest management.

Long-term intensive management reduced the soil quality of a Carya dabieshanensis forest

The evaluation of soil quality can provide new insights into the sustainable management of forests. This study investigated the effects of three types of forest management intensities (non-management (CK), extensive management (EM), and intensive management (IM)), and five management durations (0, 3, 8, 15, and 20 years) on the soil quality of a Carya dabieshanensis forest. Further, minimum data sets (MDS) and optimized minimum data sets (OMDS) were established to evaluate the soil quality index (SQI). A total of 20 soil indicators representing its physical, chemical, and biological properties were measured for the 0–30 cm layer. Using one-way ANOVA and principal component analysis (PCA), the total data set (TDS), the minimum data set (MDS), and optimized minimum data set (OMDS) were established. The MDS and OMDS contained three (alkali hydrolyzed nitrogen (AN), soil microbial biomass nitrogen (SMBN), and pH) and four (total phosphorus (TP), soil organic carbon (SOC), AN, and bulk density (BD)) soil indicators, respectively. The SQI derived from the OMDS and TDS exhibited a stronger correlation (r = 0.94, p < 0.01), which was suitable for evaluating the soil quality of the C. dabieshanensis forest. The evaluation results revealed that the soil quality was highest during the early stage of intensive management (IM-3), and the SQI of each soil layer was 0.81 ± 0.13, 0.47 ± 0.11, and 0.38 ± 0.07, respectively. With extended management times, the degree of soil acidification increased, and the nutrient content decreased. Compared with the untreated forest land the soil pH, SOC, and TP decreased by 2.64–6.24%, 29.43–33.04%, and 43.63–47.27%, respectively, following 20 years of management, while the SQI of each soil layer decreased to 0.35 ± 0.09, 0.16 ± 0.02 and 0.12 ± 0.06, respectively. In contrast to extensive management, the soil quality deteriorated more rapidly under longer management and intensive supervision. The OMDS established in this study provides a reference for the assessment of soil quality in C. dabieshanensis forests. In addition, it is suggested that the managers of C. dabieshanensis forests should implement measures such as increasing the amount of P-rich organic fertilizer and restoring vegetation to increase soil nutrient resources for the gradual restoration of soil quality.

Digital assessments of soil organic carbon storage using digital maps provided by static and dynamic environmental covariates

AbstractUnderstanding the vertical and lateral distribution of soil organic carbon (SOC) and soil organic carbon density (SOCD) is indispensable for soil use and environmental management because of their vital role in soil quality assessments. Primarily, they are needed in calculating soil organic carbon storage (SOCS). The objective of this research was to provide digital maps of SOC and SOCD variation as well as their uncertainties at multiple standardized depths (H1: 0–5, H2: 5–15, H3: 15–30, H4: 30–60 and H5: 60–100 cm) using a parsimonious model with optimized terrain‐related attributes and satellite‐derived data. SOCS were evaluated at soil subgroup levels. An area of about 808 km2 with varying elevation, plant cover and lithology from the Miandoab region, West Azerbaijan Province, Iran was selected as a case study area. A total of 386 soil samples were collected from 104 profiles comprising various soil genetic horizons. A continuous spline function was then fitted to the target properties in advance of creating a dataset at five standard depth intervals (following the GlobalSoilMap project). These were then grouped into three classes including top (H1), middle (H2, H3 and H4) and bottom (H5) depths to ease interpretation. Static and dynamic covariates (30‐m resolution) were derived from a digital elevation model (DEM) and a suite of Landsat‐8 spectral imageries, respectively. Four candidate models including stepwise multiple linear regression (SMLR), random forest (RF), cubist (CU) and extreme gradient boosting (XGBoost) Tree were tested in this study. Finally, the digital maps at 30‐m resolution of SOC and SOCD and their uncertainties were prepared using the best‐fit model and the bootstrapping method, respectively. Four soil subgroups (Gypsic Haploxerepts, Typic Calcixerepts, Typic Haploxerepts and Xeric Haplocalcids) were identified across the study area. The covariates had variable contributions on the evaluated models. The XGBoost Tree model generally outperformed other models for prediction of SOC and SOCD (R2 = 0.60, on average). Regardless of soil subgroups, the uncertainty analysis showed that the SOCD map had a low prediction interval range value indicating high accuracy. Additionally, the highest SOCS and SOCD was observed at the top followed by middle and bottom depths in the study area. All subgroups exhibited a decreasing trend of SOCD with increasing depth. A similar trend was also observed for SOCS. The highest SOCD (on average) was observed in Gypsic Haploxerepts (4.71 kg C/m2) followed by Typic Calcixerepts (4.46 kg C/m2), Typic Haploxerepts (4.45 kg C/m2) and Xeric Haplocalcids (4.40 kg C/m2). Overall, the SOCS normalized by area within soil order boundaries was greater in Inceptisols than Aridisols across the study area. The findings of this study provide critical information for sustainable management of soil resources in the area for agricultural production and environmental health in the Miandoab region of Iran.

The Impact of Grassroots Forestry Institutions on Forest Carbon Sequestration: Evidence from China’s Collective Forests

Many countries have established grassroots forestry institutions to manage and protect small-scale forestry resources and provide technology and services to private foresters. Since the inception of township forestry workstations (TFWs) in China almost 70 years ago, TFW has supported resource protection and forest property reform. In this paper, we employ fixed effect models to test the effects of TFW on collective forest carbon density and provide evidence for improving the quality of collective forests. Our results demonstrate that TFWs in China improve the carbon density of collective forests by performing forestry management and service functions. However, significant differences in TFWs exist under different management systems, and the dual leadership township forestry workstation (D_TWF) is more effective in increasing the carbon density of collective forests. The management system’s heterogeneity directly affects its performance, with D_TWF performing better management functions and the single leadership township forestry workstation (S_TWF) performing better service functions. These results underscore the importance of reforming the TFW management system in accordance with local conditions. In areas with abundant forest resources, the TFW’s management system should shift to single leadership (jurisdictional or vertical management). In forest resource-scarce regions, the TFW’s management system should change to dual leadership.

Island-Type Graphene-Nanotube Hybrid Structures for Flexible and Stretchable Electronics: In Silico Study.

Using the self-consistent charge density functional tight-binding (SCC-DFTB) method, we study the behavior of graphene-carbon nanotube hybrid films with island topology under axial deformation. Hybrid films are formed by AB-stacked bilayer graphene and horizontally aligned chiral single-walled carbon nanotubes (SWCNTs) with chirality indices (12,6) and 1.2 nm in diameter. In hybrid films, bilayer graphene is located above the nanotube, forming the so-called "islands" of increased carbon density, which correspond to known experimental data on the synthesis of graphene-nanotube composites. Two types of axial deformation are considered: stretching and compression. It has been established that bilayer graphene-SWCNT (12,6) hybrid films are characterized by elastic deformation both in the case of axial stretching and axial compression. At the same time, the resistance of the atomic network of bilayer graphene-SWCNT (12,6) hybrid films to failure is higher in the case of axial compression. Within the framework of the Landauer-Buttiker formalism, the current-voltage characteristics of bilayer graphene-SWCNT (12,6) hybrid films are calculated. It is shown that the slope of the current-voltage characteristic and the maximum values of the current are sensitive to the topological features of the bilayer graphene in the composition of graphene-SWCNT (12,6) hybrid film. Based on the obtained results, the prospects for the use of island-type graphene-nanotube films in flexible and stretchable electronic devices are predicted.

Changes in Forest Vegetation Carbon Storage and Its Driving Forces in Subtropical Red Soil Hilly Region over the Past 34 Years: A Case Study of Taihe County, China

After decades of afforestation, China’s forest coverage has increased significantly, but the forest quality and its impact on ecosystem services are still controversial. Taking forest vegetation carbon storage as an example, most assessment results based on the national forest inventory data showed that the contribution of enlarged forest area to the increase in China’s forest vegetation carbon storage was higher than that of the change in forest quality (vegetation carbon density). This means that the increase in forest carbon storage in China was mostly contributed by the increase in forest area and was less due to the increased forest vegetation carbon density. However, the national forest inventory data are based on national permanent plots that may be protected or are less disturbed to some extent. Therefore, the contribution of the change in forest quality to the increase in forest vegetation carbon storage, as evaluated from the national forest inventory data, might be overestimated, especially in regions with frequent human activities. To test the hypothesis, we selected Taihe County, a typical representative of the red soil hilly region in subtropical China, where the population is dense, economic development is fast, and the forest has experienced destruction and re-establishment. To accurately assess the impact of changes in the forest area and forest quality on forest vegetation carbon storage variation in real situations, we collected and used a series of remote sensing images from 1986 to 2019, inventory data for forest management, and field data. The results showed that the forest area and forest vegetation carbon density increased from 10.85 × 104 ha and 17.89 Mg/ha in 1986 to 16.40 × 104 ha and 26.51 Mg/ha in 2019, with an increase of 51.11% and 48.23%, respectively. Meanwhile, the forest vegetation carbon storage increased by 123.99%, from 1.94 Tg in 1986 to 4.35 Tg in 2019, suggesting a significant carbon sequestration ability. Further analysis showed that the contributions of changes in forest area and forest quality to the forest vegetation carbon storage variation were 1.23 Tg (51.19%) and 1.17 Tg (48.81%), respectively. The result implies that the increase in forest area and forest quality almost contributed equally to the increase in forest vegetation carbon storage during the 34 years of vegetation restoration in Taihe County. However, forest vegetation carbon density controlled the variation of forest vegetation carbon storage in all three forest developing stages. The precision of our results was also tested with two inventory datasets for forest management in Taihe County in 2009 and 2019. The relative contribution of forest vegetation carbon density is lower than most of the previous study results using national forest inventory data in this region, indicating that the contribution of change in the forest vegetation carbon density to the forest vegetation carbon storage variation might be overestimated based on these national permanent plots, which were protected or less disturbed to some extent.

Evolution of the water–carbon relationship at basin scale under the background of a vegetation restoration program

AbstractVegetation restoration is an important means for regional climate change mitigation and terrestrial carbon sequestration. However, large‐scale vegetation restoration in arid and semiarid regions can excessively consume local water resources, thus threatening regional water supply safety. To explore the impact of vegetation restoration on the water–carbon relationship in the Loess Plateau in China before and after the implementation of the Grain‐for‐Green Program (GfGP), the regional hydro‐ecological simulation system (RHESSys) model was used to simulate the temporal and spatial evolution of key variables of the water cycle, including the actual evapotranspiration (ET) and the surface runoff (Rs), and that of the carbon cycle, including the net primary productivity (NPP) and the total carbon density (TC). In addition, statistical methods were applied to analyze the interaction between water and carbon cycles. Results show that the RHESSys model is capable of simulating the key ecohydrological elements. In the period of 1990–2019, ET and NPP both showed significant increasing trends at rates of 8.9 mm yr−1 and 6.9 g C m−2 yr−1, respectively. The TC also had a slightly increasing trend, with rates of 0.04 t C hm−2 yr−1 (land‐use in 1995), 0.07 t C hm−2 yr−1 (land‐use in 2005), and 0.08 t C hm−2 yr−1 (land‐use in 2015). In contrast, Rs decreased at the rate of 0.35 mm yr−1. As for spatial distribution, in the middle and southeastern parts of the Yanhe River basin with the vegetation well‐restored, ET, NPP, and TC showed significant increasing trends, while the Rs showed a decreasing trend. ET was also found to have a positive correlation with NPP and TC, while the Rs has a negative correlation with NPP and TC. Moreover, the correlation of Rs‐NPP and Rs‐TC was found to increase after the implementation of the GfGP, which indicates the reinforcing effect of vegetation restoration on the water–carbon trade‐off relationship. Therefore, it is critical to coordinate and regulate the interaction between water consumption and runoff in the context of vegetation restoration in the Yanhe River basin, as well as to promote the synergy between regional ecological restoration and social economic development for water security.

Use of Drone RGB Imagery to Quantify Indicator Variables of Tropical-Forest-Ecosystem Degradation and Restoration

Recognizing initial degradation levels is essential to planning effective measures to restore tropical forest ecosystems. However, measuring indicators of forest degradation is labour-intensive, time-consuming, and expensive. This study explored the use of canopy-height models and orthophotos, derived from drone-captured RGB images, above sites at various stages of degradation in northern Thailand to quantify variables related to initial degradation levels and subsequent restoration progression. Stocking density (R2 = 0.71) and relative cover of forest canopy (R2 = 0.83), ground vegetation (R2 = 0.71) and exposed soil + rock (R2 = 0.56) correlated highly with the corresponding ground-survey data. However, mean tree height (R2 = 0.31) and above-ground carbon density (R2 = 0.45) were not well correlated. Differences in correlation strength appeared to be site-specific and related to tree size distribution, canopy openness, and soil exposure. We concluded that drone-based quantification of forest-degradation indicator variables is not yet accurate enough to replace conventional ground surveys when planning forest restoration projects. However, the development of better geo-referencing in parallel with AI systems may improve the accuracy and cost-effectiveness of drone-based techniques in the near future.

Soil organic carbon storage in Liaohe River Estuary Wetlands under restoration and multiple management strategies, based on landscape patterns

IntroductionManagement of coastal wetlands has resulted in extensive conservation of this natural resource; however, changes in carbon storage function are not yet known. There is a direct link between landscape and soil carbon storage. Predicting future changes in the landscape and carbon storage in coastal wetlands is important for developing wetland management policies.MethodHere, remote sensing and physical methods were used to measure and calculate the landscape and surface soil carbon stocks of the Liaohe River Estuary Wetland (LREW). The changes in the landscape and soil carbon stocks under three scenarios: natural development, strict protection, and culture pond transfer, were then predicted using the PLUS model.ResultThe results indicate that the surface soil organic carbon storage was 2107.97×103 t, while soil organic carbon density decreased from land to sea. Anthropogenic activity was found to be the main driver of the current landscape evolution. However, the impact of sea level rise is increasing. By 2030, considerably more storage will be gained under the culture pond transfer scenario than at present.DiscussionOur results reveal that some of the methods of ecological restoration may diminish the carbon storage capacity of coastal wetlands. Making full use of areas with high carbon storage potential may be an effective wetland carbon sink management strategy. Governments should consider more comprehensively for a better carbon pool when developing restoration strategies.

Estimating the Forest Carbon Storage of Chongming Eco-Island, China, Using Multisource Remotely Sensed Data

Urban forests are highly heterogeneous; information about the combined effect of forest classification scale and algorithm selection on the estimation accuracy for urban forests remains unclear. In this study, we chose Chongming eco-island in the mega-city of Shanghai, a national experimental carbon neutral construction plot in China, as the study object. Remote sensing estimation models (simple regression models vs. machine learning models) of forest carbon density were constructed across different classification scales (all forests, different forest types, and dominant tree species) based on high-resolution aerial photographs and Sentinel-2A remote sensing images, and a large number of field surveys and optimal models were screened by ten-fold cross-validation. The results showed that (1) in early 2020, the total forest area and carbon storage of Chongming eco-island were 307.8 km2 and 573,123.6 t, respectively, among which the areal ratios and total carbon storage ratios of evergreen broad-leaved forest, deciduous broad-leaved forest, and warm coniferous forest were 51.4% and 53.3%, 33.5% and 32.8%, and 15.1% and 13.9%, respectively. (2) The average forest carbon density of Chongming eco-island was 18.6 t/ha, among which no differences were detected among the three forest types (i.e., 17.2–19.2 t/ha), opposite to what was observed among the dominant tree species (i.e., 14.6–23.7 t/ha). (3) Compared to simple regression models, machine learning models showed an improvement in accuracy performance across all three classification scales, with average rRMSE and rBias values decreasing by 29.4% and 53.1%, respectively; compared to the all-forests classification scale, the average rRMSE and rBias across the algorithms decreased by 25.0% and 45.2% at the forest-type classification scale and by 28.6% and 44.3% at the tree species classification scale, respectively. We concluded that refining the forest classification, combined with advanced prediction procedures, could improve the accuracy of carbon storage estimates for urban forests.

Scenario Simulation of the Relationship between Land-Use Changes and Ecosystem Carbon Storage: A Case Study in Dongting Lake Basin, China.

High-frequency land-use changes caused by rapid economic development have become a key factor in the imbalance of carbon sequestration within regions. How to balance economic development and ecological protection is a difficult issue for regional planning. Studying the relationship between future land-use changes and ecosystem carbon storage (CS) is of important significance for the optimization of regional land-use patterns. The research used the gray prediction model and coupled the patch-generating land-use simulation (PLUS) model and the integrated valuation of ecosystem services and trade-offs (InVEST) model. On this basis, the evolution characteristics and spatial coordination between land-use changes and CS in the Dongting Lake Basin (DLB) in different scenarios in 2030 were simulated. The results show that: (1) The spatial distribution of CS remains stable in different scenarios, while land-use types with high carbon density in the periphery of cities are constantly invaded by construction land, which results in the greatest carbon loss in the urban areas. (2) Compared with the natural evolution scenario (NES), only 195.19 km2 of land-use types with high carbon density are transformed into construction land in the ecological protection scenario (EPS), generating a carbon sink gain of 182.47 × 104 Mg. Conversely, in the economic development scenario (EDS), a total of over 1400 km2 of farmland and ecological land are transformed into construction land, which weakens the carbon sequestration capacity of ecosystems, and more than 147 × 104 Mg of carbon loss occurs in the urban areas. (3) The planned development scenario (PDS) takes ecological protection and economic development both into consideration, which not only generates a carbon sink gain of 121.33 × 104 Mg but also reduces the carbon loss in urban areas by more than 50%. The PDS performs well in both land use and CS growth and can better motivate the effect of land-use changes in increasing the carbon sink, which is also proved by analysis of the coordination between land-use intensity (LUI) and CS. Therefore, the PDS better satisfies the future development demand of DLB and can provide a reference for sustainable land use in the basin.

Soil quality index as affected by long-time continuous cultivation in a Mediterranean sub-humid region

Evaluating the Soil Quality Index (SQI affected by continuous and long-term cultivation operations to identify the threat of soil destruction and its controlling is a severe challenge. The current study has investigated the effects of cultivation operations on SQI in a wide area (37,524 ha), and with various types of soil (three soil orders including Inceptisols, Mollisols, and Vertisols) and the record of several decades of wheat cultivation. After determining the total data set (TDS) and minimum data set (MDS), the SQI was calculated using Integrated Quality Index (IQI) and Nemoro Quality Index (NQI) models. The results showed that most soil indicators (e.g., electrical conductivity, sodium adsorption ratio, organic carbon, and bulk density) were negatively affected by long-term cultivation operations. Compared to the control soils, the values of IQI-T, NQI-T, IQI-M, and NQI-M had been decreased ranging from 17 to 24%, 20 to 27%, 17 to 22%, and 21 to 26%, respectively, in the cultivated soils. The most significant decrease in the average SQI value was observed in the Vertisols (24% decrease), followed by Inceptisols (21% decrease) and Mollisols (19.5% decrease). The regression equations indicated that IQI-T, NQI-T, IQI-M, and NQI-M models could explain 59%, 39%, 53%, and 35% and 57%, 37%, 51%, and 33% of changes in the biological and grain yields of wheat, respectively. The current study provides a quantitative method for evaluating the soil quality at the soil type-scale and creatively analyzes the effects of long-term and continuous cultivation operations on the soil quality and product performance.

Conductive carbon paint: An efficient way to electrodeposit metallic nanowires by using porous membranes

The present work demonstrates that conductive carbon paint, used for sample preparation in electron microscopy, can be a more straightforward and as-effective substitute for the metallic layer deposition usually used for the electrodeposition of metallic nanowires within porous membranes. AFM images demonstrated the good surface quality of the carbon layer. Raman spectroscopy confirmed the high crystallinity of carbon and high density of π-electrons. The electrical conductivity of the carbon layer was estimated using the linear sweep voltammetry technique. This new cathode was employed to grow continuous (Ni) and composition-modulated (Ni/Cu) nanowires within alumina templates, starting from aqueous solutions of Ni2+ and Cu2+ mixed salts. The obtention of metallic copper and nickel, and their separation can be readily observed by scanning electron microscopy and elemental mapping by EDS.

Evolution Characteristics of Wetland Landscape Pattern and Its Impact on Carbon Sequestration in Wuhan from 2000 to 2020

The wetland ecosystem is an important organic carbon pool on earth, and rich carbon storage and high carbon density are present in wetland vegetation and soil. However, large areas of wetlands have been drained, the area of wetlands has shrunk, and the degree of fragmentation has intensified due to rapid urbanization in the past 20 years. Enhancing ecological carbon sequestration capacity is a key task of carbon peaking and neutrality, and studying the carbon sequestration function of urban wetland is urgently needed. In this work, the wetland in Wuhan was taken as the main research object. The landscape pattern index of Wuhan wetland was calculated and the evolution characteristics of landscape pattern were analyzed using the remote sensing image interpretation data from 2000 to 2020. CASA model was used to estimate the carbon sequestration of wetlands and explore the spatial and temporal distribution pattern of carbon sequestration. Spearman correlation was used to study the effects of different landscape pattern parameters on carbon sequestration. Results indicate the following: (1) From 2000 to 2020, the total wetland area in Wuhan has been reduced by 158.99 km2, among which the area of lakes, reservoirs and marshes decreased by 60.65 km2, 55.51 km2 and 41.77 km2, respectively. The density of wetland patches and the degree of fragmentation and irregularity increased, and the LPI, aggregation degree and diversity decreased. (2) In the past 20 years, the total carbon sequestration amount of wetland in Wuhan first decreased and then increased, among which the carbon sequestration in wetland was the lowest in 2010 (571,900 t). The amount of carbon sequestration in 2020 was the same as that in 2000, reaching 800,000 t. (3) A larger wetland area means more concentrated and denser distribution, more complex shape, more diverse type, and stronger carbon sequestration capacity for Wuhan wetland. On the contrary, the distribution of wetlands becomes more fragmented and regular, and the disordered planning leads to the reduction in wetland types in a unit area, which will decrease the carbon sink capacity of urban wetlands.

Estimating Settlement carbon stock and density using an inventory approach and quantifying their variation by land use and parcel size

A lack of very-high resolution land-cover data and in-situ carbon sampling in Settlement areas has limited the quantification of terrestrial carbon in Canadian Settlements and elsewhere. Without those data, it is difficult to quantify Settlement area terrestrial carbon for United Nations Framework Convention on Climate Change reporting within the Land Use, Land Use Change, and Forestry sector. The presented research takes a step toward filling this gap by first classifying Settlement land cover at a very-high resolution (<=80 cm, 93% overall accuracy). Then, with those data, an inventory approach is used to estimate carbon stocks based on local data, standard rulesets from the IPCC, and practice-based assumptions. Guided by FAIR principles, our approach is operationalized and available in a Jupyter Notebook for distribution, use, and extension by others. Results found that the study Settlement comprised 30% tree cover and 18% turfgrass. When analyzed by parcel size, carbon densities varied little for parcels less than 1.6 ha (3.9–4.2 kg C m-2), but then increased with larger parcels up to 6.6 Gg C m-2 for parcels > 8.1 ha. Among different land uses, industrial, commercial, and transportation had the lowest carbon densities (2.4–2.8 kg C m-2), followed by high, medium, and low-density residential (3.6, 4.0, 8.9 kg C m-2, respectively) with low-density residential almost achieving carbon densities in protected and recreation areas (10.2 kg C m-2). Our results suggest that land use is a stronger driver of carbon-storage relative to parcel size, but their combination best represents the variation in carbon-storage in low-density residential land use. Currently, only carbon change in urban trees and deforestation around urban centres is reported in Canada’s National GHG Inventory Report. The present research quantifying carbon densities provides an analysis that could inform carbon change resulting from other land-use conversions and improve deforestation estimates by better defining the final state of a land-use change.

Improving model parsimony and accuracy by modified greedy feature selection in digital soil mapping

In the context of increasing soil degradation worldwide, spatially explicit soil information is urgently needed to support decision-making for sustaining limited soil resources. Digital soil mapping (DSM) has been proven as an efficient way to deliver soil information from local to global scales. The number of environmental covariates used for DSM has rapidly increased due to the growing volume of remote sensing data, therefore variable selection is necessary to deal with multicollinearity and improve model parsimony. Compared with Boruta, recursive feature elimination (RFE), and variance inflation factor (VIF) analysis, we proposed the use of modified greedy feature selection (MGFS), for DSM regression. For this purpose, using quantile regression forest, 402 soil samples and 392 environmental covariates were used to map the spatial distribution of soil organic carbon density (SOCD) in Northeast and North China. The result showed that MGFS selected the most parsimonious model with only 9 covariates (e.g., brightness index, mean annual temperature), much lower than RFE (22 covariates), VIF (30 covariates), and Boruta (76 covariates). The repeated validation (50 times) showed that the MGFS derived model performed better (R2 of 0.60, LCCC of 0.74, RMSE of 13.80 t ha−1) than these using full covariates, Boruta, RFE and VIF (R2 of 0.48–0.57, LCCC of 0.64–0.72, RMSE of 14.24–15.79 t ha−1). Despite the similar performance of the uncertainty estimate (PICP), the model using MGFS and RFE had the lowest global uncertainty (0.86) as indicated by the uncertainty index. In addition, MGFS had the best computation efficiency when considering the steps of variable selection and map prediction. Given these advantages over Boruta, RFE and VIF, MGFS has a high potential in fine-resolution soil mapping practices, especially for these studies at a broad scale involving heavy computation on millions or billions of pixels.

Responses of net ecosystem carbon budget and net global warming potential to long-term nitrogen deposition in a temperate grassland

Grassland systems are important terrestrial carbon sinks and have great potential for carbon (C) sequestration. Increased atmospheric nitrogen (N) deposition has profoundly affected C balance and greenhouse gas emissions in grassland systems. However, the effects of long-term nitrogen (LN) deposition on net ecosystem carbon budget (NECB) and net global warming potential (NGWP) in grassland systems are still not clearly understood. A field experiment was conducted to test the effect of LN addition (0, 30, 60, 120 and 240 kg N ha−1 yr−1) on NECB and NGWP in a temperate grassland area in Inner Mongolia, China. LN addition significantly increased soil organic carbon density (SOCD) and C sequestration in surface soil (0–30 cm) with the increase of N addition rate from 2005 to 2018. In contrast, a decrease in ecosystem respiration (Re) was observed, except at low LN concentration (30 kg N ha−1 yr−1). Annual N2O flux was significantly increased, and the CH4 sink was significantly decreased by LN addition. NECB and NGWP were relatively weak in this temperate grassland, ranging from −627.29 ± 198.81 and −232.87 ± 23.11 kg CO2 ha−1yr−1, respectively, and significantly decreased with increasing LN application. The offset effect of N2O emission to ecosystem C uptake decreased significantly with increased LN addition, and was stable at high LN addition. This was related to the increase in soil C sequestration due to plant C uptake. These results indicate that LN addition significantly decreased the NECB and NGWP of this grassland. Increased long-term N deposition significantly enhanced soil C sequestration, which has important implications for mitigating climate warming.

An individual tree-based model for estimating regional and temporal carbon storage of Abies chensiensis forest ecosystem in the Qinling Mountains, China

Accurate estimation of carbon storage of forest ecosystem has significant implication for mitigating global warming. In order to get accurate estimation of carbon storage of forest ecosystem at the regional scale and temporal dynamics in the Qinling Mountains, this study established a dynamic estimation model (FCM model) for forest carbon storage at tree stand scale according to the data of forest growth and degradation characteristics of Aibes chensiensis, and constructed a regional and temporal estimation model (geo-FCM model) based on FCM model and Aibes chensiensis spatial distribution data from 3S technology. The results showed that carbon density of Aibes chensiensis forest at tree age of 80 years from FCM model were 245.56 t ha−1, which was similar to the measured carbon densities from field investigation. The simulation process of the FCM model could well reflect the growth characteristic of Aibes chensiensis. The simulated result of geo-FCM model showed carbon storage of Abies chensiensis forest in study region were 5.23 × 105 t in year 2018 with the average carbon density of 222.26 t ha−1 and increased to 5.60 × 105 t in year 2028 along with tree growth. Its carbon storage increased 7.11% compared to the state of year 2018. The constructed model provided a robust method to estimate forest carbon storage of different tree species at different age stages at the regional scale, which can provide an important reference for forest management and selection of plantation tree species.

Woody species diversity, composition, structure and carbon storage of a dry evergreen montane forest of Essimingor Nature Forest Reserve in Tanzania

Abstract. Mwakalukwa EE, Mwakisu A, Maliondo SMS. 2023. Woody species diversity, composition, structure and carbon storage of a dry evergreen montane forest of Essimingor Nature Forest Reserve in Tanzania. Intl J Trop Drylands 7: 26-36. The biodiversity status of flora and fauna of many forests found on small protruding and isolated hills in most parts of dry areas in Tanzania is largely less studied. Their contribution to climate mitigation options also remains largely unknown. This study assessed the woody species diversity, composition, structure and carbon stocks potential of a dry evergreen montane forest of Essimingor Nature Forest Reserve in Tanzania. The vegetation data were collected from 23 concentric sample plots of 5m, 15m, and 20m radius laid systematically across the entire forest area of 6,100 ha. A total of 54 trees and shrubs species belonging to 29 families were identified. Diversity indices indicated the forest to have a high diversity of woody species. The most important species were Cassipourea malosana, Diospyros abyssinica and Drypetes natalensis. Stand structure comprised 288±173 stems ha-1, basal area of 11.47±7.23 m2ha-1 and standing volume of 27.3±16.3 m3ha-1. The mean above-ground and below-ground carbon stocks were 56.93±34.60 Mg C ha-1 and 34.71±19.72 Mg C ha-1, respectively. The observed high species diversity and carbon density signify the importance of conservation efforts bestowed in this reserve. Quantification of other carbon pools for estimation of the total carbon stock potential of this forest is recommended.