Aboveground Carbon Density Research Articles

Determinants of carbon sequestration in thinned forests

Given global climate change and the projected increases in the greenhouse effect, enhancing the carbon storage capacity of forest ecosystems is especially critical. To fully realize the potential carbon sequestration, it is imperative to understand the drivers affecting carbon storage in forest ecosystems, particularly with disturbances that disrupt existing balance. In this study, we explored the effects of stem-only harvest at various thinning intensities on forest structure and carbon density in middle-aged natural secondary forests, located in the northern temperate zone. Carbon density included aboveground carbon density (ACD), soil organic carbon stocks (SOCD), and total carbon density (TCD), which was the sum of ACD and SOCD. We employed the random forest analysis method to identify significant variables influencing changes in carbon density. Structural equation modelling (SEM) was then used to determine the drivers of changes in forest carbon density. The results showed that moderate thinning (20 %–35 % trees removed), is an effective management practice for increasing the TCD in forests. Although heavy thinning (35.1 %–59.9 % trees removed) accelerated individual growth, it did not fully offset the carbon removed due to thinning. It is noteworthy that light thinning (0–19.9 % trees removed) not only reduced the species richness but also caused a significant number of tree deaths. Large live trees were an important direct determining factor of ACD, but not the only one. In addition, thinning indirectly influenced ACD by reducing canopy density and deformed tree density. The increase in dead tree density had an adverse impact on SOCD, and this phenomenon increased with the passage of recovery time. Conversely, greater thinning intensity enhanced SOCD. Moreover, TCD was directly influenced by tree height, large live trees, and stand density. Furthermore, thinning altered the conifer ratio, thereby influencing tree growth and indirectly controlling the TCD. We believe that this knowledge will be highly beneficial for successful forest management and enhancing the carbon sequestration capacity of forest ecosystems.

Grassland degradation affected vegetation carbon density but not soil carbon density

BackgroundWith the profound changes in the global climate, the issue of grassland degradation is becoming increasingly prominent. Grassland degradation poses a severe threat to the carbon cycle and carbon storage within grassland ecosystems. Additionally, it will adversely affect the sustainability of food production. The grassland ecosystem in the northwest region of Liaoning Province, China, is particularly vulnerable due to factors such as erosion from the northern Horqin Sandy Land, persistent arid climate, and issues related to overgrazing and mismanagement of grassland. The degradation issue is especially pronounced in this ecological environment. However, previous research on the carbon density of degraded grasslands in Northeast China has predominantly focused on Inner Mongolia, neglecting the impact on the grasslands in the northwest of Liaoning Province. Therefore, this experiment aims to assess the influence of grassland degradation intensity on the vegetation and soil carbon density in the northwest of Liaoning Province. The objective is to investigate the changes in grassland vegetation and soil carbon density resulting from different degrees of grassland degradation.MethodologyThis study focuses on the carbon density of grasslands at different degrees of degradation in the northwest of Liaoning Province, exploring the variations in vegetation and soil carbon density under different levels of degradation. This experiment employed field sampling techniques to establish 100 × 100 m plots in grasslands exhibiting varying degrees of degradation. Six replications of 100 × 100 m plots per degradation intensity were sampled. Vegetation and soil samples were collected for analysis of carbon density.ResultsThe results indicate that in the context of grassland degradation, there is a significant reduction in vegetation carbon density. Furthermore, it was found that root carbon density is the primary contributor to vegetation carbon density. In comparison to mildly degraded grasslands, moderately and severely degraded grasslands experience a reduction in vegetation carbon density by 25.6% and 52.6%, respectively. However, with regard to the impact of grassland degradation on soil carbon density, it was observed that while grassland degradation leads to a slight decrease in soil carbon density, there is no significant change in soil carbon density in the short term under the influence of grassland degradation.ConclusionsTherefore, grassland degradation has exerted a negative impact on aboveground vegetation carbon density, reducing the carbon storage of above-ground vegetation in grasslands. However, there was no significant effect on grassland soil carbon density.

Post-Logging Canopy Gap Dynamics and Forest Regeneration Assessed Using Airborne LiDAR Time Series in the Brazilian Amazon with Attribution to Gap Types and Origins

Gaps are openings within tropical forest canopies created by natural or anthropogenic disturbances. Important aspects of gap dynamics that are not well understood include how gaps close over time and their potential for contagiousness, indicating whether the presence of gaps may or may not induce the creation of new gaps. This is especially important when we consider disturbances from selective logging activities in rainforests, which take away large trees of high commercial value and leave behind a forest full of gaps. The goal of this study was to quantify and understand how gaps open and close over time within tropical rainforests using a time series of airborne LiDAR data, attributing observed processes to gap types and origins. For this purpose, the Jamari National Forest located in the Brazilian Amazon was chosen as the study area because of the unique availability of multi-temporal small-footprint airborne LiDAR data covering the time period of 2011–2017 with five data acquisitions, alongside the geolocation of trees that were felled by selective logging activities. We found an increased likelihood of natural new gaps opening closer to pre-existing gaps associated with felled tree locations (<20 m distance) rather than farther away from them, suggesting that small-scale disturbances caused by logging, even at a low intensity, may cause a legacy effect of increased mortality over six years after logging due to gap contagiousness. Moreover, gaps were closed at similar annual rates by vertical and lateral ingrowth (16.7% yr−1) and about 90% of the original gap area was closed at six years post-disturbance. Therefore, the relative contribution of lateral and vertical growth for gap closure was similar when consolidated over time. We highlight that aboveground biomass or carbon density of logged forests can be overestimated if considering only top of the canopy height metrics due to fast lateral ingrowth of neighboring trees, especially in the first two years of regeneration where 26% of gaps were closed solely by lateral ingrowth, which would not translate to 26% of regeneration of forest biomass. Trees inside gaps grew 2.2 times faster (1.5 m yr−1) than trees at the surrounding non-gap canopy (0.7 m yr−1). Our study brings new insights into the processes of both the opening and closure of forest gaps within tropical forests and the importance of considering gap types and origins in this analysis. Moreover, it demonstrates the capability of airborne LiDAR multi-temporal data in effectively characterizing the impacts of forest degradation and subsequent recovery.

Unveiling spatial variations of high forest live biomass carbon stocks of Gabon using advanced remote sensing techniques

Gabon is one of 11 high-forest, low-deforestation (HFLD) countries in the world. It has the highest proportion of preserved forests in the Congo Basin and is the first country to create large forest carbon offset credits in the market. However, about 60% of forests in Gabon is allocated to logging concessions, causing concerns for forest degradation and the sustainability of carbon credits. Here, we use a combination of air- and space-borne remote sensing data and the-state-of-the-art gradient boosted regression trees to estimate forest structure and aboveground biomass carbon density (ACD) of trees at 100 m resolution for the year circa 2020. Mapping spatial variations of ACD across floristically diverse landscapes, we estimate average density and total living carbon storage of trees at the national and sub-national levels. The estimated ACD of trees in forestlands within the country was 142.12 ± 7.3 Mg C ha−1 with the highest values found in central Gabon (150.08 ± 5.8 Mg C ha−1) and on highlands (161.18 ± 6.7 Mg C ha−1). On average, in every region, ACD of forests found within logging concessions (149.89 ± 6.1 Mg C ha−1) was higher than unmanaged forests of unprotected areas (122.81 ± 4.4 Mg C ha−1), indicating the combined effects of logging in carbon-rich forests and increased productivity due to management. The country’s total estimated biomass carbon for trees (above and belowground) stored within the forests was 4.14 ± 0.3 Pg C with 68% found within logging concessions and 14% within protected areas. The map provides high precision and comprehensive assessment of carbon stocks of trees in Gabon’s forests, significantly improving the country’s prospects to implement climate mitigation policies and to participate in carbon markets.

Carbon pools in forest systems and new estimation based on an investigation of carbon sequestration

Forests, the ancient wooden giants, are both symbols of natural beauty and reservoirs of carbon stocks. The current climate crisis has created an urgent need for an in-depth study of forest ecosystems and carbon stocks. Based on forest inventory data from field surveys and four bioclimatic zones [Zone 1 (Z1, humid forest), Zone 2 (Z2, semi-humid forest), Zone 3 (Z3, semi-humid to semi-arid forest-grassland), and Zone 4 (Z4, semi-arid typical grassland)], two methods [Method 1 (M1) and Method 2 (M2)] were used to estimate carbon stocks in forest ecosystems in Shaanxi Province, China, and explored the spatial patterns of carbon pools and potential influences. The total forest ecosystem carbon pool amounted to 520.80 Tg C, of which 53.60% was stored aboveground, 17.16% belowground, and 29.24% in soil (depth of 0–10 cm). Spatially, there were marked north-south gradients in both biomass (Z2 > Z3 > Z1 > Z4) and soil organic carbon densities (Z1 > Z2 > Z3 > Z4). The differences between aboveground and belowground biomass carbon density across broadleaf, needle-leaf, and broadleaf and needle-leaf mixed forest were not pronounced, while soil organic carbon density had the order of broadleaf (18.38 Mg C/ha) > needle-leaf (11.29 Mg C/ha) > broadleaf and needle-leaf mixed forest (10.33 Mg C/ha). Under an ideal scenario that excludes external factors, mainly forest growth, the sequestration potential of forest biomass by 2032 was estimated by M1 as 85.43 Tg, and by M2 to be substantially higher at 176.21 Tg. As of 2062, M1 estimated 155.97 Tg of sequestration potential for forest biomass. The spatial patterns of forest biomass and soil carbon density were closely related to climatic factors, and these relationships allowed the spatial division into two distinct climatic regions. Moreover, biomass carbon density was significantly correlated with the normalized difference vegetation index, soil silt, and elevation. This study provides key information for promoting the strategic shift from light-green to deep-green forest systems in Shannxi Province and updates the estimation methods of forest ecosystems’ carbon pools based on field surveys.

Assessing contemporary Arctic habitat availability for a woolly mammoth proxy

Interest continues to grow in Arctic megafaunal ecological engineering, but, since the mass extinction of megafauna ~ 12–15 ka, key physiographic variables and available forage continue to change. Here we sought to assess the extent to which contemporary Arctic ecosystems are conducive to the rewilding of megaherbivores, using a woolly mammoth (M. primigenius) proxy as a model species. We first perform a literature review on woolly mammoth dietary habits. We then leverage Oak Ridge National Laboratories Distributive Active Archive Center Global Aboveground and Belowground Biomass Carbon Density Maps to generate aboveground biomass carbon density estimates in plant functional types consumed by the woolly mammoth at 300 m resolution on Alaska’s North Slope. We supplement these analyses with a NASA Arctic Boreal Vulnerability Experiment dataset to downgrade overall biomass estimates to digestible levels. We further downgrade available forage by using a conversion factor representing the relationship between total biomass and net primary productivity (NPP) for arctic vegetation types. Integrating these estimates with the forage needs of woolly mammoths, we conservatively estimate Alaska’s North Slope could support densities of 0.0–0.38 woolly mammoth km−2 (mean 0.13) across a variety of habitats. These results may inform innovative rewilding strategies.

Enhancing Urban Above-Ground Vegetation Carbon Density Mapping: An Integrated Approach Incorporating De-Shadowing, Spectral Unmixing, and Machine Learning

Accurately mapping urban above-ground vegetation carbon density presents challenges due to fragmented landscapes, mixed pixels, and shadows induced by buildings and mountains. To address these issues, a novel methodological framework is introduced, utilizing a linear spectral unmixing analysis (LSUA) for shadow removal and vegetation information extraction from mixed pixels. Parametric and nonparametric models, incorporating LSUA-derived vegetation fraction, are compared, including linear stepwise regression, logistic model-based stepwise regression, k-Nearest Neighbors, Decision Trees, and Random Forests. Applied in Shenzhen, China, the framework integrates Landsat 8, Pleiades 1A & 1B, DEM, and field measurements. Among the key findings, the shadow removal algorithm is effective in mountainous areas, while LSUA-enhanced models improve urban vegetation carbon density mapping, albeit with marginal gains. Integrating kNN and RF with LSUA reduces errors, and Decision Trees, especially when integrated with LSUA, outperform other models. This study underscores the potential of the proposed framework, particularly the integration of Decision Trees with LSUA, for advancing the accuracy of urban vegetation carbon density mapping.

Distribution Characteristics of Carbon Density in Plant–Soil System of Temperate Steppe and Temperate Desert in the Longzhong Loess Plateau

Grassland, as a key component of the carbon cycle in terrestrial ecosystems, is vital in confronting global climate change. Characterising the carbon density of grassland ecosystems in the Longzhong Loess Plateau is important for accurately assessing the contribution of grasslands to global climate change and achieving the goal of “peak carbon” and “carbon neutral”. In this study, the Longzhong Loess Plateau was used as the research object to explore changes in the plant–soil system carbon density in two grassland types by analysing the aboveground vegetation biomass carbon density, belowground vegetation biomass carbon density, 0–100 cm soil carbon density, and ecosystem carbon density of temperate steppe and temperate desert. The results showed that the vegetation biomass (standing and living, litter, and belowground biomass), soil, and ecosystem carbon densities of the temperate steppe were significantly higher than those of the temperate desert (p < 0.05). Their carbon densities were 700.51, 7612.95, and 8313.45 g·m−2, respectively. The vertical distribution of belowground biomass and soil carbon density in the temperate steppe was significantly higher than that in the temperate desert. The overall trend of belowground biomass carbon density in the temperate steppe and temperate desert showed a gradual decrease, whereas soil carbon density showed a steady increase. More than 91% and 96% of the carbon was stored in soil in the temperate steppe and temperate desert, respectively, and the belowground biomass carbon stock accounted for more than 84% of the total biomass carbon pools in both temperate steppe and temperate desert. Temperate steppe has a significant effect in improving the carbon stock of grassland ecosystems, so ecological protection and restoration of grassland should be strengthened in the future to enhance the capacity of grassland to sequester carbon and increase sinks.

Aboveground carbon sequestration of Cunninghamia lanceolata forests: Magnitude and drivers

Understanding the spatial variation, temporal changes, and their underlying driving forces of carbon sequestration in various forests is of great importance for understanding the carbon cycle and carbon management options. How carbon density and sequestration in various Cunninghamia lanceolata forests, extensively cultivated for timber production in subtropical China, vary with biodiversity, forest structure, environment, and cultural factors remain poorly explored, presenting a critical knowledge gap for realizing carbon sequestration supply potential through management. Based on a large-scale database of 449 permanent forest inventory plots, we quantified the spatial-temporal heterogeneity of aboveground carbon densities and carbon accumulation rates in Cunninghamia lanceolate forests in Hunan Province, China, and attributed the contributions of stand structure, environmental, and management factors to the heterogeneity using quantile age-sequence analysis, partial least squares path modeling (PLS-PM), and hot-spot analysis. The results showed lower values of carbon density and sequestration on average, in comparison with other forests in the same climate zone (i.e., subtropics), with pronounced spatial and temporal variability. Specifically, quantile regression analysis using carbon accumulation rates along an age sequence showed large differences in carbon sequestration rates among underperformed and outperformed forests (0.50 and 1.80 ​Mg⋅​ha−1·yr−1). PLS-PM demonstrated that maximum DBH and stand density were the main crucial drivers of aboveground carbon density from young to mature forests. Furthermore, species diversity and geo-topographic factors were the significant factors causing the large discrepancy in aboveground carbon density change between low- and high-carbon-bearing forests. Hotspot analysis revealed the importance of culture attributes in shaping the geospatial patterns of carbon sequestration. Our work highlighted that retaining large-sized DBH trees and increasing shade-tolerant tree species were important to enhance carbon sequestration in C. lanceolate forests.

Computational tools for assessing forest recovery with GEDI shots and forest change maps

Tropical secondary forests are ecosystems of critical importance for protecting biodiversity, buffering primary forest loss, and sequestering atmospheric carbon. Monitoring growth and carbon sequestration in secondary forests is difficult, with inventory plots sampling <0.001% of secondary forests. The Global Ecosystem Dynamics Investigation (GEDI), a space-borne LiDAR sampler, provides billions of aboveground carbon density (ACD) estimates across the tropics. We fuse these carbon density estimates with a time series of forest change maps to identify their age since last deforestation and thus estimate the average rate of carbon sequestration in secondary forests across the Amazon biome. To our knowledge, this is the first estimate of these rates made using the new GEDI dataset. Moreover, this paper addresses key statistical and computational challenges of GEDI data fusion and analysis. We propagate both GEDI ACD and geolocation uncertainty to the regrowth rate estimate through a Monte Carlo approach, and we handle heteroskedasticity, outliers, and spatial autocorrelation using robust statistical methods. The large size of the GEDI dataset combined with the proposed Monte Carlo bootstrap can be highly computationally intensive, with a naive implementation taking over a month to complete. Nevertheless, we demonstrate the feasibility of our method by developing optimized open-source code that performs this computation on the 151 million quality-filtered GEDI shots available for the Amazon biome from April 2019–August 2021 in under 25 min in benchmark tests. By resolving these statistical and computational challenges with an efficient open-source pipeline, we create a standard approach that can be used more broadly in any work seeking to combine the GEDI dataset with high-resolution classification maps. Using this approach, we identify approximately 23, 000 GEDI samples of regrowing forest at least 60 m × 60 m wide across the Amazon biome and estimate a carbon sequestration rate of 1.86 MgC/ha/yr with a 95% empirical confidence interval of 1.75–1.97 MgC/ha/yr, with rates varying from 1.27 to 1.99 MgC/ha/yr across smaller subregions.

Mapping Above-Ground Carbon Stocks at the Landscape Scale to Support a Carbon Compensation Mechanism: The Chocó Andino Case Study

(1) Background: Tropical Mountain forests (TMF) constitute a threatened major carbon sink due to deforestation. Carbon compensation projects could significantly aid in preserving these ecosystems. Consequently, we need a better understanding of the above-ground carbon (AGC) spatial distribution in TMFs to provide project developers with accurate estimations of their mitigation potential; (2) Methods: integrating field measurements and remote sensing data into a random forest (RF) modelling framework, we present the first high-resolution estimates of AGC density (Mg C ha−1) over the western Ecuadorian Andes to inform an ongoing carbon compensation mechanism; (3) Results: In 2021, the total landscape carbon storage was 13.65 Tg in 194,795 ha. We found a broad regional partitioning of AGC density mediated primarily by elevation. We report RF-estimated AGC density errors of 15% (RMSE = 23.8 Mg C ha−1) on any 10 m pixel along 3000 m of elevation gradient covering a wide range of ecological conditions; (4) Conclusions: Our approach showed that AGC high-resolution maps displaying carbon stocks on a per-pixel level with high accuracy (85%) could be obtained with a minimum of 14 ground-truth plots enriched with AGC density data from published regional studies. Likewise, our maps increased precision and reduced uncertainty concerning current methodologies used by international standards in the Voluntary Carbon Market.

Increasing forest carbon sinks in cold and arid northeastern Tibetan Plateau

Arid forest lands account for 6 % of the world's forest area, but their carbon density and carbon storage capacity have rarely been assessed. Forest inventories provide estimates of forest stock and biomass carbon density, improve our understanding of the carbon cycle, and help us develop sustainable forest management policies in the face of climate change. Here, we carried out three forest inventories at five-year intervals from 2006 to 2016 in 104 permanent sample plots covering the Qinghai spruce (Picea crassifolia) distribution in the north slope of Qilian Mountains, northeastern Tibetan Plateau. Results shows that mean biomasses for Qinghai spruce were 133.80, 144.89, and 157.01 Mg ha−1 while biomass carbon densities were 65.52, 70.92, and 76.88 Mg C ha−1, in 2006, 2011, and 2016, respectively. This shows an increase in the Qinghai spruce carbon density of 17.34 % from 2006 to 2016. Both the precipitation and temperature play crucial roles on the increase of aboveground carbon density. The average carbon densities were different among forests with different ages and were higher for older forests. Our results show that the carbon sequestration rate for Qinghai spruce in the Qilian Mountains is significantly higher than the average rates of national forest parks in China, suggesting that this spruce forest has the potential to sequester a significant amount of carbon despite the general harsh growing conditions of cold and arid ecoregions. Our findings provide important insights that are helpful for the assessment of forest carbon for cold and arid lands.

Response of Urban Ecosystem Carbon Storage to Land Use/Cover Change and Its Vulnerability Based on Major Function-Oriented Zone Planning

Vigorous emphasis has been placed on optimizing land spatial planning to protect carbon storage and enhance ecosystem resilience. What is the effectiveness of the Major Function-Oriented Zone (MFOZ) planning implemented to achieve this goal in China? Especially in urbanized areas where there are more pronounced conflicts between humans and land. Taking the Beijing-Tianjin-Hebei (BTH) urban agglomeration as the target area, this study explored the response of carbon storage to land use/cover change (LUCC) and its vulnerability to ecological service functions under MFOZ planning. The 30 m × 30 m spatially resolved Landsat TM/ETM remote sensing images from 2000 to 2020 were used. The data preprocessing was performed mainly through radiometric calibration, clipping, and reclassification through the ArcGIS 10.7 software. Applying the InVEST model, which uses the LUCC map and carbon storage density of the four carbon pools, including above-ground carbon density, below-ground carbon density, dead organic carbon density, and soil organic carbon density, to evaluate the carbon storage under the current landscape or in the future, the results show that: (1) The BTH ecosystem experienced a carbon storage reduction of about 7.25 × 107 Mg from 2000 to 2020 due to the expansion of construction land, which crowded out cropland. Carbon storage in the BTH showed a high concentration in the “northeast-southwest” direction and a tiny distribution in the “middle-east” direction. (2) From 2015, the initial effects of the MFOZ planning were seen, with the ecological land in the Central Core Zone and Eastern Coastal Development Zone decreasing while the proportion of high-carbon storage areas in the Eastern Coastal Development Zone increasing. (3) Over the two decades, the land use intensity index improved by 4.65 overall, and vulnerability worsened from 2000 to 2015 and was alleviated from 2015 to 2020. This study will provide a scientific reference for optimizing urban spatial land use planning and promoting carbon sequestration in ecosystems.

Forest Carbon Density Estimation Using Tree Species Diversity and Stand Spatial Structure Indices

The forest spatial structure and diversity of tree species, as the important evaluation indicators of forest quality, are key factors affecting forest carbon storage. To analyze the impacts of biodiversity indices and stand spatial structure on forest carbon density, five tree diversity indices were calculated from three aspects of richness, diversity and evenness, and three indices (Reineke’s stand density index, Hegyi’s competition index and Simple mingling degree) were calculated from stand spatial structure. The relationships between these eight indices and forest carbon density were explored using the Structural Equation Model (SEM). Then, these eight indices were used as characteristic variables to predict the aboveground carbon density of trees (abbreviated as forest carbon density) in the sample plots of the National Forest Resources Continuous Inventory (NFCI) in Shaoguan City in 2017. Multiple Linear Regression (MLR) and four typical machine learning models of Random Forest (RF), Tree-based Piecewise Linear Model (M5P), Artificial Neural Network (ANN) and Support Vector Regression (SVR) were used to predict the forest carbon density. The results show that: (1) Based on the analysis results of the structural equation model (SED), the species diversity and forest stand spatial structure have greater impacts on carbon density. (2) The R2 of all the five prediction models is greater than 0.6, among which the random forest model is the highest. (3) Based on the calculation results of optimal model of RF, the mean forest carbon density of Shaoguan city in 2017 was 43.176 tC/ha. The forest carbon density can be accurately estimated based on the species diversity index and stand spatial structure with machine learning algorithms. Therefore, a new method for the prediction of forest carbon density and carbon storage using species diversity indices and stand spatial structure can be explored. By analyzing the impacts of different biodiversity indices and stand spatial structure on forest carbon density, a scientific reference for the making of management measures for increasing forest carbon sinks and reducing emissions can be provided.

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.

Assessing spatiotemporal variations of forest carbon density using bi-temporal discrete aerial laser scanning data in Chinese boreal forests

Assessing the changes in forest carbon stocks over time is critical for monitoring carbon dynamics, estimating the balance between carbon uptake and release from forests, and providing key insights into climate change mitigation. In this study, we quantitatively characterized spatiotemporal variations in aboveground carbon density (ACD) in boreal natural forests in the Greater Khingan Mountains (GKM) region using bi-temporal discrete aerial laser scanning (ALS) data acquired in 2012 and 2016. Moreover, we evaluated the transferability of the proposed design model using forest field plot data and produced a wall-to-wall map of ACD changes for the entire study area from 2012 to 2016 ​at a grid size of 30 ​m. In addition, we investigated the relationships between carbon dynamics and the dominant tree species, age groups, and topography of undisturbed forested areas to better understand ACD variations by employing heterogeneous forest canopy structural characteristics. The results showed that the performance of the temporally transferable model (R2 ​= ​0.87, rRMSE ​= ​18.25%), which included stable variables, was statistically equivalent to that obtained from the model fitted directly by the 2016 field plots (R2 ​= ​0.87, rRMSE ​= ​17.47%). The average rate of change in carbon sequestration across the entire study region was 1.35 ​Mg·ha−1·year−1 based on the changes in ALS-based ACD values over the course of four years. The relative change rates of ACD decreased as the elevation increased, with the highest and lowest ACD growth rates occurring in the middle-aged and mature forest stands, respectively. The Gini coefficient, which represents forest canopy surface structure heterogeneity, is sensitive to carbon dynamics and is a reliable predictor of the relative change rate of ACD. This study demonstrated the applicability of bi-temporal ALS for predicting forest carbon dynamics and fine-scale spatial change patterns. Our research contributed to a better understanding of the influence of remote sensing-derived environmental variables on forest carbon dynamic patterns and the development of context-specific management approaches to increase forest carbon stocks.

黄河源园区高寒草地碳储量估算及其影响因素

PDF HTML阅读 XML下载 导出引用 引用提醒 黄河源园区高寒草地碳储量估算及其影响因素 DOI: 10.5846/stxb202207152024 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 2021年度全国草原资源调查项目(DD20211601);国家自然科学基金项目(41371125) Estimated carbon storage and influencing factors of alpine grassland in the source region of the Yellow River Author: Affiliation: Fund Project: The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:高寒草地碳储量及其影响因素研究是认识青藏高原草地生态系统乃至陆地生态系统碳循环和气候变化的关键之一。利用2021年8月上旬地面调查数据与同期高分6号遥感数据建立回归关系,在反演研究区植被地上、地下生物量碳密度和0-40cm土壤层有机碳密度基础上,估算了黄河源园区高寒草地有机碳储量,并通过路径分析探讨了土壤理化性质对碳密度的影响驱动机制。结果表明:(1)2021年黄河源园区地上生物量、地下生物量、0-40cm土壤层碳密度分别为37.65g/m2、1305.28g/m2、4769.11g/m2;总碳储量为100.44Tg(1Tg=1012g),植被层和土壤层碳储量分别分为22.06Tg、78.38Tg,占总碳密度的21.96%、78.04%。(2)黄河源园区高寒草甸和高寒草原两种草地类型地上生物量碳密度分别为41.27g/m2、30.76g/m2;地下生物量碳密度分别为1661.41g/m2、618.74g/m2;0-40cm土壤层有机碳密度分别为5790.99g/m2、2804.04g/m2;研究区碳密度总体呈现南高北低的空间分布现状,分布格局与地表植被类型分布特点较为吻合;(3)路径分析表明,研究区土壤理化性质与草地碳密度关系密切,其中地上生物量碳密度主要受土壤全磷含量和土壤含水率驱动,地下生物量碳密度主要受土壤全氮含量和容重驱动,土壤有机碳密度主要受地下生物量碳密度、土壤容重和pH值驱动。 Abstract:The study of alpine grassland carbon storage and its influencing factors is one of the keys to understand the carbon cycle and climate change of grassland ecosystem and even terrestrial ecosystem on the Qinghai Tibet Plateau. In this paper, the regression relationship between the ground survey data in early August 2021 and the remote sensing data of Gaofen-6 in the same period was established. Based on the inversion of the above-ground and below-ground biomass carbon density of vegetation and the organic carbon density of 0-40 cm soil layer in the study area, the organic carbon storage of alpine grassland in the source region of the Yellow River was estimated, and the driving mechanism of the impact of soil physical and chemical properties on carbon density was discussed through path analysis. The results showed that: (1) In 2021, the above-ground biomass, below-ground biomass and carbon density of 0-40 cm soil layer in the source region of the Yellow River were 37.65 g/m2, 1305.28 g/m2 and 4769.11 g/m2, respectively. The total carbon storage was 100.44 Tg (1 Tg=1012 g). The carbon storage of vegetation layer and soil layer was 22.06 Tg and 78.38 Tg, accounting for 21.96% and 78.04% of the total carbon density respectively. (2) The above-ground biomass carbon density of alpine meadow and alpine steppe in the source region of the Yellow River was 41.27 g/m2 and 30.76 g/m2 respectively; The below-ground biomass carbon density was 1661.41 g/m2 and 618.74 g/m2 respectively; The organic carbon density of 0-40 cm soil layer was 5790.99 g/m2 and 2804.04 g/m2 respectively; The spatial distribution of carbon density in the study area was generally high in the south and low in the north, and the distribution pattern was consistent with the distribution characteristics of surface vegetation types. (3) Path analysis showed that the physical and chemical properties of soil in the study area were closely related to the carbon density of grassland. The above-ground biomass carbon density was mainly driven by soil total phosphorus content and soil moisture, the below-ground biomass carbon density was mainly driven by soil total nitrogen content and soil bulk, and the soil organic carbon density was mainly driven by the below-ground biomass carbon density, soil bulk and pH. 参考文献 相似文献 引证文献

Indirect impacts of commercial gold mining on adjacent ecosystems

Mining is important for economic development in many tropical countries, but mining can have serious impacts on biodiversity, both directly through operations at extraction sites, and indirectly via wider social-economic development. However, mitigation efforts by large-scale mining operators focus almost exclusively on extraction sites. We provide a rare assessment of mining impacts on vegetation structure and biodiversity with increasing distance from three commercial gold mines of varying ages in Tanzania (0 years, 8 years, and 19 years since establishment).We show that mining is associated with impacts that manifest largely outside operational lease boundaries. At the two older mine sites, aboveground carbon and tree stem density are significantly higher within lease boundaries compared to outside, while there is no such effect at the new site. Further, tree stem density, aboveground carbon, and tree and butterfly species richness all decrease with increasing distance from extraction sites, with these effects again increasing with mine age. Frugivorous bird species richness is lower outside older mines, while abundance declines in frugivorous and granivorous birds are associated with declines in tree stem density, which may have implications for forest regeneration.These impacts result from new and expanding settlements around mining concessions between 2000 and 2019 and associated demand for timber and fuelwood. We recommend rigorous, integrated impact assessments and conservation planning in mining landscapes, to pre-empt the development of settlements and secondary industries around mining sites and so balance outcomes across the mining sector, natural resource-based and other livelihoods, and conservation agendas.

Effect of Revegetation in Extremely Degraded Grassland on Carbon Density in Alpine Permafrost Regions

Revegetation has been proposed as an effective approach to restoring the extremely degraded grassland in the Qinghai–Tibetan Plateau (QTP). However, little is known about the effect of revegetation on ecosystem carbon density (ECD), especially in alpine permafrost regions. We compared aboveground biomass carbon density (ABCD), belowground biomass carbon density (BBCD), soil organic carbon density (SOCD), and ECD in intact alpine meadow, extremely degraded, and revegetated grasslands, as well as their influencing factors. Our results indicated that (1) ABCD, BBCD, SOCD, and ECD were significantly lower in extremely degraded grassland than in intact alpine meadow; (2) ABCD, SOCD, and ECD in revegetated grassland significantly increased by 93.46%, 16.88%, and 19.22%, respectively; (3) stepwise regression indicated that BBCD was mainly influenced by soil special gravity, and SOCD and ECD were controlled by freeze–thaw strength and soil temperature, respectively. This study provides a comprehensive survey of ECD and basic data for assessing ecosystem service functions in revegetated grassland of the alpine permafrost regions in the QTP.

Estimating aboveground carbon stocks of urban trees by synergizing ICESat-2 LiDAR with GF-2 data

Accurately mapping carbon stocks of urban trees is necessary for urban managers to design strategies to mitigate climate change. However, the aboveground carbon stocks of urban trees are usually underestimated by passive remote sensing data because of the signal saturation problem. The research is the first attempt to develop a framework to map aboveground carbon density of trees in urban areas by synergizing Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) LiDAR data with Gaofen-2 (GF-2) imagery. The framework consists of three key steps. First, we used a support vector machine classifier to classify GF-2 images and extracted urban tree regions. Second, we estimated the tree carbon density of ICESat-2 strips by developing a ICESat-2 photon feature-based aboveground carbon density estimation model. Third, we mapped the carbon density of urban trees by developing a synergistic model between ICESat-2 and GF-2 data based on an object-oriented method. We tested the approach for the areas within the fifth ring road of Beijing, China. The results showed that the 50th percentile height (PH50) of nighttime photons was a good predictor for estimating carbon density of urban trees, with a R2 of 0.69 and a Root Mean Square Error (RMSE) of 2.81 kg C m−2. Using the spectral features generated by GF-2 imagery, we could further extrapolate the carbon density estimated by ICESat-2 strip data to a full coverage of accurate mapping carbon density by urban trees, resulting in a R2 of 0.64 and a RMSE of 2.32 kg C m−2. The carbon stocks within the fifth ring road of Beijing were 8.28 × 108 kg in total, with the mean carbon density of 3.52 kg C m−2. Such estimations were larger than that of previous study using passive remote sensing data only, suggesting the integration of spaceborne LiDAR and spectral data could greatly reduce the underestimation of carbon stocks of urban trees. Our approach can more accurately estimate carbon stocks of urban trees and has the potential to be applicable in other cities.