Small Rural Atlantic Forest Remnants Might Store Significant Amounts of Carbon: An Example in Southeastern Brazil

The unprecedented increase in atmospheric CO2 concentration has attracted global research attention on the potential role of tree plantations in climate change mitigation. There is an urgent need to estimate the above-ground biomass (AGB) and carbon stock in forest plantations. This is particularly essential for Sierra Leone, where above-ground biomass (AGB) and carbon stock data are presently lacking. This study estimated the above-ground biomass accumulation and carbon stock of Tectona grandis Linn.f. and Gmelina arborea Roxb. in the spacing and plantation trials at Njala University, Southern Sierra Leone. The assessment was based on a total inventory of trees having a diameter at breast height (DBH) ≥ 5 cm and tree height. Above-ground biomass (AGB) was estimated using the allometric equation by Chave et al. (2005), and above-ground carbon (AGC) stock was calculated by multiplying the biomass with a conversion factor of 0.5. The result showed that the mean above-ground carbon stock for Gmelina arborea was higher in the plantation trial (25.2 t ha-1) than in the spacing trial (7.5 t ha-1). For Tectona grandis, the mean above-ground carbon stock was similarly higher in the plantation trial (6.6 t ha-1) than in the spacing trial (1.5 t ha-1). The results further suggest that the variation in the means of above-ground carbon stock is not dependent on the tree species type and experimental site because there were no significant differences (P>0.05) between the tree species and experimental sites.

Large-sized trees and altitude drive aboveground carbon stock in Brazilian Atlantic Cloud Forests: An approach based on carbon hyperdominant taxa.

Human activities have been severely affecting forest structure and functions in humid tropics across the globe. In present study, we estimated aboveground biomass and carbon stocks along a disturbance gradient in wet tropical forests of southern Assam, India, using non-destructive sampling method. A total of 26 forest stands were surveyed and based on a disturbance index grouped into 4 categories, viz. undisturbed (UD), mildly disturbed (MLD), moderately disturbed (MD) and highly disturbed (HD) forests. Mean aboveground carbon (AGC) stocks and basal area decreased with increased disturbance index. Though phytosociological parameters such as species richness, Shannon-Wiener diversity index, tree density, basal area and AGC stocks showed a significant negative correlation with disturbance index, tree density (693 ± 11.6 trees ha−1) and Shannon-Wiener diversity index (1.98 ± 0.07) were highest in mildly disturbed forests. Aboveground carbon stocks were positively correlated with basal area (p < 0.01) and diversity indices (p < 0.01) across disturbance regimes. Tree species such as Cynometra polyandra, Mesua ferrea, Palaquium polyanthum, Mesua floribunda, Artocarpus chama and Stereospermum personatum together contributed 41.3 ± 6.2 % and 42.4 ± 6.7% of the total AGC stocks in undisturbed and mildly disturbed forests, respectively, while Artocarpu schama, Holarrhena pubescens, Mitragyna rotundifolia, Sapium baccatum, Schima wallichii and Toona ciliata contributed 47.2 ± 3.5% in moderately and 55.4 ± 4.0% in highly disturbed forests.

Greenhouse gas emission of carbon dioxide (CO2) is one of the major factors causing global climate change. Urban green space plays a key role in regulating the global carbon cycle and reducing atmospheric CO2. Quantifying the carbon stock, distribution and change of urban green space is vital to understanding the role of urban green space in the urban environment. Remote sensing is a valuable and effective tool for monitoring and estimating aboveground carbon (AGC) stock in large areas. In the present study, different remotely-sensed vegetation indices (VIs) were used to develop a regression equation between VI and AGC stock of urban green space, and the best fit model was then used to estimate the AGC stock of urban green space within the beltways of Xi’an city for the years 2004 and 2010. A map of changes in the spatial distribution patterns of AGC stock was plotted and the possible causes of these changes were analyzed. Results showed that Normalized Difference Vegetation Index (NDVI) correlated moderately well with AGC stock in urban green space. The Difference Vegetation Index (DVI), Ratio Vegetation Index (RVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI) and Renormalized Difference Vegetative Index (RDVI) were lower correlation coefficients than NDVI. The AGC stock in the urban green space of Xi’an in 2004 and 2010 was 73,843 and 126,621 t, respectively, with an average annual growth of 8,796 t and an average annual growth rate of 11.9%. The carbon densities in 2004 and 2010 were 1.62 and 2.77 t/hm2, respectively. Precipitation was not an important factor to influence the changes of AGC stock in the urban green space of Xi’an. Policy orientation, major ecological greening projects such as “transplanting big trees into the city” and the World Horticultural Exposition were found to have an important impact on changes in the spatiotemporal patterns of AGC stock.

Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks due to the spatial heterogeneity of bottom-up methods. In this study, we developed a method for analyzing space-sensing data that estimates and predicts long time series of forest carbon stock changes in an alpine region by considering the sample’s spatial characteristics. We employed a nonlinear mixed-effects model and improved the model’s accuracy by considering both static and dynamic aspects. We utilized ground sample point data from the National Forest Inventory (NFI) taken every five years, including tree and soil information. Additionally, we extracted spectral and texture information from Landsat and combined it with DEM data to obtain topographic information for the sample plots. Using static data and change data at various annual intervals, we built estimation models. We tested three non-parametric models (Random Forest, Gradient-Boosted Regression Tree, and K-Nearest Neighbor) and two parametric models (linear mixed-effects and non-linear mixed-effects) and selected the most accurate model to estimate Pinus densata’s above-ground carbon stock. The results showed the following: (1) The texture information had a significant correlation with static and dynamic above-ground carbon stock changes. The highest correlation was for large-window mean, entropy, and variance. (2) The dynamic above-ground carbon stock model outperformed the static model. Additionally, the dynamic non-parametric models and parametric models experienced improvements in prediction accuracy. (3) In the multilevel nonlinear mixed-effects models, the highest accuracy was achieved with fixed effects for aspect and two-level nested random effects for the soil and elevation categories. (4) This study found that Pinus densata’s above-ground carbon stock in Shangri-La followed a decreasing, and then, increasing trend from 1987 to 2017. The mean carbon density increased overall, from 19.575 t·hm−2 to 25.313 t·hm−2. We concluded that a dynamic model based on variability accurately reflects Pinus densata’s above-ground carbon stock changes over time. Our approach can enhance time-series estimates of above-ground carbon stocks, particularly in complex topographies, by incorporating topographic factors and soil thickness into mixed-effects models.

Purpose of the ReviewImproved forest management is a promising avenue for climate change mitigation. However, we lack synthetic understanding of how different management actions impact aboveground carbon stocks, particularly at scales relevant for designing and implementing forest-based climate solutions. Here, we quantitatively assess and review the impacts of three common practices—application of inorganic NPK fertilizer, interplanting with N-fixing species, and thinning—on aboveground carbon stocks in plantation forests.Recent FindingsSite-level empirical studies show both positive and negative effects of inorganic fertilization, interplanting, and thinning on aboveground carbon stocks in plantation forests. Recent findings and the results of our analysis suggest that these effects are heavily moderated by factors such as species selection, precipitation, time since practice, soil moisture regime, and previous land use. Interplanting of N-fixing crops initially has no effect on carbon storage in main tree crops, but the effect becomes positive in older stands. Conversely, the application of NPK fertilizers increases aboveground carbon stocks, though the effect lessens with time. Moreover, increases in aboveground carbon stocks may be partially or completely offset by emissions from the application of inorganic fertilizer. Thinning results in a strong reduction of aboveground carbon stocks, though the effect lessens with time.SummaryManagement practices tend to have strong directional effects on aboveground carbon stocks in plantation forests but are moderated by site-specific management, climatic, and edaphic factors. The effect sizes quantified in our meta-analysis can serve as benchmarks for the design and scoping of improved forest management projects as forest-based climate solutions. Overall, management actions can enhance the climate mitigation potential of plantation forests, if performed with sufficient attention to the nuances of local conditions.

Above-ground carbon stocks play an important role to support the sustainability of oil palm plantation, and vary with soil characteristics, climate conditions, and agronomic practices. The common direct approach to assess aboveground carbon stocks may be time-consuming, and laborious, so may not be suitable especially for large scale observation. Therefore, the use of remote sensing may overcome some limitations coming up for conventional methods, yet requires a considerable data validation to obtain a reliable model. In this study, the normalized difference vegetation index (NDVI) based on Sentinel-2 satellite imagery, acquired in September 2023, was used in combination with field measurements, i.e. biomass estimation, investigated in three separated of oil palm plantations differing in ages, i.e. young (< 10 years), middle (10 – 20 years) and old (> 20 years), subjected with similar soil characteristics, and climate condition, in Bojong Datar, Cibungur, and Cikasungka, West Java. Results show that above-ground carbon stocks increase with oil palm ages, i.e. from young to middle ages, with value ranging from 3.40 – 65.70 ton/ha, yet decreases down to 17 ton/ha with ages > 20 years. Similarly, it also occurred for NDVI index, i.e. increase from 0.50 – 0.59 for young to middle of oil palms, and down to 0.51 for oil palm > 20 years old. This study suggests a strong correlation between carbon stocks and NDVI values (R2 = 0.878), pointing out the potential use of vegetation index to estimate above-ground oil palm carbon stocks. In the future, with further validation, the use of NDVI to estimate plant biomass may provide a potential substitute for the field measurement methods with a considerable higher efficiency and accuracy.

Tree-size dimension inequality shapes aboveground carbon stock across temperate forest strata along environmental gradients

ABSTRACTUrban vegetation can help to offset carbon emissions. However, urban vegetation cover is vulnerable to urbanization. This study attempts to detect the change in vegetation cover and to quantify its impact on aboveground carbon (AGC) stocks in Auckland, New Zealand, between 1989 and 2014. Field-measured vegetation parameters were used to calculate the amount of carbon stored in plants at the plot-level. Plot-level AGC stocks were linked with vegetation spectral/structural features derived from Landsat images and Light Detection and Ranging (LiDAR) data. These data were also used to map vegetation cover and to estimate AGC stock. Vegetation cover decreased from 394.0 km2 in 1989 to 379.4 km2 in 2014. AGC stock in 1989 was estimated at 1,001,184 Mg C from Landsat 4 data. The total AGC in 2014 was estimated at 1,459,530 Mg C from Landsat 8 data. Thus, total AGC stock increased by 458,346 Mg C (45.8%) in spite of a 3.7% decrease in vegetation cover (14.6 km2) during the same period. The increase in AGC stock was derived partly from tree growth and tree plantings. Vegetation growth contributed more to the increase in AGC stock than its gain from non-vegetation to vegetation changes. The AGC stored in trees and shrubs estimated at 1,333,011 Mg C from the 2014 Landsat data is 5.7% lower than 1,414,607 Mg C estimated from the 2013 LiDAR data, due to the inability of optical imagery to capture the sub-canopy structure of forests and the saturation effect. Thus, LiDAR data provided a more accurate estimate of AGC stock, especially when the stock density is high (e.g. >97.9 Mg C ha–1).

&#x0D; &#x0D; &#x0D; Homegardens are one of the most significant and oldest types of land use systems in Sri Lanka which have been recognized as an essential component in providing a variety of ecosystem services. In these systems, trees and shrubs are grown together with food crops under family labor, creating a multitude of biological interactions. Due to rich tree diversity and density, homegarden agroforestry systems are known to have a great capacity to capture and store carbon in their biomass and soil, and thus greatly contribute to mitigation of climate change. Even though the importance of homegardens with regard to the above is highlighted significantly, large knowledge gaps remain on their total carbon storage potential, particularly in low country wet zone homegardens of Sri Lanka. Therefore, the current study aims to estimate the total aboveground and belowground carbon stocks of homegardens in Kalutara district. The study was conducted in ten homegardens ranging from 0.15 Ha to 0.43 Ha. The study focused on all perennial woody trees present in the homegardens. Heights and diameters at breast height (DBH) were measured in a total of 966 woody trees. Aboveground biomass of each tree was calculated nondestructively, using allometric equations which incorporated wood density, DBH and tree height. Belowground biomass was calculated using root: shoot ratios of trees. Total biomass of each tree was converted to total carbon stocks using a conversion factor of 0.5 extracted from literature, considering that total carbon stock of a tree is equivalent to half of its biomass. In order to get the total carbon stock, soil organic carbon (SOC) content of each home garden was analyzed in the laboratory from collected soil samples using the Loss-on-ignition method. Belowground biomass carbon stock and the SOC stock together were taken as the total belowground carbon stock of each homegarden. Estimated mean aboveground carbon stock was 91.4±11.4 Mg ha-1, while the mean belowground carbon stock was determined as 134.3±12.3 Mg ha-1 in low country wet zone home gardens. Aboveground carbon stock, together with the belowground carbon stock, was taken as the total carbon stock of the homegardens. Calculated total carbon stock per unit area for low country wet zone homegardens ranged between 179.873 Mg ha-1 and 286.606 Mg ha-1 with a mean value of 225.7±11.9 Mg ha-1. Above findings of the study present evidence for significant carbon storage capacity of low country wet zone homegardens.&#x0D; Keywords: Homegarden, Low country, Wet zone, Carbon stock, Climate change&#x0D; &#x0D; &#x0D;

Species diversity and stand structural diversity of woody plants predominantly determine aboveground carbon stock of a dry Afromontane forest in Northern Ethiopia

Monitoring temporal changes of aboveground carbon (AGC) stocks distribution in subtropical thicket is key to understanding the role of vegetation in carbon sequestration. The main objectives of this research paper were to model and quantify the temporal changes of AGC stocks between 1972 and 2010 in the Great Fish River Nature Reserve and its environs, Eastern Cape Province, South Africa. We used a method based on the integration of remote sensing and geographical information systems to estimate AGC stocks in a time series framework. A non-linear regression model was developed using Normalised Difference Vegetation Index values generated from SPOT 5 High Resolution Geometric satellite imagery of 2010 as an independent variable and AGC stock estimates from field plots as the dependent variable. The regression model was used to estimate AGC stocks from satellite imagery for 1972 (Landsat TM), 1982 (Landsat 4 TM), 1992 (Landsat 7 ETM), 2002 (Landsat ETM+) and 2010 (SPOT 5) satellite imagery. AGC stocks for the respective years were compared by means of change detection analysis at the subtropical thicket class level. The results showed a decline of AGC stocks in all the classes from 1972 to 2010. Degraded and transformed thicket classes had the highest AGC stock losses. The decline of AGC stocks was attributed to thicket transformation and degradation, which were attributed to anthropogenic activities.

Changes of vegetated land to built-up land will affect the above ground carbon (AGC) stocks in an area. These changes have an impact on climate change. This study aimed to estimate AGC stocks changes in Mijen Sub-District during 2017-2022. Mijen Sub-District is one of the suburban areas of Semarang City which has the main function as the lungs of the city. To determine the AGC stock, remote sensing data and GIS analysis are used. The results showed that during 2017-2022 AGC stock in the area decreased by 3,234.24-ton C. Estimation of changes of AGC stocks in Mijen Sub-District needed as a reference for local governments for policy making in regional and urban planning that integrates sustainable development.

Atlantic Forest recovery after long-term eucalyptus plantations: The role of zoochoric and shade-tolerant tree species on carbon stock

Carbon and biodiversity cobenefits of second-growth tropical forest: The role of leaf phenology