Aboveground biomass and carbon stock of Rhizophora apiculata forest in Ca Mau, Vietnam

Aboveground biomass and carbon stock in tropical forest play an important role in global carbon cycle. Assessment of biomass and carbon pool in different forest stands may provide information in making decisions about the carbon management within the forest. Gmelina arborea , a fast growing species that is widely distributed and an important timber species of Mizoram has been chosen to assess its biomass and carbon stock. The present study was carried out to estimate the aboveground biomass and carbon stock in G. arborea in different forest stands of Mamit District, Mizoram, north-east India. The result shows that the total aboveground biomass ranged between 66-108 Mg ha -1 and carbon stock (30.00-53.20 mg C ha -1 ). The aboveground biomass and carbon stock was maximum in forest stands (site-III) with highest tree density and diameter class of 30-40cm and 40-50cm indicating the forest site was mature and undisturbed. The result demonstrates that G. arborea contribute in carbon sequestration and helps in mitigating global warming. Further, the aboveground biomass and carbon sequestration potential was greatly affected by the tree composition, population pressure and anthropogenic activities. Keywords: Aboveground biomass, carbon stock, diameter class,Gmelina arborea, tropical forest.

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.

Plantation forests are crucial for landscape rehabilitation and carbon sequestration. However, the efficacy of these plantations in carbon sequestration in Ethiopia remains uncertain due to a lack of robust, species- and site-specific biomass estimation models. This study addresses this gap by developing and validating allometric models for estimating aboveground biomass (AGB) and carbon (C) stocks of plantation forests. Allometric models were developed using data from 69 harvested trees of three species: Eucalyptus globulus, Cupressus lusitanica, and Pinus patula. AGB was regressed against diameter at breast height (DBH) as the sole predictor, with stepwise inclusion of height (H), crown area and wood density. Model performance was evaluated using fit statistics, including Pseudo-R2 and mean prediction error (MPE). The model that incorporated both DBH and H as predictors provided the best fit (Pseudo-R2 > 0.90, p < 0.001) and achieved the lowest MPE (1.47-5.67%) across all species. The findings indicated a mean C stock of 121.6 Mg C ha-1 in the plantations studied. Our models provide valuable insights for forest management and improve the accuracy of AGB and C stock estimations in the study area and similar ecosystems. The estimated C stocks can serve as a benchmark for assessing future C dynamics.

Mangrove forests are considered potential carbon sinks in the atmosphere, surpassing other terrestrial ecosystems and playing a crucial role in the global carbon cycle. As the world strives towards climate neutrality and zero greenhouse gas emissions, the importance of mangrove forests is becoming increasingly evident. The application of technology and science to measure, monitor, and manage mangrove forests for enhanced efficiency, accuracy, and cost reduction is paramount. The study was conducted in mangrove forests in southern Vietnam, a total of 96 trees from various species were measured in the field to validate the accuracy of the UAV method using statistical indices such as Root Mean Square Error (RMSE) and Coefficient of Determination (R2). We constructed a correlation model between canopy height and diameter at breast height (DBH), where canopy height was the independent variable and DBH was the dependent variable. The ground-based biomass model based on height variables was used to estimate mangrove forests biomass and above-ground carbon stocks. We estimated mangrove species using an object-oriented classification method to determine mangrove species boundaries. The estimated heights from UAV correlated closely with ground-truth heights, with R2 = 0.99 and RMSE = 0.2 m. There was a strong correlation between canopy height from UAV (CHMuav) and DBH, with R2 = 0.95 and RMSE = 0.40 cm. The estimated canopy height (CHMuav) ranged from 1 m to 21.5 m. The object-oriented classification model for mangrove forests achieved an overall classification accuracy (OA) of 89% and a Kappa coefficient of 0.85. Above-ground biomass of Rhizophora apiculata forest with an average of 45 Mg ha−1; Avicennia alba species with an average of 22 Mg ha−1; Above-ground biomass of mixed-species with an average of 25 Mg ha−1. The above-ground carbon stocks of Rhizophora apiculata, Avicennia alba, and mixed-species have been estimated. Using the Unmanned Aerial Vehicle (UAV) and Real-Time Kinematic (RTK) methods reduced the uncertainty in estimating above-ground biomass and carbon stocks of mangrove forest.

Quantify global carbon stock in tropical forests to climate change mitigation requires availability of data and tools such as allometric models. The study aimed to estimate aboveground biomass (AGB) and carbon stock in natural and artificial lowland forests. The study retained two study sites. The first site is located in the planted forest in urban commune of Kintélé, on the northern outskirts of Brazzaville, in the Pool Department, Republic of Congo. The second site is located at the Lisanga natural forest in Mbamou Island, which is in the sub-prefecture of Brazzaville Department, Republic of Congo. A total of six plots were recorded, with 50 m × 50 m, i.e. 2500 m2 each for this study. DBH ≥ 10 cm at 1.30 m above ground level for each tree was measured. The results show that for planted forest, Plot 2 has a high biomass (582.8 t∙ha−1) and carbon stock (273.9 t∙ha−1) compared to plots 1 and 3, which have 242.3 t∙ha−1 of biomass and 113.9 t∙ha−1 of carbon, and 206 t ha-1 of biomass and 96.8 t∙ha−1 of carbon. About natural forest, there was an increase in the amount of biomass in the lower height classes (with 47.4 t∙ha−1 of biomass and 22.2 t∙ha−1 of carbon stocks for the 10 m to 19.9 m class) towards the middle class, reaching its peak in class II, from 20 to 29.9 m with 582.8 t∙ha−1 of AGB and 273.9 t∙ha−1 of carbon. The variations in biomass by diameter class and height class in the two forest types studied are remarkable. The planted forest has a higher biomass and carbon stock than the natural forest.

The loss of ecosystem services due to deforestation is of global concern. Financial mechanisms such as REDD+ (reducing emissions from deforestation and forest degradation) have been proposed as ways to support the conservation of tropical forests. Crucial steps in the implementation of REDD+ are to estimate national-level carbon emissions from deforestation and forest degradation and to collect data on local biomass and carbon stocks. In this research, above-ground biomass (AGB) values and associated carbon stocks in a lowland secondary forest are estimated and compared with those in an adjacent primary forest, both growing on limestone in Seram, the Moluccas, Indonesia.<br>Suitable allometric equations for secondary forests in this region and on limestone were not available, so destructive sampling was necessary to determine the AGB in the secondary forest. An allometric equation was developed that makes it possible to estimate the AGB when tree diameter, height and wood density data are available. This biomass estimate was compared with AGB values that were calculated using existing allometric equations for secondary forests. To calculate the biomass and carbon values for the primary forest, an allometric equation from the literature was used.<br>The AGB for trees =10 cm dbh in the secondary forest (140.7 Mg ha–1) was 2.5 times lower than that in the primary forest (349.9 Mg ha–1). Converting these biomass estimates into carbon stocks gave a value of 70.3 Mg ha–1 for the secondary forest and 175.0 Mg ha–1 for the primary forest. The AGB estimate for the secondary forest differs from published values for other areas within the region, because age, type of disturbance and original forest type are non-uniform. The AGB value for the primary forest is comparable to that found in a biomass study conducted in a Malaysian primary limestone forest, but lower than those found in primary forests in Borneo that are dominated by dipterocarps. Ecological limestone studies in the tropics are very rare and more studies of this forest type, and comparisons with adjacent forests on different soil types, are recommended.<br>When the biomass of understory vegetation and other life forms was included, the total AGB in the secondary forest was equal to 176.5 Mg ha–1. As much as 20% of the total AGB was found in life forms other than trees =10 cm dbh. Because secondary forests generally contain many small stems, it is recommended that understory vegetation be included in total AGB estimates for secondary forests.<br>The AGB estimate in the secondary forest varied greatly depending on which of the existing allometric equations was used. Therefore, this study confirms the importance of choosing suitable allometric equations for each forest type and the need to consider destructive sampling when suitable equations are not available. We stress that the allometric equation developed in this study should be used only for old secondary lowland limestone forests in the Moluccas.<br>The fieldwork for this research was carried out in Seram, the Moluccas, Indonesia, from April to June 2011. This research project received financial support from the CoLUPSIA project, Hendrik Muller Fonds and Het Miquel Fonds.

The impact of integrating WorldView-2 sensor and environmental variables in estimating plantation forest species aboveground biomass and carbon stocks in uMgeni Catchment, South Africa

The global climatic crisis along with the threat to the forests has increased the need to research for more accurate and accessible methods and techniques to quantify biomass and carbon in forest while supporting the REED+ and other world objectives. With the aim of reaching zero net deforestation, all participant countries of the United Nation Framework Convention on Climate Change (UNFCCC) have to present an up-to-date report of their carbon balance periodically, as well as compensation actions of REDD+ programme. In the 2020s, REDD+ compensation payments should start to be implemented along with the compensation actions in which money from emission countries should be paid to carbon stock countries. Therefore, accuracy, transparency and accessibility of the carbon quantification processes are essential to achieve REDD+ objectives and ultimately the conservation and enhancement of forest carbon stocks. Measurement, Recording and Verification (MRV) is the mechanism to make sure that the claim of countries that they have more carbon stock than emitted is correct. For ages, assessment of forest aboveground biomass (AGB)Above ground biomass (AGB) and aboveground carbon (AGC) or carbon stockCarbon stocks has relied on the classical forest inventoryForest inventories approach. Usually, DBHDiameter at breast height (DBH) and tree height are measured in the field to assess forest AGBAbove ground biomass (AGB) using an allometric equationAllometric equations. Although forest inventoryForest inventories data provide the needful information, it is time-consuming and less accessible, and datasets are often limited to a small area. Therefore, having a robust method using remote sensingRemote sensing technology to assess AGBAbove ground biomass (AGB) and AGC is essential in monitoring forest biomassForest biomass and carbon stockCarbon stocks. This technology is reasonably accurate, economical and operational along with the complement of field measurement. This chapter will review several latest remote sensingRemote sensing sensorSensors systems (e.g. VHRS, AirborneAirborne RGB, SARSynthetic aperture radar (SAR), AirborneAirborne LiDAR, terrestrial laser scanner and UAVUnmanned aerial vehicle (UAV) RGB and MSSMultispectral Scanner System (MSS) images) and analysis techniques and their applications in the assessment of AGBAbove ground biomass (AGB) and carbon stockCarbon stocks and sequestration.

This research assesses the spatial distribution of aboveground biomass (AGB) and aboveground carbon (AGC) in evergreen forests across six socio-economic regions of Vietnam. The classification outcomes of evergreen forests utilizing the Support Vector Machine (SVM) model achieved an overall accuracy (OA) of 86.59% and CI (F1_forest) of 0.845-0.955, suggesting that evergreen forests represent over 44% of the total forest area in Vietnam. The amalgamation of diverse data sources, encompassing optical indices, radar, and topographical information, considerably enhanced the precision of AGB estimations, with the Genetic Algorithm-Adaptive Neuro Fuzzy Inference System (GA-ANFIS) model reaching an R2 value exceeding 0.82. The findings indicate a cumulative AGB figure of 1,377,020.22 tons/ha, which is 2.2 times higher than the total AGC figure of 605,026.24 tons/ha. Regions characterized by lower AGB and AGC values are primarily concentrated in the Northern Midland and Mountain areas (NMR). Conversely, areas with elevated AGB and AGC values are predominantly located in the North Central and Central Coast (NCR), Central Highlands (CHR), and specific provinces within the Southeast region (SER). While the GA-ANFIS model exhibited commendable performance, a gap persists between forecasted and actual figures, with an overall discrepancy of 16,749.81 ha for AGB and 8156.6 tons/ha for AGC, likely attributable to challenges in field data acquisition, particularly in pristine and mature forest ecosystems. This study serves as an instrumental resource for extensive forest resource management and advocates for targeted restoration actions in zones exhibiting diminished AGB and AGC levels.

Abstract. Estimates of aboveground biomass (AGB) in forests have been made in the context of climate change mitigation. There were limited studies about Falcata aboveground biomass and carbon stock estimation using the traditional method; however, that is time-intensive and expensive. Hence, this study was executed to utilize remote sensing and assess the potential of the synergistic use of Sentinel-1 and Sentinel-2 images in estimating the AGB and carbon stock of Falcata. The methodology consists of boundary demarcation of Falcata plantations in Butuan City, development of aboveground biomass models, and estimation and mapping of aboveground biomass and carbon stock. Among the developed models, the model which is the combination of Sentinel-1 and Sentinel-2 has the highest coefficient of determination (R2) of 0.632 and lowest Root Mean Square Error (RMSE) of 1.94 ton/pixel and was found to perform best in predicting the AGB and carbon stock of 4-year-old Falcata and performed poorly in 1-year-old Falcata. Nevertheless, its overall R2 and RMSE have proven that the model is moderately good and acceptable in predicting AGB of all stand ages of Falcata and, indirectly, the carbon stock. This study demonstrates that combining satellites generates a robust and more accurate AGB and Carbon Stock model than the models derived from the individual satellites.

Biomass stocks and carbon storage in Barringtonia acutangula floodplain forests in North East India

The objective of this study is to precisely quantify the forest aboveground biomass (AGB) and carbon stock in a tropical deciduous forest with synergistic use of space-borne L-band ALOS PALSAR and X-band COSMO-Skymed SAR data, along with field inventory data. AGB serves as a decisive parameter for preparing global decision making policy targeting the impact of reducing emissions from deforestation and forest degradation and climate change. The study proposed optimal regression models for assessing above-ground bole biomass over tropical deciduous mixed forests of Munger (Bihar, India) with synergistic use of ALOS PALSAR and COSMO-Skymed sigma nought images. Coefficient of determination (r2) of 0.89 and RMSE of 15.12 Mg ha−1 were calculated for the best fit integrated model. On validation, the integrated model produced a model accuracy of 78%, r2 = 0.89, RMSE = 16.64 Mg ha−1 and Willmott’s index of agreement of 0.934. Resulting modeled AGB were converted to carbon and carbon dioxide equivalents using conversion factors. L-band showed higher accuracy in the estimation in comparison to X-band; but the estimation accuracy improved with the synergistic use of both X- and L-band SAR data. Hence, the study recommends the combined use of X- and L-band SAR with exceptional capabilities for improved assessment of tropical forest stand AGB and carbon with significant contribution towards operational forestry and policy making.

BackgroundTropical forests play a crucial role as source and sink in global carbon cycle. Development and other anthropogenic activities have led to degradation of forest land, and ultimately, it results in loss of biodiversity and increases concentration of CO2 in atmospheres. Therefore, there is urgent need to estimate regional and national level carbon stock for making forest-based policies and strategies for mitigation of CO2. Patchy and sporadic information is available on biomass and carbon stock of Indian forests. The paper presents a systematic review and comprehensive account of studies conducted in different forest types in India.ResultThere are six major forest types found in India consisting of 15 groups and other subgroups with peculiar characteristics. Methodologies used by researchers for biomass/carbon stock estimation are destructive, nondestructive, tree inventories data, species-specific biomass estimation, and remote sensing. Majority of estimates are based on nondestructive allometric equation approach. Studies showed positive correlation between tree species, diameter at breast height, and biomass/carbon stock. Small- and medium-sized growing trees, invasive species, mixed forest, Agroforestry, and Agrosilviculture also play an important role in atmospheric carbon assimilation. The results of diverse forest carbon stock studies are broadly categorized in North, Central, and Southern India. Present review will be helpful for developing conservation policies and decision to increase carbon stock and also REDD+ program for particular forest ecosystem.ConclusionThe systematic literature review was carried out to gather and summarize information from different studies conducted on forest ecosystems and quantification methods used for biomass estimation and carbon stock in different forests types and states of India. In general, great variability occurs in aboveground biomass and carbon stock on account of climatic and geographic differences. To obtain good and accurate estimations, following nondestructive approach, species-specific density-based equations are required from different habitats and also in relation to degradation status of forests. As such regional volume equations would increase error of estimations. The comprehensive account of data would be helpful to formulate strategies based on carbon sequestration in Indian forests for CO2 mitigation.

The present study aimed to estimate above-ground biomass and carbon stocks of different life forms in tropical dry and moist deciduous forests of Nallamalais, one of the centers of plant diversity of India, located in central part of the Eastern Ghats. The present study used a non-destructive method of biomass estimation. From the sampled inventory it is found that the dry deciduous vegetation with 114 species comprising a total of 1737 tree individuals with a mean basal area of 16.37±9.12 m2 ha-1, 61.52±41.66 Mg ha-1 (Mega gram=106 g) above-ground biomass and 26.83±15.69 Mg ha-1 carbon, the moist deciduous vegetation with 115 species, comprising 1431 tree individuals with a mean basal area of 29.78±4.83 m2 ha-1, contributing 110.37±26.12 Mg ha-1 above-ground biomass and 52.24±12.48 Mg ha-1 carbon. It is revealed that the moist deciduous forests are more efficient in terms of sequestering atmospheric carbon.

Mangrove forest is a unique ecosystem that plays important roles to climate change control, as carbon sink and CO2 absorbing from the atmosphere. This research was aimed to estimate the potential of above-ground biomass and carbon stocks of Ciletuh mangrove forest, West Java, Indonesia. Mangrove forest area and occupation of dominant species were mapped using Geographical Information System, meanwhile species composition and forest structure was sampled using 198 plots (20 m × 20 m each) systematically spread out at studied forest area. Vegetation analysis data were used to estimate the potential of above-ground biomass and carbon stocks. Above-ground biomass and carbon stock of mangrove species was estimated using allometric models that already available. The results showed that mangrove forest in Ciletuh covered an area amounted to 8 ha. There were 18 tree mangrove species dominated by species of non-Rhizophoraceae belonging to 14 genera of 11 families. Above-ground biomass of Ciletuh mangrove forest was estimated at 31.78 t ha−1, carbon stock at 14.93 t C ha−1, and CO2 absorption at 54.68 t ha−1.