Estimation Of Carbon Storage Research Articles

Estimating carbon storage of Eucalyptus urophylla vegetation in Mutis Timau Nature Reserve, East Nusa Tenggara, Indonesia using remote sensing analysis

Abstract. Sadono R, Wardhana W, Wirabuana PYAP. 2023. Estimating carbon storage of Eucalyptus urophylla vegetation in Mutis Timau Nature Reserve, East Nusa Tenggara, Indonesia using remote sensing analysis. Biodiversitas 24: 1946-1952. The presence of Eucalyptus urophylla S.T. Blake in Mutis Timau Nature Reserve (Cagar Alam) in Timor Island, East Nusa Tenggara Province, Indonesia plays an important role in long-term climate change mitigation. Therefore, this study aimed to investigate the carbon storage of E. urophylla in Mutis Timau Nature Reserve in varying tree densities using remote sensing analysis. Sentinel 2B satellite image acquired on 10 September 2022 was used to generate the Normalized Difference Vegetation Index (NDVI) using ArcGis 10.8. The NDVI values were then classified to generate the land cover type and density of E. urophylla. The NDVI values greater than 0.7 were assigned as the stands of E. urophylla and classified into low, moderate, and high density. The total carbon storage of each density area was calculated using the estimated area and secondary data of total carbon storage per ha for corresponding densities. The results of remote sensing analysis showed that E. urophylla vegetation was found to cover 73.82% or 9,084.56 ha of the total 12,306.43 ha estimated study area. The estimated area for moderate vegetation density of E. urophylla was 5,150.26 ha followed by low and high densities of 3,857.48 ha and 96.81 ha, respectively. The carbon storage of low, moderate, and high density was calculated to be 577,273.38, 996,655.61 and 27,467.90 tons C, respectively, totaling 1,601,396.89 tons C across the nature reserve. The findings of this study recommend that the coverage of E. urophylla vegetation needs to be expanded, specifically to become high-density to enhance carbon stock, for example through restoration.

Above ground biomass after 39 years logging in Kintap Research Forest, South Kalimantan

Land degradation causes the fragmentation of forest areas. The fragmentation can be in the form of a decrease in species diversity and carbon storage in an area including logged-over areas. This study aims to obtain stand abundance, basal area, and potential of above ground biomass that can be used in estimating natural recovery in logged-over areas. Stand measurements were carried out in 3 different areas with the distance of the plot from the road as a differentiating factor. The measurement parameters include tree species, diameter, and tree height with the smallest diameter being 10 cm. Making plots at each location covering an area of 1 ha with an observation sub-plot measuring 20 m x 20 m (400 m2). Estimation of carbon storage used the allometric model compiled by Brown, Ketterings, and Basuki. The results showed that the biomass potential reached 190 tones/hectares – 290 tones/hectares. This potential is mostly contributed by trees with a diameter of more than 20 cm. Trees with a diameter of 20 cm accounted for more than 50% of the total biomass. This condition indicates that silvicultural technique in the form of maintaining the remaining stands after felling are very necessary for accelerating the growth of small-diameter trees so that natural succession can occur more quickly.

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.

A Simple Method Using an Allometric Model to Quantify the Carbon Sequestration Capacity in Vineyards.

Winegrapes are an important component of agroecosystems. They are endowed with great potential to sequester and store carbon to slow down greenhouse gas emissions. Herein, the biomass of grapevines was determined, and the carbon storage and distribution features of vineyard ecosystems were correspondingly analyzed using an allometric model of winegrape organs. Then, the carbon sequestration of Cabernet Sauvignon vineyard in the Helan Mountain East Region was quantified. It was found that the total carbon storage of grapevines increased with vine age. The amounts of the total carbon storage in the 5-year-old, 10-year-old, 15-year-old, and 20-year-old vineyards were 50.22 t·ha-1, 56.73 t·ha-1, 59.10 t·ha-1, and 61.06 t·ha-1, respectively. The soil held the majority of the carbon storage, which was concentrated in the top and subsurface layers (0-40 cm) of the soil. Moreover, the biomass carbon storage was mainly distributed in the perennial organs (perennial branches and roots). In young vines, carbon sequestration increased each year; however, the increased rate in carbon sequestration decreased with winegrape growth. The results indicated that vineyards have a net carbon sequestration capacity, and within certain years, the age of grapevines was found to be positively correlated with the amount of carbon sequestration. Overall, the present study provided accurate estimations of the biomass carbon storage in grapevines using the allometric model, which may help vineyards become recognized as important carbon sinks. Additionally, this research can also be used as a basis for figuring out the ecological value of vineyards on a regional scale.

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.

Carbon storage estimation of mountain forests based on deep learning and multisource remote sensing data

Remote sensing monitoring of forest carbon storage against the background of climate change is a popular research topic, and multisource remote sensing is significant for improving its accuracy at a regional scale. Improving accuracy at a low cost is a current challenge. In this study, the spaceborne light detection and ranging (lidar) ICESat-2/advanced terrain laser altimeter system (ATLAS), along with an optimized random forest regression (RF) model and 54 sample plots, was used to obtain early estimates of carbon storage at the canopy scale in the footprints of ICESat-2/ATLAS. On this basis, combined with Landsat 8 operational land imager (OLI) data and a deep neural network (DNN) model, a regional-scale remote sensing estimation of forest carbon storage in Shangri-La, a mountainous area in Southwest China, was performed. The coefficient of determination (R2) and root mean square error (RMSE) were used to assess the estimation results. The results showed that (1) the apparent surface reflectance (ASR) and other parameters of the ATLAS spots had a strong correlation with the carbon storage of mountain forests, and the optimized RF model estimated the carbon storage well at the canopy scale. The modeling accuracy was R2 = 0.8890, the verification accuracy was R2 = 0.7750, and the contribution rate of the ASR was 22.76% in the model. (2) Using the carbon storage of 74,873 ATLAS footprints obtained by the RF model as the training sample for deep learning modeling, along with the Landsat 8 OLI vegetation index, a regional-scale forest carbon storage (DNN) estimation model was constructed. The model verification accuracy was R2 = 0.5433 and RMSE = 6.6402 Mg / ha. (3) The estimated population carbon storage based on the DNN model was 4.717 × 107 Mg, with an average value of 49.04 Mg / ha in the study area. The model estimates were valid. In conclusion, the results estimated by a small sample were used as a large sample training dataset for region-scale deep learning, which can be effective in reducing costs.

Carbon accumulation and storage across contrasting saltmarshes of Scotland

Saltmarshes are acknowledged to be “carbon hotspots” due to their capacity to trap and store large quantities of carbon (C) within their soils and potentially have the ability to regulate climate over different timescales. In-turn governments and international organizations are now recognizing the need to include these intertidal ecosystems in national and global C accounting. Yet, in many regions, estimates of organic carbon (OC) storage and the rate at which OC is buried in saltmarsh soils either do not exist or are not at the scale necessary for inclusion in national C budgets. Here we bring together tools from across the geosciences to investigate the quantity of OC held within the soil and above/belowground biomass, alongside estimates of the rate at which OC accumulates and the source of the OC within the soils of four contrasting Scottish saltmarshes. Using radiometric dating techniques it is estimated that OC accumulates at a rate of between 29.1 and 198.1 g C m−2 yr−1 across the different study sites. In contrast, the source of the OC varies little across the sites with 73%–99% of the OC within the saltmarsh soil originating from terrestrial/in situ sources; marine-derived OC plays a minor role in the development of the saltmarsh OC stocks. Using average values derived from the four sites it is possible to make first-order estimates of saltmarsh OC stocks and accumulation rates for all Scotland's 240 mapped saltmarshes (58.68 km2). It is estimated that across Scotland saltmarsh habitat stores 1.15 ± 0.21 Mt OC which is supplemented by an additional 4385 ± 481 tonnes of OC each year.

Sources and sequestration rate of organic carbon in sediments of the bare tidal flat ecosystems: A model approach

Humans have been contributing adversely to greenhouse gas emissions by generating a vast amount of CO2, primarily causing climate change. Nature-based climate solutions, consisting of both terrestrial and marine ecosystems, are tremendous potential for sequestering and storing significant amounts of carbon, which can help to slow the progression of climate change. In this study, we use a carbon balance model to simulate the carbon sequestration rate and carbon stored in bare tidal flat (BTF) areas along Korea's west and south coasts from 2018 to 2050. Furthermore, the percentage of potential carbon sources deposited at BTF sites was calculated using a two-terminal mixing model and δ13C data. The carbon deposited on the BTF areas is the result of lateral carbon transport from upslope terrestrial regions as well as marine sources. Based on the δ13C isotope, this study classified potential carbon sources in BTFs sediment into two categories: terrestrial and marine. The results indicate that the proportion of organic carbon contribution from terrestrial sources ranged from 7.63% to 49% in the BTF studied areas. We discuss the validity of projection which was investigated over three years, from 2018 to 2020. A preliminary conclusion is that future carbon storage at BTF sites will increase significantly. Carbon accumulation increases linearly over time in nearly all areas studied, with carbon sequestration rates ranging from 0.053 to 0.623 (MgC ha−1 yr−1). This study found that a significant amount of carbon is sequestered for a long time in the BTF regions based on model simulation results. In addition, it also contributes to projects that seek to promote and conserve these climate benefits by providing estimates of carbon storage in coastal BTFs that can be included in NDCs for the Paris Agreement.

Estimation of carbon storage in Shaanxi Yellow River Wetland Provincial Nature Reserve

Estimation of carbon storage in Shaanxi Yellow River Wetland Provincial Nature Reserve

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

Research on the Establishment of Carbon Inversion Model in Engebei Ecological Demonstration Area of the Kubuqi Desert Based on Remote Sensing Data

Studying the changes of vegetation coverage and carbon storage in terrestrial ecosystem will provide important information for ecosystem optimization. The Kubuqi Desert is a typical ecological fragile area, and its ecological construction is not only to build an ecological barrier to protect northern China, but also to be a case of similar regions in the world. Engebei ecological demonstration area, located in the northern margin of the central Kubuqi Desert, was selected as the study area. The objective is to establish a carbon inversion model through the LASSO regression model in this area which can promote its ecological development. Taking Landsat, Sentinel-2A remote sensing data and field sample data as data sources. The vegetation coverage was calculated through the remote sensing data. The carbon reserves in plots were calculated through the biomass equation. Then, the appropriate carbon storage estimation model was found out, which was established by the LASSO regression model and the Sentinel-2A remote sensing images. Finally, the carbon reserves over the years were calculated by this estimation model. Research shows that: first, the overall vegetation coverage of the study area was not high, which was around 27% – 37%; second, the proportion of lower (Ⅰ) level vegetation coverage in the study area decreased overall, and the proportion of higher (Ⅴ) level vegetation coverage generally stabilized; third, carbon reserves remain roughly above 71752.64t, up by 20,209.61t in 2021 compared to 2020, and up by 14,867.02t in 2021 compared to 1991.

PEMODELAN PENGINDERAAN JAUH UNTUK ESTIMASI SIMPANAN KARBON DI BLOK 1 PT ALAM BUKIT TIGAPULUH

Biomass calculations are useful for knowing the amount of carbon stored in a forest ecosystem and essential steps to mitigate global warming and climate change. Restoration activities at PT Alam Bukit Tigapuluh (PT ABT) have indirectly made efforts to absorb carbon dioxide (CO2). This study aimed to analyze and measure carbon storage as well a model and map carbon storage at PT ABT. The method used the survey method of species, diameter at breast height, and vegetation height with a plot size of 20mx20m. The research conducted to analyze carbon storage and build a regression model. The results showed that the average carbon storage of the 30 plots was 259.32 tons/ha and the estimated carbon storage is 5,496,233 tC. The best regression model’s modelling result are Y = -817 + 331.5 MSRVI with the correlation of determination being 76.40%. The distribution of carbon storage in PT ABT is divided into 5 classes, namely, non-vegetated class (<0 tons/ha) of 57 ha, low (0-112.55 tons/ha) of 291 ha, medium (112.55-225.11 tons/ha) covering an area of ​​2,059 ha, high (225.11-337.67 tons/ha) covering an area of ​​18,533 ha and very high (>337.67 tons/ha) covering an area of ​​1,256 ha.

Estimation of aboveground carbon storage based on remote sensing and inventory data: A case study from Türkiye

Forest ecosystems are one of the most important systems in capturing atmospheric carbon dioxide (CO2) and play an essential role in mitigating climate change. Forest ecosystems provide biodiversity, soil conservation, recreation areas, wildlife habitat, nutrient cycling, carbon storage and oxygen production. According to the Climate Change Framework Convention, determining and reporting the amount of carbon accumulation in forest ecosystem is of great importance. Different methods such as allometric equations, carbon expansion factor and remote sensing methods are used to determine aboveground carbon (AGC) storage. Especially with the developing technology, remote sensing techniques are used intensively to determine AGC storage. Normalized Difference Vegetation Index (NDVI) are commonly used, especially in determining the amount of AGC storage. The aim of this study is to estimate AGC storage according to different methods and to investigate whether there are statistical differences in AGC storage estimation of these approaches. The research was carried out in pure Calabrian pine (Pinus brutia Ten.) stands in the Burmahanyayla planning unit in Antalya province in the Mediterranean region of Türkiye. Within the scope of the study, the amount of AGC storage was calculated by using the inventory study conducted in 2022 and the Landsat 9 satellite data. A paired sample t-test was used to statistically examine the difference in the quantity of AGC storage based on inventory data and remote sensing data using SPSS 23.0. The results reveal that there was no statistically significant difference in the AGC storage values between the two approaches in all sample plots.

Microclimate, forage production and carbon storage in a eucalypt-based silvopastoral system

This study aimed to assess microclimate regulation, forage production, and carbon storage as Environmental Services (ES) of a Silvopastoral System (SPS). The evaluations were conducted from June 2019 to June 2021 in a ranch establishment in Uruguay. The SPS was implanted in 2012 with trees of Eucalyptus grandis in triple rows and alleys of Campo Natural (CN), the grazed native grassland. The area of assessment presented a density of 425 trees ha−1, and the treatments were: i) Full Sun (FS), representing the CN without trees, the standard condition at the zone of study; ii) SPS alley; iii) SPS under trees. The microclimatic variables evaluated were air temperature and relative humidity, wind speed, black globe temperature, flux density of photosynthetic active radiation, and solar global radiation, which were used to calculate indexes of animal thermal stress. Events of heat waves were identified and used for data analysis. The forage biomass production was seasonally evaluated, and the carbon storage estimation considered 60 cm of soil depth, aerial forage carbon, and the stem of trees. The results indicated higher thermal comfort in the SPS during heat waves, especially under trees. The inclusion of convection and radiation processes in thermal exchange allowed obtaining differences between positions. The forage biomass was reduced in the SPS with respect to the FS condition, presenting the highest difference during winter and the lowest in spring. Carbon storage was promoted in the SPS due to the biomass accumulation in trees. The results obtained suggest that the SPS is an integrated agricultural production scheme in which multifunctionality can be achieved, and specific ES can be promoted.

Modelling the Dynamics of Carbon Storages for Pinus densata Using Landsat Images in Shangri-La Considering Topographic Factors

Accurate estimation of forest carbon storage is essential for understanding the dynamics of forest resources and optimizing decisions for forest resource management. In order to explore the changes in the carbon storage of Pinus densata in Shangri-La and the influence of topography on carbon storage, two dynamic models were developed based on the National Forest Inventory (NFI) and Landsat TM/OLI images with a 5-year interval change and annual average change. The three modelling methods used were partial least squares (PLSR), random forest (RF) and gradient boosting regression tree (GBRT). Various spectral and texture features of the images were calculated and filtered before modelling. The terrain niche index (TNI), which is able to reflect the combined effect of elevation and slope, was added to the dynamic model, the optimal model was selected to estimate the carbon storage, and the topographic conditions in areas of change in carbon storage were analyzed. The results showed that: (1) The dynamic model based on 5-year interval change data performs better than the dynamic model with annual average change data, and the RF model has a higher accuracy compared to the PLSR and GBRT models. (2) The addition of TNI improved the accuracy, in which R2 is improved by up to 10.48% at most, RMSE is reduced by up to 7.32% at most, and MAE is reduced by up to 8.89% at most, and the RF model based on the 5-year interval change data has the highest accuracy after adding TNI, with an R2 of 0.87, an RMSE of 3.82 t-C·ha−1, and a MAE of 1.78 t-C·ha−1. (3) The direct estimation results of the dynamic model showed that the carbon storage of Pinus densata in Shangri-La decreased in 1987–1992 and 1997–2002, and increased in 1992–1997, 2002–2007, 2007–2012, and 2012–2017. (4) The trend of increasing or decreasing carbon storage in each period is not exactly the same on the TNI gradient, according to the dominant distribution, as topographic conditions with lower elevations or gentler slopes are favorable for the accumulation of carbon storage, while the decreasing area of carbon storage is more randomly distributed topographically. This study develops a dynamic estimation model of carbon storage considering topographic factors, which provides a solution for the accurate estimation of forest carbon storage in regions with a complex topography.

ESTIMASI SIMPANAN KARBON PADA BIOMASSA MANGROVE DI TAHURA NGURAH RAI

Mangroves contribute to mitigating climate change by storing carbon. As mangrove forests have a high rate of carbon sequestration and storage, carbon stock calculations are required to quantify the accumulation of carbon stored in a site to determine its involvement in decreasing greenhouse gas emissions. This study aims to estimate carbon storage at the Mangrove Information Center (MIC) by using an allometric calculation of the mangrove above-ground biomass data. Data collection was carried out on January 10-14, 2022 at the MIC, with an area of 322 ha. The method used is a 1000 meters line transect plot divided into 15 measuring plots in three mangrove ecological zones (front, middle, and back). Biomass data were taken in a non-destructive way based on measurements of the circumference girth at breast height (GBH) and the results were calculated using allometric. According to data analysis, the average total surface biomass of mangroves in MIC is 781.30 kg, with estimated carbon storage in MIC is 36,727 tons/ha. Overall, the total carbon stock in the stratum MIC is 11.826,253 tons. The highest estimated carbon storage is in Plot 9, which is 91,799 tons/ha, while the lowest estimated carbon storage is in Plot 2, which is 13,873 tons/ha. The high numbers of carbon storage from mangrove above-ground biomass are mostly found in the middle and front zones because of the very dense mangrove density.
 Keywords: mangrove; above-ground carbon estimation; climate change mitigation

Dendrometric data from the silvicultural scenarios developed by Office National des Forêts (ONF) in France: a tool for applied research and carbon storage estimates

Key messageWe provide a database of 52 silvicultural scenarios recommended in French public forests including relevant dendrometric variables and metrics for carbon accounting. The dataset is available at https://doi.org/10.57745/QARRFS. Associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/f76ed27f-325d-493b-8731-0995dcaa7805. Special attention was paid to offer carbon metrics required for the French Label Bas Carbone offset projects.

Precision of mangrove sediment blue carbon estimates and the role of coring and data analysis methods.

Carbon accumulation in coastal wetlands is normally assessed by extracting a sediment core and estimating its carbon content and bulk density. Because carbon content and bulk density are functionally related, the latter can be estimated gravimetrically from a section of the core or, alternatively, from the carbon content in the sample using the mixing model equation from soil science. Using sediment samples from La Paz Bay, Mexico, we analyzed the effect that the choice of corer and the method used to estimate bulk density could have on the final estimates of carbon storage in the sediments. We validated the results using a larger dataset of tropical mangroves, and then by Monte Carlo simulation. The choice of corer did not have sizable influence on the final estimates of carbon density. The main factor in selecting a corer is the operational difficulties that each corer may have in different types of sediments. Because of the multiplication of errors in a product of two variables subject to random sampling error, when using gravimetric estimates of bulk density, the dispersion of the data points in the estimation of total carbon density rises rapidly as the amount of carbon in the sediment increases. In contrast, the estimation of total carbon density using only the carbon fraction as a predictor is very precise, especially in sediments rich in organic matter. This method, however, depends critically on the accurate estimation of the two parameters of the mixing model: the bulk density of pure peat and the bulk density of pure mineral sediment. The estimation of carbon densities in peaty sediments can be very imprecise when using gravimetric bulk densities. Estimating carbon density in peaty sediments using only the estimate of organic fraction can be much more precise, provided the model parameters are estimated with accuracy. These results open the door for simplified and precise estimates of carbon dynamics in mangroves and coastal wetlands.

An improved estimate of soil carbon pool and carbon fluxes in the Qinghai-Tibetan grasslands using data assimilation with an ecosystem biogeochemical model

The accurate estimation of soil carbon (C) pool and fluxes is a prerequisite to better understand the terrestrial C feedback to climate change. However, recent studies showed considerable uncertainties in soil C estimates. To provide a reliable C estimate in the grasslands of the Qinghai-Tibet Plateau (QTP), we calibrated key parameters in a process-based ecosystem model (the CENTURY model) through data assimilation based on 570 soil samples and 21 sites of eddy covariance measurements. Two assimilating strategies (Opt1 – assimilating C pool observations; Opt2 –assimilating both C pool and C flux) were examined. Compared to default parameterization, our results showed both Opt1 and Opt2 improved the soil organic carbon density (SOCD) estimation, with R2 increasing from 0.59 to 0.75 and 0.73, respectively. Opt2 was superior to Opt1 in constraint of parameters dominating aboveground processes and yield a better estimation of net ecosystem production (NEP). Based on different parameterization, the spatial variability of SOCD and NEP across the QTP grassland were generated. Both Opt1 and Opt2 ameliorated the overestimation of SOCD by the default model, estimating a total soil C of 6.63 Pg and 6.48 Pg C for the topsoil (0–30 cm) of the QTP grasslands, respectively. Opt2 showed lower uncertainties in the NEP estimation and predicted a net sink of 14.33 Tg C annually. Compared with existing datasets, our study provided a more reliable estimation of carbon storage and fluxes in the QTP grassland with the calibrated ecosystem model. The results highlight that data assimilation with multiple observational data sets is promising to constrain process-based ecosystem models and increase the robustness of model predictions for terrestrial C cycle feedback to future climate change.

Measurement uncertainty in a national forest inventory: results from the northern region of the USA

Statistical confidence in estimates of timber volume, carbon storage, and other forest attributes depends, in part, on the uncertainty in field measurements. Surprisingly, measurement uncertainty is rarely reported, even though national forest inventories routinely repeat field measurements for quality assurance. We compared measurements made by field crews and quality assurance crews in the Forest Inventory and Analysis program of the U.S. Forest Service, using data from 2790 plots and 51 740 trees and saplings across the 24 states of the Northern Region. We characterized uncertainty in 12 national core tree-level variables; seven tree crown variables used in forest health monitoring; three variables describing seedlings; and 11 variables describing the site, such as elevation, slope, and distance from a road. Discrepancies in measurement were generally small but were higher for some variables requiring judgment, such as tree class, decay class, and cause of mortality. When scaled up to states, forest types, or the region, uncertainties in basal area, timber volume, and aboveground biomass were negligible. Understanding all sources of uncertainty is important to designing forest monitoring systems, managing the conduct of the inventory, and assessing the uncertainty of forest attributes required for making regional and national forest policy decisions.