Model Of Carbon Storage Research Articles

Carbon storage-driven multi-objective spatial planning: simulation and practice at the management unit level in Northeast China

In recent years, global attention to forest ecological environment, carbon, wood yield, and biodiversity has been increasing, which requires forest managers to make reasonable forest planning in time and space to meet the needs of multi-objectives and multi-resources. In this study, the resources of business units in Northeast China were analyzed, and the FPS-ATLAS model was established. A multi-objective decision supporting spatial forest planning scheme for 16629.53 ha of forest in Jingouling forest farm of Wangqing Forestry Bureau in Jilin Province was proposed. In this paper, the volume growth model of six forest types was established, and the carbon storage model was introduced to show the total carbon, biomass carbon, dissolved organic carbon, and total soil carbon of the ecosystem. The dynamic change in carbon during the whole planning period has achieved good results. After 20 time periods, the total soil carbon increased to 7,627,208 Mg (+6.63%), the total ecosystem carbon increased to 14,889,876 Mg (+27.51%), the biomass carbon increased to 5,362,779 Mg (+59.70%), and the dissolved organic carbon increased to 9,531,906 Mg (+14.59%). The purpose of this article is to achieve dynamic management of forest resources, meet multiple constraint settings, achieve multiple management objectives, harvest wood products, and ensure the ecological and social functions of forests so that forests ultimately achieve the ideal state of sustainability.

A new method of estimating shelterbelt carbon storage on the regional scale: Combined the single tree carbon storage with tree numbers

The carbon storage potential of planted shelterbelts was gradually recognized in recent years, but there was a lack of shelterbelt knowledge for carbon inventory analyses because of their narrow linear feature and extensive distribution. Considering its own characteristics of the shelterbelt, this paper proposed a new remote sensing model for estimating the carbon storage of farmland shelterbelt (CSBelt) based on the single tree carbon storage and tree numbers. In this model, the single tree carbon storage model was established by shelterbelt age, which could be identified from time series images. Tree numbers of each shelterbelt, as another important parameter in the CSBelt model, was detected by numbers of rows, columns, and preservation rate, which were monitored by high resolution remote sensing image and geographic information system method. Finally, overall accuracy (OA), mean absolute error (MAE), and mean error (ME) was used to validate the remotely identified data. The results showed that, the OA of the single tree carbon storage was 80.4% within the error range of 0.05 MgC/tree, the MAE was 0.036 MgC/tree, and ME was 0.020 MgC/tree; the OA of tree numbers was 71.7% within the error range of 100 trees/km, the MAE was 94 trees/km, and ME was 73 trees/km; the OA of shelterbelt carbon storage was 82.6% within the error range of 100 MgC/km, the MAE was 49.63 MgC/km, and ME was 33.78 MgC/km. Compared to other carbon storage models, this model requires fewer parameters and is not restricted by the stand conditions, which has better universality and realizability. This research is helpful for the accurate assessment of shelterbelt ecosystem carbon storage, which is of great significance for predicting climate change and formulating strategies. The results of this research will also provide data support for shelterbelt management on a regional scale.

Comparison of the CASA and InVEST models’ effects for estimating spatiotemporal differences in carbon storage of green spaces in megacities

Urban green space is a direct way to improve the carbon sink capacity of urban ecosystems. The carbon storage assessment of megacity green spaces is of great significance to the service function of urban ecosystems and the management of urban carbon zoning in the future. Based on multi-period remote sensing image data, this paper used the CASA model and the InVEST model to analyze the spatio-temporal variation and driving mechanism of carbon storage in Shenzhen green space and discussed the applicability of the two models to the estimation of carbon storage in urban green space. The research results showed that, from 2008 to 2022, in addition to the rapid expansion of construction land, the area of green space and other land types in Shenzhen showed a significant decrease trend. The estimation results of the carbon storage model showed that the carbon storage of green space shows a significant trend of reduction from 2008 to 2022, and the reduction amounts are 0.8 × 106 t (CASA model) and 0.64 × 106 t (InVEST model), respectively. The evaluation results of the model show that, in megacities, the spatial applicability of InVEST model is lower than that of CASA model, and the CASA model is more accurate in estimating the carbon storage of urban green space. The research results can provide a scientific basis for the assessment of the carbon sink capacity of megacity ecosystems with the goal of "dual carbon".

Estimating changes of forest carbon storage in China for 70 years (1949–2018)

In the realm of forest resource inventory and monitoring, stand-level biomass carbon models are especially crucial. In China, their importance is underscored as they form the bedrock for estimating national and international forest carbon storage. This study, based on the data from 52,700 permanent plots in the 9th National Forest Inventory (NFI) of China, was directed towards developing these models. After computing biomass and carbon storage per hectare using specific tree models for 34 species groups, we devised robust volume-derived biomass and carbon storage models for 20 forest types. The application of these models and historical data reveals notably a decline in China's forest carbon storage to 4.90Pg by the late 1970s due to aggressive forest exploitation. However, subsequent conservation and afforestation campaigns have affected a recovery, culminating in a storage of 8.69Pg by the 9th NFI. Over the past 40 years, China's forest carbon storage has surged by 3.79Pg, split between natural forests (2.25Pg) and planted forests (1.54Pg). In benchmarking against three pre-existing models, we discerned discernible biases, underscoring the need for larger modeling sample sizes. Overall, our models stand as a monumental stride in accurately gauging forest carbon storage fluctuations in China, both regionally and nationally.

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.

InVEST Soil Carbon Stock Modelling of Agricultural Landscapes as an Ecosystem Service Indicator

Soil carbon storage results from interactions between ecological processes and contributes to the global chemical regulation of the atmosphere, a vital ecosystem service. Within the ecosystem services approach, measuring soil carbon stock is used as an indicator of landscapes that function as terrestrial carbon sinks and sources. Soil carbon stock models of agricultural landscapes use national carbon stock data and are used to determine environmental benchmarks and develop land-use management strategies for improved landscape-scale carbon sequestration. The InVEST Carbon Storage model has been used as a tool to map carbon stock based on these data. However, the accuracy of the national carbon inventories of Hungary is unknown. In this study, the InVEST soil carbon stock models of two agricultural landscapes in Hungary were produced based on national soil carbon stock data and in-field collected soil sample carbon stock data. Carbon stock inventories were collated and used as InVEST carbon model inputs, and the models were mapped, compared, and evaluated to determine their usefulness in the planning of maximizing soil carbon storage in sustainable land-use management and policy development. Five InVEST soil carbon stock spatial models were produced for both agricultural landscapes, which showed great variation based on the data used to develop it. Aggregate carbon stock potentially stored in the landscape-scale study areas also varied between datasets used. Integrating soil sample data along with national carbon stock data shows prospective applicability in assessing contextual landscape-scale potential soil carbon stock storage.

Real‐time deep‐learning inversion of seismic full waveform data for CO2 saturation and uncertainty in geological carbon storage monitoring

ABSTRACTDeep‐learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep‐learning full waveform inversion method that after the neural network has been trained can image CO2 saturation and its uncertainty in real time. Our deep‐learning inversion method is based on the U‐Net architecture with the neural network trained on pairs of synthetic seismic data and CO2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO2 saturation models and once fully trained directly estimates CO2 saturation as a function of subsurface location. We further quantify uncertainties of CO2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof‐of‐concept study, the CO2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep‐learning inversion experiments using noise‐free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep‐learning inversion can estimate 2D distributions of CO2 fairly well even in the presence of Gaussian random noise and (2) both CO2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep‐learning inversion method can serve as a robust real‐time monitoring tool for geological carbon storage and/or other time‐varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

Integrating social media data and machine learning to analyse scenarios of landscape appreciation

Cultural ecosystem services can be challenging to simulate, leading to their under-representation in future scenario modelling to support decision-making. Here we use the density of landscape appreciation photographs uploaded to social media to parameterise an empirical model of landscape appreciation. We developed the model using over 150,000 photographs uploaded to the website Flickr in Aotearoa New Zealand. The current distribution of landscape appreciation photographs was influenced by a combination of biophysical and socio-ecological factors, including the land cover, altitude and distance from the coastline, and human population densities and accessibility. We used the landscape appreciation model to conduct a sensitivity analysis of the impacts of native forest restoration on agricultural land. The sensitivity analysis identified priority areas where restoration would have a larger positive impact. By comparing with a model of carbon storage gained through native forestation, we highlighted substantial spatial mismatches between these conflicting ecosystem service objectives. Empirical models of landscape appreciation derived from social media data can provide flexible, sensitive simulation tools for assessing how future changes in landscape management may impact indicators of cultural ecosystem service value. Similar models could be developed in any region with sufficient social media data for parameterisation, and could be altered to focus on management actions at smaller spatial scales.

Assessment on Changes of Ecosystem Carbon Storage in Reservoir Area due to Hydroproject.

Hydropower offers significant value for global carbon peak and carbon neutrality. However, the construction of hydropower stations leads to significant changes in land use and cover structure in reservoir areas, which affect ecosystem services including carbon balance. Furthermore, the development and operation of hydropower project require vast investment. However, the reservoir ecosystem's carbon storage and carbon emission reduction caused by hydropower could offer economic benefits when the official carbon market trading in China was launched in 2021. Therefore, it is necessary to assess comprehensively the changes in carbon storage and its value to the ecosystem in reservoir areas. The evaluation is of great importance for carbon loss reduction, land management, and hydropower development. This study provides a comprehensive and effective framework for evaluating changes in carbon storage and has its value to the reservoir ecosystem. It combines land utilization classification data obtained from remote sensing image interpretation and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) carbon storage model. Based on the case study of the Xiluodu reservoir area, they were evaluated from two aspects: physical quantity and value quantity. The results show that the carbon storage in the Xiluodu reservoir area increased by 8,504.42 Mg from 2000 to 2018. The spatial distribution of the carbon storage shows a trend of high in the north and west, but low in the south and east. The construction of hydropower stations and the rise of reservoir water level covered a large amount of land, which led to the loss of carbon storage in reservoir areas. By implementing soil and water conservation and vegetation protection policies, parts of the cultivated land and grassland were converted into forestland, which was the main source for increasing the ecosystem's carbon storage. Moreover, carbon emission reduction was achieved by hydropower. In terms of the monetary value, the carbon storage value of the reservoir ecosystem increased to 19 million RMB during the construction period (2005–2015). The carbon storage value of the reservoir ecosystem increased to 611 million RMB during the operation period (2015–2018). The latter was greater than the maintenance cost of the hydropower station and exceeded the amortized cost of hydropower development, indicating the feasibility and economic benefits of hydropower development. These findings provide guidance for future hydropower development decisions in Jinsha River Basin and also others.

Integrated assessment of land-use/land-cover dynamics on carbon storage services in the Loess Plateau of China from 1995 to 2050

Understanding the impacts of large-scale anthropogenic drivers on spatiotemporal processes in carbon storage through land-use patterns is a growing issue for the assessment of terrestrial ecosystems. However, an integrated assessment of changes in carbon storage, entailing complex land-use and land-cover (LULC) dynamics, remains a challenge. This study assessed the consequences of LULC changes on regional carbon storage in the Loess Plateau from 1995 to 2050 by integrating spatially explicit LULC and carbon models using multi-source data and typical scenarios. A Markov-CA model was used to model past LULC dynamics, which was followed by future projections under different LULC scenarios. Each scenario represented different socio-economic trajectories, including historical trend, ecological priority, and urban priority. The integrated valuation of the ecosystem service and tradeoffs (InVEST) carbon storage model and statistical methods were then used to assess spatiotemporal changes in the regional carbon storage associated with LULC dynamics. The results indicated that the ecological priority scenario could increase carbon storage by 11.36 Pg between 2015 and 2050. This is due to an increase in the carbon storage capacity in grasslands, shrub forest, and evergreen coniferous forest, indicating that ecological restoration could increase carbon sequestration. This mitigates the carbon loss caused by the rapid expansion of built-up land and fluctuations in cropland, which often takes place in the urban priority and historical trend scenarios. In favor of targeting the northwestern and central regions with socioeconomic benefits, grasslands and forests should be further promoted in the Loess Plateau. The findings will help implement long-term carbon sequestration strategies for ecosystem conservation.

Forest Contribution to Climate Change Mitigation: Management Oriented to Carbon Capture and Storage

Today, climate change is assumed by many researchers and scholars as a certainty and is presented as the biggest challenge humanity has ever faced. It is commonly accepted that anthropogenic greenhouse gas emissions are the main cause that is accelerating the process. Therefore, it is urgent to find solutions to mitigate climate change, mainly because the intense effects have already been felt, in many cases in the form of the occurrence of extremely violent weather events. Forests are undoubtedly one of the most effective and easiest ways to provide the function of carbon sinks. However, it is essential and convenient to analyze the permanence time of this carbon in forests, because this permanence time depends directly on the forest management model used. This article aims to analyze forest management models from the perspective of carbon residence time in temperate forests, dividing the models into three types, namely carbon conservation models, carbon storage models, and carbon substitution models, according to their ability to contribute to functioning as carbon sinks, thereby contributing to the mitigation of climate change.

川西米亚罗自然保护区森林地上碳储量遥感估算

PDF HTML阅读 XML下载 导出引用 引用提醒 川西米亚罗自然保护区森林地上碳储量遥感估算 DOI: 10.5846/stxb201807281610 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 四川省科技厅应用基础项目（2017JY0155）；四川省中医药管理局重大专项（2018PC005） Estimation of forest aboveground carbon storage in Sichuan Miyaluo Nature Reserve based on remote sensing Author: Affiliation: Fund Project: 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:研究米亚罗自然保护区的森林地上碳储量，有利于掌握高山峡谷区森林植被碳储量的分布特点，对区域森林资源的有效科学管理和维护森林生态环境具有重要意义。通过对保护区内的7个主要树种分别构建模型来初步估测森林地上碳储量，利用实地调查的样地数据与同期Landsat 8遥感卫星影像，得到波段信息、植被指数、主成分分析、纹理特征、地形因子、植被生长六大类共80个因子，用以构建乔木层不同树种地上碳储量的多元线性逐步回归模型，而灌木层的碳储量则由生物量密度法估算得出。研究结果表明：①模型预测值与实测值相关系数在0.675-0.775之间，精度较高，预测结果可靠；②米亚罗自然保护区森林地上碳储量为5.861 Tg，其中乔木层5.632 Tg，灌木层0.229 Tg。云冷杉林是研究区分布最广的林分类型，其碳储量为5.098 Tg，占森林地上碳储量总量的86.98%，在维持区域碳平衡方面发挥着重要作用。③研究区森林平均碳密度为53.138 t/hm2，其中冷杉的碳密度最高，达到74.467 t/hm2。④区域内森林地上碳储量以杂谷脑河流域及支流为中心，以一定缓冲距离呈树枝状发散分布，海拔3000-4000 m区域的森林植被碳储量最大，且阴坡的碳储量明显高于阳坡。 Abstract:The Miyaluo Nature Reserve is an important carbon sequestration region in Western Sichuan. Study of aboveground forest carbon storage in Miyaluo Nature Reserve is necessary for elucidating the distribution characteristics of forest vegetation carbon storage in alpine gorge areas. Furthermore, it is of great significance to the scientific management of forest resources and the maintenance of forest ecological environments in the nature reserves. In this study, carbon storage models were established for seven main tree species to estimate the carbon storage on forest land. Firstly, 897 samples were obtained from a field survey, and sample biomass and carbon storage were calculated based on the volume-derived biomass method. Secondly, Landsat 8 satellite remote sensing data were used to obtain image characteristics of forest vegetation. Thirdly, combined with forest survey data, 80 factors were determined that were classified into six categories (band information, vegetation index, principal component analysis, texture features, topographic factors, and vegetation growth). Moreover, the correlation between these factors and the biomass of different tree species was analyzed, according to the order of Pearson's correlation coefficient, the significant correlation factors were then introduced into the multiple linear stepwise regression equation as independent variables, and the estimation models of aboveground carbon storage of different tree species in the tree layer were established. Finally, the aboveground carbon storage of the arbor layer was calculated by the estimation model, and the carbon storage in the shrub layer was calculated using the average biomass density method. The results showed that ① the accuracy of models for carbon storage estimation was within the margin of error, and the correlation coefficient between the measured value and the predicted value of the model was 0.675-0.775. ② The total carbon storage in the forest in the Miyaluo Nature Reserve was 5.861 Tg, of which the aboveground carbon storage in the arbor layer was 5.632 Tg, and the carbon storage in the shrub layer was 0.229 Tg. The order of the aboveground carbon storage of the prominent species was Abies faberi > Picea asperata > Tsuga chinensis > Larix gmelimi > Quercus acutissima > Betula chinensis > Pinus tabulaeformis > Pinus armandi; The total carbon storage of fir trees was 5.098 Tg, accounting for 86.98% of the aboveground carbon storage in the study area. The spruce-fir forest was the most widely distributed forest type in the Miyaluo Nature Reserve, and it played an important role in maintaining the carbon balance. ③ The average carbon density in the forest in Miyaluo Nature Reserve was 53.138 t/hm2, and the carbon density of Chinese fir species was higher than the average. The carbon density of fir was much higher than that of other tree species and was 74.467 t/hm2. ④ The spatial distribution characteristics of aboveground forest carbon storage in the study area were obtained by overlapping the Digital Elevation Model (DEM) with carbon storage. Taking the Zagunao River and its tributaries as the center, the carbon storage spatial distribution data were extended to the surrounding area by a given buffer distance in the whole region, similar to arborization. The largest area of forest vegetation carbon storage was distributed in regions with elevation of 3000-4000 m. The aboveground carbon storage on shady slopes was higher than that on sunny slopes. 参考文献 相似文献 引证文献

Forest Management and Climate Change Mitigation: A Review on Carbon Cycle Flow Models for the Sustainability of Resources

With climate change being a certainty, which today is probably the biggest challenge humanity is facing, and also accepting that greenhouse gas emissions are the main cause accelerating climate change, there is an urgent need to find solutions that lead to the mitigation of the already intense, and in some cases, even violent, effects. Forests can most easily work as carbon sinks. However, it is convenient to analyze the residence time of this carbon in forests, as this residence time will depend on the type of forest management used. This paper aims to analyze forest management models from a perspective of carbon residence time in forests, dividing the models into three types: carbon conservation, carbon storage, and carbon substitution. Carbon conservation models are those models in which the amounts of carbon stored only replace the carbon released, mainly by the industrial use of raw materials. Carbon storage models are models that foster the growth of forest areas to ensure that the amount of carbon stored grows, and where the ratio clearly leans towards sequestration and storage. Carbon substitution models are models that move towards the substitution of fossil carbon by renewable carbon, thus contributing to the creation of a neutral flow.

Nondestructive and accurate measurement of volume and stem carbon storage for urban greening tree species based on terrestrial laser scanning point cloud

Nondestructive and accurate measurement of stem volume and carbon storage for urban green tree species play an important role in forest carbon stock. To explore terrestrial laser scanning (TLS) potential, to determine the effect of different section lengths on measurement precision, and to evaluate the accuracy of the existing models, 30 point cloud samples of Sapindus mukorossi were collected by TLS, and the stem drill cores were also sampled according to the diameter grade by CO250 to obtain stem volume and carbon storage data using different section lengths. Afterward these were compared with data derived from existing models. Results showed that. (1) The diameter at breast height (DBH) and tree height could be measured with high accuracy based on TLS with a mean relative error for DBH of 1.97% and for height of 4.85%. (2) Based on TLS, when the section length was less than 0.50 m, stem volume could be measured accurately. (3) The existing models (including one-way volume model and standard volume model and stem carbon storage model) are not suitable for the stem volume and carbon storage of urban greening tree species, as the result of one-way ANOVA are significant (P < 0.05). Therefore, TLS is reliable in the field of nondestructive and accurate measurement of stem volume and carbon storage for urban greening tree species.

Optimal harvest cycle on Nothofagus forests including carbon storage in Southern America: An application to Chilean subsidies in temperate forests

Different countries may have passed through their forest transitions from net native forest loss to net exotic plantation expansion. More investment in global and national forest monitoring is needed to provide better support to increase sustainable forest management and reduce forest loss, particularly in native forests. These slow-growing forests could get involved in the voluntary carbon markets. In this sense, some international and national initiatives based on subsidies could play a keystone role in the native forest conservation, mainly in small private lands.This approach aims to identify the optimal harvest cycle of Nothofagus forest type based on integral harvesting management (timber resources, natural regeneration and carbon storage) and three scenarios (lack of subsidies and two different national subsidies). The most suitable carbon storage models were Schumacher-Hall, Naslund and one specific model. The coefficient of determination was higher than 0.95 for these three models. If only timber harvesting was considered, the optimal harvest cycles was established between 59 and 62 years according to the presence or lack the national subsidies. However, when forest management and carbon storage were considered, the optimal harvest cycle increases three years. Therefore, the effect of this new alternative can be also observed in net present value and internal rate of return. This integral forest management could be an attractive approach for small private owners and for the global warning mitigation according to the ratified international agreements.

Modeling carbon storage across a heterogeneous mixed temperate forest: the influence of forest type specificity on regional-scale carbon storage estimates

Accurately assessing forest carbon storage on a landscape scale is critical to understanding global carbon cycles and the effects of land cover changes on ecological processes. Calculations of regional-scale forest carbon storage that rely on maps of land cover typically reflect only coarse forest classes. How differences in carbon stored by different tree species may affect such assessments is largely unexplored. We examined a range of forest carbon storage models to understand the effects of forest type specificity on carbon storage estimates in the northeastern United States. Models estimated forest carbon in total aboveground and coarse root biomass based on three levels of forest classification specificity: (1) relative basal area by species, (2) species associations, and (3) broad forest types per IPCC (in: IPCC guidelines for national greenhouse gas inventories, IPCC, Japan, 2006) guidelines. The specificity of forest type classifications influenced results with generally lower carbon storage estimates resulting from higher-specificity forest classifications. The two most specific models, with mean carbon storage estimates of 103–107 Mg/ha, were most accurate compared to field validation points. These estimates are greater than 2013 field-based U.S. Forest Service estimates (84–90 Mg/ha). There are many sources of uncertainty in landscape-scale carbon storage assessments. Here we show that improving detail in one of these sources, forest stand composition, increases the accuracy of these assessments, and better reflects carbon storage patterns across heterogeneous landscapes. While more work is needed, particularly to improve stand age maps, this information can inform the interpretation of current carbon storage estimates and improve future estimates in heterogeneous forests.

Metamodeling-based approach for risk assessment and cost estimation: Application to geological carbon sequestration planning

Carbon capture and storage (CCS) is being evaluated globally as a geoengineering measure for significantly reducing greenhouse emission. However, long-term liability associated with potential leakage from these geologic repositories is perceived as a main barrier of entry to site operators. Risk quantification and impact assessment help CCS operators to screen candidate sites for suitability of CO2 storage. Leakage risks are highly site dependent, and a quantitative understanding and categorization of these risks can only be made possible through broad participation and deliberation of stakeholders, with the use of site-specific, process-based models as the decision basis. Online decision making, however, requires that scenarios be run in real time. In this work, a Python based, Leakage Assessment and Cost Estimation (PyLACE) web application was developed for quantifying financial risks associated with potential leakage from geologic carbon sequestration sites. PyLACE aims to assist a collaborative, analytic-deliberative decision making processes by automating metamodel creation, knowledge sharing, and online collaboration. In PyLACE, metamodeling, which is a process of developing faster-to-run surrogates of process-level models, is enabled using a special stochastic response surface method and the Gaussian process regression. Both methods allow consideration of model parameter uncertainties and the use of that information to generate confidence intervals on model outputs. Training of the metamodels is delegated to a high performance computing cluster and is orchestrated by a set of asynchronous job scheduling tools for job submission and result retrieval. As a case study, workflow and main features of PyLACE are demonstrated using a multilayer, carbon storage model.

Satellite-based detection of bamboo expansion over the past 30 years in Mount Tianmushan, China

ABSTRACTThe present work aims to detect bamboo expansion and its impact on carbon storage in a thick forest in the most recent 30 years. The research area is the national nature reserve of Tianmushan, Zhejiang Province, China, and the present paper monitored bamboo expansion from 1984 to 2015. Multi-spectral band and vegetation indices from Landsat images in summer and winter are used combined to improve the accuracy of detection using a support vector machine (SVM) classifier. Expansion of bamboo over this period is evident. Total expansion is 161%, the fastest annual rate being 11.6%. However, over recent decades the growth of bamboo has been inhibited by human activity and the total area has decreased by 21%. Evergreen broadleaf forest is the most vulnerable to invasion by bamboo at a ratio of about 65%, and this expanding trend has been brought under effective control. Carbon storage was estimated using sample plot surveys and modelling based on key ecological forests. According to our estimation using carbon storage models, the total carbon storage of Tianmushan has declined by circa 4.7% due to bamboo expansion in the past three decades.

Plant functional diversity and carbon storage – an empirical test in semi‐arid forest ecosystems

Summary Carbon storage in vegetation and soil underpins climate regulation through carbon sequestration. Because plant species differ in their ability to capture, store and release carbon, the collective functional characteristics of plant communities (functional diversity) should be a major driver of carbon accumulation in terrestrial ecosystems. Three major components of plant functional diversity could be put forward as drivers of carbon storage in ecosystems: the most abundant functional trait values, the variety of functional trait values and the abundance of particular species that could have additional effects not incorporated in the first two components. We tested for associations between these components and carbon storage across 16 sites in the Chaco forest of Argentina under the same climate and on highly similar parental material. The sites differed in their plant functional diversity caused by different long‐term land‐use regimes. We measured six plant functional traits in 27 species and weighted them by the species abundance at each site to calculate the community‐weighted mean (CWM) and the functional divergence (FDvar) of each single trait and of multiple traits (FDiv). We also measured plant and soil carbon storage. Using a stepwise multiple regression analysis, we assessed which of the functional diversity components best explained carbon storage. Both CWM and FDvar of plant height and wood‐specific gravity, but no leaf traits, were retained as predictors of carbon storage in multiple models. Relationships of FDvar of stem traits and FDiv with carbon storage were all negative. The abundance of five species improved the predictive power of some of the carbon storage models. Synthesis. All three major components of plant functional diversity contributed to explain carbon storage. What matters the most to carbon storage in these ecosystems is the relative abundance of plants with tall, and to a lesser extent dense, stems with a narrow range of variation around these values. No consistent link was found between carbon storage and the leaf traits usually associated with plant resource use strategy. The negative association of trait divergence with carbon storage provided no evidence in support to niche complementarity promoting carbon storage in these forest ecosystems.

ECONOMIC VALUE OF THE CARBON SINK SERVICES OF TROPICAL SECONDARY FORESTS AND ITS MANAGEMENT IMPLICATIONS

This paper explores the economic feasibility of secondary forest regeneration and conservation as an alternative in the campaign addressing the problem of global warming. Detailed measurements of tropical secondary forests over time, in different ecological zones of Costa Rica, are used to evaluate carbon storage models. The paper addresses key issues in the international discussion about cross- and within-country compensation for carbon storage services and illustrates a method to compute/predict their economic value over time under a variety of scenarios. The procedure is applicable to other developing countries where secondary forest growth is increasingly important.