Assessment Of Carbon Storage Research Articles

Land use/cover changes lead to a decrease in carbon storage in arid regions-a case study of Northwest China.

Land use/cover changes lead to a decrease in carbon storage in arid regions-a case study of Northwest China.

Multi-scenario simulation and carbon storage assessment of land use in a multi-mountainous city

Multi-scenario simulation and carbon storage assessment of land use in a multi-mountainous city

Impacts of Ecological Restoration Projects on Ecosystem Carbon Storage of Tongluo Mountain Mining Area, Chongqing, in Southwest China

Surface mining activities cause severe disruption to ecosystems, resulting in the substantial destruction of surface vegetation, the loss of soil organic carbon stocks, and a decrease in the ecosystem’s ability to sequester carbon. The ecological restoration of mining areas has been found to significantly enhance the carbon storage capacity of ecosystems. This study evaluated ecological restoration strategies in Chongqing’s Tongluo Mountain mining area by integrating GF-6 satellite multispectral data (2 m panchromatic/8 m multispectral resolution) with ground surveys across 45 quadrats to develop a quadratic regression model based on vegetation indices and the field-measured biomass. The methodology quantified carbon storage variations among engineered restoration (ER), natural recovery (NR), and unmanaged sites (CWR) while identifying optimal vegetation configurations for karst ecosystems. The methodology combined the high-spatial-resolution satellite imagery for large-scale vegetation mapping with field-measured biomass calibration to enhance the quantitative accuracy, enabling an efficient carbon storage assessment across heterogeneous landscapes. This hybrid approach overcame the limitations of traditional plot-based methods by providing spatially explicit, cost-effective monitoring solutions for mining ecosystems. The results demonstrate that engineered restoration significantly enhances carbon sequestration, with the aboveground vegetation biomass reaching 5.07 ± 1.05 tC/ha, a value 21% higher than in natural recovery areas (4.18 ± 0.23 tC/ha) and 189% greater than at unmanaged sites (1.75 ± 1.03 tC/ha). In areas subjected to engineered restoration, both the vegetation and soil carbon storage showed an upward trend, with soil carbon sequestration being the primary form, contributing to 81% of the total carbon storage, and with engineered restoration areas exceeding natural recovery and unmanaged zones by 17.6% and 106%, respectively, in terms of their soil carbon density (40.41 ± 9.99 tC/ha). Significant variations in the carbon sequestration capacity were observed across vegetation types. Bamboo forests exhibited the highest carbon density (25.8 tC/ha), followed by tree forests (2.54 ± 0.53 tC/ha), while grasslands showed the lowest values (0.88 ± 0.52 tC/ha). For future restoration initiatives, it is advisable to select suitable vegetation types based on the local dominant species for a comprehensive approach.

Multi-Scenario Land Use and Carbon Storage Assessment in the Yellow River Delta Under Climate Change and Resource Development

Land use and land cover change (LULCC) is a key driver of carbon storage changes, especially in complex coastal ecosystems such as the Yellow River Delta (YRD), which is jointly influenced by climate change and resource development. The compounded effects of sea-level rise (SLR) and land subsidence (LS) are particularly prominent. This study is the first to integrate the dual impacts of SLR and LS into a unified framework, using three climate scenarios (SSP1–26, SSP2–45, SSP5–85) provided in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), along with LS monitoring data, to comprehensively assess future inundation risks. Building on this, and taking into account land use and ecological protection policies in the YRD, three strategic scenarios—Ecological Protection Scenario (EPS), Natural Development Scenario (NDS), and Economic Growth Scenario (EGS)—are established. The PLUS and InVEST models are used to jointly simulate LULCC and carbon storage changes across these scenarios. Unlike previous studies focusing on single driving factors, this research innovatively develops a dynamic simulation system for LULCC and carbon storage driven by the SLR-LS compound effects, providing scientific guidance for land space development and coastal zone planning in vulnerable coastal areas, while enhancing carbon sink potential. The results of the study show the following: (1) Over the past 30 years, the land use pattern of the YRD has generally extended toward the sea, with land use transitions mainly from grasslands (the largest reduction: 1096.20 km2), wetlands, reservoirs and ponds, and paddy fields to drylands, culture areas, construction lands, salt pans, and tidal flats. (2) Carbon storage in the YRD exhibits significant spatial heterogeneity. Low-carbon storage areas are primarily concentrated in the coastal regions, while high-carbon storage areas are mainly found in grasslands, paddy fields, and woodlands. LULCC, especially the conversion of high carbon storage ecosystems to low carbon storage uses, has resulted in an overall net regional carbon loss of 2.22 × 106 t since 1990. (3) The risk of seawater inundation in the YRD is closely related to LS, particularly under low sea-level scenarios, with LS playing a dominant role in exacerbating this risk. Under the EGS, the region is projected to face severe seawater inundation and carbon storage losses by 2030 and 2060.

Spatially explicit assessment of carbon storage and sequestration in forest ecosystems

Spatially explicit assessment of carbon storage and sequestration in forest ecosystems

Mangrove biomass productivity and sediment carbon storage assessment at selected sites in Mauritius: The effect of tidal inundation, forest age and mineral availability

Abstract CO2 is the most abundant anthropogenic greenhouse gas released in the environment and is considered one of the main drivers of global warming and ensuing climate change. Biological carbon sequestration mitigates CO2 through ecosystems like forests, wetlands, and agricultural lands. Mangrove forests, being highly carbon-dense ecosystems, sequester significant carbon in their biomass and soils. This study aims at evaluating the carbon storage potential of R.mucronatanatural and planted forests in Mauritius and the effect of tidal inundation and mineral availability.
Plant height and diameter at breast height were measured in situ with the GLOBE Observer application and a measuring tape, respectively. Rate of canopy coverage over the past twenty years was assessed using historical Landsat images available on Google Earth Pro. Allometric equations were used to estimate the above-ground biomass (AGB) and below-ground biomass (BGB) of R.mucronata. Total organic carbon (TOC) and all essential nutrients for plant growth were analysed using standard methods. 
Our findings show that in both natural and planted forests, the more inundated zones were first established. However, tree and sapling density and AGB were negatively correlated with sodium (r = -0.830; -0.880, respectively). Positive correlations between AGB and NO3-N, NH4-N, phosphate, and manganese suggest that these minerals were limiting factors. Nevertheless, the combination of forest age and salinity was found to play key roles on the AGB and TOC, which is linked to materials originating from the mangroves. It is noteworthy that the Ferney forest with a relatively lower salinity (5-15 ppt) and the only forest that had already reached a steady state in 2010, had a relatively much higher AGB (326.2±26.3 t ha-1) than the global average for R.mucronata (94.8 t ha-1), let alone Rhizophora spp. (281 t ha-1). The TOC registered at Ferney (47.34%) was also higher than the global values reported (2.00±2.20% to 40.00±2.20%)

Dynamic Simulation of Land Use Change and Assessment of Carbon Storage Based on the PLUS Model: A Case Study of the Most Livable City, Weihai, China

Analyzing and monitoring land use/cover (LULC) changes is critical for improving regional ecosystem service functions and developing strategies for long-term socio-economic development. Exploring future changes in land use and carbon storage under different scenarios is important for optimizing regional ecosystem service functions and formulating sustainable socio-economic development policies. In the present work, we evaluate LULC changes and carbon storage changes in the Rapid Urbanization Area (RUA) of Weihai City from 2000 to 2020 using satellite images. Using five Landsat images, the spatio-temporal dynamics of the LULC changes were measured, using a supervised classification algorithm of the neural net and the intensity analysis techniques in GIS. The Landsat images from 2000, 2005, 2010, 2015, and 2020 were categorized into five main land use categories in the researched region: urban areas, woodlands, cultivated areas, bare soil, and water bodies. Our results reveal that urban areas, woodlands, and bare soil increased by about 129.63 km2 (13.29%), 53.07 km2 (5.44%), and 40.99 km2 (4.2%) from 2000 to 2020, respectively. On the contrary, the cultivated areas decreased by 218.35 km2 (22.36%) and the water bodies decreased by 5.44 km2 (0.56%). To summarize, the conversion of cultivated areas into urban areas has been the most significant transformation in the RUA during the period 2000–2020. Regarding carbon storage, in the study area, it decreased by 14.92 × 104 t from 2000 to 2020. Moreover, according to the prediction of the LULC changes for 2030 by the patch-generating land use simulation (PLUS) model, the cultivated areas and carbon storage will continue to decline. The slow increase in woodland brings good ecological benefits. But the sharp reduction in the per capita cultivated areas will bring environmental and socio-economic problems to the RUA. Therefore, it is time to strengthen the implementation of cultivated area protection policy. Monitoring and managing LULC changes are critical for establishing relationships between policy choices, regulatory measures, and future LULC operations, especially because many potential concerns remain in the RUA territories.

Research on Multi-scenario Prediction and Assessment Methods for Regional Carbon Storage Based on Large Language Models

With the background of global climate change and carbon neutrality, the urgent requirement for more accurate estimates of carbon storage at the regional scale has been increasingly required for environmental issues. Due to subjective influences, traditional carbon storage prediction methods have poor prediction performance in a multi-scenario setting, with a lack of effective technical support. Considering the above-mentioned limitation, the novelty of the present study lies in its efforts to improve the accuracy and objectivity of carbon storage predictions on regional and urban scales using large language models. To this end, this work developed a carbon storage prediction simulation system with multi-scenario settings based on LLMs. It can be matched and retrieved by inputting regional information from national land cover and carbon stock databases in the system. It processes user-scenario settings, matches them with a simulation database, then selects proper driving factors and simulation parameters to generate multi-group scenario prediction results. These are estimated for carbon storage by means of InVEST software in comparison and optimization with historical data. According to the experimental results, the multi-scenario simulation based on LLM has a significant improvement in the accuracy of carbon storage prediction and a substantial reduction of subjective influences when setting the scenarios. This approach simulates more effectively the land use changes in future carbon storage, using better accuracy and stability than the traditional methods. The approach proposed in this study greatly extends the technical boundary for regional carbon storage assessment and introduces large language models that might provide an effective strategy for multi-scenario simulations. In such a way, that would be very useful to make future carbon storage predictions and provide optimization strategies for low-carbon urban planning in support of achieving carbon neutrality.

Land Cover Simulation and Carbon Storage Assessment in Daqing City based on FLUS-InVEST Model

Considering Daqing City as the research area, the impact of land cover change on carbon storage in the future was discussed, and the hot spots of carbon sequestration capacity were identified. The future land use simulation （FLUS） model was used to simulate the land cover pattern of a natural succession scenario, ecological protection scenario, urban development scenario, and comprehensive development scenario in 2030, and the integrated valuation of ecosystem services and trade-offs （InVEST） model was combined to estimate carbon storage in 2010, 2020, and 2030. Finally, the hot spot analysis tool was used to identify the cold hot spots of carbon sequestration capacity. The results showed the following： ① From 2010 to 2020, the area of cultivated land, water, and artificial surface increased, whereas the area of other land cover types decreased, and the total carbon storage decreased by 8.6×105 t. ② The land cover change of the natural succession scenario and urban development scenario in 2030 was similar to that of 2010-2020, with carbon storage decreasing by 1.16×106 t and 1.20×106 t, respectively. The carbon storage of the comprehensive development scenario decreased by 1.00×106 t compared with that in 2020, and carbon storage of the ecological protection scenario was 5.677 7×108 t, which increased by 2.53×106 t compared with that in 2020. ③ The conversion of grassland and wetland to cultivated land was the main cause of carbon storage loss, and the main contributor of carbon storage in the ecological protection scenarios was wetland. ④ The hot spots of carbon sequestration capacity were mainly located in the wetland area, and the cold spots were mainly distributed in the central part of Daqing City. The carbon sequestration capacity of cultivated land was not significant. According to the research results, to realize the urban transformation of Daqing City, we should insist on returning farmland to forest and grass, increase the intensity of returning moisture, improve the utilization rate of urban land, and increase green infrastructure in the main urban area.

Dry season assessment of carbon storage and emission from upland and riparian soils in the Ganga River basin

Dry season assessment of carbon storage and emission from upland and riparian soils in the Ganga River basin

Ecosystem carbon storage assessment and multi-scenario prediction in the Weihe River Basin based on PLUS-InVEST model.

Land use changes are the main cause for the changes of carbon storage, which is of great importance for maintaining regional carbon balance to make multi-scenario projections of future land use change and explore its impacts on carbon storage. In recent years, under the combination of natural factors and policies, with the land use changing significantly, carbon storage of the Weihe River Basin has also changed. Based on the PLUS-InVEST model, we assessed and predicted the spatial and temporal variations of ecosystem carbon storage in the Weihe River Basin and explored the impacts of land-use change. The results showed that land use distribution pattern of the Weihe River Basin did not change much from 2000 to 2020, which was characterized by the decreases of cropland area and the increases of the area of the remaining land use types. The main ways of land use type conversion were cropland to built-up land and inter-conversion of cropland, forest, grassland. Carbon storage in the Weihe River Basin showed an upward trend from 2000 to 2020, with a total increment of 15.31×106 t. The areas with high carbon storage presented the characteristics of "northeast patch-western scatter-central and southern belt", while low carbon storage distributed in the Guanzhong Plain urban agglomeration located in the lower basin. Compared to 2020, carbon storage in the Weihe River Basin in 2030 would increase under the four scenarios. Carbon storage would increase the least under the economic development scenario, and the most under the ecological protection scenario. The variation of carbon storage in spatial distribution would be embodied in the staggered zone of cropland, forest, and grassland in the upper basin. The results could provide data support for land use management decisions and carbon storage enhancement in the Weihe River Basin.

Forest Canopy Height Retrieval Model Based on a Dual Attention Mechanism Deep Network

Accurate estimation of forest canopy height is crucial for biomass inversion, carbon storage assessment, and forestry management. However, deep learning methods are underutilized compared to machine learning. This paper introduces the convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM) model and proposes a Convolutional Neural network–spatial channel attention–bidirectional long short-term memory (CNN-SCA-BiLSTM) model, incorporating dual attention mechanisms for richer feature extraction. A dataset comprising vegetation indices and canopy height data from forest regions in Luoyang, specifically within the 8–20 m range, is used for a comparative analysis of multiple models, with accuracy evaluated based on the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results demonstrate that (1) the CNN-BiLSTM model exhibits strong potential (MAE = 1.6554 m, RMSE = 2.2393 m, R2 = 0.9115) and (2) the CNN-SCA-BiLSTM model, while slightly less efficient (<1%), demonstrates improved performance. It reduces the MAE by 0.3047 m, the RMSE by 0.6420 m, and increases the R2 value by 0.0495. Furthermore, the model is utilized to generate a canopy height map (MAE = 5.2332 m, RMSE = 7.0426 m) for Henan in the Yellow River Basin for the year 2022. The canopy height is primarily distributed around 5–20 m, approaching the accuracy levels of global maps (MAE = 4.0 m, RMSE = 6.0 m).

Session 16. Oral Presentation for: Building a carbon storage portfolio for the Barrow and Dampier sub-basins – a national resource

Presented on Wednesday 22 May: Session 16 The Australian government has recently legislated a 43% emissions reduction target for 2030 to drive net zero ambitions for 2050. The government plans for carbon capture and storage (CCS) to play a significant role in achieving this through greenhouse gas storage licence rounds. Rapid screening of Australia’s carbon storage opportunities is necessary to position future acreage release and ultimately identify and mature carbon storage sites. An industry collaboration between Geoscience Australia and SLB has conducted a basin-scale carbon storage assessment project for the Barrow and Dampier sub-basins. The project has built and modernised a subsurface database fit for CCS screening. The dataset was used as the foundation to create a carbon storage opportunity portfolio and storage resource assessment for the area. The project has three major streams: (1) CCS-focussed reprocessing, depth imaging and merging of 29 legacy seismic surveys covering 26,150 km2 – focussed on detailed image resolution for containment analysis. (2) A basin-wide audit of 1620 wells and databasing of available log data, 129 wells were carried through for petrophysical and rock physics analysis relevant to CCS. (3) A portfolio of carbon storage opportunities and associated capacity assessment for the Barrow–Dampier including saline aquifers and depleted fields ranked on storage capacity, reservoir and seal properties and integrity. The results of this study will be released via the National Offshore Petroleum Information Management System as a public resource to expedite CCS implementation for the region. To access the Oral Presentation click the link on the right. To read the full paper click here

Assessment of Carbon Storage in a Multifunctional Landscape: A Case Study of Central Asia

The robust carbon storage (CS) capacity of terrestrial ecosystems is crucial in mitigating climate change and holds indispensable significance for global sustainable development. The diverse topography of Central Asia (CA), comprising oases, grasslands, forests, deserts, and glaciers, has fostered industries like animal husbandry, irrigation agriculture, and mining. However, the fragile arid ecosystems of CA render it highly sensitive to climate change and human activities, with their impact on the sustainable development of multifunctional landscapes in this region remaining ambiguous in the future. This study linked land use changes with multiple socio-economic and ecological indicators to predict the dynamics of land use and changes in CS in CA. The findings reveal a significant spatial heterogeneity in CS, with considerable variations among five countries driven by differences in landscape composition. Kyrgyzstan and Kazakhstan, characterized by grasslands, demonstrate higher CS per unit area, whereas Turkmenistan, dominated by barren land, exhibits the lowest CS per unit area. Strategies involving innovative development and improved biodiversity conservation have proven effective in augmenting CS. Meanwhile, high economic and population growth stimulates the expansion of cropland and urban land, reducing the CS capacity of ecosystems. This study contributes to a more precise assessment of CS dynamics in CA. Furthermore, by elucidating the interrelationships between future socio-economic development and environmental conservation in CA, it offers solutions for enhancing the conservation of multifunctional landscapes in CA.

Comparison of 16 national methods in the life cycle assessment of carbon storage in wood products in a reference building

Wood and bio-based construction products are perceived as a way to use renewable resources, to save energy and to mitigate greenhouse gas (GHG)-emissions

Building a carbon storage portfolio for the Barrow and Dampier sub-basins – a national resource

The Australian government has recently legislated a 43% emissions reduction target for 2030 to drive net zero ambitions for 2050. The government plans for carbon capture and storage (CCS) to play a significant role in achieving this through greenhouse gas storage licence rounds. Rapid screening of Australia’s carbon storage opportunities is necessary to position future acreage release and ultimately identify and mature carbon storage sites. An industry collaboration between Geoscience Australia and SLB has conducted a basin-scale carbon storage assessment project for the Barrow and Dampier sub-basins. The project has built and modernised a subsurface database fit for CCS screening. The dataset was used as the foundation to create a carbon storage opportunity portfolio and storage resource assessment for the area. The project has three major streams: (1) CCS-focussed reprocessing, depth imaging and merging of 29 legacy seismic surveys covering 26,150 km2 – focussed on detailed image resolution for containment analysis. (2) A basin-wide audit of 1620 wells and databasing of available log data, 129 wells were carried through for petrophysical and rock physics analysis relevant to CCS. (3) A portfolio of carbon storage opportunities and associated capacity assessment for the Barrow–Dampier including saline aquifers and depleted fields ranked on storage capacity, reservoir and seal properties and integrity. The results of this study will be released via the National Offshore Petroleum Information Management System as a public resource to expedite CCS implementation for the region.

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".

Mangrove wetlands distribution status identification, changing trend analyzation and carbon storage assessment of China

Mangrove wetlands distribution status identification, changing trend analyzation and carbon storage assessment of China

Carbon storage and ecological characteristics of restored and natural mixed deciduous forests in western Thailand

Importance of the work: Assessment of carbon storage and ecological characteristics in mixed deciduous forests is crucial for understanding its succession and mitigating global warming. Objectives: This study examine carbon storage and ecological characteristics between mining-disturbed and undisturbed mixed deciduous forests in Ratchaburi, western Thailand. Materials & Methods: 2022, twelve 20 × 20 m plots were established in natural and restored mixed deciduous forests in Ratchaburi Province, Thailand. Trees with DBH ≥ 4.5 cm were inventoried. Ecological parameters (density, basal area, IVI, diversity) and forest carbon stocks (aboveground biomass, soil carbon) were assessed. Results: A total of 658 trees, representing 67 species from 22 families, were identified. The composition differed between two forests, with a 48.06% similarity in species. The Shannon-Wiener diversity was 2.90 in the restored forest (RF) and 3.36 in the natural forest (NF). The basal area was 231.67 ± 71.15 m2/ha in the RF and 416.41 ± 261.59 m2/ha in the NF. Mean tree height was 10.51 ± 3.72 m in the RF and 13.97 ± 4.41 m in the NF. Tree density was 1,779.17 ± 729.97 trees/ha in the RF and 962.50 ± 147.27 trees/ha in the NF. The total carbon stock was higher in the NF (366.27 ± 76.51 MgC/ha) compared to the RF (194.14 ± 45.80 MgC/ha). Main finding: The restored forest showed lower carbon storage and ecological parameters, indicating long-term mining impacts. However, the restored forest still serves as a carbon reservoir, contributing to the reduction of atmospheric CO2 levels and preserving biodiversity after prolonged restoration.

Carbon Storage Assessment under Mangrove Restoration of Dongzhai Harbor in Hainan Island, China

Mangrove restoration is recognized as an effective strategy for enhancing the carbon storage capacity of natural ecosystems, advancing toward the “carbon neutrality” goal. The carbon storage effects of ecological restoration efforts remain insufficiently understood as previous studies have focused on carbon storage dynamics in ecosystems, yet the specific impacts of targeted mangrove restoration are less explored. This study utilizes multi-temporal remote sensing data and actual restoration data from Dongzhai Harbor Hainan Island to identify the mangrove wetland coverage and quantify the spatiotemporal evolution of carbon storage under various restoration efforts using the InVEST model. Additionally, we employed the PLUS model to simulate and compare carbon storage potential under multiple development goals. The findings reveal the following: (a) Mangrove restoration significantly increased the area of land with high carbon sink capability, resulting in a regional carbon storage increase of 210,001.68 tons from 2015 to 2021, with 97% of this increase attributable to ecological restoration. (b) Mangrove coverage is crucial for regional carbon storage, with an average of 443 tons of carbon stored per hectare. Decreases in carbon storage occurred mainly during the conversion of mangroves to aquaculture, and forests/agriculture to residential areas. Increases in carbon storage were seen in the reverse transitions. (c) Comparing the scenarios focused solely on mangrove protection with cultivated land protection, the carbon storage in Dongzhai Harbor is projected to reach its maximum by 2045 under the carbon storage priority scenario. Our findings build a scientific foundation for formulating effective mangrove conservation and restoration strategies.