Spatio-temporal evolution characteristics and simulation prediction of carbon storage: A case study in Sanjiangyuan Area, China

Wu, Xinyan; Shen, Caiting; Shi, Linna; Wan, Yuanyuan; Ding, Jinmei; Wen, Qi

doi:10.1016/j.ecoinf.2024.102485

Cited by 15 publications

(4 citation statements)

References 66 publications

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“…The expansion of construction land and water 4 caused the loss of CS, among which the transfer of woodland to water and construction land caused 5 CS losses of 1.19×10^6 t and 1.36×10^6 t, respectively, and the transfer of farmland to water and 6 construction land caused CS losses of 0.17×10^6 t and 0.78×10^6 t, respectively. Overall, the shift 7 from ecological land such as woodland and grassland to artificial land was the main source of CS loss, 8 which is consistent with previous studies 16,40 Getis-Ord G*i was used to further analyze the spatial aggregation and distribution of hot spots and changes. The hot spots in all of Zunyi were clustered in the western and central regions but gradually decreased over the study period.…”

Section: Urban Development (Cds)supporting

confidence: 78%

“…Furthermore, remote sensing technology can be combined with it to visually represent and reflect spatial distributions. This approach facilitates further analysis of the spatiotemporal evolution characteristics of CS and its response to land use changes 16 . In addition, many current studies have combined the InVEST model with predictive models such as CLUE 17 , FLUS 18,19 , and CA-Markov 20 to evaluate changes in land use patterns and ecosystem CS under future scenarios based on analyses of historical evolutionary patterns and the driving factors of CS.…”

Section: Introductionmentioning

confidence: 99%

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Response of carbon storage to land use change and Multi-Scenario predictions in Zunyi, China

Liu,

Mei,

Yue

et al. 2024

Preprint

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Evaluating and predicting how carbon storage (CS) responds to land use change can optimize future spatial layouts and coordinate the relationship between land use and ecosystem services. This study aimed to explore the spatiotemporal evolution of CS in Zunyi from 2000 to 2020, investigate the driving factors of CS, and predict spatiotemporal changes under multiple scenarios in 2030. Woodland (> 62%) and farmland (> 30%) are the main land use types in Zunyi. The land use changes included decreases in farmland (-0.92%) and grassland (-0.76%) and an increase in construction land (+ 0.92%). In 2000, 2010, and 2020, CS in Zunyi was 658.77×10^6 t, 661.44×10^6 t, and 658.35×10^6 t, respectively, initially increasing then decreasing. Woodland, farmland and grassland conversions to construction land and water were primarily responsible for CS loss. The NDVI is the main driving factor influencing the spatial pattern of CS (q > 10%). Furthermore, the impacts of the human footprint index and population density are increasing. In 2030, the CS in Zunyi is projected to decrease under the NDS, ECS and CDS scenarios. Under the ECS, CS is estimated to be 656.67×10^6 t, with the smallest decrease (-0.26%); moreover, this scenario effectively alleviates the decreases in farmland and woodland.

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Section: Urban Development (Cds)supporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Response of carbon storage to land use change and Multi-Scenario predictions in Zunyi, China

Liu,

Mei,

Yue

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…However, these models suffer from drawbacks such as long sampling periods, complex data requirements, and large workloads (Zhu et al 2021;Jiang et al 2017). In contrast, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, with its minimal data requirements, high precision, and convenient operation, has widely applied its carbon storage module in terrestrial ecosystem carbon storage research (He et al 2023;Wu et al 2024). For example, Sun Tiancheng used the InVEST model to assess carbon storage in the southeastern coastal zone of Hainan Island and proposed ecosystem restoration strategies for coastal zones (Sun et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Study on the variation of carbon storage in the Chang-Zhu-Tan urban agglomeration in China based on topographic relief

Mi,

Li,

2024

Preprint

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Regional carbon storage is a significant manifestation of ecosystem service functions. Examining the impact of changes in land use on carbon stock in the Chang-Zhu-Tan urban agglomeration under different topographic reliefs is paramount for sustainable land resource utilization and realizing carbon peaking and neutrality goals. Taking the Chang-Zhu-Tan Urban Agglomeration as an example, based on the InVEST model and combined with land use data to analyze the spatio-temporal variation of carbon storage, using the mean change point method to extract topographic relief. From the perspective of topographic relief, this study explores the correlation between changes in land use and carbon storage distribution patterns in the study area. The research results show that the optimal analysis window for topographic reliefs in the study area is 17×17, covering an area of 0.26 km2, with an average topographic relief of 78.58 m. From 2000 to 2020, changes in land use in the Chang-Zhu-Tan urban agglomeration primarily involved a gradual decrease in forest land, cultivated land, and grassland areas and a gradual increase in water and construction land. The carbon stock in the Chang-Zhu-Tan urban agglomeration showed a decreasing trend, with a total reduction of 13.37×106 tons over the past 20 years. The distribution of carbon stock across topographic reliefs mainly exhibits low-value and moderate-value carbon stock concentrations in flat and slightly undulating areas. In contrast, moderately high-value and high-value carbon stock concentrations are concentrated in gently and moderately undulating areas. These research findings provide a scientific foundation for optimizing the spatial pattern of the study area and formulating carbon peak and carbon-neutral policies.

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“…In this study, we combined the carbon density data under ecological zoning with the secondary categorization of land use types to reduce the error between the carbon density parameters and the actual carbon density caused by spatial heterogeneity, and to improve the accuracy of carbon stock estimation by the InVEST model. Secondly, existing studies mainly focus on the assessment of carbon storage capacity for single development scenarios in the history or the future [38,39], and the simulation of future multi-scenario carbon storage capacity is still to be optimized [40]. Therefore, we assessed the land use changes in the Yellow River Basin (YRB) and their impacts on the carbon stock under various development scenarios in the future through the coupling of the Future Land Use Simulation (FLUS) 2.4 model and the InVEST 3.13 model.…”

Section: Introductionmentioning

confidence: 99%

Effects of Land Use/Cover Change on Terrestrial Carbon Stocks in the Yellow River Basin of China from 2000 to 2030

Zhang,

Yang,

Liu

et al. 2024

Remote Sensing

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Accurately assessing and predicting the impacts of land use changes on ecosystem carbon stocks in the Yellow River Basin (YRB) and exploring the optimization of land use structure to increase ecosystem carbon stocks are of great practical significance for China to achieve the goal of “double carbon”. In this study, we used multi-year remote sensing data, meteorological data and statistical data to measure the ecosystem carbon stock in the YRB from 2000 to 2020 based on the InVEST model, and then simulated and measured the ecosystem carbon stock under four different land use scenarios coupled with the FLUS model in 2030. The results show that, from 2000 to 2020, urban expansion in the YRB continued, but woodland and grassland grew more slowly. Carbon stock showed an increasing trend during the first 20 years, with an overall increase of 7.2 megatons, or 0.23%. Simulating the four land use scenarios in 2030, carbon stock will decrease the most under the cropland protection scenario, with a decrease of 17.7 megatons compared with 2020. However, carbon stock increases the most under the ecological protection scenario, with a maximum increase of 9.1 megatons. Furthermore, distinct trends in carbon storage were observed across different regions, with significant increases in the upstream under the natural development scenario, in the midstream under the ecological protection scenario and in the downstream under the cropland protection scenario. We suggest that the upstream should maintain the existing development mode, with ecological protection prioritized in the middle reaches and farmland protection prioritized in the lower reaches. This study provides a scientific basis for the carbon balance, land use structure adjustment and land management decision-making in the YRB.

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Spatio-temporal evolution characteristics and simulation prediction of carbon storage: A case study in Sanjiangyuan Area, China

Cited by 15 publications

References 66 publications

Response of carbon storage to land use change and Multi-Scenario predictions in Zunyi, China

Response of carbon storage to land use change and Multi-Scenario predictions in Zunyi, China

Study on the variation of carbon storage in the Chang-Zhu-Tan urban agglomeration in China based on topographic relief

Effects of Land Use/Cover Change on Terrestrial Carbon Stocks in the Yellow River Basin of China from 2000 to 2030

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