https://orcid.org/0000-0002-8641-1104
Figures
Abstract
Different land-use types have different carbon sequestration capacities, and changes in land-use type directly cause changes in carbon sequestration capacity. To understand the change trend of carbon sequestration capacity, it is necessary to quantify the specific quantity of the impact of land use change on carbon sequestration capacity to indirectly estimate the change in carbon sequestration capacity by monitoring the change in land use type. Based on the analysis of the changing trends of land use type data and ecological carbon sequestration data in the research area, this study establishes an impact model (IM) of land use change on ecological carbon sequestration capacity, quantifying the specific quantity of the impact of land use change on ecological carbon sequestration capacity from two aspects: the impact of land use type transition in and the impact of land use type transition out. Through verification of the accuracy of the model estimation, it is expected that the impact of land use change on carbon sequestration capacity in Sichuan mountainous areas in 2025 and 2030 will be -20754 tons and -30837 tons, respectively, and the total ecological carbon sinks will be 132 and 133 million tons, respectively. Based on the trend analysis of the total carbon sequestration and the IM estimation data, the impact of land use change on ecological carbon sequestration capacity tends to stabilize (the impact will fluctuate around -31156 tons), and the trend of a significant increase in total carbon sequestration is not obvious. The IM quantifies the specific quantity of the influence of land-use type change on carbon sequestration capacity and can dynamically analyze the external factors that cause the change in carbon sequestration capacity, which is of great significance for monitoring the change law of carbon sequestration capacity.
Citation: Huang X, Lyu W, Ye H, Su L (2025) A model of the impact of land use change on its carbon sequestration capacity. PLoS One 20(5): e0323645. https://doi.org/10.1371/journal.pone.0323645
Editor: Laxmi Kant Sharma, Central University of Rajasthan, INDIA
Received: February 18, 2025; Accepted: April 13, 2025; Published: May 29, 2025
Copyright: © 2025 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting information files.
Funding: This paper is supported by the Neijiang Normal University Project (2024QNZ26).
Competing interests: The authors have declared that no competing interests exist.
Introduction
With the proposal of carbon neutrality targets, research on vegetation carbon sequestration has gradually become a hot topic [1–3]. Many studies have been conducted on the estimation methods, models, trends, and impact of external conditions on ecological carbon sinks for carbon sequestration. Research on the impact of land use change on ecological carbon sink function has also achieved good results [4]. The machine learning model proposed by Mo et al. shows that human activities significantly reduce forest carbon storage compared to the natural potential, and optimizing land resource management can significantly enhance carbon sinks [5]. Sha et al. proposed that by optimizing land management measures, such as forest nurturing and grassland restoration, the global terrestrial ecosystem can add approximately 3.5–4 billion tons of carbon annually, equivalent to one-third of global fossil fuel emissions [6]. Studies have specifically pointed out that grasslands and farmland outside of forests have a higher potential for carbon sequestration, and the rational use of land has a greater impact on carbon sequestration capacity. Researchers have studied land-use conflicts in the study area from the perspective of ecological carbon sinks and identified land-use conflicts in the study area from the perspective of improving ecological carbon sink capacity [7–9]. Raj et al. evaluated the land use dynamics in the Aravalli Mountains of India using a combination of geographic space and CART methods, and predicted future land use changes, indirectly reflecting the impact of future land use changes on ecological carbon sequestration capacity [10]. Pekka et al. conducted a study on land use and carbon emissions in Finland and found that forests can effectively reduce carbon emissions, and strengthening forest management can slow down the occurrence of greenhouse effects [11]; Zhou et al. conducted a survey of 7800 plots nationwide to evaluate the current status and rate of carbon sequestration in China’s forest ecosystems, predict their carbon sequestration potential, and explore the mechanisms of carbon sequestration in forest ecosystems [12]; Wang et al. quantitatively evaluated the impact of land use change on carbon balance and found that returning farmland to forests can increase carbon dioxide absorption [13]; Liu et al. analyzed the correlation between land use change and ecological carbon sink based on remote sensing data from 2005 and 2015, and found that the reduction of ecological risks caused by land use change can be directly reflected in the improvement of ecological carbon sink capacity [14]; Chen et al. analyzed the spatiotemporal distribution characteristics between carbon sink values and climate regulation values on the Qinghai Tibet Plateau based on land use data, and found that the interannual variation of carbon sinks was very small [15]. Zeng et al. used the Carnegie Ames Stanford method (CASA) model to estimate ecological carbon sequestration in the study area and studied the ecological carbon sequestration capacity of vegetation under different land-use type [16]. Strengthening research on the impact of land use change on ecological carbon sinks can help understand the effects of land use change, ecological carbon sink capacity, and future trends, thereby contributing to achieving carbon neutrality goals.
Although there have been many studies on the correlation between land use and carbon sequestration capacity, current research has only revealed the correlation between the two and has not specifically quantified it [17,18]. As far as the current research status is concerned, there is a lack of calculation methods and models to calculate the impact of land-use type changes on carbon sequestration capacity, and the specific impact of land use changes on carbon sequestration capacity cannot be clarified. Therefore, relevant models are needed to quantify the impact of land use change on carbon sequestration capacity.
How can we quantify the impact of land use change on ecological carbon sequestration capacity? This study focused on the mountainous areas of Sichuan Province, which have strong carbon sequestration capabilities, as the research area. Based on the analysis of the changes in land use and carbon sequestration capacity in the Sichuan mountainous areas, the IM was established from two aspects: the impact of land use type transition in and the impact of land use type transition out. The model was used to quantify the impact of land-use type changes on carbon sequestration capacity to more accurately grasp the basic situation of carbon sequestration capacity in the study area.
Study area, data and methods
Study area
Considering the abundant ecological resources and frequent changes in land-use type in Sichuan mountainous areas, this study chose Sichuan mountainous areas as the research area. However, classification methods for mountainous areas are diverse, and a unified classification standard has not yet been established. Based on the literature, the selection of mountainous area definition indicators is consistent, and mountainous areas are qualitatively and quantitatively described by factors such as absolute height, relative height, and slope [19]. According to the results of Zhang et al., the following definition criteria for Sichuan mountainous areas were selected [20,21]:
- The area with elevation less than 500 m, the terrain fluctuation is greater than 50 m;
- The elevation between 500m-2500m area, terrain ups and downs higher than 100m or slope greater than 25 °area;
- Area with elevations above 2500 m.
Using ArcGIS software and Sichuan ASTER DEM data (spatial resolution 30m, Fig 1A) [22], the DEM dataset was provided by the National Ecosystem Science Data Center and the National Science and Technology Infrastructure (http://www.nesdc.org.cn). The terrain undulation data (Fig 1B) was obtained through DEM calculations. Generate range data for Sichuan mountainous areas based on the above standards (Fig 1C).
Data collection, processing, and trend analysis
Land use data.
The data used in this study were mainly based on Landsat TM remote sensing images from 2000 to 2020 as data sources. Erdas Imagine, ENVI, and ArcGIS were used to preprocess remote sensing images from different periods in Sichuan mountainous areas. Supervised classification and human-computer interaction methods are used to interpret and test the accuracy. Combined with Google Earth software to correct the interpretation results, the vector data of land use in Sichuan in different years are obtained and the area of land types are counted. The total accuracy of image interpretation obtained by the accuracy test was higher than 83%, and the kappa coefficient was higher than 0.81. The accuracy of the land use data meets the research requirements [22–24]. The land-use type in Sichuan mountainous areas include construction land, forest, grassland, cultivated land, shrub, lake, wetland, wasteland, ice, and snow, with a spatial resolution of 30 m. The land-use distribution map for the research area in 2020 is shown in Fig 2.
Ecological carbon sequestration data.
Different carbon sequestration estimation models are suitable for estimating carbon sequestration in areas with different topographic features. Based on the small- and medium-scale characteristics of Sichuan mountainous areas, accuracy of the model estimation, and difficulty of data acquisition, the CASA model was selected to estimate the total amount of ecological carbon sequestration in Sichuan mountainous areas. The CASA model not only has the advantages of simple structure, high reliability, easy access to parameters, and relatively high accuracy of estimation results, but it is also widely used in the estimation of vegetation NPP at medium and large spatial scales [25]. However, according to existing research, the CASA model assigns a unified value of maximum light energy utilization efficiency (e = 0.389) without considering the influence of vegetation types, terrain factors, and other factors on the estimation results. Based on these shortcomings, Huang et al. improved the model and used the improved CASA model to estimate the total ecological carbon sequestration in Sichuan mountainous areas (Fig 3) [26]. The total ecological carbon sequestration showed an increasing trend (R2 = 0.6346). As the carbon sequestration area of the ecosystem remains unchanged, it can be seen that the carbon density in Sichuan mountainous areas generally shows an increasing trend, but the increase gradually slows.
Combined with the spatial distribution of total ecological carbon sequestration (Fig 4) and land-use status data (Fig 2) in the Sichuan mountainous areas, the main types of utilization in the high-value areas of carbon sequestration in the mountainous areas are forests and shrubs, which are mainly concentrated in the mountainous altitude transition zone. The carbon sequestration capacity of the low-altitude area in mountainous areas in general and the main land use type is cultivated land. In high-altitude mountainous areas, the ecological carbon sequestration capacity is weak, and the main land-use type is grassland. Overall, the regional change in carbon sequestration capacity is not large, and the interannual change in total carbon sequestration mainly comes from the low-altitude area of mountainous areas, the transition zone between low altitude and high altitude. This is mainly because the ecological carbon sequestration capacity is affected by external factors, particularly the influence of temperature and precipitation.
Trend analysis of data changes.
Based on the basis of obtaining the above data, the ecological carbon sequestration data and land use data of the research area from 2000 to 2020 were used to calculate the carbon density data of different land-use type and analyze their changes over time. These three types of data and their changing patterns serve as the basic data for estimating the IM. The basic data are listed in Table 1, and the variation pattern of the basic data is shown in Figs 5–11.
Combined with Table 1 and Fig 5, the area of cultivated land in mountainous areas shows a trend of stage reduction, whereas carbon sequestration and carbon density show an increasing trend, and the increasing trend is more consistent. The carbon sequestration and carbon density of cultivated land did not decrease with a decrease in area, and the average annual growth of carbon density was 7 g/m2.
As shown in Table 1 and Fig 6, the forest area in Sichuan mountainous area is increasing, the growth of forest carbon sequestration and carbon density is relatively gentle, and the average annual growth of carbon density is 5 g/m2.
Combined with Table 1 and Fig 7, the shrub area in the Sichuan mountainous areas fluctuates greatly, but the carbon sequestration and carbon sequestration density of shrubs show a stable increasing trend. The carbon density of shrubs does not change with the change of area, and the carbon density increases by 5.3 g/ m2 annually.
Combined with Table 1 and Fig 8, the grassland area in Sichuan mountainous area decreased year by year, and the carbon sequestration and carbon density of grassland increased with the decrease in grassland area, and the change was relatively stable. The carbon density of grasslands showed a slow growth trend over time, with an average annual growth of 1.9 g/m2.
Combined with Table 1 and Fig 9, the lake area in Sichuan mountainous areas changes greatly, which is mainly affected by precipitation factors, but it is basically stable in general, and the trend line is basically the same as the horizontal line. The carbon sequestration capacity of the lake does not change with the change of area, and its carbon sequestration capacity is relatively stable. The carbon density of the lake increased slowly, with an average annual increase of 2.65 g/m2.
Combined with Table 1 and Fig 10, the area of the mountain wetland and the total amount of carbon sequestration fluctuate greatly between years and are mainly affected by climate, but the change in carbon density is relatively stable, and the trend line is basically the same as the horizontal line.
Combined with Table 1 and Fig 11, the areas of ice and snow in the Sichuan mountainous areas show a slow growth trend, while the area of wasteland changes more smoothly, and the trend line is consistent with the horizontal line.
Establishment, estimation, and verification of IM
Establishment of the IM.
To accurately grasp the impact of land use change on ecological carbon sequestration, the paper constructed and used to estimate the specific impact of land use change on ecological carbon sequestration. The equations for the model are as follows:
In the equation 1–3, GTYX is the total impact of land use on ecological carbon sequestration, ZRi is the impact of the transfer of other land-use type to the i-th land use type, ZCi is the impact of the transfer of the i-th land use type to other land-use type, St is the area of the t-th land use type, Si is the area of the i-th land use type, n is the number of land-use type, a (t, i) is the transfer probability of the t-th land use type to the i-th land use type, a (i, t) is the transfer probability of the i-th land use type to the t-th land use type. ρt and ρi are the carbon density of the t-th and i-th current land use type, and βt and βi are the increase and decrease coefficients of the carbon density of the t-th and i-th land use type.
The IM divides the impact of land-use change on ecological carbon sequestration capacity into the impact of land-use types, namely, transfer-in (ZR) and transfer-out (ZC). The transfer of unfavorable carbon sequestration was negative (from higher carbon density to lower carbon density), and the transfer of strong carbon sequestration was positive (from lower carbon density to higher carbon density). The sum of the transfer values of the nine land-use types can determine the impact of land-use change on carbon sequestration.
The specific data-processing process of the IM is shown (Fig 12).
- Land use data and ecological carbon sequestration datasets from 2000 to 2020 were collected in the research area, along with the changes in land use type area (S), carbon sequestration amount, and carbon sequestration density (β).
- Combining land use data and ecological carbon sequestration data, analyze the trend (ρ) of carbon sequestration density changes in different land-use type from 2000 to 2020, and calculate the transition probability between different land-use type (a)
- Using β, ρ, a, and S obtained in the above steps as four important parameters to calculate the transfer-in (ZR) and transfer-out (ZC) of different land-use type in the IM. By integrating the impact of different land-use types on ecological carbon sequestration, the total impact of land-use changes on ecological carbon sequestration capacity (GTYX in Fig 12) in the study area was obtained, and the future trend of changes in land use and ecological carbon sequestration was estimated.
Estimation and verification of the IM.
From Equations 1–3, it can be seen that the unknown main parameters in the model include land use transition probability a, land use carbon density increase/decrease coefficient β, and land use-type area S. Before conducting the estimation in the IM, it is necessary to use existing land use data and carbon sequestration data to estimate a, β, and S.
Calculation of transition probability (a): Different land-use types have different ecological carbon sequestration capabilities, and the transition probability between different land types is a key parameter for estimating the impact of input and output in the IM. This study used the established land use type transfer matrix equation (Equation 4) and land use data to calculate the average value of land use transfer probability from 2000 to 2020 (Table 4), which serves as the transfer probability between different land-use type in Sichuan mountainous areas in the future.
In the equation, ai is the transition probability of the i-th land-use type; M (i, t) is the i-th land-use type number in t time period, and M (q, t) is the q-th land-use type number in t + 1 time period. The utilization types are divided into cultivated land-1, forest-2, shrub-3, grassland-4, water body-5, ice and snow-6, wasteland-7, construction land-8, wetland-9, generally i = q; n is the time interval, and the unit is year; k is the grid area of the raster data; count () is the counting function; S (i, t) is the total area of the i-th utilization types in t time.
To reflect the mutual transfer relationship between land-use type in Sichuan mountainous areas more intuitively, the transfer ball of land-use type is drawn according to the transfer probability in Table 2 (Fig 13). In the Fig, most types of outward transfer are grassland and lakes, and the least is construction land. The most inward transfer type was grassland and the least was wetland. There are 47 transfer routes of land-use types in Sichuan mountainous areas, of which 21 are conducive to the transfer of carbon sequestration, 24 are not conducive to the transfer of carbon sequestration, and 2 are the transfer between the same ability of carbon sequestration.
β and S estimation: To estimate the change in land use type (S) and carbon density (β) in Sichuan mountainous areas, the Grey Prediction Model and ARIMA prediction model in the most commonly used time series prediction model were selected to predict the area and carbon density of different land-use type. The Grey Prediction model is suitable for small samples, obvious trends, and medium-to long-term predictions, and has the advantage of being simple and efficient. The ARIMA prediction model is suitable for data sufficiency, complex temporal patterns, and short-term high-precision predictions, with the advantages of flexibility and statistical rigor. In practical applications, the two models are usually combined and used interchangeably to balance the prediction needs in different scenarios.
The grey prediction model: The Grey Prediction Model is a time-series prediction method for predicting related systems with important uncertain parameters. The degree of difference in the development trend between the factors of the grey prediction identification system, that is, a detailed analysis of the similarity, generation, and processing of the original data, to find the general law of the establishment and change of the system, calculate the data sequence with strong regularity, and then establish the corresponding differential equation model to predict the future development trend. The grey prediction model is constructed using a series of quantitative values that reflect the characteristics of the predicted object observed at equal time intervals to predict the characteristic quantity at a certain time in the future or the time to reach a certain characteristic quantity [27,28]. The land use and carbon sequestration data of Sichuan mountainous areas from 2000 to 2020 were imported into the grey prediction model to estimate the area and carbon density data of land-use type for 2025 and 2030. The results are as follows (Table 3):
ARIMA prediction model: The basic idea of the ARIMA model is to regard the data sequence formed by the predicted object over time as a random sequence and use a mathematical model to describe this sequence. Once the model is identified, it can predict the future value from past and current values of the time series [29,30]. The land use and carbon sequestration data of Sichuan mountainous areas from 2000 to 2020 were imported into the ARIMA prediction model to estimate the area and carbon density data of land-use types for 2025 and 2030.. The results are as follows (Tables 5 and 6):
IM estimation results: The average value of the estimated results of the Grey Prediction model and the ARIMA model was used as the estimated carbon density and land use areas, and the average value of the land use transfer probability of the Sichuan mountainous areas from 2000 to 2020 was used as the transfer probability in 2025 and 2030. The impact of land use change on ecological carbon sequestration in Sichuan mountainous areas in 2025 and 2030 was estimated by incorporating them into the calculation equation (Equation 1–3) of the impact model. However, the Grey Prediction model and ARIMA prediction model are based on existing basic data to estimate future trends. They are based only on the data calculation in the state of the data itself, ignoring the impact of the environment on the data. Combined with the trend analysis of land use, carbon density, and total carbon sequestration data (Table 1, Figs 5–11), the average values of lake area, ice and snow area, wasteland area, wetland carbon density, and lake carbon density from 2000 to 2020 were used as the estimated data for 2025 and 2030. In summary, the results of land use data and carbon density estimation in Sichuan mountainous areas in 2025 and 2030 (Tables 7 and 8):
The IM calculates the impact of land-use type changes on ecological carbon sequestration in 2025 and 2030: the transfer-in (TI) impact and transfer-out (TO) impact of nine land-use type changes on ecological carbon sequestration in the Sichuan mountainous areas (Table 9).
The changes in forest, grassland, ice, and snow are conducive to the improvement of ecological carbon sequestration capacity, while the changes in other land-use type are not conducive to the improvement of ecological carbon sequestration capacity. In 2025, the impact of land use change on ecological carbon sequestration capacity is -20754 tons, and the impact in 2030 is -30837 tons. At the same time, the impact of transfer between different land-use types on ecological carbon sequestration was different, but the impact of changes in the same land-use type at different periods on ecological carbon sequestration capacity was small. Comparing the impact of the same type of transfer in 2025 with that in 2030 (Fig 14), it is found that the impact of the same type of use change in 2025 accounts for 95% of the impact in 2030, and R2 is equal to 0.0104. The increase in the impact of future land-use change on ecological carbon sequestration capacity in Sichuan mountainous areas tends to be stable, and the impact will fluctuate around −31,156 tons (-30,837 * (1 + 0.0104)) in the future.
Accuracy verification: The improved CASA model improved the estimation accuracy of the CASA model, and its estimation results were considered close to the actual carbon sequestration amount. To verify the estimation accuracy of the IM, the IM was used to estimate the total amount of ecological carbon sequestration and the impact of land use change on ecological carbon sequestration in the study area in 2021 and 2022, and the improved CASA model was used to estimate the total amount of ecological carbon sequestration in 2021 and 2022. The two estimation results were compared to verify the estimation accuracy of IM. The equation for calculating the ecological carbon sequestration in 2021 and 2022 using IM is as follows:
In the equation, GTZG is the total amount of ecological carbon sequestration in Sichuan mountainous areas, n is the number of land-use type in Sichuan mountainous areas, S(i,t) is the estimated area of the i-th type of land-use type during the t period, ρ(i,t) is the estimated carbon density of the i-th type of land-use type during the t period, and GTYXt is the impact of land use change on ecological carbon sequestration. According to the land-use type areas and carbon density estimated by the IM, it is calculated that the total ecological carbon sequestration in 2021 without the impact of land use change is 121,335,540 tons, and the total ecological carbon sequestration in 2022 is 119,588,769 tons (Table 10).
The land use data and ecological carbon sequestration estimation data of Sichuan mountainous areas in 2021 and 2022 were imported into the IM, and the impacts of land use change on ecological carbon sequestration in 2021 and 2022 were calculated to be -82226 tons and -45892 tons, respectively (Table 11). Combined with the calculation results, the total amount of ecological carbon sequestration in 2021 was 12125.3314 million tons, and the total amount of ecological carbon sequestration in 2022 was 11954.2877 million tons.
To verify the accuracy tiof the impact model, this study used the ecological carbon sequestration estimated by the IM to compare with the improved CASA model estimation and the ecological carbon sequestration calculated by MODIS data. The improved CASA model estimated that the total amount of ecological carbon sequestration in 2021 and 2022 will be 122.915 million tons and 1255.195 million tons, respectively, and the total amount of carbon sequestration calculated by MODIS data will be 123.7702 and 1258.101 million tons, respectively. In 2021, the estimated amount of IM accounted for 98.65% of the improved CASA model and 97.97% of the MODIS data, and the estimated IM in 2022 accounted for 95.24% of the improved CASA model and 95.19% of the MODIS data. Compared with the estimation results for 2021 and 2022, the estimation accuracy of the IM is higher, indirectly reflecting the accuracy of the IM in quantifying the impact of land use change on ecological carbon sequestration.
Result and discussion
Result
This study used the IM model to quantify the impact of land use change on ecological carbon sequestration, and the results showed the following:
- The total ecological carbon sequestration in the Sichuan mountainous areas in 2025 and 2030 was 132 and 133 million tons, respectively (Table 12).
- According to the model estimation, the impact of land use change on ecological carbon sequestration in the study area will be -20754 tons and -30837 tons in 2025 and 2030, respectively.
- The impact of land use change on ecological carbon sequestration capacity tends to stabilize, with fluctuations of approximately −31156 tons.
Discuss
Advantages and disadvantages: Compared with other studies on the correlation between land use and carbon sequestration capacity, the biggest feature of this study is that it directly quantifies the impact of land-use type changes on carbon sequestration capacity by constructing an impact model and further analyzes the changing trends of their mutual influence. However, there are still some shortcomings in IM that need to be addressed. First, there is inevitably a certain degree of error in the analysis of the changing trends of land-use classification data and carbon sequestration data in the research area from 2000 to 2020, and the changing trends of both are key parameters required for IM. The accuracy of these key parameters has a significant impact on the IM estimation results. At present, the classification accuracy of land use data is not entirely accurate, and the estimation accuracy of carbon sequestration data is affected by many factors. Therefore, it is necessary to further improve the classification accuracy of land use and the accuracy of carbon sequestration estimation data to enhance the estimation accuracy of the IM. In addition, because of the large scope of the research area, it is difficult to consider the impact of local land policies, climate change, and other factors on the estimation results, and the influence of these factors on the estimation conclusions is difficult to assess. Finally, but not the endpoint, there are very few key parameters that affect the model, and there may still be some factors that are not fully included in the model.
Research direction: based on the above shortcomings, in future research, considering the variability of climate, human factors, policy factors, as well as the emergence of new methods and technologies, the IM should incorporate more influencing factors and continuously optimize calculation methods to improve the accuracy of impact model estimation.
Conclusion
This study is based on land use data and ecological carbon sequestration data in mountainous areas of Sichuan Province from 2000 to 2020 and constructs an impact model from two aspects: the impact of land use transfer in and the impact of land use transfer out. The estimation model is used to estimate the impact of land use change on ecological carbon sequestration. The conclusions of this study are as follows.
- The amount of ecological carbon sequestration in Sichuan mountainous areas shows a slow growth trend from 2000 to 2020, and the total amount of ecological carbon sequestration in 2025 and 2023 shows little difference (a difference of 0.01 million tons). This shows that the ecological carbon sequestration capacity of Sichuan mountainous areas is close to saturation, and the scientific management of the conversion between land-use type is conducive to the improvement of the ecological carbon sequestration function..
- The impact of land-use change on ecological carbon sequestration tends to stabilize, and the current land-use change is not conducive to improving the ecological carbon sequestration capacity.
- The model can effectively quantify the impact of land use on ecological carbon sequestration. The accuracy of the impact model is verified through comparison with relevant data from 2021 and 2022, which can intuitively demonstrate the impact of land-use change on ecological carbon sequestration, help policymakers grasp the impact of land-use change on ecological carbon sequestration capacity and its change trend, and have a clear understanding of the change law of land use change and ecological carbon sequestration capacity in the future.
Supporting information
S1 File. Paper dataset 1 Carbon sequestration data: Carbon sequestration data in Sichuan mountainous areas.
https://doi.org/10.1371/journal.pone.0323645.s001
(ZIP)
S2 File. Paper dataset 2 Vector data within the research area: Vector data within the research area.
https://doi.org/10.1371/journal.pone.0323645.s002
(ZIP)
S3 File. Paper dataset 3 Land use data (2000-2020): 2000-2002.
https://doi.org/10.1371/journal.pone.0323645.s003
(ZIP)
S4 File. Paper dataset 3 Land use data (2000-2020): 2003-2005.
https://doi.org/10.1371/journal.pone.0323645.s004
(ZIP)
S5 File. Paper dataset 3 Land use data (2000-2020): 2006-2008.
https://doi.org/10.1371/journal.pone.0323645.s005
(ZIP)
S6 File. Paper dataset 3 Land use data (2000-2020): 2009-2011.
https://doi.org/10.1371/journal.pone.0323645.s006
(ZIP)
S7 File. Paper dataset 3 Land use data (2000-2020): 2012-2014.
https://doi.org/10.1371/journal.pone.0323645.s007
(ZIP)
S8 File. Paper dataset 3 Land use data (2000-2020): 2015-2017.
https://doi.org/10.1371/journal.pone.0323645.s008
(ZIP)
S11 File. Paper dataset 3 Land use data (2000-2020): 2018-2020.
https://doi.org/10.1371/journal.pone.0323645.s011
(ZIP)
Acknowledgments
The completion of the paper not only relies on the members of the project team but also thanks to the researchers who shared data online, and provided the basic data for the completion of the paper; Secondly, I would like to express my sincere gratitude to the reviewers for their valuable suggestions on the paper revisions, which have contributed significantly to the improvement of the paper and dataset is provided by National Ecosystem Science Data Center, National Science & Technology Infrastructure of China. (http://www.nesdc.org.cn).
References
- 1. Pan J, Li Y, Zhang Y. Transformational revolution and risk prevention of carbon neutrality. Qinghai Soc Sci. 2022;2022(256):1–9.
- 2. Wan H., Li H., Gao J. Study on the spatial pattern of carbon sequestration potential of vegetation ecosystems in China. Acta Ecologica Sinica. 2022;42 (21): 8568–80.
- 3. Wang J, Feng L, Palmer PI, Liu Y, Fang S, Bösch H, et al. Large Chinese land carbon sink estimated from atmospheric carbon dioxide data. Nature. 2020;586(7831):720–3. pmid:33116288
- 4. Fu B, Zhang L. Land use change and ecosystem services: concepts, methods and progress. Advances in Geographic Science. 2014;33(04):441–6.
- 5. Mo L, Zohner CM, Reich PB, Liang J, de Miguel S, Nabuurs G-J, et al. Integrated global assessment of the natural forest carbon potential. Nature. 2023;624(7990):92–101. pmid:37957399
- 6. Sha Z, Bai Y, Li R, Lan H, Zhang X, Li J, et al. The global carbon sink potential of terrestrial vegetation can be increased substantially by optimal land management. Commun Earth Environ. 2022;3(1).
- 7. Yang Y, Ren P, Hong B. Land use conflict identification based on ecological security in chongqing section of the three gorges reservoir area. Resour Environ Yangtze Basin. 2019;28(02):322–32.
- 8. Tian R, Chen Y, Zhou H. Spatial conflict identification of land use in mining cities from the perspective of ecological security pattern. Min Res Dev. 2020;40(06):153–9.
- 9. Dong G, Liu Z, Niu Y, Jiang W. Identification of land use conflicts in shandong province from an ecological security perspective. Land. 2022;11(12):2196.
- 10. Raj A, Sharma LK. Assessment of land-use dynamics of the Aravalli range (India) using integrated geospatial and CART approach. Earth Sci Inform. 2022;15(1):497–522.
- 11. Vanhala P, Bergström I, Haaspuro T, Kortelainen P, Holmberg M, Forsius M. Boreal forests can have a remarkable role in reducing greenhouse gas emissions locally: land use-related and anthropogenic greenhouse gas emissions and sinks at the municipal level. Sci Total Environ. 2016;557–558:51–7. pmid:26994793
- 12. Guo-Yi Z. Evaluation on the carbon pools of China’s forest ecosystems―current status, capacities and sinks and studies on the mechanisms. Chinese J Plant Ecol. 2016;40(4):279–81.
- 13. Wang X, Zhou M, Li T, Ke Y, Zhu B. Land use change effects on ecosystem carbon budget in the Sichuan Basin of Southwest China: conversion of cropland to forest ecosystem. Sci Total Environ. 2017;609:556–62. pmid:28763653
- 14. Liu J, Xu H, Wang Y. Evaluation of ecological risk and carbon sequestration function based on land use pattern change-taking Huanghua City, Hebei Province as an example. China Ecol Agric. 2018;26(08):1217–26.
- 15. Chen X, Wang X, Feng X. Trade-offs and synergies of ecosystem services in the Qinghai-Tibet Plateau. Geogr Res. 2021;40(1):18–34.
- 16. Zeng P, Zhang F, Feng Q. Estimation of carbon sequestration value and spatial-temporal evolution analysis of different vegetation ecosystems in Qilian Mountains. Glacier Permafrost. 2019;41(6):1348–58.
- 17. Kong J, Yang R, Su Y. Effect of land use and cover change on carbon stock dynamics in a typical desert oasis. Acta Ecol Sin. 2018;38(21):7801–12.
- 18. Houghton RA, Nassikas AA. Negative emissions from stopping deforestation and forest degradation, globally. Glob Chang Biol. 2018;24(1):350–9. pmid:28833909
- 19.
Chen Q. Study on landscape pattern-function coupling and optimization of land use in mountainous areas. Chongqing: Southwest University.
- 20. Zhang W, Li A, Jiang X. Quantitative definition of mountain spatial range in China based on DEM. Geogr Geogr Inf Sci. 2013;29(05):58–63.
- 21. Zhang W, Li A. Study on the suitable calculation scale of topographic relief in China based on DEM. Geo Geographic Inform Sci. 2012;28 (04): 8–12.
- 22. Zhao X, Su Y, Hu T, Chen L, Gao S, Wang R, et al. A global corrected SRTM DEM product for vegetated areas. Remote Sensing Letters. 2018;9(4):393–402.
- 23. Appiah D, Schröder D, Forkuo E, Bugri J. Application of geo-information techniques in land use and land cover change analysis in a Peri-Urban district of Ghana. IJGI. 2015;4(3):1265–89.
- 24. Ellis EA, Baerenklau KA, Marcos-Martínez R, Chávez E. Land use/land cover change dynamics and drivers in a low-grade marginal coffee growing region of Veracruz, Mexico. Agroforest Syst. 2010;80(1):61–84.
- 25. Hadian F, Jafari R, Bashari H, Tartesh M, Clarke KD. Estimation of spatial and temporal changes in net primary production based on Carnegie Ames Stanford Approach (CASA) model in semi-arid rangelands of Semirom county, Iran. J Arid Land. 2019;11(4):477–94.
- 26. Huang X, Li H, He Z. An improved Carnegie-Ames-Stanford approach model for estimating ecological carbon sequestration in mountain vegetation. Front Ecol Evol. 2022;10:1–17.
- 27. Lv X, Ya Q, Jiaqi Y. Analysis and prediction of forest carbon storage and carbon sequestration capacity in China. Acad J Environ Earth Sci. 2022;:40–50.
- 28. Zuo L, Jiang Y, Gao J. Quantitative separation of multidimensional driving forces of ecosystem services in the ecological protection red line area. Acta Geogr Sin. 2022;77(09):2174–88.
- 29. Bo Y, Li X, Liu K. Three decades of gross primary production (GPP) in China: variations. Trends, attributions, and prediction inferred from multiple datasets and time series modeling. Remote Sens. 2022; 14(11):2564.
- 30. Zhang S, Bai Y, Liu Q. Estimations of winter wheat yields in Shandong province based on remote sensed vegetation indices data and CASA model. Spectrosc Spectral Anal. 2021;41(1):257–64.