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Article

Vegetation Greening Enhanced the Regional Terrestrial Carbon Uptake in the Dongting Lake Basin of China

by
Shihan Wang
1,
Huihui Feng
1,2,*,
Bin Zou
1,2,
Zhuolin Yang
1 and
Shu Wang
1,3
1
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
2
Key Laboratory of Spatio-Temporal Information and Intelligent Services, Chinese Ministry of Natural Resources, Changsha 410083, China
3
Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518000, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(4), 768; https://doi.org/10.3390/f14040768
Submission received: 2 March 2023 / Revised: 4 April 2023 / Accepted: 6 April 2023 / Published: 8 April 2023
(This article belongs to the Special Issue Advances in Monitoring and Assessment of Forest Carbon Storage)
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Abstract

:
Terrestrial ecosystem carbon uptake is essential to achieving a regional carbon neutrality strategy, particularly in subtropical humid areas with dense vegetation. Due to the complex spatial and temporal heterogeneity of the carbon uptake of ecosystems, it is difficult to quantify the influence and contribution of different factors. With the aid of multisource remote sensing data, the spatiotemporal characteristics of carbon uptake and the impact of vegetation change were explored in the Dongting Lake Basin from 2001 to 2020. Based on the Conversion of Land Use and its Effects at Small regional extent (CLUE-S) model, we simulated the land use of the basin under different development scenarios in 2030. Our results showed that the basin has demonstrated a significant greening trend in the last 20 years, with a multiyear average NDVI (normalized difference vegetation index) of 0.60 and an increasing trend (y = 0.0048x − 9.069, R2 = 0.89, p < 0.01). In this context, the multiyear mean of net ecosystem productivity (NEP) was 314.95 g C·m−2·a−1 and also showed a significant increasing trend (y = 1.8915x + 295.09, R2 = 0.23, p < 0.05). Moreover, though the future carbon uptake might decrease because of the enhanced anthropogenic activities, the ecological conservation scenario might mitigate the reduction by 0.05 × 107 t. In conclusion, the greening trend enhanced the ecosystem carbon uptake in the Dongting lake basin. Considering the representativeness of the Dongting Lake Basin, the results of our study would provide useful clues for understanding the trend and pattern of terrestrial carbon uptake and for guiding the carbon neutrality strategy in the subtropical humid area.

Graphical Abstract

1. Introduction

The considerable carbon emissions (i.e., the extensive use of fossil fuels) have caused detrimental effects on human health, socioeconomic development, and the environment, which has aroused global environmental concern [1]. Consequently, many countries have prioritized reducing carbon emissions as a vital solution to tackle the issue of global climate change [2]. For example, on 22 September 2020, China officially announced at the 75th session of the United Nations General Assembly that it will achieve the goal of carbon peaking by 2030 and carbon neutrality by 2060 [3]. Current carbon emission reduction mainly focuses on energy restructuring and less on the role of terrestrial ecosystems in carbon sequestration. According to statistics, global anthropogenic CO2 emissions during 2010–2019 were 110 × 108 t C·a−1, of which approximately 31% (approximately 34 × 108 t C·a−1) were absorbed and fixed by terrestrial ecosystems [4]. Moreover, terrestrial ecosystems acted as a carbon sink with an annual uptake of 0.096~0.106 Pg C a−1 during 1981–2000 in China, which offset 14.6%~16.1% of the fossil fuel emissions during the same period [5]. Therefore, the regional terrestrial ecosystem with dense vegetation (i.e., the subtropical humid area) has a greater potential in sequestering and storing carbon dioxide [6]. Accurately assessing terrestrial ecosystem carbon sequestration has also become an important prerequisite for regional carbon trading and management [7].
The main methods for monitoring carbon uptake in ecosystems are the sample plot inventory method, model simulation method, and quantitative remote sensing method [5]. Among them, the sample plot inventory method is used to investigate the carbon stocks of vegetation, litterfall, soil, and other carbon pools in ecosystems by setting up typical sample plots and to obtain the changes in carbon stocks over a certain period of time based on long-term continuous measurements, which is more accurate but difficult to apply to regional large-scale uptake monitoring and analysis [8]. The model simulation method uses photosynthesis and respiration in vegetation growth to simulate its growth cycle and its key processes with solid physical mechanisms, but the assumptions and structures of different models vary greatly. Additionally, the complexity of input parameters would also generate the unavoidable uncertainty [8]. The quantitative remote sensing method provides an efficient way to track and analyze regional large-scale ecosystem carbon uptake based on remote sensing techniques to obtain various vegetation parameters. This method is then combined with ground survey data and empirical models to estimate carbon storage [9]. Carbon uptake monitoring of terrestrial ecosystems based on satellite remote sensing mainly utilizes the accurate inversion of ecological indicators such as gross primary productivity (GPP), net primary productivity (NPP), and net ecosystem productivity (NEP) [10]. NEP shows the net CO2 exchange between ecosystems and the atmosphere, showing how much carbon is stored in a certain area. When NEP > 0, the ecosystem behaves as a carbon sink, meaning that it sequesters more carbon from the atmosphere than it releases to the atmosphere. In addition, when NEP < 0, it behaves as a carbon source, meaning that it releases more carbon to the atmosphere [11]. The physical indication of NEP indicators is clear and easy to calculate. Therefore, they are widely used in studies related to carbon uptake in regional and even global terrestrial ecosystems.
Many factors affect carbon uptake in terrestrial ecosystems, including meteorological factors (temperature, precipitation, atmospheric radiation, etc.), soil conditions, human activities, and land use changes [12]. Among them, meteorology is an important factor in vegetation growth. It affects vegetation carbon uptake by influencing vegetation growth cycles, the magnitude of photosynthesis and respiration, and the activity of microorganisms in the soil [13]. Different vegetation types exhibit different levels of carbon uptake, and land use patterns directly reflect vegetation types and their spatial and temporal distribution characteristics, thus having a direct impact on regional vegetation carbon uptake [14]. In addition, the driving factors affect vegetation carbon uptake with a complex interaction [15]. For example, the meteorological effect is the main external factor affecting vegetation growth. However, the process of land use change considerably alters the flux of heat and water interactions at the “land–atmosphere” interface, exerting a significant impact on local climate characteristics [16]. Under the influence of these complex interactions, it is difficult to quantify the effect and contribution of specific factors on vegetation carbon uptake, making it challenging to analyze the causes of carbon uptake in terrestrial ecosystems.
The Dongting Lake basin is an important subtropical humid area in China’s middle and lower reaches of the Yangtze River, which is one of the 200 global conservation priority eco-regions [17]. It is also a biodiversity conservation hotspot in the world, which has three Ramsar sites in the Dongting Lake Basin [18]. The Food and Agriculture Organization of the United Nations (FAO) and the Worldwide Fund for Nature (WWF) have utilized it as a research sample because of its abundance in natural resources, its high percentage of forest cover, and its substantial potential as a carbon sink. The carbon sink function of terrestrial ecosystems in the Dongting Lake basin not only plays an essential role in the sustainable development of the region but also provides a reference for the “carbon peaking and carbon neutral” strategy in China and other regions of the world and therefore has a substantial demonstration value [19]. This study aimed to explore the spatial and temporal patterns of net ecosystem productivity in the Dongting Lake basin and the influencing factors, providing a scientific basis for understanding the characteristics of regional carbon fluxes and assisting in ecosystem management.

2. Materials and Methods

2.1. Study Area

The Dongting Lake basin (24°39′–30°24′ N, 107°16′–114°14′ E) covers most of Hunan Province and parts of Hubei, Guangxi, Guizhou, Chongqing, and Guangdong (Figure 1), with a basin area of approximately 14% of the Yangtze River basin area. The average multiyear temperature is 17 °C, the average multiyear precipitation is approximately 1437 mm, and the annual runoff is 2016 billion m3. The vegetation in the basin is dominated by evergreen broad-leaved forests, with mixed forests, shrubs, grasslands, and bamboo forests [19]. The ecological and economic zone of the Dongting Lake basin is an important national development area and a crucial part of the national strategic development. It played a significant role in ecosystem protection and other aspects.

2.2. Data Sources and Processing

In this study, the carbon budget response characteristics of vegetation changes in the Dongting Lake basin were studied based on remote sensing and ground multisource data. The primary data and their descriptions were as follows.
(1) NDVI data. The data were obtained from NASA’s MODIS NDVI (MOD 13A3) product for 2001–2020 (https://ladsweb.modaps.eosdis.nasa.gov/search, accessed on 1 January 2022), and the data resolution is 1 km with synthetic products every month. The product is calculated from atmospheric corrected bidirectional surface reflectance to remove water, clouds, heavy aerosols, and cloud shadows. It is widely used to monitor vegetation change and ecological environment at regional and global scales [20].
(2) NPP data. The data were obtained from NASA’s MODIS NPP (MOD17A3HGF) dataset for 2001–2020 (https://ladsweb.modaps.eosdis.nasa.gov/search/order, accessed on 20 January 2022), and the data resolution is 500 m with synthetic products every year. These data are based on MODIS/TERRA satellite remote sensing metrics. The annual NPP information is calculated by using the MME-BGG model and the light energy usage model, which is an upgraded version of the MOD17 data product. This product has a good correlation with Chinese field observation data (R2 = 0.81) [21], showing that it is reliable.
(3) Meteorological data. These mainly include monthly precipitation and temperature data, which are used for calculating soil microbial heterotrophic respiration and as input parameters of land use simulation models. The data were obtained from the National Science and Technology Infrastructure Platform-National Earth System Science Data Center (http://www.geodata.cn, accessed on 16 February 2022), with a 1 km data resolution and monthly synthesis products [22].
(4) Land use data. These were used to analyze the spatial variability in NEP for different land use types and as input parameters of the land use change model (CLUE-S). The data were obtained from Globalland30 (http://www.globallandcover.com, accessed on 12 March 2022), and it is a global Land cover dataset with a 30 m spatial resolution developed in China [23].
(5) Natural and social driving factor data. As the input parameters of the land use simulation model (CLUE-S), the driving factors of LUCC mainly include natural geographical factors, which are relatively stable on a short time scale, and socioeconomic factors, such as population economy and road traffic. Population density data were obtained from Worldpop (https://www.worldpop.org/, accessed on 30 March 2022) [24] and GDP data from the Resource and Environment Science and Data Center of the Chinese Academy of Sciences (http://www.resdc.cn, accessed on 1 April 2022) [25]. DEM data were obtained from the Geospatial Data Cloud (https://www.gscloud.cn/search, accessed on 6 April 2022) of ASTER GDEM data of 30 m, and the 2020 global NASA resolution 30 m DEM data that the National Aeronautics and Space Administration (NASA) has released (https://lpdaac.usgs.gov/news/release-nasadem-data-products/, accessed on 15 April 2022). The road and river data were provided by the Geographic Remote Sensing Ecology Network (http://www.gisrs.cn/, accessed on 20 April 2022).
The data sources in this study were varied, and there were significant differences among the data, so all data were unified to the WGS-84 UTM projection and resampled to a 1 km resolution. In addition, each driving factor data must be processed to raster format for storage, and the number of rasters was kept consistent during processing.

2.3. Methodologies

To explore the carbon budget response characteristics of vegetation changes in the Dongting Lake basin, this study first analyzed the spatial and temporal characteristics of vegetation changes in the Dongting Lake basin by combining the Theil–Sen Median and Mann–Kendall methods with vegetation NDVI data. Then, based on the soil microbial respiration model, the spatiotemporal changes in net ecosystem productivity (NEP) and the carbon sink levels of different land use types were analyzed. Finally, the CLUE-S model was used to predict the carbon budget in different development scenarios of the basin by combining the carbon sequestration characteristics of different land use types.

2.3.1. Trend Test Method

The Sen trend analysis method was used to calculate the trend of NDVI and NEP on the time series of the Dongting Lake basin and combined with the Mann–Kendall method to test the significance of the trend in the Dongting Lake basin [26,27,28]. The Sen trend analysis method has high calculation efficiency, which can sufficiently reduce noise interference, so it is commonly used in the trend analysis of long-time series data [29,30]. In addition, the Mann–Kendall method was further employed to test the significance of the NDVI and NEP data series. By combining these two methods, we were able to identify trends in the time series data and quantify the significance and direction. The trend test method implemented in this study was calculated based on the value of a raster pixel.

2.3.2. Estimation of NEP

The assessment of carbon sources/sinks of vegetation is mainly measured by the net carbon budget. Net ecosystem productivity (NEP) represents the net CO2 exchange between terrestrial ecosystems and the atmosphere [11]. Without considering the influence of other natural and anthropogenic factors, vegetation NEP can be expressed as the difference between vegetation net primary productivity (NPP) and carbon emissions from the heterotrophic respiration of soil microorganisms with the following equation.
N E P = N P P − R H
Pei et al. studied the relationship between soil carbon emissions and environmental factors. They established a regression model of soil microbial respiration with temperature and precipitation to estimate the distribution of soil microbial heterotrophic respiration. This empirical model has now achieved more satisfactory results in applying a carbon uptake estimation in different vegetation and climate zones, such as the Qinghai-Tibet Plateau, Southwest Arid Zone, Northeast Forest Zone, and Xuzhou city, and Jiangsu Province. Its calculation equation is as follows [31].
R H = 0.22 × E x p 0.0913 T + L n 0.3145 R + 1 × 30 × 0.465
where RH is the monthly soil microbial heterotrophic respiration value (g C), T is the monthly mean temperature (°C), and R is the monthly precipitation (mm).

2.3.3. LUCC Scenario Prediction and the Response of Carbon Uptake

In this study, the CLUE-S model was used to predict the change in vegetation land, and then to explore the response of the carbon budget under different LUCC scenarios. The CLUE-S model was developed by the research group “Land Use Change and its Effects” at Wageningen University, the Netherlands, on the basis of the earlier CLUE model [32,33,34], which can realize the dynamic simulation of land use change under multiple scenarios in a region based on the quantitative relationship between the spatial distribution of land use and the driving factors [35,36].
Considering the land use planning under different RCP scenarios [37] and the development trend of Dongting Lake Basin, three land use simulation scenarios were adopted in this study: (1) Natural development scenario: Based on the conversion probability of each land type in the basin from 2010 to 2020, the demand for each land use type in 2030 was calculated based on the current land use situation in 2020. (2) Ecological protection priority scenario: Priority was given to the protection of all vegetation land, the conversion probability of cultivated land and forestland to urban land was reduced by 30%, the transfer of grassland waters to urban land was reduced by 20%, and the conversion probability of urban land to forestland and grassland was increased by 10%. (3) Economic development priority scenario: This scenario advocates that regional economic development should be given priority, the probability of transferring cultivated land to urban land should be increased by 20%, the probability of transferring woodland and grassland to urban land should be increased by 10%, and the probability of transferring urban land to land other than cultivated land should be reduced by 30%.
Combined with the historical law of LUCC response characteristics of terrestrial ecosystem carbon uptake, the overall characteristics of carbon uptake under different scenarios are predicted to provide comparable options for future regional territorial spatial planning and optimization.

3. Results

3.1. Temporal and Spatial Variation of Vegetation in the Dongting Lake Basin

The average NDVI in the Dongting Lake Basin was 0.60 (Figure 2), which showed a decreasing trend from west to east. The high-value zone of NDVI (>0.60) is mainly located in the hilly and woodland-covered areas with high terrain, while the low-value zone (<0.40) is mainly located near the Dongting Lake area and in the central part of the basin. From the interannual variation in the NVDI (Figure 3), the NDVI of the basin showed a smooth upward trend in general (y = 0.0048x − 9.069, R2 = 0.89, p < 0.01). The greening trend was greater in 2011–2020 (0.06) than in 2001–2010 (0.03), which is consistent with the previous studies [19,38]. With a series of ecological restoration projects (i.e., the Returning Farmland to Forest), the vegetation has shown a continuous greening trend in the past two decades [38].
Figure 4 shows the spatial characteristics of changes in NDVI from 2001 to 2020. The vegetation index (NDVI) significantly increased over a wide range, mainly distributed in the forestland, arable land, and grassland areas in the northwest and central east of the basin, accounting for 88.11% of the total area of the basin. The main reasons may be related to the construction of shelterbelts in the middle and upper reaches of the Yangtze River; the growth of vegetation on the banks was caused by the use of the Three Gorges project, afforestation in mountain and lake areas, returning farmland to lakes, and closing mountains for forest cultivation and other national policy-oriented factors [38,39]. The zones where NDVI decreased significantly were mainly distributed around urban construction land such as Changzhutan and local cultivated areas in the Dongting Lake plain, accounting for 1.66% of the total basin area, which is closely related to the anthropogenic disturbances and economic development [40]. Overall, the area with an increasing trend of NDVI (90.10%) was much larger than the area with a decreasing trend (2.08%), indicating a significant greening trend in the Dongting Lake Basin.

3.2. Spatial and Temporal Characteristics of the Terrestrial Carbon Uptake

Figure 5 shows the regional distribution of the vegetation’s multiyear average NEP values in the Dongting Lake basin. Overall, the vegetation ecosystem in the Dongting Lake basin exhibited a carbon sink, with a multiyear average NEP value of 314.95 g C·m−2·a−1. The zones with a strong carbon sink effect (NEP > 600 g C·m−2·a−1) were mainly located in the western, southeastern edge, and northwestern edge regions of the watershed. The weaker (NEP < 300 g C·m−2·a−1) zones were mainly located near the Dongting Lake area, the urban construction sites, and their surrounding areas in the central part of the basin. In terms of temporal changes (Figure 6), the overall NEP of the basin showed an increasing trend (y = 1.8915x + 295.09, R2 = 0.23, p < 0.05), where the maximum NEP value appeared in 2015 (366.49 g C·m−2·a−1) and the minimum value appeared in 2005 (276.23 g C·m−2·a−1).
Figure 7 shows the spatial characteristics of changes in vegetation NEP from 2001 to 2020. The results of the Sen-MK trend test indicated that the areas with a significant increase in the vegetation carbon uptake accounted for 42.52% of the total area, mainly located in the flat topographic agricultural production areas in the central part of the basin [41]. The areas with decreasing vegetation carbon uptake effect were mainly located in the hilly woodland areas at the northern edge, accounting for 29.91% of the total area. In general, the overall NEP level in the basin showed a fluctuating upward trend (1.40 g C·m−2·a−1). However, it is still necessary to strengthen the protection of forests to avoid the risk of weakening the vegetation carbon uptake due to forest degradation.
The carbon uptake effect varied significantly over different vegetation types (Figure 8). We found that forestland had the highest carbon uptake effect (347.53 g C·m−2·a−1), followed by grassland (327.76 g C·m−2·a−1), and cultivated land (261.82 g C·m−2·a−1) had the lowest. In addition, the carbon uptake of the different vegetation types all showed a significant upward trend. The growth rate of cultivated land was fastest (1.87 g C·m−2·a−1), while that of forestland (1.13 g C·m−2·a−1) and grassland (1.71 g C·m−2·a−1) was lower. The main reason may be the influence by the development of agricultural production, technological progress, investment increase, and crop yield, which consequently resulted in the increasing carbon sequestration effect of crops during the growth period [38,42,43]. The statistical results of the whole region showed that forestland is the main component of carbon uptake in the Dongting Lake basin, with an annual average carbon uptake of 5.17 × 107 t C, accounting for 65.39% of the total carbon uptake in the basin. In addition, cultivated land is also an important carbon uptake element, with an annual average carbon uptake of 2.07 × 107 t C, accounting for 26.21% of the total carbon uptake. In comparison, grassland has limited carbon uptake effect due to its small percentage of area, with an average annual carbon uptake of 0.66 × 107 t C, accounting for 8.40% of the total carbon uptake.

3.3. Prediction of Carbon Budget Scenarios under Different Vegetation Changes

The CLUE-S model was employed to simulate and predict land use change in 2030 under different scenarios to further explore the influence of land use types on the carbon budget of vegetation ecosystems in the basin. The Kappa coefficient of the 2020 LUCC simulation results based on the 2000 and 2010 data was 0.83, which indicated that the model had high confidence in the LUCC simulation results in the Dongting Lake basin.
Figure 9 displays the outcomes of various scenarios from the LUCC simulation. In general, driven by the demand of regional socioeconomic development, urban construction land showed an increasing trend in different scenarios, while cultivated land, forestland, and grassland continued to decrease (Table 1). However, the changes in each land type varied significantly under different scenarios. Under the natural development scenario, urban land continued to increase significantly (3467 km2). Land types such as cultivated land, forestland, and grassland continued to decrease by 985 km2, 2561 km2, and 757 km2, respectively, while the water area continued to expand (975 km2). Under the ecological protection scenario, vegetated land types were effectively protected, with cultivated land, forestland, and grassland being 681 km2, 421 km2, and 82 km2 higher than the natural growth scenario, respectively, while the expansion of urban land area (2257 km2) slowed down compared to the natural development scenario. In the priority economic development scenario, urban construction land expanded dramatically, increasing by 4182 km2 compared to 2020, while all vegetation types of land showed a clear trend of decrease, with cultivated land, forestland, grassland, and other land types decreasing by 1428 km2, 2755 km2, and 807 km2, respectively, compared to 2020.
The corresponding ecosystem carbon uptake characteristics were further analyzed based on the predicted results of the above LUCC scenarios (Figure 10, Table 2). Due to the increase in urban construction land and the continuous decrease in vegetation area, the ecosystem absorption under the three scenarios showed a downward trend. However, the vegetation carbon uptake in Dongting Lake Basin under the ecological simulation scenario was highest (7.83 × 107 t C), which was the lowest decrease compared with 2020. However, the carbon uptake in the natural development scenario (7.80 × 107 t C) and economic development scenario (7.78 × 107 t C) was significantly reduced, which was 0.14 × 107 t C and 0.16 × 107 t C lower than those in 2020, respectively. Therefore, there is an urgent need to integrate socioeconomic and ecological environmental protection under the ecological protection scenario to promote collaborative and sustainable development in the future.

4. Discussion

Using multisource remote sensing data, this study examined the role of vegetation change on the terrestrial carbon uptake in a subtropical humid area, which would enhance our knowledge in the physical mechanism and support the land planning and eco-environment management. Results revealed that the vegetation greening enhanced the carbon sink level of the Dongting Lake Basin in the past two decades. In addition, the ecological conservation scenario could offset the potential decrease in future carbon uptake resulting from increased anthropogenic activities by 0.05 × 107 t.
LUCC plays an important role in the terrestrial carbon uptake at regional and global scales. Previous studies warned that global deforestation weakened the magnitude of the terrestrial carbon uptake in recent decades [44,45,46]. Our work suggested that the degradation might be mitigated in the subtropical regions. The latest works also showed an overall greening trend at the global scale, particularly of China and India [47,48,49], which would therefore be expected to be an increased terrestrial carbon uptake in the foreseeable future.
On the other hand, a contradiction always remains between socioeconomic development and eco-environment conservation. Urbanization unavoidably invaded the natural lands (i.e., the farmland, grassland, and forest) for the target of socioeconomic development [50], leaving great pressure on the terrestrial carbon uptake. Despite this, the ecological protection priority scenario would help to minimize the negative impact of urbanization. In this study, the regional carbon uptake would decrease in all the scenarios of recent decades. However, the magnitude of the carbon uptake decrease is minor in the ecological protection priority scenario, which strongly meets the sustainable goal for balancing socioeconomic development and eco-environment conservation.
However, the advances in new technologies will have a new impact on the management of carbon sinks in the future. For example, the reduction in the cost of wood biochar would help to improve the biological carbon sequestration for carbon sinks management [51]. Moreover, although the application of solid biofuel technology has the potential to reduce the dependence on fossil fuels [52], the relationship is complex [53,54]. Therefore, a comprehensive consideration of technical, economic, environmental, and policy factors is required to promote sustainable development and address the challenges of climate change.
There are still several uncertainties of our investigation. The first challenge refers to the accurate data source, which is crucial for the analysis. In this study, we adopted the MODIS NPP product for the investigation, which had been widely used in the previous studies [55,56]. However, it still contains unavoidable uncertainty because of the algorithm assumption and the complexity of terrestrial carbon uptake, which requires a continuous improvement of the dataset [21]. Furthermore, the vegetation exerts a nonlinear impact on the terrestrial ecosystem. For example, some researchers have pointed out that forest age, even in the same species, is also an essential driver of regional carbon uptake [57,58]. Moreover, it should be noted that plant phenology, soil microbial diversity, and plant diversity, which have significant impacts on plant growth and development, may also introduce uncertainties in plant production [59,60,61]. In general, the spatial and temporal resolution of the data limits the commercial use of the method and the heterogeneity of the model limits the application in other regions, but this study provides a new idea for carbon uptake estimation and prediction to other regions. Such uncertainties should be considered in future studies for improving the understanding on the terrestrial carbon uptake and its driving factors.

5. Conclusions

This study analyzed the relationship between vegetation change and terrestrial carbon uptake in the Dongting Lake basin. The greening trend in the Dongting Lake basin was characterized by an increasing trend of average NDVI (y = 0.0048x − 9.069, R2 = 0.89, p < 0.01). As a result, the regional terrestrial carbon uptake also showed an overall upward trend, with the average annual NEP increasing by y = 1.8915x + 295.09 (R2 = 0.23, p < 0.05). This means that the greening trend of vegetation in the basin has enhanced the carbon uptake level of vegetation in the past two decades. Moreover, the forests played an important role in the carbon uptake, with a higher contribution (65.39%) than the grassland (26.21%) and cultivated land (8.40%), indicating that optimal forest management of the basin is necessary in the future. Furthermore, we found that the magnitude of carbon uptake decreased under all development scenarios in future, while the reduction is minor under the ecological protection scenario (7.83 × 107 t C) when compared to the natural growth scenario (7.80 × 107 t C) and the economic development priority scenario (7.78 × 107 t C), suggesting that carbon sinks in subtropical regions may be mitigated by optimal land management in the future to prevent a decrease in magnitude.
The results of this study can support the ecological governance of the Dongting Lake basin and present a scientifically supported supplemental foundation for comprehending how vegetation carbon uptake varies and for achieving the region’s “carbon peaking and carbon neutrality” goals. It helped to capture the overall spatial pattern and temporal trend of the terrestrial carbon uptake through satellite observation. The ground observations can be used to explore the impact mechanism of the regional carbon uptake in future research. Furthermore, it needed to carry out spatial optimization simulations of national land for regional carbon neutrality targets, which may provide a scientific decision-making basis for regional ecosystem conservation and promote regional sustainable development.

Author Contributions

Conceptualization, H.F., B.Z. and S.W. (Shihan Wang); Methodology, S.W. (Shihan Wang) and H.F.; Writing—Original Draft Preparation, S.W. (Shihan Wang); Writing—Review and Editing, H.F., S.W. (Shu Wang) and Z.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported in part by the National Natural Science Foundation of China (Grant No. 42071378), the Nature Science Foundation of Hunan Province (Grant No. 2020JJ3045), and the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Minis-try of Natural Resources (Grant No. KF-2022-07-021).

Data Availability Statement

The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. The Dongting Lake Basin.
Figure 1. The Dongting Lake Basin.
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Figure 2. Spatial distribution in the average NDVI in the Dongting Lake basin from 2001 to 2020.
Figure 2. Spatial distribution in the average NDVI in the Dongting Lake basin from 2001 to 2020.
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Figure 3. Temporal trends of the average NDVI in the Dongting Lake basin from 2001 to 2020.
Figure 3. Temporal trends of the average NDVI in the Dongting Lake basin from 2001 to 2020.
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Figure 4. Spatial patterns of the NDVI trends in the Dongting Lake Basin from 2001 to 2020: (a) The Sen trend analysis of NDVI shows the spatial distribution of the greening trend, (b) The significance test of NDVI shows different levels of change (*: p < 0.05).
Figure 4. Spatial patterns of the NDVI trends in the Dongting Lake Basin from 2001 to 2020: (a) The Sen trend analysis of NDVI shows the spatial distribution of the greening trend, (b) The significance test of NDVI shows different levels of change (*: p < 0.05).
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Figure 5. Spatial distribution of the average NEP in the Dongting Lake basin from 2001 to 2020.
Figure 5. Spatial distribution of the average NEP in the Dongting Lake basin from 2001 to 2020.
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Figure 6. Temporal trends of the average NEP in the Dongting Lake basin from 2001 to 2020.
Figure 6. Temporal trends of the average NEP in the Dongting Lake basin from 2001 to 2020.
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Figure 7. Spatial patterns of the NEP trends in the Dongting Lake Basin from 2001 to 2020: (a) The Sen trend analysis of NEP reveals the spatial distribution of the trend of carbon uptake, (b) The significance test of NEP shows different levels of change (*: p < 0.05).
Figure 7. Spatial patterns of the NEP trends in the Dongting Lake Basin from 2001 to 2020: (a) The Sen trend analysis of NEP reveals the spatial distribution of the trend of carbon uptake, (b) The significance test of NEP shows different levels of change (*: p < 0.05).
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Figure 8. Comparison of carbon uptake effect of different vegetation types.
Figure 8. Comparison of carbon uptake effect of different vegetation types.
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Figure 9. Multi-scenario simulated land use map of Dong Ting Lake Basin in 2030: (a) land use in 2020, and (b) the natural development scenario, (c) the ecological protection priority scenario, and the (d) economic development priority scenario in 2030.
Figure 9. Multi-scenario simulated land use map of Dong Ting Lake Basin in 2030: (a) land use in 2020, and (b) the natural development scenario, (c) the ecological protection priority scenario, and the (d) economic development priority scenario in 2030.
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Figure 10. Comparison of vegetation carbon uptake in the Dongting Lake basin under different scenarios (×107 t C).
Figure 10. Comparison of vegetation carbon uptake in the Dongting Lake basin under different scenarios (×107 t C).
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Table
Table 1. Area of different land use types under multi-scenario simulation (km2).
Table 1. Area of different land use types under multi-scenario simulation (km2).
Cultivated LandForestlandGrasslandWater AreaUrban LandUnused Land
Present Situation of Land Use in 202080,336149,12019,82671696852482
Natural development scenario in 203079,351146,55919,069814410,319343
Ecological protection priority scenario in 203080,032146,98019,15181679109346
Economic development priority scenario in 203078,908146,36519,019811411,034345
Table
Table 2. Carbon uptake of different scenarios in the Dongting Lake basin in 2030 (×107 t C).
Table 2. Carbon uptake of different scenarios in the Dongting Lake basin in 2030 (×107 t C).
Cultivated Land Carbon UptakeForestland Carbon UptakeGrassland Carbon UptakeThe Total of
Carbon Uptake
Present Situation in 20202.105.180.657.94
Natural development scenario in 20302.085.090.637.80
Ecological protection priority scenario in 20302.105.110.637.83
Economic development priority scenario in 20302.075.090.627.78
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Wang, S.; Feng, H.; Zou, B.; Yang, Z.; Wang, S. Vegetation Greening Enhanced the Regional Terrestrial Carbon Uptake in the Dongting Lake Basin of China. Forests 2023, 14, 768. https://doi.org/10.3390/f14040768

AMA Style

Wang S, Feng H, Zou B, Yang Z, Wang S. Vegetation Greening Enhanced the Regional Terrestrial Carbon Uptake in the Dongting Lake Basin of China. Forests. 2023; 14(4):768. https://doi.org/10.3390/f14040768

Chicago/Turabian Style

Wang, Shihan, Huihui Feng, Bin Zou, Zhuolin Yang, and Shu Wang. 2023. "Vegetation Greening Enhanced the Regional Terrestrial Carbon Uptake in the Dongting Lake Basin of China" Forests 14, no. 4: 768. https://doi.org/10.3390/f14040768

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