A study of the effects of climate change and human activities on NPP of marsh wetland vegetation in the Yellow River source region between 2000 and 2020

Feng, Xueke; Zhao, Zhizhong; Ma, Tao; Hu, Bixia

doi:10.3389/fevo.2023.1123645

Cited by 5 publications

(3 citation statements)

References 51 publications

(40 reference statements)

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“…The dynamic changes in NPP of marsh vegetation on the north and south sides of the Hu Line are significantly affected by SD and LUCC. This is because marshes, as fragile ecosystems, are greatly affected by light and heat conditions and are more susceptible to the impact of human activities [53].…”

Section: Discussionmentioning

confidence: 99%

Analysis of Dynamic Changes in Vegetation Net Primary Productivity and Its Driving Factors in the Two Regions North and South of the Hu Huanyong Line in China

Liu,

Yan,

et al. 2024

Land

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Human activities and global environmental changes have transformed terrestrial ecosystems, notably increasing vegetation greenness in China. However, this greening is less effective across the Hu Huanyong Line (Hu Line). This study analyzes dynamic changes and driving factors of nine vegetation net primary productivities (NPPs) in regions divided by the Hu Line using remote sensing data, trend analysis, and the Geodetector model. Findings reveal that from 2001 to 2022, 38.22% of regional vegetation NPP in China increased, especially in the Loess Plateau, Sichuan Basin, and Northeast Plains, while 2.39% decreased, primarily in the southeastern region and southern Tibet. Grasslands contributed 39.71% to NPP north of the Hu Line, and cultivated vegetation contributed 50.58% south. The driving explanatory power of factors on vegetation NPP on the north side of the Hu Line is generally greater than that on the south side. Natural factors primarily drive NPP changes, with human activities having less impact. Combined factors, particularly climate and elevation, significantly enhance the driving explanatory power (q, 0–1). The joint effects of elevation and precipitation on grassland NPP dynamics (q = 0.602) are notable. GDP’s influence on broadleaf forests north of the Hu Line (q = 0.404) is significant. Grasslands respond strongly to land use changes and population density, with a combined effect of q = 0.535. Shrubs, alpine vegetation, and meadows show minimal response to individual factors (q < 0.2). These findings offer insights for devising ecological protection measures tailored to local conditions.

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Section: Discussionmentioning

confidence: 99%

Analysis of Dynamic Changes in Vegetation Net Primary Productivity and Its Driving Factors in the Two Regions North and South of the Hu Huanyong Line in China

Liu,

Yan,

et al. 2024

Land

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“…Vegetation CO 2 uptake is achieved through plant photosynthesis, and the sunshine duration, which is one of the three main factors of meteorological plant photosynthesis (sunshine duration, temperature, and rainfall), was relatively constant. Thus, the temperature and humidity became the main factors influencing plant photosynthesis and, therefore, NPP [19]. As such, we included temperature and rainfall as some of the natural factors in our analysis.…”

Section: Natural Factorsmentioning

confidence: 99%

Evaluation of Future Trends Based on the Characteristics of Net Primary Production (NPP) Changes over 21 Years in the Yangtze River Basin in China

et al. 2023

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As the third largest river basin in the world, the Yangtze River basin in China has vegetation ecosystems in its plain, mountain, and alpine regions. Studying the change characteristics of the vegetation’s net primary productivity (NPP) and its relationship with natural factors and human activities can aid with understanding, to a certain extent, the response of the ecosystem to global climate change. Based on a total of 21 years of MOD17A3 data products from 2000 to 2020, this paper analyzed the spatial variation characteristics and future trends of the NPP in this region by using the coefficient of variation (CV), trend analysis (β), and Hurst index (H) methods. Meanwhile, correlation analysis was used to explore the influence of natural factors and human activities on the NPP. The results show the following: (1) the total amount of the NPP in the Yangtze River Basin was relatively high, and the overall change trend is rising, while the inter-annual fluctuation is evident. The total amount of NPP ranges from 0.786 PgC (2000) to 1.024 PgC (2020), and the annual average was 0.932 PgC. This increase was mainly caused by the increase in the average NPP of forest land, cultivated land, and construction land. (2) The mean value of the NPP in the different regions of the Yangtze River Basin ranged from 0 (construction land) to 1902.89 gC/m2·a. The mean value of the NPP in the Yangtze River Basin was high in the south and low in the north, as well as high in the middle and low in the east and west. The main high-value areas were located in the Hengduan Mountains and the Yunnan-Guizhou Plateau. The coefficient of variation (CV) was 0.0009–0.9980, and the mean CV was only 0.1126. Regarding the future development trend, 77.90% and 22.10% of the regions showed an increase, 22.10% showed a decrease, and 75.25% showed an anti-sustainable state. (3) The effect of human activities on the NPP was generally negative, and the loss of NPP due to land use change in 2020 was around 9.85 TgC when compared with the same in 2000. (4) The rainfall and temperature in the Yangtze River basin both showed a non-significant increase, and the correlation coefficient between the NPP and rainfall was between −0.874 and 0.910. Furthermore, the correlation coefficient of the temperature ranged from −0.928 to 0.929, with a positive correlation overall and a negative correlation locally, and the NPP changes were more susceptible to the influence of temperature than rainfall.

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“…Exploratory research on wetland spatiotemporal evolution patterns has made continuous progress due to the rapid development of RS and GIS, and research on the causes of natural wetlands variation is gradually becoming a hot issue in the field of natural wetlands [24][25][26]. Natural wetlands are particularly sensitive to human activities (urbanization, long-term groundwater extraction, species invasion, and industrial pollution) [27,28] and are influenced by natural factors such as climate change and topographic conditions [29][30][31][32]. Due to the lack of effective methods to control excessive human activity, the fifth assessment reports from the IPCC (Intergovernmental Panel on Climate Change) predicted further global warming trends, including the temperature rising, a precipitation regime shift, a sea level rise, and an increasing frequency of extreme climate events [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Driving Force Analysis of Natural Wetland in Northeast Plain Based on SSA-XGBoost Model

Liu,

Lin,

Zhang

et al. 2023

Sensors

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Globally, natural wetlands have suffered severe ecological degradation (vegetation, soil, and biotic community) due to multiple factors. Understanding the spatiotemporal dynamics and driving forces of natural wetlands is the key to natural wetlands’ protection and regional restoration. In this study, we first investigated the spatiotemporal evolutionary trends and shifting characteristics of natural wetlands in the Northeast Plain of China from 1990 to 2020. A dataset of driving-force evaluation indicators was constructed with nine indirect (elevation, temperature, road network, etc.) and four direct influencing factors (dryland, paddy field, woodland, grassland). Finally, we built the driving force analysis model of natural wetlands changes to quantitatively refine the contribution of different driving factors for natural wetlands’ dynamic change by introducing the sparrow search algorithm (SSA) and extreme gradient boosting algorithm (XGBoost). The results showed that the total area of natural wetlands in the Northeast Plain of China increased by 32% from 1990 to 2020, mainly showing a first decline and then an increasing trend. Combined with the results of transfer intensity, we found that the substantial turn-out phenomenon of natural wetlands occurred in 2000–2005 and was mainly concentrated in the central and eastern parts of the Northeast Plain, while the substantial turn-in phenomenon of 2005–2010 was mainly located in the northeast of the study area. Compared with a traditional regression model, the SSA-XGBoost model not only weakened the multicollinearity of each driver but also significantly improved the generalization ability and interpretability of the model. The coefficient of determination (R2) of the SSA-XGBoost model exceeded 0.6 in both the natural wetland decline and rise cycles, which could effectively quantify the contribution of each driving factor. From the results of the model calculations, agricultural activities consisting of dryland and paddy fields during the entire cycle of natural wetland change were the main driving factors, with relative contributions of 18.59% and 15.40%, respectively. Both meteorological (temperature, precipitation) and topographic factors (elevation, slope) had a driving role in the spatiotemporal variation of natural wetlands. The gross domestic product (GDP) had the lowest contribution to natural wetlands’ variation. This study provides a new method of quantitative analysis based on machine learning theory for determining the causes of natural wetland changes; it can be applied to large spatial scale areas, which is essential for a rapid monitoring of natural wetlands’ resources and an accurate decision-making on the ecological environment’s security.

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A study of the effects of climate change and human activities on NPP of marsh wetland vegetation in the Yellow River source region between 2000 and 2020

Cited by 5 publications

References 51 publications

Analysis of Dynamic Changes in Vegetation Net Primary Productivity and Its Driving Factors in the Two Regions North and South of the Hu Huanyong Line in China

Analysis of Dynamic Changes in Vegetation Net Primary Productivity and Its Driving Factors in the Two Regions North and South of the Hu Huanyong Line in China

Evaluation of Future Trends Based on the Characteristics of Net Primary Production (NPP) Changes over 21 Years in the Yangtze River Basin in China

Driving Force Analysis of Natural Wetland in Northeast Plain Based on SSA-XGBoost Model

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