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Dai T, Dai X, Lu H, He T, Li W, Li C, Huang S, Huang Y, Tong C, Qu G, Shan Y, Liang S, Liu D. The impact of climate change and human activities on the change in the net primary productivity of vegetation-taking Sichuan Province as an example. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:7514-7532. [PMID: 38159188 DOI: 10.1007/s11356-023-31520-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 12/09/2023] [Indexed: 01/03/2024]
Abstract
Vegetation is an essential component of terrestrial ecosystems, influenced by climate change and human activities. Quantifying the relative contributions of climate change and human activities to vegetation dynamics is crucial for addressing global climate change. Sichuan Province is one of the essential ecological functional areas in the upper reaches of the Yangtze River, and its vegetation change is of great significance to the environmental function and ecological security of the Yangtze River Basin and southwest China. In this paper, the modified Carnegie-Ames-Stanford Approach(CASA) model was used to estimate the monthly NPP (Net Primary Productivity) of vegetation in Sichuan Province from 2000 to 2018, and the univariate linear regression analysis was used to analyze the temporal and spatial variation of vegetation NPP in Sichuan Province from 2000 to 2018. In addition, taking vegetation NPP as an index, Pearson correlation analysis, partial correlation analysis, and second-order partial correlation analysis were carried out to quantitatively analyze the contribution of climate change and human activities to vegetation NPP. Finally, the Hurst index and nonparametric Man-Kendall significance test were used to predict the future change trend of vegetation NPP in Sichuan Province. The results show that (1) from 2000 to 2018, the NPP of vegetation in Sichuan Province has a significant increasing trend (Slope = 6.09gC·m-2·a-1), with a multi-year average of 438.72 gC·m-2·a-1, showing a trend of low in the east and high in the middle. The response of vegetation NPP to altitude is different at different elevations; (2) the contribution rates of climate change and human activities to vegetation NPP change are 4.12gC·m-2·a-1 and 1.97gC·m-2·a-1, respectively. In contrast, the impact of human activities on NPP is more significant than climate change. Human activities are the main factors affecting vegetation restoration and degradation in Sichuan Province. However, the positive contribution to NPP change is less than climate change; (3) the future vegetation NPP change trend in Sichuan Province is mainly rising, and the same direction change trend is much larger than the reverse change trend. The areas with an increasing trend in the future account for 89.187% of the total area. This research helps understand the impact of climate change and human activities on vegetation change in Sichuan Province. It offers scientific bases for vegetation restoration and ecosystem management in Sichuan and the surrounding areas.
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Affiliation(s)
- Tangrui Dai
- College of Earth Sciences, Chengdu University of Technology, Chengdu, 610059, China
| | - Xiaoai Dai
- College of Earth Sciences, Chengdu University of Technology, Chengdu, 610059, China.
| | - Heng Lu
- State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, 610065, China
| | - Tao He
- College of Earth Sciences, Chengdu University of Technology, Chengdu, 610059, China
| | - Weile Li
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China
| | - Cheng Li
- College of Earth Sciences, Chengdu University of Technology, Chengdu, 610059, China
| | - Shengqi Huang
- College of Earth Sciences, Chengdu University of Technology, Chengdu, 610059, China
| | - Yiyang Huang
- College of Earth Sciences, Chengdu University of Technology, Chengdu, 610059, China
| | - Chenbo Tong
- College of Earth Sciences, Chengdu University of Technology, Chengdu, 610059, China
| | - Ge Qu
- College of Earth Sciences, Chengdu University of Technology, Chengdu, 610059, China
| | - Yunfeng Shan
- College of Earth Sciences, Chengdu University of Technology, Chengdu, 610059, China
| | - Shuneng Liang
- Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing, 100048, China
| | - Dongsheng Liu
- PIESAT Information Technology Co., Ltd., Beijing, 100195, China
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Yang C, Zhai G, Fu M, Sun C. Spatiotemporal characteristics and influencing factors of net primary production from 2000 to 2021 in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:91084-91094. [PMID: 37466838 DOI: 10.1007/s11356-023-28666-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023]
Abstract
With the rapid development of remote sensing, variously high temporal and spatial resolution products of different sensors were gradually applied aspects of researches, which could achieve rapid and low-cost monitors of terrestrial environment. It was meaningful to analyze the latest and long-term changes of net primary production (NPP), which could reflect the human-induced effects on ecological environment. In our study, we used Sen's slope and Mann-Kendall test to analyze the spatiotemporal changes of NPP. Then, we used fluctuation model and Moran model to reveal the stability and clusters of NPP, respectively. Next, we quantitatively analyzed NPP changes in the perspectives of land use types and provinces. Finally, we used geographically weighted regression (GWR) model to analyze effects of different factors on NPP. The result showed that NPP presented significant increase in most areas of China from 2000 to 2021. Especially, Loess Plateau showed obvious NPP increase. Meanwhile, "high-high" cluster of NPP difference were mainly distributed in the ecological policies-influenced areas. The slope in 5-15° has the highest growth trend, and the slope > 25° has the slowest growth trend. Cropland, forests, and shrub revealed an obvious improvement of NPP, which indicated afforestation and intensive farmed played a key role. Temperature, precipitation, population density, and elevation had significant effects on NPP (p < 0.05) in 2000, 2011, and 2021. The degree of effects of human activities was gradually increase in GWR model. In this scenario, related ecological policies had vita influencing on NPP improvement. Our study could provide a help for monitor of ecological environment and government policies.
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Affiliation(s)
- Chen Yang
- School of Land Science and Technology, China University of Geosciences (Beijing), Haidian District, 29 Xueyuan Road, Beijing, 100083, China
- Real Estate Registration Center, Ministry of Natural Resources, Beijing, 100034, China
| | - Guohui Zhai
- Real Estate Registration Center, Ministry of Natural Resources, Beijing, 100034, China
| | - Meichen Fu
- School of Land Science and Technology, China University of Geosciences (Beijing), Haidian District, 29 Xueyuan Road, Beijing, 100083, China.
| | - Chang Sun
- School of Modern Science & Technology, Hebei Agricultural University, Hebei, 071001, China
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Chen S, Yang L, Liu X, Zhu Z. Net Primary Productivity Variations Associated with Climate Change and Human Activities in Nanjing Metropolitan Area of China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14798. [PMID: 36429517 PMCID: PMC9691230 DOI: 10.3390/ijerph192214798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/03/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
Rapid economic development has changed land use and population density, which in turn affects the stability and carbon sequestration capacity of regional ecosystems. Net primary productivity (NPP) can reflect the carbon sequestration capacity of ecosystems and is affected by both climate change and human activities. Therefore, quantifying the relative contributions of climate change and human activities on NPP can help us understand the impact of climate change and human activities on the carbon sequestration capacity of ecosystems. At present, researchers have paid more attention to the impact of climate change and land use change on NPP. However, few studies have analyzed the response of the NPP to gross domestic product (GDP) and population density variations on a pixel scale. Therefore, this paper analyzes the impact of climate change and human activities to NPP on a pixel scale in the Nanjing metropolitan area. During the period 2000-2019, the annual mean NPP was 494.89 g C·m-2·year-1, and the NPP in the south of the Nanjing metropolitan area was higher than that in the north. The NPP was higher in the forest, followed by unused land, grassland, and cropland. In the past 20 years, the annual mean NPP showed a significant upward trend, with a growth rate of 3.78 g C·m-2·year-1. The increase in temperature and precipitation has led to an increasing trend of regional NPP, and the impact of precipitation on NPP was more significant than that of temperature. The transformation of land use from low-NPP type to high-NPP type also led to an increase in NPP. Land use change from high-NPP type to low-NPP type was the main cause of regional NPP decline. Residual analysis was used to analyze the impact of human activities on NPP. Over the last 20 years, the NPP affected by human activities (NPPhum) showed a high spatial pattern in the south and a low spatial pattern in the north, and the annual mean NPPhum also showed a fluctuating upward trend, with a growth rate of 2.00 g C·m-2·year-1. The NPPhum was influenced by both GDP and population density, and the impact of population density on NPP was greater than that of GDP.
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