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Qin J, Ma M, Shi J, Ma S, Wu B, Su X. The Time-Lag Effect of Climate Factors on the Forest Enhanced Vegetation Index for Subtropical Humid Areas in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:799. [PMID: 36613120 PMCID: PMC9819476 DOI: 10.3390/ijerph20010799] [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: 10/30/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
Forests represent the greatest carbon reservoir in terrestrial ecosystems. Climate change drives the changes in forest vegetation growth, which in turn influences carbon sequestration capability. Exploring the dynamic response of forest vegetation to climate change is thus one of the most important scientific questions to be addressed in the precise monitoring of forest resources. This paper explores the relationship between climate factors and vegetation growth in typical forest ecosystems in China from 2007 to 2019 based on long-term meteorological monitoring data from six forest field stations in different subtropical ecological zones in China. The time-varying parameter vector autoregressive model (TVP-VAR) was used to analyze the temporal and spatial differences of the time-lag effects of climate factors, and the impact of climate change on vegetation was predicted. The enhanced vegetation index (EVI) was used to measure vegetation growth. Monthly meteorological observations and solar radiation data, including precipitation, air temperature, relative humidity, and photosynthetic effective radiation, were provided by the resource sharing service platform of the national ecological research data center. It was revealed that the time-lag effect of climate factors on the EVI vanished after a half year, and the lag accumulation tended to be steady over time. The TVP-VAR model was found to be more suitable than the vector autoregressive model (VAR). The predicted EVI values using the TVP-VAR model were close to the true values with the root mean squares error (RMSE) < 0.05. On average, each site improved its prediction accuracy by 14.81%. Therefore, the TVP-VAR model can be used to analyze the relationship of climate factors and forest EVI as well as the time-lag effect of climate factors on vegetation growth in subtropical China. The results can be used to improve the predictability of the EVI for forests and to encourage the development of intensive forest management.
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Affiliation(s)
- Jushuang Qin
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
- Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China
| | - Menglu Ma
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
- Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China
| | - Jiabin Shi
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
- Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China
| | - Shurui Ma
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
- Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China
| | - Baoguo Wu
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
- Research Institute of Forestry Informatization, Beijing Forestry University, Beijing 100083, China
| | - Xiaohui Su
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
- Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China
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Ma B, Jing J, Liu B, Xu Y, Dou S, He H. Quantitative assessment of the relative contributions of climate change and human activities to NPP changes in the Southwest Karst area of China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:80597-80611. [PMID: 35723822 DOI: 10.1007/s11356-022-21433-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/08/2022] [Indexed: 06/15/2023]
Abstract
Net primary production (NPP) is an essential component of the terrestrial carbon cycle and an essential factor of ecological processes. In global change research, it was the core content to study the driving forces of NPP change. In this paper, we focused on the Southwest Karst area of China and analyzed the response mechanisms of NPP to topography, land-use types, climatic change, and human activities. Our results showed that (1) changes in elevation and slope lead to significant differences in the spatial distribution of NPP. With the increase of elevation and slope, NPP first increased and then decreased, their critical values were 2000 m and 15°, respectively. (2) NPP varied significantly among different land-use types. The average NPP of the forest was the highest, and the average NPP of cultivated land increased fastest. (3) Temperature and precipitation had the most substantial influence on NPP, both of them promoted the increase of NPP, and the effect of temperature was more obvious in the Southwest Karst area. (4) Ecological engineering significantly promoted the change of NPP, while animal husbandry significantly inhibited the change of NPP. (5) There were significant spatial differences in the driving effects and corresponding contributions of climatic change and human activities; both of them promoted the increase of NPP in the Southwest Karst area of China. Under climatic change and human activities, NPP increased by 1.24 gC·m-2·year-1 and 2.29 gC·m-2·year-1, respectively. The contributions rates of climatic change and human activities separately accounted for 35% and 65%. The contribution of human activities on NPP was much higher than that of climatic change in the Southwest Karst area, and the results suggested that we should focus on the role of human activities on NPP change.
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Affiliation(s)
- Bingxin Ma
- College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, Guangxi Province, China
| | - Juanli Jing
- College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, Guangxi Province, China.
- Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin, 541006, Guangxi Province, China.
| | - Bing Liu
- College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, Guangxi Province, China
| | - Yong Xu
- College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, Guangxi Province, China
| | - Shiqing Dou
- College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, Guangxi Province, China
| | - Hongchang He
- College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, Guangxi Province, China
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Spatiotemporal Changes and Driver Analysis of Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands. REMOTE SENSING 2022. [DOI: 10.3390/rs14153563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Ecosystem respiration (RE) plays a critical role in terrestrial carbon cycles, and quantification of RE is important for understanding the interaction between climate change and carbon dynamics. We used a multi-level attention network, Geoman, to identify the relative importance of environmental factors and to simulate spatiotemporal changes in RE in northern China’s grasslands during 2001–2015, based on 18 flux sites and multi-source spatial data. Results indicate that Geoman performed well (R2 = 0.87, RMSE = 0.39 g C m−2 d−1, MAE = 0.28 g C m−2 d−1), and that grassland type and soil texture are the two most important environmental variables for RE estimation. RE in alpine grasslands showed a decreasing gradient from southeast to northwest, and that of temperate grasslands showed a decreasing gradient from northeast to southwest. This can be explained by the enhanced vegetation index (EVI), and soil factors including soil organic carbon density and soil texture. RE in northern China’s grasslands showed a significant increase (1.81 g C m−2 yr−1) during 2001–2015. The increase rate of RE in alpine grassland (2.36 g C m−2 yr−1) was greater than that in temperate grassland (1.28 g C m−2 yr−1). Temperature and EVI contributed to the interannual change of RE in alpine grassland, and precipitation and EVI were the main contributors in temperate grassland. This study provides a key reference for the application of advanced deep learning models in carbon cycle simulation, to reduce uncertainties and improve understanding of the effects of biotic and climatic factors on spatiotemporal changes in RE.
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NDVI-Based Greening of Alpine Steppe and Its Relationships with Climatic Change and Grazing Intensity in the Southwestern Tibetan Plateau. LAND 2022. [DOI: 10.3390/land11070975] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Alpine vegetation on the Southwestern Tibetan Plateau (SWTP) is sensitive and vulnerable to climate change and human activities. Climate warming and human actions (mainly ecological restoration, social-economic development, and grazing) have already caused the degradation of alpine grasslands on the Tibetan Plateau (TP) to some extent. However, it remains unclear how human activities (mainly grazing) have regulated vegetation variation under climate change and ecological restoration since 2000. This study used the normalized difference vegetation index (NDVI) and social statistic data to explore the spatiotemporal changes and the relationship between the NDVI and climatic change, human activities, and grazing intensity. The results revealed that the NDVI increased by 0.006/10a from 2000 to 2020. Significant greening, mainly distributed in Rikaze, with partial browning, has been found in the SWTP. The correlation analysis results showed that precipitation is the most critical factor affecting the spatial distribution of NDVI, and the NDVI is correlated positively with temperature and precipitation in most parts of the SWTP. We found that climate change and human activities co-affected the vegetation change in the SWTP, and human activities leading to vegetation greening since 2000. The NDVI and grazing intensity were mainly negatively correlated, and the grazing caused vegetation degradation to some extent. This study provides practical support for grassland use, grazing management, ecological restoration, and regional sustainable development for the TP and similar alpine areas.
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Long Term Observation of Fractional Vegetation Cover in Qingyang of Gansu Province and Its Response to Climate Change. ATMOSPHERE 2022. [DOI: 10.3390/atmos13020288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Vegetation is seen as a sensitive indicator of global change because of its crucial role in connecting the atmosphere, soil, and water. Fractional vegetation cover (FVC), in turn, is an important indicator of vegetation status. Qingyang is a typically ecologically sensitive region, with a range of changes in vegetation in the last decade as a result of climatic and non-climatic factors. However, the exact impact of climate change and human activities remains unclear. Satellite observations can help to clarify that impact, allowing us to assess trends in vegetation change in the last two decades (2000–2019). In this study, daily and composite time series vegetation variations were derived from moderate resolution imaging spectroradiometer (MODIS) data and the impact of climate and human activity factors was examined for different administrative districts. By deploying multiple regression models, the research revealed that human activity has contributed 46% to the FVC variation, while the remaining 54% was led by climate factors. In areas where FVC was increasing, human activity contributed 55.89% while climate factors contributed 44.11%. In areas where FVC was decreasing, human activity and climate factors contributed 24.58% and 75.42%, respectively. The study also looks at the impacts of El Nino/IOD events in FVC dynamics in the study site. The FVC inversion result from MODIS proved capable of capturing long-term and seasonal vegetation patterns and thus provide a valuable archive for decadal-scale vegetation dynamics in the study area. Moreover, the improvement in FVC was a dual effect of climatic and human activities, while the latter owns a higher contribution especially for the implementation of ecological construction projects.
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Evaluation of Eco-Environmental Quality in Qaidam Basin Based on the Ecological Index (MRSEI) and GEE. REMOTE SENSING 2021. [DOI: 10.3390/rs13224543] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
As the population has increased and the economy has developed in the Qaidam Basin, the demand for food and energy in the basin has increased, and the contradiction between economic development and ecological protection is gradually becoming prominent. In this study, the eco-environmental quality of the Qaidam Basin from 1986 to 2019 was evaluated and analyzed based on the Modified Remote Sensing Ecological Index (MRSEI) retrieved by the Google Earth Engine (GEE) and meteorological and socioeconomic auxiliary data. The results show that (1) the Qaidam Basin had a lower overall level of eco-environmental quality, with higher eco-environmental quality in the southeastern part of the basin and lower eco-environmental quality in the central and northwestern parts of the basin. (2) During the period of 1986 to 2019, the eco-environmental quality of the Qaidam Basin started to reverse in 2003; it decreased first and then increased, and the overall performance showed an upward trend over the past 34 years. The most obvious changes were in the northwestern, northeastern, southwestern and central parts of the basin. The eco-environmental quality continued to decline in the northwestern and rise in the northeastern and southwestern regions, and in the central part, it decreased first and then plateaued. (3) The eco-environmental quality of the Qaidam Basin was affected by both natural and human factors. From 1986 to 2019, the “warm and wet” climate in the basin promoted the growth of vegetation. Furthermore, the optimization of industrial structures alleviated the pressure of agriculture and livestock and jointly improved the ecological environment in the Qaidam Basin.
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