Vegetation Greenness Variations and Response to Climate Change in the Arid and Semi-Arid Transition Zone of the Mongo-Lian Plateau during 1982–2015

Na, Risu; Na, Li; Du, Haibo; He, Hong S.; Yin, Shuiqing; Zong, Shengwei; Huang, Lirong; Yang, Young-Hoon; Wu, Zhengfang

doi:10.3390/rs13204066

Cited by 17 publications

(9 citation statements)

References 48 publications

(76 reference statements)

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“…On the other hand, increasing temperatures can prolong the growing period, thus promoting increased NPP [13]. Previous studies showed that vegetation is mainly affected by precipitation in arid and semi-arid areas, but it is mainly affected by the temperature in alpine regions [7,14]. However, other researchers have reported that the change in vegetation productivity in both semi-arid and alpine regions was dominated by temperature [15].…”

Section: Introductionmentioning

confidence: 99%

Changes in Net Primary Productivity and Factor Detection in China’s Yellow River Basin from 2000 to 2019

et al. 2023

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Net primary productivity (NPP) is a main contributor to ecosystem carbon pools. It is crucial to monitor the spatial and temporal dynamics of NPP, as well as to assess the impacts of climate change and human activities to cope with global change. The dynamic of the NPP in China’s Yellow River Basin (YRB) from 2000 to 2019 and its influencing factors were analyzed by using trend and persistence tests and the GeoDetector method. The results show that the NPP had strong spatial heterogeneity, with a low NPP in the west and north, and a high NPP in the east and south. From 2000 to 2019, the NPP showed a statistically significant increase (at a mean of 5.5 g C m−2 yr−1, for a cumulative increase of 94.5 Tg C). A Hurst analysis showed that for the NPP in 76.3% of the YRB, the time series was anti-persistent. The spatial heterogeneity of the NPP in the YRB was mainly explained by precipitation and relative humidity (q value ranged from 0.24 to 0.44). However, the strength of the precipitation explained the decreased variation over time (q value decreased from 0.40 in 2000 to 0.26 in 2019). Interactions between the climate factors and human activities affected the NPP more strongly than individual factors. The results emphasize the importance of strengthening future research on the interaction between climate change and human activities. The results reveal the risk and optimal ranges of the driving factors and provide a quantification of the impacts of those factors regarding NPP. These findings can provide a scientific basis for vegetation restoration in the YRB.

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Section: Introductionmentioning

confidence: 99%

Changes in Net Primary Productivity and Factor Detection in China’s Yellow River Basin from 2000 to 2019

et al. 2023

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“…Vegetation growth status is affected by many factors, especially in arid and semi-arid areas (Jaber 2019;Herrmann et al 2005; Liu et al 2016). Previous studies have shown that temperature and precipitation are important in uencing factors in arid and semi-arid areas, and the growth status of vegetation is more sensitive to the response of temperature and precipitation (Na et al 2021;Parizi et al 2020;Cañón et al 2011). The coal mining area of Northern Shaanxi has complex natural conditions, fragile ecological environment, and close response of vegetation to climate factors (Qiao et al 2016).…”

Section: Introductionmentioning

confidence: 99%

NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM

Gao

Du²,

Lei³

et al. 2023

Water Resour Manage

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Normalized difference vegetation index(NDVI) is the most commonly used factor to re ect vegetation growth status, and improving the prediction accuracy of NDVI is of great signi cance to the development of regional ecology. In this study, a new NDVI forecasting model based on the combination of time series decomposition(TSD), convolutional neural network (CNN) and long short-term memory (LSTM) was proposed. In order to verify the performance of TSD-CNN-LSTM model and explore the response of NDVI to climatic factors, two forecasting models of temperature and precipitation based on its own historical information and four NDVI forecasting models that based on Temperature, precipitation and its own historical information were established. The results show that TSD-CNN-LSTM model based on its own historical information had the best forecasting performance, with the RMSE, NSE, MAE to be 0.4996, 0.9981, 0.4169 for temperature, 5.6941, 0.9822, 3.9855 for precipitation and 0.0573, 0.9617, 0.0447 for NDVI, respectively. Meanwhile, the NDVI forecasting models based on climatic factors show that the model based on the combination of temperature and precipitation has the better effect than that based on single temperature and single precipitation. Combined with the results of correlation analysis it can be inferred that NDVI changes are most signi cantly in uenced by the combination of temperature and precipitation, followed by temperature, and least in uenced by precipitation. The above ndings can provide a meaningful reference and guidance for the study of vegetation growth with climate changes.

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“…Precipitation is another key factor affecting the ecosystem, being directly or indirectly involved in the physiological activities of vegetation [17]. In arid and semi-arid regions, such as shrublands and grasslands, precipitation (rain and snow) is a pivotal driver of ecosystems [18]. Fluctuations in precipitation can also trigger changes in nutrient cycling and productivity [19].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations of Global Terrestrial Typical Vegetation EVI and Their Responses to Climate Change from 2000 to 2021

Li,

Song,

Qin

et al. 2023

Remote Sensing

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With the increasing impact of climate change on ecosystems, it is crucial to analyze how changes in precipitation and temperature affect global ecosystems. Therefore, this study aims to investigate the spatiotemporal variation characteristics of the Enhanced Vegetation Index (EVI) in the global forest, grassland, shrubland, and tundra (FGST) from 2000 to 2021. We utilized partial correlation analysis and grey relation analysis to assess the responses of different vegetation types to precipitation, temperature, and extreme water and heat indicators. The result shows that, despite a “warmer and drier” trend in FGST (excluding tundra), global climate change has not adversely affected the ongoing vegetation growth. It presents a favorable implication for global carbon dioxide assimilation. Different vegetation types displayed different sensitivities to changes in precipitation and temperature. Shrubland proved to be the most sensitive, followed by grassland, forest, and tundra. As the impacts of global climate change intensify, it becomes crucial to direct our attention toward dynamics of vegetation types demonstrating heightened sensitivity to fluctuations in precipitation and temperature. Our study indicates that, except for forests, extreme precipitation indicators have a stronger impact on EVI than extreme temperature indicators. Forests and tundra have demonstrated heightened susceptibility to the intensity of extreme climatic events, while grasslands and shrublands have been more sensitive to the duration of such events. Understanding these responses can offer valuable insights for developing targeted strategies for adaptation and preservation. Our study enhances comprehension of the feedback relationship between global climate change and vegetation, offering scientific evidence for global climate change evaluation.

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Vegetation Greenness Variations and Response to Climate Change in the Arid and Semi-Arid Transition Zone of the Mongo-Lian Plateau during 1982–2015

Cited by 17 publications

References 48 publications

Changes in Net Primary Productivity and Factor Detection in China’s Yellow River Basin from 2000 to 2019

Changes in Net Primary Productivity and Factor Detection in China’s Yellow River Basin from 2000 to 2019

NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM

Spatiotemporal Variations of Global Terrestrial Typical Vegetation EVI and Their Responses to Climate Change from 2000 to 2021

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