Investigating causal relationships between grassland deterioration and climate and socioeconomic changes through time-series computational learning

Zhou, Chenghu; Liang, Jianyuan; Xie, Yichun

doi:10.1016/j.jclepro.2022.132963

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…In order to further coordinate economic development, some grasslands in the region have been used for urban construction, coal mining, and cultivation of cash crops (Hu & Nacun, 2018). The research results of Zhou et al (2022) show that there is a causal relationship between the economic transformation of Inner Mongolia from 2000 to 2017 and grassland degradation; socioeconomic activities are the main driving factor of grassland productivity changes, far exceeding the influence of CC (Zhou et al, 2022). The influence of economic factors and urbanization factors is higher than the influence of CC (Li, Zhang, et al, 2022;Zhou et al, 2022).…”

Section: Refine the Impact Of Human Activities On Vegetationmentioning

confidence: 99%

Identifying the drivers of vegetation changes in Inner Mongolia based on residual analysis and Hasse diagram technique

Mu,

Tong,

Batunacun

et al. 2024

Ecosphere

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Exploring the effect of climate change and human activities on vegetation is a key requisite for the reconstruction of regional ecological environments. Therefore, based on long‐term vegetation GIMMS Normalized Difference Vegetation Index (NDVI) data, climate data, and statistical data, the present study applied the Hasse diagram technique and combined the multivariate regression residual analysis to quantitatively analyze the impact of human activities and climate change on vegetation in Inner Mongolia from detailed human activities with some innovations. The results show that (1) NDVI shows an overall increasing trend over the last 39 years, with an abrupt change in 2000; moreover, vegetation growth was better before the abrupt change (PI: 1982–2000) than after it (PII: 2001–2020), with significant downward trends in Xilin Gol and Hulunbuir. (2) Human activities can promote as well as inhibit vegetation, and the promotion effect was larger during 1982–2000 than during 2001–2020, whereas the inhibition effect was larger during 2001–2020. In addition, during PI, vegetation in Inner Mongolia generally experienced promotion by human activities and climate change, while during PII, climate‐driven promotion had the strongest effect, followed by human‐driven inhibition mainly distributed in Xilin Gol. (3) The result of the Hasse diagram analysis shows that the dominant pathways of human activities affecting most of the cities were economic factors and urbanization during PI and economization during PII.

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Section: Refine the Impact Of Human Activities On Vegetationmentioning

confidence: 99%

Identifying the drivers of vegetation changes in Inner Mongolia based on residual analysis and Hasse diagram technique

Mu,

Tong,

Batunacun

et al. 2024

Ecosphere

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“…Considerable evidences have shown that urbanization is one of the substantial contributors to local climate change [14][15][16][17][18][19][20][21][22] . In some rapidly urbanized areas, urbanization has been found to account for over 80% of the local temperature increase.…”

Section: Introductionmentioning

confidence: 99%

Local Climate Change Induced by Urbanization on a South China Sea Island

HAO,

LI,

CHAN

et al. 2024

JTM

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The South China Sea is a hotspot for regional climate research. Over the past 40 years, considerable improvement has been made in the development and utilization of the islands in the South China Sea, leading to a substantial change in the land-use of the islands. However, research on the impact of human development on the local climate of these islands is lacking. This study analyzed the characteristics of local climate changes on the islands in the South China Sea based on data from the Yongxing Island Observation Station and ERA5 re-analysis. Furthermore, the influence of urbanization on the local climate of the South China Sea islands was explored in this study. The findings revealed that the 10year average temperature in Yongxing Island increased by approximately 1.11 °C from 1961 to 2020, and the contribution of island development and urbanization to the local warming rate over 60 years was approximately 36.2%. The linear increasing trend of the annual hot days from 1961-2020 was approximately 14.84 days per decade. The diurnal temperature range exhibited an increasing trend of 0.05 °C per decade, whereas the number of cold days decreased by 1.06 days per decade. The rapid increase in construction on Yongxing Island from 2005 to 2021 led to a decrease in observed surface wind speed by 0.32 m s -1 per decade. Consequently, the number of days with strong winds decreased, whereas the number of days with weak winds increased. Additionally, relative humidity exhibited a rapid decline from 2001 to 2016 and then rebounded. The study also found substantial differences between the ERA5 re-analysis and observation data, particularly in wind speed and relative humidity, indicating that the use of re-analysis data for climate resource assessment and climate change evaluation on island areas may not be feasible.

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“…The normalized difference vegetation index (NDVI), as an index to characterize the vegetation coverage, is widely used in the study of the laws and driving forces of long-timeseries vegetation evolution [6][7][8][9][10]. In recent years, using wavelet analysis and empirical mode decomposition (EMD) methods, combined with NDVI data, to extract the periodic characteristics of time series and study vegetation cover change has attracted extensive attention from domestic scholars [11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Study on NDVI Periodic Change Characteristics of Inner Mongolia Grassland Based on Variational Mode Decomposition Algorithm

Zhang

Tian

et al. 2023

Atmosphere

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Based on the NDVI data of vegetation in Inner Mongolia from 1982 to 2015, the variational mode decomposition (VMD) method, which has been well applied in the field of signal decomposition, is introduced to study the periodicity of vegetation index in Inner Mongolia. The VMD method is used to extract the monthly and annual NDVI and the long time series cycle characteristics of temperature and precipitation in the same period from April 1982 to October 2015 in Inner Mongolia. The results show that temperature and precipitation are important factors affecting the growth of vegetation, and there are 6.99 and 3.49 months of the same oscillation cycle for monthly NDVI and temperature and precipitation time series; when the central frequency is the same, the amplitude of the monthly temperature and precipitation time series increases with the increase of the lag period. The annual scale NDVI has the same period of 16.95, 6.8a, and 4.85a with precipitation, and the same period of 6.8a and 4.85a with temperature. The Residue component shows that the overall NDVI and temperature in Inner Mongolia have shown a significant slow growth trend in the past 30 years. Although the precipitation has shown a significant slow decline trend in the same time period (p = 0.000), the grassland is still in the process of continuous improvement.

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Investigating causal relationships between grassland deterioration and climate and socioeconomic changes through time-series computational learning

Cited by 5 publications

References 52 publications

Identifying the drivers of vegetation changes in Inner Mongolia based on residual analysis and Hasse diagram technique

Identifying the drivers of vegetation changes in Inner Mongolia based on residual analysis and Hasse diagram technique

Local Climate Change Induced by Urbanization on a South China Sea Island

Study on NDVI Periodic Change Characteristics of Inner Mongolia Grassland Based on Variational Mode Decomposition Algorithm

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