Surrogate multivariate Hurst exponent analysis of gait dynamics

Marín-López, A; Martínez-Cadena, J.A.; Martinez-Martinez, F.; Alvarez-Ramı́rez, José

doi:10.1016/j.chaos.2023.113605

Cited by 1 publication

(2 citation statements)

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“…The Hurst index (H) is frequently utilized in climatology, hydrology, and other domains to examine the continuity and correlation of changes in long-time-series data, reflecting future trends [39][40][41]. H > 0.5 indicates that the future trend is consistent with the study phase, implying that the trend in the study period will continue in the future; H = 0.5 reveals an indeterminate future trend in change; and H < 0.5 demonstrates that the next trend in change is opposite to the study phase [42][43][44]. The reclassification results were multiplied using the raster calculator of ArcGIS 10.2 based on the Z of the MK trend test, divided by 0; and the p divided by 0.01 and 0.05; combined with the Hurst index, divided by 0.5; and were used to predict the trend in the future vegetation changes.…”

Section: Trend Analysis Correlation Analysis and Forecasting Of Futur...mentioning

confidence: 99%

“…They have weak self-regulation abilities and poor stability, making it challenging to establish a sustainable ecosystem in such a short time [47]. Research has indicated that the effects of human activity on vegetation are far less than those of regional climate change [44,83]. Therefore, climatic elements still play a leading part in the vegetation change in the Yinshanbeilu grassland region.…”

Section: Influence Of Human Factors On Vegetation Changementioning

confidence: 99%

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Contribution of Climatic Factors and Human Activities to Vegetation Changes in Arid Grassland

Tuo,

Xu,

Zhang

et al. 2024

Sustainability

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Clarifying the changing trend in vegetation and its affecting variables is extremely valuable for natural resource management. Vegetation changes in the Yinshanbeilu grassland region, which is situated in the centre of Inner Mongolia in northern China and is part of the arid steppe region, are extremely sensitive to climatic factors. In this study, we investigated the changes in vegetation in the Yinshanbeilu grassland zone from the year 2000 to 2020 using the Normalized Difference Vegetation Index (NDVI) data. The contribution of climatic conditions and human activities to the annual and growing season vegetation changes was quantified. The findings revealed that vegetation cover in the Yinshanbeilu grassland zone increased at a rate of 0.00267/a between 2000 and 2020. Throughout the year and during the growing season, precipitation had a greater influence on the growth of vegetation than other climatological factors. In most places, there was a significant positive correlation between the NDVI and precipitation, which negatively correlated with other climatic factors. The average rates at which precipitation, temperature, cumulative sunshine hours, and potential evapotranspiration contributed to changes in NDVI were 0.00173/a, −0.00027/a, 0.00006/a, and 0.00074/a, respectively, for the entire year, and 0.00180/a, −0.00001/a, 0.00021/a, and 0.00059/a for the growing season. The impact of climate change on vegetation activities was more pronounced, accounting for 84.76% of annual NDVI change and 97.36% of growing season NDVI change. Humans contributed 15.24% of total annual NDVI change and 2.64% of growing season NDVI change. This research’s findings serve as scientific support for preserving the environment in the Yinshanbeilu grassland region, as well as an essential reference for government decision making.

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