A Comprehensive Analysis of Vegetation Dynamics and Their Response to Climate Change in the Loess Plateau: Insight from Long-Term kernel Normalized Difference Vegetation Index Data

Forests, Journal Year: 2024, Volume and Issue: 15(3), P. 471 - 471

Published: March 2, 2024

The Loess Plateau (LP) is a typical climate-sensitive and ecologically delicate area in China. Clarifying the vegetation–climate interaction LP over 40+ years, particularly pre- post-Grain to Green Program (GTGP) implementation, crucial for addressing potential climate threats achieving regional ecological sustainability. Utilizing kernel Normalized Difference Vegetation Index (kNDVI) key climatic variables (precipitation (PRE), air temperature (TEM), solar radiation (SR)) between 1982 2022, we performed an extensive examination of vegetation patterns their reaction changes using various statistical methods. Our findings highlight considerable widespread greening on from evidenced by kNDVI slope 0.0020 yr−1 (p < 0.001) 90.9% significantly increased greened area. GTGP expedited this process, with increasing 0.0009 0.0036 expanding 39.1% 84.0%. Over past 40 experienced significant warming 0.001), slight humidification, marginal decrease SR. Post-GTGP rate decelerated, while PRE SR growth rates slightly accelerated. Since hurst index exceeded 0.5, most vegetated expected be greening, warming, humidification future. In long term, 75% benefited increase PRE, especially relatively dry environments. LP, 61% areas showed positive correlation TEM, 4.9% exhibited negative correlation, mainly arid zones. promoted 23% area, mostly eastern LP. enhanced sensitivity corresponding 15.3% 59.9%. Overall, has emerged as dominant driver dynamics followed TEM These insights contribute comprehensive understanding climate-impact-related response mechanisms, providing guidance efforts toward sustainable development amid changing climate.

Language: Английский

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Dynamics of solar-induced chlorophyll fluorescence (SIF) and its response to meteorological drought in the Yellow River Basin

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 360, P. 121023 - 121023

Published: May 10, 2024

Language: Английский

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Quantifying the drought sensitivity of grassland under different climate zones in Northwest China

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 910, P. 168688 - 168688

Published: Nov. 20, 2023

Language: Английский

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Impacts of drought and heat events on vegetative growth in a typical humid zone of the middle and lower reaches of the Yangtze River, China

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 620, P. 129452 - 129452

Published: March 28, 2023

Language: Английский

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Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China

Atmosphere, Journal Year: 2024, Volume and Issue: 15(2), P. 155 - 155

Published: Jan. 25, 2024

Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production cause large economic losses. The accurate prediction of drought effectively reduce impacts droughts. Deep learning methods have shown promise in prediction, with convolutional neural networks (CNNs) being particularly effective handling spatial information. In this study, we employed deep approach to predict Fenhe River (FHR) basin, taking into account meteorological conditions surrounding regions. We used daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as evaluation index. Our results demonstrate effectiveness CNN model predicting events 1~10 days advance. evaluated predictions made by model; average Nash–Sutcliffe efficiency (NSE) between predicted true values for next 10 was 0.71. While accuracy slightly decreased longer lengths, remained stable heavy are typically difficult predict. Additionally, key variables were identified, found training these led higher than it all variables. This study approves an when considering

Language: Английский

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Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change

Journal of Arid Land, Journal Year: 2024, Volume and Issue: 16(1), P. 46 - 70

Published: Jan. 1, 2024

Language: Английский

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Seasonal Response of the NDVI to the SPEI at Different Time Scales in Yinshanbeilu, Inner Mongolia, China

Land, Journal Year: 2024, Volume and Issue: 13(4), P. 523 - 523

Published: April 15, 2024

Recently, the frequent occurrence of droughts has caused a serious impact on vegetation growth and progression. This research is based upon normalized difference index (NDVI) from 2001 to 2020. The correlation between NDVI standardized precipitation evapotranspiration (SPEI) at disparate time scales was used assess response drought in Yinshanbeilu region. levels SPEI1, SPEI3, SPEI6, SPEI12 increased prominently eastern region country, while decreased significantly east west spring, summer, autumn but reversed winter. area with an upward trend (33.86%) slightly lower than that downward (66.14%). coefficients SPEI over entire year timescale. elevated values were concentrated southeastern western regions survey Additionally, best timescales SPEI6 SPEI12. Grassland most sensitive type NDVI. SPEI1–12 0.313, 0.459, 0.422, 0.406. Both spring summer more responsive SPEI12, whereas winter SPEI3. exhibited complex soil texture features respect different seasonal scales, showed strong both autumn. Loam, sandy loam, silty loam all highest 0.509, 0.474, 0.403, respectively.

Language: Английский

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Effects of precipitation on vegetation and surface water in the Yellow River Basin during 2000–2021

Journal of Geographical Sciences, Journal Year: 2024, Volume and Issue: 34(4), P. 633 - 653

Published: April 1, 2024

Language: Английский

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Investigating the Response of Vegetation to Flash Droughts by Using Cross-Spectral Analysis and an Evapotranspiration-Based Drought Index

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(9), P. 1564 - 1564

Published: April 28, 2024

Flash droughts tend to cause severe damage agriculture due their characteristics of sudden onset and rapid intensification. Early detection the response vegetation flash is utmost importance in mitigating effects droughts, as it can provide a scientific basis for establishing an early warning system. The commonly used method determining time drought, based on index or correlation between precipitation anomaly growth anomaly, leads late irreversible drought vegetation, which may not be sufficient use analyzing earning. evapotranspiration-based (ET-based) indices are effective indicator identifying monitoring drought. This study proposes novel approach that applies cross-spectral analysis ET-based index, i.e., Evaporative Stress Anomaly Index (ESAI), forcing vegetation-based Normalized Vegetation (NVAI), response, both from medium-resolution remote sensing data, estimate lag vitality status An experiment was carried out North China during March–September period 2001–2020 using products at 1 km spatial resolution. results show average water availability estimated by over 5.9 days, shorter than measured widely (26.5 days). main difference phase lies fundamental processes behind definitions two methods, subtle dynamic fluctuation signature signal (vegetation-based index) correlates with (ET-based versus impact indicated negative NDVI anomaly. varied types irrigation conditions. rainfed cropland, irrigated grassland, forest 5.4, 5.8, 6.1, 6.9 respectively. Forests have longer grasses crops deeper root systems, mitigate impacts droughts. Our method, innovative earlier impending impacts, rather waiting occur. information detected stage help decision makers developing more timely strategies ecosystems.

Language: Английский

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Inferring vegetation response to drought at multiscale from long-term satellite imagery and meteorological data in Afghanistan

Ecological Indicators, Journal Year: 2024, Volume and Issue: 158, P. 111567 - 111567

Published: Jan. 1, 2024

Drought caused by climate change has significantly increased vegetation vulnerability in Afghanistan during the last decades. This paper investigates response to drought at multiple scales across country based on historical data from 1980 2020. It explores multiscale relationships between as indicated grid-based standardised precipitation evapotranspiration index (SPEI) and condition represented satellite-derived anomaly (VAI). also examines links of dominant land cover with their implications for agriculture. We assess spatiotemporal correlations integrating TerraClimate grids timeseries sourced NOAA AVHRR (National Oceanic Atmospheric Administration Advanced Very High-Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer) images ESA CCI (European Space Agency Climate Change Initiative) maps. evaluated effect cumulative predominant covers. Our results show years 2000–2001 2017–2018 driest four decades, substantially correlated spatial temporal variations Afghanistan. The most sensitive months are June July significant impacts 6 8 months. covers more prone negative effects under severe (in order) shrubland, rainfed cropland, grassland, trees, irrigated cropland. These findings suggest sensitivity that future national scale management should be focused on. study demonstrates usefulness open-access researchers planners data-poor countries.

Language: Английский

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