Spatiotemporal changes of vegetation in the northern foothills of Qinling Mountains based on kNDVI considering climate time-lag effects and human activities

Environmental Research, Journal Year: 2025, Volume and Issue: unknown, P. 120959 - 120959

Published: Jan. 1, 2025

Language: Английский

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Central Asia’s desertification challenge: Recent trends and drives explored with Google Earth Engine

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 460, P. 142595 - 142595

Published: May 17, 2024

Language: Английский

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Spatiotemporal Changes in Water-Use Efficiency of China’s Terrestrial Ecosystems During 2001–2020 and the Driving Factors

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(1), P. 136 - 136

Published: Jan. 3, 2025

Water-use efficiency (WUE) is an important indicator for understanding the coupling of carbon and water cycles in terrestrial ecosystems. It provides a comprehensive reflection ecosystems’ responses to various environmental factors, making it essential how ecosystems adapt complex changes. Using satellite-based estimates gross primary productivity (GPP) evapotranspiration (ET), our study investigated spatiotemporal variations WUE across China’s from 2001 2020. We employed geographic detector method, partial correlation analysis, ridge regression assess contributions different factors (temperature, precipitation, solar radiation, vapor pressure deficit, leaf area index, soil moisture) GPP, ET, WUE. The results show significant increases during period, with increase rates 6.70 g C m−2 yr−1, 2.68 kg H2O 0.007 respectively. More than three-quarters regions trends (p < 0.05) displayed notable 0.05). Among all driving index (LAI) made largest contribution WUE, particularly warm temperate semi-humid regions. Precipitation radiation were climatic influences arid northern China humid southwestern China,

Language: Английский

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Quantifying the relative importance of natural and human factors on vegetation dynamics in China’s western frontiers during 2010-2021

Environmental Research, Journal Year: 2025, Volume and Issue: unknown, P. 121120 - 121120

Published: Feb. 1, 2025

Language: Английский

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Quantifying the Combined and Individual Impacts of Climate and Human Activity on the Urban Green Space Carbon Sink Capacity in Beijing

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106253 - 106253

Published: Feb. 1, 2025

Language: Английский

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Research Trends in Vegetation Spatiotemporal Dynamics and Driving Forces: A Bibliometric Analysis (1987–2024)

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 588 - 588

Published: March 28, 2025

Under the dual pressures of climate change and rapid urbanization, a comprehensive analysis vegetation’s spatiotemporal patterns their driving forces plays pivotal role for addressing global ecological challenges. However, systematic bibliometric analyses in this field remain limited. This study involved 18,270 related publications from 1989 to 2024 retrieved Web Science SCI-Expanded database, elucidating research trends, methodologies, key thematic areas. Utilizing bibliometrix biblioshiny tools, results reveal an annual average growth rate 17.62% number published articles, indicating expansion. Climate emerged as core force, with high-frequency keywords such “vegetation”, “dynamics”, “variability”. China (18,687 papers), United States (14,502 Germany (3394 papers) are leading contributors domain, showing fastest output, albeit relatively lower citation rates. Core journals, including Remote Sensing Environment Global Change Biology, have played roles advancing vegetation dynamics research, remote sensing techniques dominating field. The highlights shift single-variable (e.g., temperature, precipitation) multi-scale multidimensional approaches around 2010. Regional studies, those focusing on Loess Plateau, gaining importance, while advancements machine learning technologies enhanced precision scalability research. provides summary current state development trends forces, offering valuable insights future

Language: Английский

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Research on Interval Probability Prediction and Optimization of Vegetation Productivity in Hetao Irrigation District Based on Improved TCLA Model

Agronomy, Journal Year: 2025, Volume and Issue: 15(6), P. 1279 - 1279

Published: May 23, 2025

Vegetation productivity, as an essential global carbon sink, directly influences the variety and stability of ecosystems. Precise vegetation productivity monitoring forecasting are crucial for cycle. Traditional machine learning algorithms frequently experience overfitting when processing high-dimensional time-series data or substantial numbers outliers, impeding accurate prediction various metrics. We propose a multimodal regression model utilizing TCLA framework—comprising Transient Trigonometric Harris Hawks Optimizer (TTHHO), Convolutional Neural Networks (CNN), Least Squares Support Vector Machine (LSSVM), Adaptive Bandwidth Kernel Density Estimation (ABKDE)—with Hetao Irrigation District, vast irrigation basin in China, serving study area. This employs TTHHO to effectively navigate search space adaptively optimize network node positions, integrates CNN-LSSVM feature extraction analysis, incorporates ABKDE probability density function estimation outlier detection, resulting interval enhanced resilience interference. Experimental indicate that improves accuracy by 10.57–26.47% compared conventional models (Long Short-Term Memory (LSTM), Transformer). In presence 5–15% fusion results drop RMSE (p < 0.05), with reduction 45.18–69.66%, yielding values between 0.079 0.137, thereby demonstrating model’s high robustness resistance interference predicting next three years. work introduces scientific approach precisely alterations regional using proposed model, significantly enhancing resource management ecological conservation techniques.

Language: Английский

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Investigating the temporal lag and accumulation effect of climatic factors on vegetation photosynthetic activity over subtropical China

Ecological Indicators, Journal Year: 2024, Volume and Issue: 166, P. 112406 - 112406

Published: July 27, 2024

Monitoring vegetation photosynthesis in China's subtropical regions using remote sensing is challenging because of the complex ecosystems and climate variability. Previous studies often pay less attention on influence multiple climatic factors temporal effects (lag accumulation) photosynthesis, thereby underestimating their impact. This study utilizes a dataset comprising Solar-induced chlorophyll fluorescence (SIF) data (GOSIF product), MODIS Land Cover product (MCD12C1), various variables. Analytical methods including Theil-Sen Median trend analysis, Mann-Kendall test, partial correlation optimal parameter-based geographical detector (OPGD) model were employed to explore dynamics SIF responses identify drivers China. The findings indicate that (1) as indicated by SIF, exhibited an increasing majority Chinese regions, which constitute over 80 % area, with particularly pronounced enhancements southern central western parts subtropics. (2) Soil moisture primarily exhibits lag evergreen needleleaf forests, deciduous broadleaf mixed whereas temperature does not exhibit significant effects. Solar radiation vapor pressure deficits impact through both accumulation Under effects, proportion correlations between increases 36.71 ∼ 43.8 %, excluding temperature. (3) Temperature dominant factor affecting forest. Interactions have significantly stronger than individual factors. Notably, explanatory power deficit substantially when it interacts other Studying aids accurately predicting change, improving accuracy global carbon cycle models guiding development sequestration management strategies.

Language: Английский

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Monitoring vegetation dynamics across land use types in Iran: spatiotemporal relationships with soil temperature and water volume

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Feb. 1, 2025

Language: Английский

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Quantifying the impacts of climate change and human activities on vegetation in ecologically fragile regions: a case study of Northern China

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(5)

Published: April 25, 2025

Language: Английский

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