Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis

Land, Journal Year: 2025, Volume and Issue: 14(3), P. 598 - 598

Published: March 12, 2025

As global climate change intensifies, its impact on the ecological environment is becoming increasingly pronounced. Among these, land surface temperature (LST) and vegetation cover status, as key indicators, have garnered widespread attention. This study analyzes spatiotemporal dynamics of LST Kernel Normalized Difference Vegetation Index (KNDVI) in 11 provinces along Yangtze River their response to based MODIS Terra satellite data from 2000 2020. The linear regression showed a significant KNDVI increase 0.003/year (p < 0.05) rise 0.065 °C/year 0.01). Principal Component Analysis (PCA) explained 74.5% variance, highlighting dominant influence urbanization. K-means clustering identified three regional patterns, with Shanghai forming distinct group due low variability. Generalized Additive Model (GAM) analysis revealed nonlinear LST–KNDVI relationship, most evident Hunan, where cooling effects weakened beyond threshold 0.25. Despite 0.07 increase, high-temperature areas Chongqing Jiangsu expanded by over 2500 km2, indicating limited mitigation. reveals complex interaction between KNDVI, which may provide scientific basis for development management adaptation strategies.

Language: Английский

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Explainable machine learning-based fractional vegetation cover inversion and performance optimization – A case study of an alpine grassland on the Qinghai-Tibet Plateau

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102768 - 102768

Published: Aug. 10, 2024

Fractional Vegetation Cover (FVC) serves as a crucial indicator in ecological sustainability and climate change monitoring. While machine learning is the primary method for FVC inversion, there are still certain shortcomings feature selection, hyperparameter tuning, underlying surface heterogeneity, explainability. Addressing these challenges, this study leveraged extensive field data from Qinghai-Tibet Plateau. Initially, selection algorithm combining genetic algorithms XGBoost was proposed. This integrated with Optuna tuning method, forming GA-OP combination to optimize learning. Furthermore, comparative analyses of various models inversion alpine grassland were conducted, followed by an investigation into impact heterogeneity on performance using NDVI Coefficient Variation (NDVI-CV). Lastly, SHAP (Shapley Additive exPlanations) employed both global local interpretations optimal model. The results indicated that: (1) exhibited favorable terms computational cost accuracy, demonstrating significant potential tuning. (2) Stacking model achieved among seven (R2 = 0.867, RMSE 0.12, RPD 2.552, BIAS −0.0005, VAR 0.014), ranking follows: > CatBoost LightGBM RFR KNN SVR. (3) NDVI-CV enhanced result reliability excluding highly heterogeneous regions that tended be either overestimated or underestimated. (4) revealed decision-making processes perspectives. allowed deeper exploration causality between features targets. developed high-precision scheme, successfully achieving accurate proposed approach provides valuable references other parameter inversions.

Language: Английский

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Use of Unmanned Aerial Vehicles for Monitoring Pastures and Forages in Agricultural Sciences: A Systematic Review

Drones, Journal Year: 2024, Volume and Issue: 8(10), P. 585 - 585

Published: Oct. 17, 2024

With the growing demand for efficient solutions to face challenges posed by population growth and climate change, use of unmanned aerial vehicles (UAVs) emerges as a promising solution monitoring biophysical physiological parameters in forage crops due their ability collect high-frequency high-resolution data. This review addresses main applications UAVs crop characteristics, addition evaluating advanced data processing techniques, including machine learning, optimize efficiency sustainability agricultural production systems. In this paper, Scopus Web Science databases were used identify assessment. Based on inclusion exclusion criteria, search resulted 590 articles, which 463 filtered duplicates 238 selected after screening. An analysis revealed an annual rate 35.50% evidencing interest theme. 1086 authors, 93 journals 4740 citations reviewed. Finally, our results contribute scientific community consolidating information precision farming, offering solid basis future research practical applications.

Language: Английский

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Mapping Soil Cadmium Content Using Multi-Spectral Satellite Images and Multiple-Residual-Stacking Model: Incorporating Information from Homologous Pollution and Spectrally Active Materials

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 485, P. 136755 - 136755

Published: Dec. 6, 2024

Language: Английский

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