Desertification in northern China from 2000 to 2020: The spatial–temporal processes and driving mechanisms

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102769 - 102769

Опубликована: Авг. 11, 2024

Desertification is one of the most significant environmental and social challenges globally. Monitoring desertification dynamics quantitatively identifying contributions its driving factors are crucial for land restoration sustainable development. This study develops a standardized methodological framework that combines with mechanisms at pixel level, applied to northern China from 2000 2020. Using multisource data employing Time Series Segmentation Residual Trend analysis (TSS-RESTREND) method alongside geographical detector, we assessed reversion, expansion, abrupt change processes, along impacts interactions natural human were assessed. Over past two decades, proportion desertified decreased by 5.60%. Notably, 32.88% area experienced while only 5.86% underwent expansion. Abrupt changes in both reversed expanding areas observed, primarily central western regions, these concentrated periods 2009–2011 2014–2016. The various different sub-regions exhibited spatial heterogeneity. Increased precipitation, temperature, evapotranspiration contributed reversion area, wind speed influenced eastern area. Additionally, population density afforestation activities also promoted reversion. In contrast, precipitation increased temperature expansion areas, respectively, exacerbating this process. Overall, between enhanced. Future control ecological engineering planning should focus on coupling effects relevant vegetation changes.

Язык: Английский

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Optimized SVR model for predicting dissolved oxygen levels using wavelet denoising and variable reduction: Taking the Minjiang River estuary as an example

Ecological Informatics, Год журнала: 2025, Номер 86, С. 103007 - 103007

Опубликована: Янв. 11, 2025

Язык: Английский

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A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103014 - 103014

Опубликована: Янв. 1, 2025

Язык: Английский

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Global soil respiration predictions with associated uncertainties from different spatio-temporal data subsets

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102777 - 102777

Опубликована: Авг. 23, 2024

Soil respiration (Rs), the second-largest flux in global carbon cycle, is a crucial but uncertain component. To improve understanding of Rs, we constructed single models, and specific models classified by climate type, land cover year data record, elevation range using random forest algorithm to predict Rs values explore associated uncertainty models. The results showed similar overall predictive performance for with an R-squared value greater than 0.63; however, significant differences were observed compared estimate (23 Pg C). All estimated larger model, mainly owing imbalances sample on which prediction based. One exception this result estimates smaller 2020 (95.1 Overall, model closer those obtained temperate zones training distribution, resulted other classification-specific Prediction observations before 2000 tend underestimate Rs. However, use proved helpful addressing persistent temporal spatial sampling. Expanding coverage records both temporally spatially updating database promptly would estimation accuracy while enhancing budget feedback soil regard warming.

Язык: Английский

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Quantification of the Influencing Factors of Stand Productivity of Subtropical Natural Broadleaved Forests in Eastern China Using an Explainable Machine Learning Framework

Forests, Год журнала: 2025, Номер 16(1), С. 95 - 95

Опубликована: Янв. 8, 2025

Natural broadleaf forests (NBFs) are the most abundant zonal vegetation type in subtropical regions. Understanding mechanisms influencing stand productivity NBFs is important for developing “nature-based” solutions climate change mitigation. However, minimal research has captured effects of nonlinearities and feature interactions that often have nonlinear impacts on factors. To address this gap, we used continuous forest inventory data, a machine learning model was constructed. Subsequently, through leveraging interpretable framework SHapley Additive explanation (SHAP) partial dependence plot, determined global local explanations factors productivity. Our findings indicate following: (1) The Autogluon performed strongest based R2, RMSE, rRMSE metrics. (2) basal area (BA), neighborhood comparison diameter at breast height (NC), age (AGE) were key Stand increased with increasing BA decreased NC AGE. maintained above 15 m2ha−1 below 0.45, which represent favorable conditions to maintain optimal growth. (3) SHAP interaction values calculated determine five major study provides reference sustainable management NBFs, thereby highlighting role mitigating change.

Язык: Английский

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Ml assisted techniques in power side channel analysis for trojan classification

Cluster Computing, Год журнала: 2025, Номер 28(3)

Опубликована: Янв. 21, 2025

Язык: Английский

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Regression analysis and artificial neural networks for predicting pine species volume in community forests

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103203 - 103203

Опубликована: Май 1, 2025

Язык: Английский

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Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102951 - 102951

Опубликована: Дек. 1, 2024

Язык: Английский

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Modelling height to crown base using non-parametric methods for mixed forests in China

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102957 - 102957

Опубликована: Дек. 1, 2024

Язык: Английский

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