Unveiling Fractional Vegetation Cover Dynamics: A Spatiotemporal Analysis Using MODIS NDVI and Machine Learning

Environmental and Sustainability Indicators, Год журнала: 2024, Номер unknown, С. 100485 - 100485

Опубликована: Сен. 1, 2024

Язык: Английский

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Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions

Trees Forests and People, Год журнала: 2024, Номер 18, С. 100657 - 100657

Опубликована: Авг. 20, 2024

Язык: Английский

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Spatiotemporal dynamics of vegetation cover: integrative machine learning analysis of multispectral imagery and environmental predictors

Earth Science Informatics, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 1, 2025

Язык: Английский

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Machine Learning and Spatio Temporal Analysis for Assessing Ecological Impacts of the Billion Tree Afforestation Project

Ecology and Evolution, Год журнала: 2025, Номер 15(2)

Опубликована: Фев. 1, 2025

ABSTRACT This study evaluates the Billion Tree Afforestation Project (BTAP) in Pakistan's Khyber Pakhtunkhwa (KPK) province using remote sensing and machine learning. Applying Random Forest (RF) classification to Sentinel‐2 imagery, we observed an increase tree cover from 25.02% 2015 29.99% 2023 a decrease barren land 20.64% 16.81%, with accuracy above 85%. Hotspot spatial clustering analyses revealed significant vegetation recovery, high‐confidence hotspots rising 36.76% 42.56%. A predictive model for Normalized Difference Vegetation Index (NDVI), supported by SHAP analysis, identified soil moisture precipitation as primary drivers of growth, ANN achieving R 2 0.8556 RMSE 0.0607 on testing dataset. These results demonstrate effectiveness integrating learning framework support data‐driven afforestation efforts inform sustainable environmental management practices.

Язык: Английский

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Estimation of potato above-ground biomass based on the VGC-AGB model and deep learning

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 232, С. 110122 - 110122

Опубликована: Фев. 17, 2025

Язык: Английский

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Incorporating stand parameters in nonlinear height-diameter mixed-effects model for uneven-aged Larix gmelinii forests

Frontiers in Forests and Global Change, Год журнала: 2025, Номер 7

Опубликована: Янв. 7, 2025

Tree attributes, such as height (H) and diameter at breast (D), are essential for predicting forest growth, evaluating stand characteristics developing yield models sustainable management. Measuring tree H is particularly challenging in uneven-aged forests compared to D. To overcome these difficulties, the development of updated reliable H-D crucial. This study aimed develop robust Larix gmelinii by incorporating variables. The dataset consisted 7,069 trees sampled from 96 plots Northeast China, encompassing a wide range densities, age classes, site conditions. Fifteen widely recognized nonlinear functions were assessed model relationship effectively. Model performance was using root mean square error (RMSE), absolute (MAE), coefficient determination (R 2 ). Results identified Ratkowsky (M8) best performer, achieving highest R (0.74), lowest RMSE (16.47%) MAE (12.50%), statistically significant regression coefficients (p < 0.05). Furthermore, M8 modified into 5 generalized (GMs) adding stand-variables (i.e., height, volume their combination), results indicate that GM2 0.82% 13.7%. We employed mixed-effects modeling approach with both fixed random effects account variations individual plot level, enhancing predictive accuracy. explained 71% variability trends residuals. calibrated response calibration method, through EBLUP theory. Our findings suggest stand-level variables representing plot-specific can further improve fit mixed- models. These advancements provide authorities enhanced tools supporting

Язык: Английский

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Correcting forest aboveground biomass biases by incorporating independent canopy height retrieval with conventional machine learning models using GEDI and ICESat-2 data

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103045 - 103045

Опубликована: Янв. 1, 2025

Язык: Английский

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Derivation of Allometric Equations and Carbon Content Estimation in Mangrove Forests of Malaysia

Environmental and Sustainability Indicators, Год журнала: 2025, Номер unknown, С. 100618 - 100618

Опубликована: Янв. 1, 2025

Язык: Английский

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Advancing forest fire prediction: A multi-layer stacking ensemble model approach

Earth Science Informatics, Год журнала: 2025, Номер 18(3)

Опубликована: Фев. 19, 2025

Язык: Английский

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Comparative analysis of sensors and classification algorithms for land cover classification in Islamabad, Pakistan

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Янв. 29, 2025

Язык: Английский

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