
Ecological Indicators, Год журнала: 2025, Номер 173, С. 113391 - 113391
Опубликована: Март 30, 2025
Язык: Английский
Ecological Indicators, Год журнала: 2025, Номер 173, С. 113391 - 113391
Опубликована: Март 30, 2025
Язык: Английский
Environmental and Sustainability Indicators, Год журнала: 2024, Номер unknown, С. 100485 - 100485
Опубликована: Сен. 1, 2024
Язык: Английский
Процитировано
24Trees Forests and People, Год журнала: 2024, Номер 18, С. 100657 - 100657
Опубликована: Авг. 20, 2024
Язык: Английский
Процитировано
19Earth Science Informatics, Год журнала: 2025, Номер 18(1)
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
13Ecology 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
Процитировано
6Computers and Electronics in Agriculture, Год журнала: 2025, Номер 232, С. 110122 - 110122
Опубликована: Фев. 17, 2025
Язык: Английский
Процитировано
5Frontiers 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
Процитировано
2Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103045 - 103045
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
2Environmental and Sustainability Indicators, Год журнала: 2025, Номер unknown, С. 100618 - 100618
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
2Earth Science Informatics, Год журнала: 2025, Номер 18(3)
Опубликована: Фев. 19, 2025
Язык: Английский
Процитировано
2Earth Science Informatics, Год журнала: 2025, Номер 18(2)
Опубликована: Янв. 29, 2025
Язык: Английский
Процитировано
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