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

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Tác giả: Shoaib Ahmad Anees, Mohammad Javed Ansari, Timothy Dube, Waseem Razzaq Khan, Qijing Liu, Kaleem Mehmood, Sultan Muhammad, Fahad Shahzad, Mansour Shrahili

Ngôn ngữ: eng

Ký hiệu phân loại: 697.72 Radiant panel heating

Thông tin xuất bản: England : Ecology and evolution , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 471883

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 in tree cover from 25.02% in 2015 to 29.99% in 2023 and a decrease in barren land from 20.64% to 16.81%, with an accuracy above 85%. Hotspot and spatial clustering analyses revealed significant vegetation recovery, with high-confidence hotspots rising from 36.76% to 42.56%. A predictive model for the Normalized Difference Vegetation Index (NDVI), supported by SHAP analysis, identified soil moisture and precipitation as primary drivers of vegetation growth, with the ANN model achieving an
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