Статья

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

Kaleem MehmoodCollege of Forestry Beijing Forestry University Beijing ChinaShoaib Ahmad AneesDepartment of Forestry The University of Agriculture Dera Ismail Khan PakistanSultan MuhammadInstitute of Forest Science University of Swat Swat PakistanFahad ShahzadPrecision Forestry Key Laboratory of Beijing Beijing Forestry University Beijing ChinaQijing LiuCollege of Forestry Beijing Forestry University Beijing ChinaWaseem Razzaq KhanDepartment of Forestry Science and Biodiversity, Faculty of Forestry and Environment Universiti Putra Malaysia Serdang MalaysiaMansour ShrahiliDepartment of Statistics and Operations Research, College of Science King Saud University Riyadh Saudi ArabiaMohammad Javed AnsariDepartment of Botany Hindu College Moradabad (Mahatma Jyotiba Phule Rohilkhand University Bareilly) Moradabad IndiaTimothy DubeInstitute for Water Studies, Faculty of Science University of the Western Cape Cape Town South Africa

2025en

ABI

Аннотация

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 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 R 2 of 0.8556 and an RMSE of 0.0607 on the testing dataset. These results demonstrate the effectiveness of integrating machine learning with remote sensing as a framework to support data‐driven afforestation efforts and inform sustainable environmental management practices.

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Идентификаторы

DOI: 10.1002/ece3.70736

Цитирования и источники

Цитирований: 2Использованных источников: 0

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