Machine learning and spatio temporal analysis for assessing ecological impacts of the billion tree afforestation project

dc.contributor.authorDube, Timothy
dc.contributor.authorMehmood, Kaleem
dc.contributor.authorAnees, Shoaib Ahmad
dc.date.accessioned2025-08-01T12:46:47Z
dc.date.available2025-08-01T12:46:47Z
dc.date.issued2025
dc.description.abstractThis 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 R2 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.
dc.identifier.citationMehmood, K., Anees, S.A., Muhammad, S., Shahzad, F., Liu, Q., Khan, W.R., Shrahili, M., Ansari, M.J. and Dube, T., 2025. Machine learning and spatio temporal analysis for assessing ecological impacts of the billion tree afforestation project. Ecology and Evolution, 15(2), p.e70736.
dc.identifier.urihttps://doi.org/10.1002/ece3.70736
dc.identifier.urihttps://hdl.handle.net/10566/20640
dc.language.isoen
dc.publisherJohn Wiley and Sons Ltd
dc.subjectAfforestation
dc.subjectLand-use change
dc.subjectMachine learning
dc.subjectNDVI
dc.subjectRemote sensing
dc.titleMachine learning and spatio temporal analysis for assessing ecological impacts of the billion tree afforestation project
dc.typeArticle

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