[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121500-en":3,"doc-seo-121500-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121500,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning and Spatio Temporal Analysis for Assessing Ecological Impacts of the Billion Tree Afforestation Project - Research Article","This study evaluates the Billion Tree Afforestation Project (BTAP) in Pakistan’s Khyber Pakhtunkhwa (KPK) province by combining remote sensing with machine learning. Using Random Forest (RF) classification on Sentinel-2 imagery, tree cover increases from 25.02% (2015) to 29.99% (2023) while barren land decreases from 20.64% to 16.81%, with accuracy above 85%. Hotspot and spatial clustering analyses indicate strong vegetation recovery. An NDVI predictive model, supported by SHAP, highlights soil moisture and precipitation as key drivers, achieving an ANN R2 of 0.8556 and RMSE of 0.0607.","Ecology and Evolution  \nRESEARCH ARTICLE  OPEN ACCESS   \nMachine Learning and Spatio Temporal Analysis for Assessing Ecological Impacts ofthe Billion Tree Afforestation Project  \nKaleem Mehmood1,2,3 | Shoaib Ahmad Anees4  | Sultan Muhammad3 | Fahad Shahzad5 | Qijing Liu1,2 | Waseem Razzaq Khan6 | Mansour Shrahili7 | Mohammad Javed Ansari8 | Timothy Dube9  \n1College of Forestry, Beijing Forestry University, Beijing, China | 2Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing, China | 3Institute of Forest Science, University of Swat, Swat, Pakistan | 4Department of Forestry, The University of Agriculture, Dera Ismail Khan, Pakistan | 5Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing, China | 6Department of Forestry Science and Biodiversity, Faculty of Forestry and Environment, Universiti Putra Malaysia, Serdang, Malaysia | 7Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh, Saudi Arabia | 8Department of Botany, Hindu College Moradabad (Mahatma Jyotiba Phule Rohilkhand University Bareilly), Moradabad, India | 9Institute for Water Studies, Faculty of Science, University of the Western Cape, Cape Town, South Africa  \nCorrespondence: Shoaib Ahmad Anees ([anees.shoaib@gmail.com](anees.shoaib@gmail.com)) | Waseem Razzaq Khan ([khanwaseem@upm.edu.my](khanwaseem@upm.edu.my))  \nReceived: 29 September 2024 | Revised: 28 November 2024 | Accepted: 3 December 2024  \nFunding: This research was funded by the Universiti Putra Malaysia, Malaysia, Vote No. 9750500.  \nKeywords: afforestation | land-use change | machine learning | NDVI | remote sensing | Sentinel-2  \nABSTRACT  \nThis 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.  \n1 | Introduction  \nAfforestation is important to global climate change mitigation, land rehabilitation, and biodiversity enhancement strategies. It has recently been announced that the BTAP Project in Pakistanis one of seven ambitious global initiatives and policy tools emphasizing scaling up forest landscape restoration (Kamal, Ali, and Yingjie 2018; Ullah et al. 2020) . This afforestation activity  \nremoves large volumes of CO2 from the atmosphere, which is crucial in combating global warming (Haider et al. 2017; Jallatet al. 2021; Khan et al. 2020) . It also enhances ecosystem services related to carbon sequestration and wildlife conservation (Chen and Zhang 2023; Wang et al. 2022; Wang et al. 2024) . Remote sensing technologies have dramatically changed how afforestation programs are monitored and evaluated, predominantly through high-resolution satellite imagery (Shawky  \n\n| Kaleem Mehmood and Shoaib Ahmad Anees contributed equally to this work. |\n| --- |\n| This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.\u003Cbr>© 2025 The Author(s). Ecology and Evolution published by John Wiley & So","cbCaib6BKW0n2kqH","https://ap.wps.com/l/cbCaib6BKW0n2kqH","pdf",8863622,1,29,"English","en",105,"# Introduction\n## Remote sensing and afforestation monitoring\n## Machine learning methods for land-cover change\n## Study context: BTAP and Sentinel/Landsat data","[{\"question\":\"How does the study measure ecological impacts of the Billion Tree Afforestation Project?\",\"answer\":\"It evaluates BTAP using remote sensing and machine learning, applying Random Forest classification to Sentinel-2 imagery and analyzing NDVI-related patterns.\"},{\"question\":\"What trends in land cover does the Sentinel-2 classification reveal?\",\"answer\":\"Tree cover rises from 25.02% in 2015 to 29.99% in 2023, while barren land declines from 20.64% to 16.81%, with classification accuracy above 85%.\"},{\"question\":\"Which factors are identified as primary drivers of vegetation growth in the NDVI model?\",\"answer\":\"SHAP-supported modeling indicates soil moisture and precipitation are the primary drivers of vegetation growth, validated with ANN performance metrics.\"}]","Machine Learning and Spatio Temporal Analysis for Assessing Ecological Impacts of the Billion Tree Afforestation Project - Research Article | PDF",1785735956,73,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-and-spatio-temporal-analysis-for-assessing-ecological-impacts-of-the-billion-tree-afforestation-project-research-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-spatio-temporal-analysis-for-assessing-ecological-impacts-of-the-billion-tree-afforestation-project-research-article/121500/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the study measure ecological impacts of the Billion Tree Afforestation Project?","Question",{"text":75,"@type":76},"It evaluates BTAP using remote sensing and machine learning, applying Random Forest classification to Sentinel-2 imagery and analyzing NDVI-related patterns.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What trends in land cover does the Sentinel-2 classification reveal?",{"text":80,"@type":76},"Tree cover rises from 25.02% in 2015 to 29.99% in 2023, while barren land declines from 20.64% to 16.81%, with classification accuracy above 85%.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors are identified as primary drivers of vegetation growth in the NDVI model?",{"text":84,"@type":76},"SHAP-supported modeling indicates soil moisture and precipitation are the primary drivers of vegetation growth, validated with ANN performance metrics.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]