[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122823-en":3,"doc-seo-122823-105":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":4,"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},122823,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning XGBoost Method for Detecting Mangrove Cover Using Unmanned Aerial Vehicle Imagery","Mangrove ecosystems are vital yet declining, making accurate, spatially explicit mapping essential for socioeconomic and ecological management. This study classifies mangrove cover at Tanjung Lapin Beach (~18.3 ha) in North Rupat, Bengkalis Regency, Riau Province by applying an XGBoost machine-learning model to UAV imagery. Orthomosaic imagery was processed into 3,500 tiles, and 224 visually recognized mangrove sample points were used for training. The model achieved 99% overall accuracy with kappa ~0.98, producing a mangrove cover estimate of ~11.9 ha (64% of the study area).","Department of Earth Science and Technology, Universitas Negeri Gorontalo  \nMachine Learning XGBoost Method for Detecting Mangrove Cover Using Unmanned Aerial Vehicle Imagery  \nMinati 1 , Iksal Yanuarsyah 1  , Sahid Agustian Hudjimartsu 1   \n1 Ibnu Khaldun University, Bogor, Indonesia  \nARTICLE INFO  \nArticle history:  \nReceived: 22 June 2023  \nAccepted: 24 July 2023  \nPublished: 30 July 2023  \nKeywords:  \nMangrove Cover; RStudio; UAV; XGBoost  \nCorresponding author:  \nIksal Yanuarsyah  \nEmail: iksal.yanuarsyah@ft.uika[bogor.ac.id](bogor.ac.id)  \nRead online:  \nScan this QR code with your smartphone or mobile device to read online.  \nABSTRACT  \nThe mangrove ecosystem can be understood as a unique and different typeof ecosystem that can benefit the surrounding ecosystem from the socioeconomic and ecological perspective. The purpose of this study is to classify mangrove cover in Tanjung Lapin Beach, about 18.3 hectares, North Rupat District Bengkalis Regency, Riau Province, by applying machine learning XGBoost methods of UAV images by producing interpretations of mangrove cover in the research area. The use of machine learning with a high level of accuracy resulting from the XGBoost method is expected to help the availability of spatial data in identifying better mangrove forest cover. The data obtained from the orthomosaic results from the 3,500 tiles image is used as a reference for making sample points for the analysis process using the XGBoost method, with 224 sample points of mangrove objects visually recognized as training data. Regarding training data, the XGBoost method's iteration result obtained 99% overall accuracy and Kappa accuracy of about 0.98. It means the analysis process continues to the mangrove object cover detection stage. Based on the detection results, it was obtained about 11.9 hectares of mangrove forest cover (64% of the total study area) . It has 68 sample points as test data used as an accuracy test tool from the detection results of mangrove objects, where an overall accuracy of 87% and kappa accuracy of 0.82 were obtained. This shows the successful use of the XGBoost method in identifying the mangrove's cover.  \nHow to cite: Minati., Yanuarsyah, I., & Hudjimartsu, S. (2023) . Machine Learning XGBoost Method for Detecting Mangrove Cover Using Unmanned Aerial Vehicle Imagery. Jambura Geoscience Review, 5(2), 127-136. doi:[https://doi.org/10.34312/jgeosrev.v5i2.20782](https://doi.org/10.34312/jgeosrev.v5i2.20782)  \n1. INTRODUCTION  \nThe mangrove forest ecosystem can be interpreted as a unique and distinctive form of the ecosystem, so it can bring many benefits to the surrounding ecosystem, starting from a socioeconomic and ecological perspective (Rahmat Maulana et al. , 2021) . Amidst the vast expanse of Southeast Asia lies a crucial ecosystem known as mangrove forests, sustaining the livelihoods of millions. However, the unfortunate reality persists as these vital coastal habitats rapidly decline. Between 2000 and 2012, these irreplaceable mangrove forests were lost at an average annual rate of 0. 18%, putting the well-being of nature and the people who depend on it at risk (Richards & Friess, 2016) . Without substantial intervention in the brackish water aquaculture sectors and palm oil plantations, the current business-as-usual policy approach paints a grim picture for mangrove forests. Over the next two decades, a staggering 700,000 hectares of these vital ecosystems are at risk of being lost, with the majority converted into fish and shrimp ponds. Urgent action is needed to avert this impending catastrophe and safeguard the future of our precious mangrove forests (Ilman et al. , 2016) . At the Asian level, the area of Indonesian mangrove forests is around 49% of the total area of mangrove forests in Asia, followed by Malaysia (10%) and Myanmar (9%)  \n(Schaduw, 2019) . The area tends to decrease from year to year. In 25 years (1980-2005), Indonesia lost 30. 1% or 1.3 million hectares of mangroves. E","cbCaiqfCcDlAVsUt","https://ap.wps.com/l/cbCaiqfCcDlAVsUt","pdf",834685,1,10,"English","en",105,"# Introduction\n## Mangrove ecosystem importance and decline\n## Remote sensing and UAV technology\n## Machine learning background: XGBoost\n# Methods\n## Study area and data source (UAV orthomosaic)\n## Image tiling and sample point generation\n## Training and testing with XGBoost\n# Results and Discussion\n## Training performance (accuracy and kappa)\n## Detection output (estimated mangrove area)\n## Testing performance (test accuracy and kappa)\n# Conclusion","[{\"question\":\"What is the main goal of using XGBoost in this study?\",\"answer\":\"To classify mangrove cover using UAV imagery and produce spatial interpretation of mangrove distribution in the study area.\"},{\"question\":\"How was the training data prepared for the XGBoost model?\",\"answer\":\"UAV orthomosaic results were divided into 3,500 tiles, and 224 visually recognized mangrove sample points were used as training data.\"},{\"question\":\"What accuracy did the model achieve, and what does it imply?\",\"answer\":\"The training produced 99% overall accuracy with kappa ~0.98, and the testing achieved 87% overall accuracy with kappa 0.82, indicating successful mangrove cover identification.\"}]","Machine Learning XGBoost Method for Detecting Mangrove Cover Using Unmanned Aerial Vehicle Imagery | 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is the main goal of using XGBoost in this study?","Question",{"text":75,"@type":76},"To classify mangrove cover using UAV imagery and produce spatial interpretation of mangrove distribution in the study area.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the training data prepared for the XGBoost model?",{"text":80,"@type":76},"UAV orthomosaic results were divided into 3,500 tiles, and 224 visually recognized mangrove sample points were used as training data.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy did the model achieve, and what does it imply?",{"text":84,"@type":76},"The training produced 99% overall accuracy with kappa ~0.98, and the testing achieved 87% overall accuracy with kappa 0.82, indicating successful mangrove cover 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