[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119133-en":3,"doc-seo-119133-105":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},119133,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Comparative Analysis of Machine Learning Models for Tree Species Classification from UAV LiDAR Data","Forest ecosystems sustain global biodiversity and climate balance, making accurate tree species identification essential for ecological surveillance and forest stewardship. This study performs a comparative evaluation of machine learning algorithms for binary tree species classification using UAV LiDAR data. A dataset of 192 trees from a diverse forest is modeled with Logistic Regression, SVM, Random Forest, KNN, Gradient Boosting, and Decision Trees. Performance is measured using accuracy-related metrics including precision, recall, and F1-scores. Results indicate Logistic Regression and SVM achieve the strongest predictive capability, while KNN underperforms and ensembles show higher overfitting risk. Preprocessing and feature engineering are discussed to improve outcomes, supporting sustainable forest management.","Comparative Analysis of Machine Learning Models for Tree Species Classification from UAV LiDAR Data  \nGregorius Airlangga  \nInformation System Study Program, Universitas Katolik Indonesia Atma Jaya, Indonesia  \nARTICLE INFORMATION ABSTRACT  \n\n| Article History:\u003Cbr>Submitted 13 January 2024 Revised 21 February 2024 Accepted 12 March 2024 | \u003Cbr>Forest ecosystems play a pivotal role in maintaining global biodiversity and climate balance. The precise identification of tree species via remote sensing technologies is vital for effective ecological surveillance and forest stewardship. This research conducts a comparative analysis of various machine learning algorithms for the binary classification of tree species utilizing LiDAR data captured by Unmanned Aerial Vehicles (UAVs) . We analyzed a dataset featuring 192 trees from a diverse forest, employing models such as Logistic Regression, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), Gradient Boosting, and Decision Trees. These models were assessed on their accuracy, precision, recall, and F1-scores to ascertain their efficacy. Our findings reveal that Logistic Regression and SVM were superior, achieving precision and recall scores up to 0.96, indicating their robust predictive capability. In contrast, KNN underperformed, suggesting the need for parameter refinement. Although ensemble methods demonstrated resilience, they were more prone to overfitting in comparison to the more straightforward Logistic Regression and SVM models. Preliminary data preprocessing and feature engineering techniques are discussed, enhancing the models' performance. This work enriches the domain of remote sensing and ecological monitoring by offering an in-depth evaluation of machine learning models for tree species classification, underscoring their advantages and constraints. It underscores the transformative potential of machine learning in refining ecological analysis precision, thereby aiding in the pursuit of sustainable forest management. Future research directions could include model refinement through advanced feature selection or the exploration of novel machine learning algorithms for improved classification accuracy. |\n| --- | --- |\n| Keywords:\u003Cbr>UAV LiDAR Data;\u003Cbr>Tree Species Classification; Machine Learning;\u003Cbr>Remote Sensing; Forest Biodiversity |  |\n| Corresponding Author:\u003Cbr>Gregorius Airlangga, Universitas Katolik Indonesia Atma Jaya, Jakarta, Indonesia. Email:\u003Cbr>gregorius.airlangga@atmajaya. [ac.id](ac.id) |  |\n| This work is licensed under a Creative Commons Attribution-Share Alike 4.0\u003Cbr> |  |\n\nDocument Citation:  \nG. Airlangga, “Comparative Analysis of Machine Learning Models for Tree Species Classification from UAV LiDAR Data,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 6, no. 1, pp. 54-62, 2024, DOI: 10. 12928/biste.v6i1 .10059.  \n1. INTRODUCTION  \nThe essential role of forests in maintaining ecological balance, supporting biodiversity, and sequestering carbon has positioned forest conservation and management at the forefront of global environmental priorities [1]–[3] . Amidst the dual crises of climate change and biodiversity loss, the urgency to develop accurate, efficient, and scalable methods for monitoring forest health, structure, and species composition has intensified [4]–[6] . Advanced remote sensing technologies, particularly Light Detection and Ranging (LiDAR), offer unprecedented opportunities to address these challenges [7]–[9] . However, the full potential of LiDAR in discriminating between tree species in mixed and dense forests—a critical aspect for biodiversity assessments and ecological monitoring—remains underexploited [10]–[12] . This gap highlights the pressing need for innovative research that bridges advanced computational methods with ecological science to enhance our understanding and management of forest ecosystems [5],[13],[14] . The advent of high-resolution LiDAR technology has transformed ecological monitoring, allowing for d","cbCaijoHcIELm4z7","https://ap.wps.com/l/cbCaijoHcIELm4z7","pdf",550149,1,9,"English","en",105,"# Introduction\n# Keywords\n# Article Information and Abstract\n# Machine Learning Models and Evaluation\n## Performance Metrics\n## Findings and Overfitting Considerations\n# Data Preprocessing and Feature Engineering\n# Conclusion and Future Research","[{\"question\":\"What is the document’s main goal?\",\"answer\":\"To compare multiple machine learning models for binary tree species classification using UAV LiDAR data and evaluate their effectiveness with standard performance metrics.\"},{\"question\":\"Which models were tested on the UAV LiDAR dataset?\",\"answer\":\"Logistic Regression, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), Gradient Boosting, and Decision Trees.\"},{\"question\":\"What results indicate the best-performing approaches?\",\"answer\":\"Logistic Regression and SVM achieve superior precision and recall values, up to 0.96, showing stronger predictive capability than the other methods.\"}]","Comparative Analysis of Machine Learning Models for Tree Species Classification from UAV LiDAR Data | 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