[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120703-en":3,"doc-seo-120703-105":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},120703,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Comparing Different Machine Learning Options to Map Bark Beetle Infestations in Croatia","The paper evaluates approaches for mapping bark beetle–infested forests in Croatia, addressing the challenge of producing accurate maps over large, difficult-to-access areas. It compares supervised machine learning and classification workflows implemented in open-source GIS platforms QGIS and SAGA GIS, using Copernicus Sentinel-2 satellite imagery. Tested methods include maximum likelihood, minimum distance, artificial neural networks, decision trees, K-nearest neighbor, random forest, support vector machine, spectral angle mapper, and naive Bayes. Results are assessed via Kappa values, identifying neural networks as highest accuracy and support vector machines as lowest.","COMPARING DIFFERENT MACHINE LEARNING OPTIONS TO MAP BARK BEETLE  \nINFESTATIONS IN CROATIA  \nN. Kranjčić1*, V. Cetl 2, H. Matijević 2, D. Markovinović 2  \n1 Faculty of Geotechnical Engineering, University of Zagreb, Varaždin, [Croatia-nikola.kranjcic@gfv.unizg.hr](Croatia-nikola.kranjcic@gfv.unizg.hr)  \n2 Department for Geodesy and Geomatics, University North, Varaždin, Croatia- (vcetl, hmatijevic, dmarkovinovic)@[unin.hr](unin.hr)  \nKEY WORDS: supervised classification, machine learning options, QGIS, SAGA GIS, Copernicus data  \nABSTRACT:  \nThis paper presents different approaches to map bark beetle infested forests in Croatia. Bark beetle infestation presents threat to forest ecosystems. Due to large unapproachable area, it also presents difficulties in mapping infested areas. This paper analyses available machine learning options in open-source software QGIS and SAGA GIS. All options are performed on Copernicus data, Sentinel 2 satellite imagery. Machine learning and classification options are maximum likelihood classifier, minimum distance, artificial neural network, decision tree, K Nearest Neighbor, random forest, support vector machine, spectral angle mapper and Normal Bayes. Kappa values respectively are: 0.71; 0.72; 0.81; 0.68; 0.69; 0.75; 0.26; 0.60; 0.41 which shows highest classification accuracy for artificial neural networks method and lowest for support vector machine accuracy.  \n1. INTRODUCTION  \nRemote sensing is the process of acquiring information about an object or phenomenon without making physical contact with it. It involves usage of various sensors to capture data from a distance, such as aerial photography, satellite imagery, and LiDAR. One of the most important applications of remote sensing is classification. It is the process of categorizing objects or areas based on their characteristics in the acquired data. In recent years, machine learning methods have become increasingly popular for remote sensing classification. Machine learning algorithms, such as artificial neural networks, support vector machines, and random forests, are used to automatically learn and recognize patterns in the data, and then assign classification labels to the objects or areas. These methods have been shown to be effective for a wide range of remote sensing applications, including land use and land cover mapping, vegetation monitoring, and urban growth analysis. Within this paper we explore the use of machine learning methods for remote sensing data classification (Feng et al., 2015; Foody, 2002; Jain et al., 2016; Jog and Dixit, 2016; Kranjčić et al., 2019a; Singh et al., 2017) . We discuss various algorithms, their strengths and weaknesses, and their suitability for different types of remote sensing data. We also investigate the impact of different input features, such as spectral, textural, and contextual information, on the performance of the classifiers. Finally, we compare the results of different machine learning methods with traditional classification techniques and discuss the potential for future research and development in this field. However, due to the page limitations, each method and comparations are defined partially. Following methods are used and discussed: maximum likelihood, minimum distance, artificial neural network, decision tree, K nearest neighbour, random forest, support vector machine, spectral angle mapper and naïve Bayes. We executed all the classification methods using OpenCV library from within QGIS and SAGA GIS software. The paper is organised as it follows, second chapter presents methods, study area and data sets used. Chapter three deals with results and discussion, chapter four presents’conclusions and lest chapter shows references used.  \n* Corresponding author  \n2. METHODS, STUDY AREA AND DATA SETS  \nIn this chapter, each method is shortly explained, study area is presented together with data sets used.  \n2.1 Maximum likelihood  \nMaximum likelihood (ML) is a supervised classification me","cbCais8qzsjWOwVq","https://ap.wps.com/l/cbCais8qzsjWOwVq","pdf",1328699,1,6,"English","en",105,"# Introduction\n# Methods, Study Area and Data Sets\n## Maximum likelihood\n## Minimum distance\n## Artificial neural network\n## Decision tree\n## K-nearest neighbor\n# Results and Discussion\n# Conclusions\n# References","[{\"question\":\"Why is mapping bark beetle infestations challenging in Croatia?\",\"answer\":\"Bark beetle infestation threatens forest ecosystems and is difficult to map accurately over large areas that are hard to access.\"},{\"question\":\"Which data source and software platforms are used in the study?\",\"answer\":\"The study uses Copernicus Sentinel-2 satellite imagery and runs classification options in open-source tools QGIS and SAGA GIS.\"},{\"question\":\"How do the compared classifiers perform according to Kappa values?\",\"answer\":\"Kappa values indicate the best performance for artificial neural networks (0.81) and the lowest accuracy for support vector machine (0.26), with other methods in between.\"}]","Comparing Different Machine Learning Options to Map Bark Beetle Infestations in Croatia | 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is mapping bark beetle infestations challenging in Croatia?","Question",{"text":75,"@type":76},"Bark beetle infestation threatens forest ecosystems and is difficult to map accurately over large areas that are hard to access.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data source and software platforms are used in the study?",{"text":80,"@type":76},"The study uses Copernicus Sentinel-2 satellite imagery and runs classification options in open-source tools QGIS and SAGA GIS.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the compared classifiers perform according to Kappa values?",{"text":84,"@type":76},"Kappa values indicate the best performance for artificial neural networks (0.81) and the lowest accuracy for support vector machine (0.26), with other methods in 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