[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121495-en":3,"doc-seo-121495-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121495,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Validation of Sentinel-2 Based Machine Learning Models for Czech National Forest Inventory","The National Forest Inventory (NFI) of the Czech Republic underpins forest management but demands substantial time and resources. This study validates Sentinel-2–based machine learning models against official NFI data to verify real-world reliability for forest monitoring. Four common algorithms—Classification and Regression Trees, Random Forest, Support Vector Machine, and Naive Bayes—were applied to estimate forest cover conditions from Sentinel-2 imagery. Random Forest achieved the highest overall accuracy (98.3%). Systematic comparison with NFI records addresses a key remote-sensing gap: validation beyond training datasets. Proper validation enables scalable, accurate forest monitoring, reducing the financial and labor burden of traditional field surveys while supporting sustainable management decisions.","Ecological Informatics 87 (2025) 103133  \nContents lists available at ScienceDirect  \nEcological Informatics  \njournal [homepage: www.elsevier.com/locate/ecolinf](homepage: www.elsevier.com/locate/ecolinf)  \n| Validation of sentinel 2 based machine learning models for Czech National Forest Inventory\u003Cbr>Richard Kov´arník *, Jitka Janov´a\u003Cbr>Department of Statistics and Operation Analysis, Faculty of Business and Economics, Mendel University in Brno, Zemˇedˇelsk´a 1, 61300 Brno, Czech Republic |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords: Machine learning Remote sensing Data science\u003Cbr>Forest management National forest inventory Automation |  | The National Forest Inventory (NFI) of the Czech Republic provides essential data for forest management but requires significant time and resources. This study highlights the critical role of validating Sentinel-2-based machine learning models against real NFI data to ensure their reliability for forest monitoring. While satellitebased models offer a cost-effective alternative, their practical applicability depends on rigorous validation. We applied four commonly used machine learning models—Classification and Regression Trees, Random Forest, Support Vector Machine, and Naive Bayes—to Sentinel-2 imagery to estimate forest cover conditions. The Random Forest model achieved the highest overall accuracy (98.3 %). By systematically comparing model predictions with official NFI data, we address a key gap in remote sensing applications: the need for real-world validation beyond training datasets. Our findings demonstrate that properly validated Sentinel-2-based models can enhance large-scale forest monitoring, reducing the financial and labor burdens of traditional field surveys while ensuring data accuracy for sustainable forest management. |\n\n1. Introduction  \nThe accelerating pace of urbanization and environmental change necessitates the development and application of advanced technologies to monitor landscape conditions and dynamics, thereby facilitating prompt and effective decision-making (Panahi et al., 2024). Remote sensing, machine learning, and geospatial analysis have emerged as crucial tools for tracking ecological changes, particularly in the field of forestry. These technologies enhance our ability to conduct large-scale environmental assessments with high precision and efficiency, reducing the reliance on traditional field-based data collection methods (Fassnacht et al., 2024).  \nIn the Czech Republic, the National Forest Inventory (NFI), established under Forest Act 289/1995, serves as the primary method for systematically collecting data on forest stand conditions at the national level (Institute for Forest Management, 2024). Initiated as a national project in 2001, the NFI employs field surveys supplemented by photogrammetry and digital aerial image analysis to monitor and assess forests across extensive areas. While this approach is valued for its accuracy and reliability, it faces challenges such as high costs, timeconsuming data collection, limited update frequency, and the need for substantial personnel and technological resources (Kangas and  \nMaltamo, 2006). Consequently, there is a growing interest in innovative technologies that can complement or partially replace traditional methods, offering faster and more cost-effective solutions (McRobertsand Tomppo, 2007).  \nThe advancement of computer technology has led to the emergence of systems capable of significantly reducing costs, notably through the acquisition and analysis of satellite imagery. These images are now obtained in high quality and at regular intervals, enhancing their utility in monitoring landscape changes (Coops et al., 2023). Remote sensing data is increasingly employed in scientific studies addressing forest structure, biomass estimation, and land cover classification (Fassnacht et al., 2024). The integration of Earth observation data with machine learning techni","cbCaiaKcY2UXNk6X","https://ap.wps.com/l/cbCaiaKcY2UXNk6X","pdf",2881760,1,"English","en",105,"# Introduction\n# Materials and Methods\n## Sentinel-2 data and preprocessing\n## Machine learning models\n# Results\n## Model accuracy and comparison\n# Discussion\n# Conclusion","[{\"question\":\"Why is validation of Sentinel-2 machine learning models against NFI data necessary?\",\"answer\":\"The study emphasizes that satellite-based models can be cost-effective, but only rigorous validation against official NFI measurements ensures their reliability for practical forest monitoring.\"},{\"question\":\"Which machine learning models were tested and which performed best?\",\"answer\":\"Four models were used: Classification and Regression Trees, Random Forest, Support Vector Machine, and Naive Bayes. Random Forest achieved the highest overall accuracy at 98.3%.\"},{\"question\":\"How does the work reduce reliance on traditional field surveys?\",\"answer\":\"By using properly validated Sentinel-2–based models to estimate forest cover conditions at scale, the approach can lower financial and labor demands associated with conventional field data collection while maintaining data accuracy.\"}]","Validation of Sentinel-2 Based Machine Learning Models for Czech National Forest Inventory | PDF",1785735927,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"validation-of-sentinel-2-based-machine-learning-models-for-czech-national-forest-inventory","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/validation-of-sentinel-2-based-machine-learning-models-for-czech-national-forest-inventory/121495/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is validation of Sentinel-2 machine learning models against NFI data necessary?","Question",{"text":74,"@type":75},"The study emphasizes that satellite-based models can be cost-effective, but only rigorous validation against official NFI measurements ensures their reliability for practical forest monitoring.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning models were tested and which performed best?",{"text":79,"@type":75},"Four models were used: Classification and Regression Trees, Random Forest, Support Vector Machine, and Naive Bayes. Random Forest achieved the highest overall accuracy at 98.3%.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the work reduce reliance on traditional field surveys?",{"text":83,"@type":75},"By using properly validated Sentinel-2–based models to estimate forest cover conditions at scale, the approach can lower financial and labor demands associated with conventional field data collection while maintaining data accuracy.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]