[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124450-en":3,"doc-seo-124450-105":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},124450,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Application of Machine Learning and Remote Sensing in Monitoring Land Use Dynamics in Tourism Area","The study investigates spatial-temporal changes in land use and land cover (LULC) across the Lake Toba tourism area during the past 35 years, linking increasing tourism pressure to landscape transformation. Remote sensing and machine learning are combined to analyze satellite imagery from Landsat 5, 8, and 9 using the Random Forest algorithm with GIS integration. NDVI and SAVI support ecosystem health monitoring, while thermal and water-related indices track environmental effects, including possible growth of water hyacinths. Results inform sustainable land management for tourism development.","Application of machine learning and remote sensing in monitoring land use dynamics in tourism area  \nSalsa Muafiroh1*, Kuncoro Adi Pradono1, Priyo Sunandar1, Parluhutan Manurung1  \n1 Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, West Java 16424, Indonesia.  \n* Correspondence: [salsa.muafiroh@ui.ac.id](salsa.muafiroh@ui.ac.id)  \nReceived Date: January 25, 2025 Revised Date: February 27, 2025 Accepted Date: February 28, 2025  \nABSTRACT  \nBackground: This study uses remote sensing and machine learning techniques to investigate the spatialtemporal changes in land use and land cover (LULC) within the Lake Toba tourism area over the past 35 years. Increasing tourism activities have significantly altered the region's landscape, particularly leading to a reduction in forest cover and an expansion of built-up areas. Method: By applying the Random Forest algorithm to satellite imagery data from Landsat 5, 8, and 9, and integrating Geographic Information System (GIS) technology, we analyzed and accurately predicted these changes. Additionally, indices such as NDVI and SAVI were used to monitor ecosystem health in detail, particularly for tracking the growth of invasive species like water hyacinths. Findings: LULC analysis of the Lake Toba tourism area reveals significant changes, including an increase in built-up areas, a decrease in vegetation, and the potential growth of water hyacinths. Surface temperature analysis indicates higher temperatures in built-up areas and cooler temperatures in natural vegetation. Using NDVI, SAVI, and MDWI indices also helped in monitoring water hyacinth growth, supporting improved ecosystem management for sustainability. Conclusion: This study highlights the environmental impacts of tourism and emphasizes the need for sustainable land management practices to balance development with ecological preservation. Novelty/Originality of this Research: This research demonstrates the effectiveness of combining machine learning with spatial technologies to support informed decision-making inland use planning.  \nKEYWORDS: land use and land cover (LULC); machine learning; tourism area.  \n1. Introduction  \nThe tourism sector in Indonesia continues to play a vital role in supporting the national economy (Widari, 2020) . The issue of land use and land cover (LULC) in the Lake Toba tourism area has become increasingly significant (Junef, 2017; Sinuhaji et al., 2019), particularly after Lake Toba's designation as a National Tourism Strategic Area (Kawasan Strategis Pariwisata Nasional or KSPN) (Marikena & Setiwannie, 2022; Yuli, 2022) . The government's policy granting priority status to the development of this region underscores the critical need for meticulous land management to support sustainable tourism development (Sinaga, 2018; Siregar, 2018; Tanjung et al., 2024) . As a tourism destination with potential for economic, socio-cultural, and environmental growth, changes in land use within this area have become a focal point in ensuring the sustainability of tourism development (Pardede & Suryawan, 2016; Tanjung et al., 2024) .  \nDespite its promising potential, the increase in tourist visits and tourism-related activities around Lake Toba has brought about numerous environmental challenges (Saputra, 2020; Hakim, 2024) . These include the uncontrolled growth of settlements, inadequate waste management, and limited space allocation for tourism activities (Widhijanto & Tisnaningtyas, 2018). Addressing these challenges requires a comprehensive and sophisticated approach to land use management to ensure a harmonious balance between tourism development and environmental conservation (Safitri, 2021; Zaenal et al., 2016) .  \nThis balance is particularly essential given the interconnectedness of economic growth, environmental sustainability, and socio-cultural resilience in strategic tourism areas like Lake Toba (Delita etal., 2017; Hakim et a., 2024; Kartomihardjo et al.,","cbCaiiN4IFUjLzu6","https://ap.wps.com/l/cbCaiiN4IFUjLzu6","pdf",785866,1,15,"English","en",105,"# Introduction\n## Land use and tourism development context\n## Environmental challenges and need for integrated methods\n# Methods\n## Study area and geographic setting\n## Data sources and analytical approach","[{\"question\":\"What is the main objective of this research?\",\"answer\":\"To examine spatial-temporal LULC changes in the Lake Toba tourism area over the past 35 years and assess environmental impacts of tourism activities.\"},{\"question\":\"Which machine learning method is used to analyze land use changes?\",\"answer\":\"The study applies the Random Forest algorithm to satellite imagery data from Landsat 5, 8, and 9, integrated with GIS.\"},{\"question\":\"How do the indices like NDVI, SAVI, and MDWI contribute to the analysis?\",\"answer\":\"NDVI and SAVI are used to monitor ecosystem health and vegetation conditions, while MDWI helps monitor water hyacinth growth and related water-ecosystem dynamics.\"}]","Application of Machine Learning and Remote Sensing in Monitoring Land Use Dynamics in Tourism Area | 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