[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121029-en":3,"doc-seo-121029-105":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":20,"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},121029,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Primary forest characteristics estimation through remote sensing data and machine learning - Sakhalin case study","Remote sensing supports diverse environmental applications by enabling consistent observation and spatial analysis. Machine learning further helps uncover dependencies between satellite measurements and vegetation properties, which is essential for estimating forest characteristics and tracking change over time. This study develops a tailored approach for remote sensing and forestry needs, processing Sentinel-2 multispectral data by building feature spaces, selecting training samples, and training separate models for forest species groups, height, basal area, and timber stock. In the Sakhalin case, MAE reaches 1.6 m for height, 0.084 for basal area, and 47.8 m³/ha for timber stock, indicating potential for AI-driven forestry decisions.","Primary forest characteristics estimation through remote sensing data and machine learning: Sakhalin case study  \nSvetlana Illarionova*, Alina Smolina, and Dmitrii Shadrin  \nSkolkovo Institute of Science and Technology, 143026 Skolkovo, Moscow, Russia  \nAbstract. Currently, remote sensing techniques assist in various environmental applications and facilitate observation and spatial analysis.  \nMachine learning algorithms allow researchers to find dependencies in satellite data and vegetation cover properties. One of the significant tasks for ecological assessment is associated with estimating forest characteristics and monitoring changes over time. In contrast to the general computer vision domain, remote sensing data and forestry measurements have their own specific requirements and necessitate tailored approaches that involve processing multispectral satellite data, creating feature spaces, and selecting training samples. In this study, we focus on extracting primary forest characteristics, including forest species groups, height, basal area, and timber stock. We utilise Sentinel-2 multispectral data to develop a machine learning-based solution for vast and remote territories. Timber stock is calculated using empirical formulas based on measurements of forest species groups, height, and basal area. These intermediate forest parameters are estimated using individually trained machine learning algorithms for each parameter. As a case study, we examine the Sakhalin region (Russia), which encompasses several forestries with varying vegetation properties. In Nevelskoye forestry, we achieved a mean absolute error (MAE) of 1.6m for height, 0.084 for basal area, and 47.8 m3/ha for timber stock. The results obtained demonstrate promise for further integrating artificial intelligencebased solutions into forestry decision-making processes and natural resources management.  \n1 Introduction  \nForests play an important role in the ecosystem and have a significant impact on climate, biodiversity, and the economy. They are a source of oxygen, food, timber production, and help retain water in the soil and prevent erosion. Conducting forest inventory allows for obtaining information on the state of forest resources, assessing their potential, and determining necessary measures for their conservation and management [1] . This is crucial for forest management planning, fire prevention, monitoring of deforestation, and  \n* Corresponding author: [s.illarionova@skoltech.ru](s.illarionova@skoltech.ru)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nbiodiversity protection. Additionally, forest inventory serves as the basis for developing a strategy for sustainable forest resource use and reducing negative environmental impacts [2] .  \nThe class or group of tree species in a forest is one of the primary parameters for assessing forest resources. Determining tree species can be done through sample plots or individual stands, typically focusing on dominant species that make up more than 50% of the area [3] . Another key parameter for forest assessment is the distinction between broadleaf and coniferous species. This differentiation helps address various ecological and forest management tasks [4] . Assessment can be conducted using artificial intelligence algorithmsand available satellite data [5] .  \nForest height can be assessed through various methods and is considered an independent parameter, for example, in monitoring regrowth in power line corridors [6], as well as in conjunction with other parameters for comprehensive forest assessment. Lidar surveys are commonly used to assess forest canopy height by constructing a canopy height model from point clouds [7]. An alternative and less costly approach is the use of satellite data [8] .  \nAbsolute basa","cbCaii7aCDqwlfDq","https://ap.wps.com/l/cbCaii7aCDqwlfDq","pdf",621732,1,19,"English","en",105,"# Introduction\n## Forest role and the need for inventory\n## Key forest assessment parameters\n## Methods for height estimation\n## Basal area and timber stock estimation","[{\"question\":\"Which forest characteristics does the study estimate for Sakhalin forests?\",\"answer\":\"The approach estimates primary forest characteristics including forest species groups, height, basal area, and timber stock.\"},{\"question\":\"How is Sentinel-2 used in the proposed machine learning pipeline?\",\"answer\":\"Sentinel-2 multispectral data are processed to create feature spaces, and training samples are selected to train machine learning models for each forest parameter.\"},{\"question\":\"What accuracy results were reported for the Sakhalin case study?\",\"answer\":\"In Nevelskoye forestry, the mean absolute error (MAE) was 1.6 m for height, 0.084 for basal area, and 47.8 m³/ha for timber stock.\"}]","Primary forest characteristics estimation through remote sensing data and machine learning - Sakhalin case study | PDF",1785733389,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"primary-forest-characteristics-estimation-through-remote-sensing-data-and-machine-learning-sakhalin-case-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/primary-forest-characteristics-estimation-through-remote-sensing-data-and-machine-learning-sakhalin-case-study/121029/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which forest characteristics does the study estimate for Sakhalin forests?","Question",{"text":75,"@type":76},"The approach estimates primary forest characteristics including forest species groups, height, basal area, and timber stock.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is Sentinel-2 used in the proposed machine learning pipeline?",{"text":80,"@type":76},"Sentinel-2 multispectral data are processed to create feature spaces, and training samples are selected to train machine learning models for each forest parameter.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy results were reported for the Sakhalin case study?",{"text":84,"@type":76},"In Nevelskoye forestry, the mean absolute error (MAE) was 1.6 m for height, 0.084 for basal area, and 47.8 m³/ha for timber stock.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]