[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121899-en":3,"doc-seo-121899-105":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},121899,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Remotely sensed desertification modeling using ensemble of machine learning algorithms - Article","Due to having a sensitive and fragile ecosystem, dry areas are constantly exposed to land degradation and desertification, making accurate quantitative assessment essential. This study first evaluates regional desertification using the MEDALUS model, then selects eight remote-sensing indicators with the highest correlation to field data for risk modeling. Four machine learning methods—SVM, GBM, GLM, and RF—are used, and an ensemble weighted average is produced to predict desertification patterns in northeastern Iran, with combined results indicating the least uncertainty.","Remote Sensing Applications: Society and Environment 34 (2024) 101149  \nContents lists available at ScienceDirect  \nRemote Sensing Applications: Society and Environment  \njournal [homepage: www.elsevier.com/locate/rsase](homepage: www.elsevier.com/locate/rsase)  \n| Remotely sensed desertification modeling using ensemble of machine learning algorithms\u003Cbr>Abdolhossein Boalia, Hamid Reza Asgarib, *, Ali Mohammadian Behbahanic, Abdolrassoul Salmanmahinyd, Babak Naimie\u003Cbr>a Department of Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Golestan, Iran b Department of Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Golestan, Iran c Department of Watershed and Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Golestan, Iran d Department of Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Golestan, Iran e Department of Biology, University of Utrecht, Padualaan 8, Utrecht, 3584 CH, The Netherlands |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Modeling\u003Cbr>Machine learning methods Desertification maps Remote sensing MEDALUS |  | Due to having a sensitive and fragile ecosystem, dry areas are constantly exposed to land degradation and desertification. Therefore, it is necessary to formulate appropriate strategies for quantitative assessment of desertification that are highly accurate. In this research, desertification of the region was first evaluated using MEDALUS model, then according to the results of MEDALUS model and reviewing the results of other researchers, 8 indicators remote sensing that had the highest correlation with field data were selected for modeling. Four machine learning methods Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Generalized Linear Models (GLM) and Random Forests (RF) were used to model the risk of desertification in northeastern Iran. Finally, the weighted average of the ensemble model in the SDM statistical package was used to predict the desertification of the region. Based on the results obtained from MEDALUS model, the indicators of drought resistance (score 162), conservation operations (score 158) and soil salinity (score 155), in the working units of abandoned lands, wetland lands, and Salty lands located in the north East of the region, have increased the process of desertification. The results of modeling using machine learning methods showed that in 2002, the SVM model (AUC = 0.91, TSS = 0.93, and Kappa = 0.86) and in 2021, the RF model (AUC = 0.94, TSS = 0.94, and Kappa = 0.90) have performed best. The forecast of the combined model for desertification in 2021 in the studied area showed that the northeastern and sporadically in the central parts of the studied area are affected by the progress of the desertification process. Therefore, by considering the results of the combined model (as a model with the least uncertainty), it is possible to reduce the progress of the desertification process by planning, optimal management and applying corrective methods in the areas affected by desertification. |\n\n1. Introduction  \nArid regions around the world are highly susceptible to environmental changes that can result in land degradation and  \n* Corresponding author.  \nE-mail [addresses:](addresses: Hossien.boali@yahoo.com)[ Hossien.boali@yahoo.com](addresses: Hossien.boali@yahoo.com) (A. Boali), [hamidreza.asgari@gau.ac.ir](hamidreza.asgari@gau.ac.ir) (H.R. Asgari), [ali.mohammadian@gau.ac.ir](ali.mohammadian@gau.ac.ir) (A. Mohammadian Behbahani),  \n[mahini@gau.ac.ir](mahini@gau.ac.ir) (A. Salmanmahiny), [naimi.b@gmail.com](naimi.b@gmail.com) (B. Naimi).  \n[https://doi.org/10.1016/j.rsase.2024.101149](https://doi.org/10.1016/j.rsase.2024.101149)  \nReceived 15 May 2023; Received in revised form 10 January 2024; Accepted 26 January 2024 Available online 13 Februar","cbCaidyHWghfXemp","https://ap.wps.com/l/cbCaidyHWghfXemp","pdf",9222196,1,17,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Background and importance\n## Need for quantitative assessment\n# Materials and methods\n## MEDALUS evaluation\n## Indicator selection using remote sensing\n## Machine learning models and ensemble prediction\n# Results\n## Indicator impacts and trends\n## Model performance by year\n# Discussion\n## Interpretation and management implications","[{\"question\":\"How is desertification assessed in this study before machine learning modeling?\",\"answer\":\"The research first evaluates regional desertification using the MEDALUS model, then uses its results to guide subsequent indicator selection and modeling.\"},{\"question\":\"Which machine learning methods are used to model desertification risk?\",\"answer\":\"SVM, Gradient Boosting Machine (GBM), Generalized Linear Models (GLM), and Random Forests (RF) are used to model desertification risk.\"},{\"question\":\"What does the ensemble prediction indicate about desertification in northeastern Iran?\",\"answer\":\"The combined model for 2021 suggests that northeastern areas, and sporadically the central parts, are affected by the progression of desertification, supporting the use of low-uncertainty results for mitigation planning.\"}]","Remotely sensed desertification modeling using ensemble of machine learning algorithms - Article | PDF",1785807639,43,{"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},"remotely-sensed-desertification-modeling-using-ensemble-of-machine-learning-algorithms-article","",{"@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/remotely-sensed-desertification-modeling-using-ensemble-of-machine-learning-algorithms-article/121899/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How is desertification assessed in this study before machine learning modeling?","Question",{"text":75,"@type":76},"The research first evaluates regional desertification using the MEDALUS model, then uses its results to guide subsequent indicator selection and modeling.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are used to model desertification risk?",{"text":80,"@type":76},"SVM, Gradient Boosting Machine (GBM), Generalized Linear Models (GLM), and Random Forests (RF) are used to model desertification risk.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the ensemble prediction indicate about desertification in northeastern Iran?",{"text":84,"@type":76},"The combined model for 2021 suggests that northeastern areas, and sporadically the central parts, are affected by the progression of desertification, supporting the use of low-uncertainty results for mitigation planning.","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":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]