{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T23:00:41Z","timestamp":1776207641776,"version":"3.50.1"},"reference-count":221,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T00:00:00Z","timestamp":1728604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Tianshan Talent Development Program","award":["2022TSYCCX0006"],"award-info":[{"award-number":["2022TSYCCX0006"]}]},{"name":"Tianshan Talent Development Program","award":["2022-XBQNXZ-001"],"award-info":[{"award-number":["2022-XBQNXZ-001"]}]},{"name":"Western Young Scholars Project of the Chinese Academy of Sciences","award":["2022TSYCCX0006"],"award-info":[{"award-number":["2022TSYCCX0006"]}]},{"name":"Western Young Scholars Project of the Chinese Academy of Sciences","award":["2022-XBQNXZ-001"],"award-info":[{"award-number":["2022-XBQNXZ-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper provides a comprehensive review of advancements in the detection; evaluation; and management of invasive plant species (IPS) using diverse remote sensing (RS) techniques and machine learning (ML) methods. Analyzing the high-resolution datasets received from drones, satellites, and aerial photography enables the perfect cartography technique and analysis of the spread and various impacts of ecology on IPS. The majority of current research on hyperspectral imaging with unmanned aerial vehicle (UAV) enhanced by ML has significantly improved the accuracy and efficiency of identifying mapping IPS, and it also serves as a powerful instrument for ecological management. The integrative association is essential to manage the alien species better, as researchers from multiple other fields participate in modeling innovative methods and structures. Incorporating advanced technologies like light detection and ranging (LiDAR) and hyperspectral imaging shows potential for improving spatial and spectral analysis approaches and utilizing ML approaches such as a support vector machine (SVM), random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and deep convolutional neural network (DCNN) analysis for detecting complex IPS. The significant results indicate that ML methods, most importantly SVM and RF, are victorious in recognizing the alien species via analyzing RS data. This report emphasizes the importance of continuous research efforts to improve predictive models, fill gaps in our understanding of the connections between climate, urbanization and invasion dynamics, and expands conservation initiatives via utilizing RS techniques. This study also highlights the potential for RS data to refine management plans, enabling the implementation of more efficient strategies for controlling IPS and preserving ecosystems.<\/jats:p>","DOI":"10.3390\/rs16203781","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T07:47:05Z","timestamp":1728892025000},"page":"3781","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Advances in Remote Sensing and Machine Learning Methods for Invasive Plants Study: A Comprehensive Review"],"prefix":"10.3390","volume":"16","author":[{"given":"Muhammad Murtaza","family":"Zaka","sequence":"first","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9091-6033","authenticated-orcid":false,"given":"Alim","family":"Samat","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"China-Kazakhstan Joint Laboratory for RS Technology and Application, Al-Farabi Kazakh National University, Almaty 050012, Kazakhstan"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1093\/aob\/mcab011","article-title":"Intraspecific trait variation in plants: A renewed focus on its role in ecological processes","volume":"127","author":"Westerband","year":"2021","journal-title":"Ann. 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