Advances in vegetation mapping through remote sensing and machine learning techniques: a scientometric review

Abstract

This study explores the rapid growth in remote-sensing technologies for vegetation mapping, driven by the integration of advanced machine learning techniques. An analysis of publication trends from Scopus indicates significant expansion from 2019 to 2023, reflecting technological advancements and improved accessibility. Incorporating algorithms like random forest, support vector machines, neural networks, and XGBRFClassifier has enhanced the monitoring and analysis of vegetation dynamics at various scales. This progress supports addressing global environmental challenges such as climate change by providing timely data for conservation strategies. China leads in research output, followed by the United States and India, underscoring the field’s global significance. Key journals, including “Remote Sensing,” and conferences like IGARSS, play pivotal roles in disseminating findings. The majority of publications are research articles, emphasizing the reliance on original research and empirical data. The field’s multidisciplinary nature is evident, with contributions spanning Earth Sciences, Agriculture, Environmental Science, and Computer Science. Visualisations using VOSviewer reveal interconnected themes, highlighting topics like land use, climate change, and aboveground biomass. The findings emphasise the importance of continued research and international collaboration to develop innovative solutions for environmental sustainability.