Vegetation Classification and Mapping by Remote Sensing and Machine Learning

Dear Colleague,

Vegetation classification and mapping by remote sensing and machine learning is a rapidly growing field that has gained significant attention recently. The use of machine learning algorithms in remote sensing has created high-resolution, accurate, and efficient maps of vegetation cover and types. Recent trends in vegetation classification and mapping by remote sensing and machine learning include the development of more advanced algorithms that can handle complex and heterogeneous landscapes. Additionally, there has been a focus on integrating multiple data sources, such as LiDAR and hyperspectral data, to improve the accuracy and increase the level of detail in vegetation maps.

Vegetation classification and mapping by remote sensing and machine learning have significant implications for understanding ecosystem dynamics, monitoring changes in vegetation cover and types, and informing land-use and conservation planning efforts. Accurate vegetation mapping can help understand climate change's impact on ecosystems and inform efforts to mitigate and adapt to these changes. Developing new algorithms and techniques for vegetation mapping using remote sensing and machine learning can help drive advances in remote sensing technologies, including developing new sensors and platforms. There has also been a push towards developing open-source software and data platforms to democratize access to remote sensing data and machine learning tools for vegetation mapping. This has led to the creation of large-scale global vegetation mapping initiatives.

This Special Issue invites the submission of studies covering vegetation classification and mapping by remote sensing and machine learning acquired by different sensors and platforms. Topics may cover anything from the application of a case study to modern technology within this theme. Articles may address, but are not limited, to the following topics:

• Application of classic machine learning methodology to vegetation classification and mapping;
• Modern machine learning methodology for feature extraction;
• High-performance machine learning algorithms for vegetation mapping;
• Accuracy assessment of machine learning in remote sensing;
• Vegetation classification and mapping by remote sensing and machine learning using multi-sensors;
• Regional/global scale programs for vegetation classification and mapping by machine learning.

Prof. Dr. Kiwon Lee
Prof. Dr. No-Wook Park
Dr. Kwangseob Kim
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.