Dear Colleagues,

Vegetation analysis and mapping is a critical component of monitoring the earth's ecosystems and understanding the impact of environmental changes on biodiversity and ecosystem services. Accurate vegetation mapping enables researchers and managers to identify and track changes in vegetation cover over time, detect the onset of ecosystem degradation, and identify areas in need of restoration or conservation. Furthermore, the analysis of vegetation data provides critical information for climate change research, land-use planning, and agricultural management. Remote sensing has revolutionized vegetation mapping and trend analysis, providing data with different spatial and spectral resolutions on vegetation cover at global scales. However, the accurate interpretation of remote sensing data requires advanced analytical techniques that can handle the complexity and scale of the data. In recent years, Convolutional Neural Networks (CNNs), a type of deep learning algorithm, have emerged as a powerful approach for analysing remote sensing data and extracting valuable information about vegetation patterns and dynamics. CNNs enable researchers to extract complex features from large-scale remote sensing datasets, providing critical insights into vegetation distribution, composition, and dynamics. The ability of CNNs to accurately classify vegetation types and detect changes in vegetation cover over time has the potential to transform our understanding of global vegetation dynamics and its response to environmental changes.

The aim of the forthcoming Special Issue (SI) is to highlight the latest developments and applications of CNNs in vegetation remote sensing. The SI welcomes all types of manuscripts (e.g., original research articles, review articles, etc.) with an added value of using time series remote sensing data in all aspects regarding the mapping, change detection, trend analysis, and studies of drivers of vegetation change in all ecosystems using CNNs. Some suggested themes and topics for submission include, but are not limited to, the following:

• CNN architectures for vegetation remote sensing:
• Novel CNN architectures specifically designed for vegetation classification, segmentation, or change detection.
• Comparative studies of different CNN architectures for vegetation remote sensing.

• Applications of CNNs in vegetation remote sensing:
• Use of CNNs for vegetation mapping, classification, and segmentation in different regions and ecosystems.
• Analysis of the performance of CNNs compared to traditional remote sensing methods in vegetation mapping and monitoring.
• Retrieving time series of biophysical parameters for vegetation monitoring using CNNs.
• Integration of CNNs with other remote sensing data sources, such as LiDAR or hyperspectral data, to improve vegetation mapping accuracy.

• CNNs for monitoring vegetation dynamics:
• Development of time-series CNN models for vegetation change monitoring and trend analysis.
• Analysis of the spatio-temporal patterns of vegetation changes using CNNs.

• CNNs for addressing key challenges in vegetation remote sensing:
• Use of CNNs for accurate classification of mixed pixel areas, such as urban or agricultural landscapes.
• Response of vegetation dynamics to climatic variables change.
• Analysis of the effects of different data preprocessing techniques on the performance of CNNs in vegetation remote sensing.
• Development of CNN-based techniques for handling missing data in vegetation remote sensing datasets.

Dr. Abolfazl Abdollahi
Guest Editor

Dr. Chandrama Sarker
Guest Editor Assistant
Environmental Unit, The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, Australia
Email: [email protected]

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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

• convolutional neural networks (CNNs)
• remote sensing
• vegetation mapping
• vegetation dynamics and trend analysis
• time series analysis
• change detection
• deep learning
• mixed pixels classification
• land use change
• environmental change
• accuracy assessment