Remote Sensing for Landscape Dynamics

Dear Colleagues,

Landscape dynamics—referring to the spatial and temporal changes in land use, land cover, vegetation patterns, and ecological processes—are crucial indicators of environmental sustainability and ecosystem resilience. Understanding, monitoring, and predicting landscape dynamics are essential for informed decision-making in land management, biodiversity conservation, disaster risk reduction, and climate adaptation strategies.

Recent advancements in remote sensing technologies, including multispectral and hyperspectral sensors, SAR, LiDAR, and UAS, offer unprecedented opportunities for capturing landscape change at multiple spatial and temporal scales. Furthermore, the integration of satellite data with in situ observations, geospatial modeling, and machine learning methods has enabled more accurate, real-time, and scalable analyses of landscape patterns and processes. In particular, long-term time series data (e.g., Landsat, MODIS) support trend analysis and landscape evolution characterization. Meanwhile, large language models (LLMs) offer innovative applications in landscape research, including automated geospatial metadata interpretation, remote sensing product annotation, and decision support for land management.

This Special Issue invites high-quality submissions that focus on the remote sensing monitoring and analysis of landscape dynamics, and mainly collects the following three types of research results: First, landscape change detection and process monitoring, including land use/cover change, vegetation succession and degradation recovery, the impact of natural disasters on landscape structure, as well as data acquisition and analysis based on high-resolution remote sensing and unmanned aerial vehicles. Second, landscape pattern analysis and ecological modeling, covering landscape fragmentation, ecological connectivity assessment, evolution of urban-rural transition zones, as well as the integrated application of remote sensing and ecological models. Third, advanced technologies for spatio-temporal analysis and prediction, focusing on multi-temporal and multi-source data fusion, long-term time series analysis, climate change impacts, and the innovative application of machine learning, deep learning, and large language models in landscape dynamics monitoring and modeling.

Original research, methodological innovations, and case studies with practical relevance are welcome for submission.

The aim of this Special Issue is to advance the understanding of landscape dynamics through innovative applications of remote sensing technologies. By focusing on the spatial and temporal patterns of land use change, vegetation dynamics, and ecological processes, this issue seeks to promote interdisciplinary research that links environmental monitoring, geospatial analysis, and landscape management. The topics covered in this Special Issue are highly aligned with the scope of Remote Sensing, which emphasizes the development and application of remote sensing methods and data for studying Earth system processes. In particular, this issue encourages contributions that leverage satellite, airborne, or UAV-based remote sensing to monitor landscape change, assess ecosystem responses, and support sustainable development and climate adaptation goals at local, regional, or global scales.

Submissions are welcome on, but not limited to, the following topics:  

• Land use/land cover change detection using remote sensing
• Vegetation succession, degradation, and recovery monitoring
• Impacts of natural disturbances on landscape structure
• Fine-scale change detection with UAV and high-resolution data
• Landscape fragmentation and ecological connectivity analysis
• Remote sensing integrated with ecological modeling
• Multi-temporal and multi-sensor landscape dynamics analysis
• Dynamic monitoring of artificial ecosystems and their interface with natural landscapes
• Machine learning applications in landscape change modeling
• Long-term time series data analysis and prediction
• Long time series and large-scale landscape pattern modeling
• Landscape pattern and spatial planning

Dr. Peipei Wang
Dr. Wei Han
Dr. Yuanyuan Zhao
Prof. Dr. Xinqi Zheng
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 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.

• landscape dynamics
• land use/land cover change
• landscape pattern analysis
• remote sensing
• machine learning
• time series analysis
• landscape resilience
• land degradation and recovery
• geospatial modeling
• multi-sensor data fusion
• vegetation monitoring
• large language models