Multi-Source Remote Sensing of Urban, Forest and Wetland Ecosystems
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 115
Editor
Interests: tropical forest ecology; landscape ecology; remote sensing (multispectral; hyperspectral; LiDAR; sonar) and GIS; plant ecology; conservation GIS; ecological modelling; disturbance ecology; biodiversity conservation; climate change ecology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The cost of remote sensing platforms and equipment—including terrestrial laser scanners (TLSs), drones, drone-based LiDAR, and multispectral and hyperspectral cameras—has decreased dramatically over the past few decades. Along with the free and open availability of medium-resolution imagery (Sentinel and Landsat) and growing accessibility of commercial high-resolution imagery, this has resulted in an unprecedented diversity of remote sensing tools now available to researchers, managers, and planners seeking to map, monitor, and inform the conservation and management of forests, wetlands, and urban green spaces. However, integrating data from multiple sensors and platforms remains technically challenging as differences in spatial resolution, temporal and spatial coverage, and the physical vantage point of each sensor must be reconciled. Moreover, standardized multi-source fusion workflows are still lacking for most ecosystem applications. With the rapid and increasing availability of artificial intelligence—encompassing deep learning architectures for point cloud and image analysis, deep learning-based multi-modal fusion, and large language model-assisted workflow and code generation—the barriers to combining disparate datasets are falling significantly. Novel multi-source pipelines or workflows that once demanded specialist programming expertise can now be designed and implemented by a far broader community of researchers and practitioners.
This Special Issue aims to advance the science and practice of multi-source remote sensing as applied to three ecosystem contexts: forests, wetlands, and urban green spaces. It seeks contributions that move beyond single-sensor studies to demonstrate the added value of integrating data from multiple platforms, sensors, or scales—whether through classical statistical approaches, machine learning, or emerging AI-driven workflows. These aims relate directly to the journal’s scope in covering the following topics: multispectral and hyperspectral remote sensing, LiDAR and laser scanning, active and passive microwave remote sensing, data fusion and data assimilation, image processing and pattern recognition, change detection, spaceborne, airborne, and terrestrial platforms, and remote sensing applications in ecology, forestry, and environmental management.
This Special Issue welcomes original research and review articles that leverage the complementarity of data acquired by different sensors and platforms and applied to urban, forest, and wetland ecosystems. Topics may range from the estimation of forest structural variables at the tree or stand level to more comprehensive landscape- and regional-scale analyses. Multi-source data integration (e.g., multispectral, hyperspectral, LiDAR, SAR, SfM), multiscale approaches, AI-driven analytical pipelines or workflows, and studies focused on long-term monitoring and ecosystem service assessments are all within scope.
Dr. Kurt McLaren
Guest Editor
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-anonymized 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.
Keywords
- data fusion
- LiDAR
- deep learning
- ecosystem monitoring
- multispectral remote sensing
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