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Special Issue "Advances in Satellite-Based Land Cover Mapping and Monitoring"
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".
Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 4281
Special Issue Editors
Interests: landscape ecology; computational methods; modelling; remote sensing; land cover monitoring
Interests: large-scale land cover mapping and monitoring; land cover meta-standards; remote sensing of land surface processes
Special Issue Information
Satellite-based Earth Observation (EO) is now in its seventh decade! The first satellite images of the Earth came from America’s Earth Explore 6 in 1959 and, since then, the technological progress has been impressive. However, until recently, access to the satellite images needed for large-scale land cover mapping has been very challenging. Images have been infrequent, expensive, and stored in independent, distributed government and private archives. Having located and obtained a collection of images, specialist skills were then needed to prepare for analysis. Analysis was typically performed on local machines with a limited processing capacity. The full potential of satellite-EO for land-cover analyses could not be realised.
The tide began to turn in 2008 when the USGS allowed access to its Landsat archive, thus providing free access to sub-30-m spatial-resolution data. Since 2014, the EU’s Copernicus programme has launched a fleet of Sentinel satellites, all of which provide images with full, free and open access, with some satellites providing a similar or better spatial resolution to Landsat and increased revisit frequency. Detailed global views of the land surface are, therefore, now freely available every few days. Cloud-based services provide centralized, straightforward access to vast collections of analysis-ready satellite images, which can be combined with sophisticated analysis tools through high-powered scalable processing abilities. Image access and processing constraints are now rarely a problem, and opportunities for innovative land cover research have never been greater. Major barriers have come down, but others remain.
Ground data are essential in the interpretation of satellite images and validation of the results. Observations are needed from widespread, often difficult-to-access locations, so they are expensive to collect. Many landscapes are dynamic and require regular updates. Consequently, ground data are often either inadequately available or not available at all. Scientists and service providers have had to compromise, making use of data collected for alternative purposes, which are often too sparse, out of date and require semantic translations. Potential solutions are crowd-sourced data, manipulation and re-use of old data, using information from historical maps and creative ways to expand the reach of geographically limited collections.
Once satellite and ground data are available, there are many ways to describe land cover. This has led to product heterogeneity, bringing compatibility and comparability issues, which limit key applications. For example, this is most important when monitoring large-scale land cover change in response to changes in climate and land use. The development of land-cover and land-use meta-language standards and semantic translation tools has the potential to harmonise cross-border inventories to support continental and global challenges.
In this Special Issue, we want to tap into the amazing research and development that is currently underway in land-cover mapping. In particular, we are seeking to expand the understanding of operational systems for large-scale, and even global, land-cover mapping and monitoring. In the last 12 months, a number of regional and global land cover products have been released, which provide real possibilities in land-cover mapping today and in the future.
Therefore, in this Special Issue, we are particularly interested in state-of-the-art submissions that cover the following:
- The use of analysis-ready image collections and cloud computing services;
- Highly automated operational systems;
- Near-real-time land-cover mapping;
- Methods for overcoming the shortage of adequate training and validation data;
- Tools for harmonising disparate land cover and ground data collections to support continental-to-global-scale land-cover analyses;
- Land-cover change detection;
- Any other novel and innovative developments in the field of EO-based land-cover mapping.
Dr. R. Daniel Morton
Dr. Geoff Smith
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 2500 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.
- land cover
- land use
- change detection
- operational systems
- cloud processing
- semantic translation
- Earth Observation