Special Issue "Remote Sensing Applications in Coastal Environment"
Deadline for manuscript submissions: 31 December 2020.
Interests: terrestrial laser scanner; remote sensing modeling of coastal environment; shoreline erosion; coastal geomorphology; coastal hazards; flood risk
Interests: image processing; shoreline extraction; spatial analyses; digital terrain modeling; sea bottom modeling; big data set reduction; neural networks
Interests: coastal floods; sea level rise; coastal land use; flood risk; flood vulnerability; statistical methods; hydrodynamic modeling
Coastal regions are susceptible to rapid changes as they constitute the boundary between the land and sea. The resilience of a particular segment of coast depends on many factors including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment.
This Special Issue invites high-quality and innovative scientific papers that advance the science of remote sensing for coastal ecosystems through innovative approaches and novel applications. Articles that explore, evaluate, or implement the use of remote sensing sensors from UAVs through terrestrial scanners to spaceborne equipment within both natural or built coastal environments are welcome.
The following topics are particularly encouraged:
- Coastal storms and floods
- Sea-level rise
- Shoreline erosion
- Demographic and economic growth
- Land cover and use changes
- (Integrated) coastal zone management
Technical and algorithmic advances in
- coastal modeling
- RS/GIS applications
- airborne and terrestrial LiDAR
- detection of temporal changes
We are looking forward to your submissions.
Dr. Paweł Terefenko
Dr. Jacek Lubczonek
Dr. Dominik Paprotny
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 papers will be 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 2200 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.
- Coastal process
- Terrestrial, airborne, and spaceborne remote sensing
- Sea-level rise
- Shoreline change
- Coastal geomorphology
- Coastal land use
- Risk and vulnerability assessment
- Climate change
- Coastal hazards
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Mapping Coastal Dune Vegetation by Remotely Sensed Rao's Q Temporal Diversity
Authors: F. Marzialetti 1, M. Di Febbraro 1, M. Malavasi 2, S. Giulio 3, A.T.R. Acosta 3, M.L. Carranza 1
Affiliation: 1 Envix-Lab, Department of Bioscience and Territory, University of Molise, Contrada Fonte Lappone, 86090 Pesche (Is), Italy
2 Department of Applied Geoinformatics and Spatial Planning, Faculty of Environmental Sciences, Czech University of Life Sciences, Kamycka 129, 165 00 Prague 6, Czech Republic
3 Department of Sciences, University of Roma Tre, Viale G. Marconi 446, 00146 Rome, Italy
Abstract: The increasing human pressure on coastal areas points up the need of monitoring tools that offer updated information to decision makers and planners seeking to improve coastal management. The raising availability of free remotely sensed data at high spatial and temporal resolution allows developing cost-effective ways to evaluate the distribution of ecosystems at present and over time.
As coastal dune landscapes are heterogeneous-dynamic mosaics, which represent a challenge for static habitat mapping, we propose to use the variability of temporal series of Sentinel-2 images as a basis for coastal ecosystems mapping. In particular, we aim at properly describing the annual fluctuations of remotely sensed ecological parameters by applying Rao’s Q diversity index as a basis for classifying and mapping coastal dune vegetation. We analyzed a representative tract of the Adriatic Coast in central Italy (Molise Region). Using Sentinel-2 imagery, we described the monthly behavior of three ecologically relevant parameters on coastal areas: (1) Modified Soil Adjusted Vegetation Index 2 (MSAVI2), (2) Normalized Difference Water Index (NDWI2) and (3) Brightness Index (BI2), as surrogates of phenological fluctuations of vegetation, soil water content seasonality and bare soil amount, respectively. Based on Rao's Q temporal diversity index calculated on yearly stacks of 12 months for each ecological parameter, we implemented an unsupervised classification through three hierarchical runs of Random Forests algorithm (RF). We evaluated the accuracy of the obtained classes by inspecting 300 checkpoints collected in the field and by photo-interpretation of high resolution aerial photos (less than 1 meter). We identified seven vegetation classes, with high performance of RF and high accuracy values. Rao's Q temporal diversity looks effective for mapping coastal dune mosaics, and offers promising perspectives for exploring the utilization of synthetic indices over time with wider areas and different landscapes. Effectiveness of the proposed procedure for mapping coastal dune landscape also suggests the advantages of Sentinel-2 free images and open source RF classification algorithms, even at the fine scale of highly fragmented sand dunes.
Keywords: ecosystem monitoring, random forest classifier, Sentinel-2, Normalized Difference Vegetation Index NDVI, Normalized Difference Water Index NDVWI, Brightness Index BI