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Remote Sensing
Special Issues
Open Access Satellite Imagery Processing and Applications
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Open Access Satellite Imagery Processing and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 9468

Special Issue Editors

Dr. Cheng-Chien Liu

E-Mail Website
Guest Editor
Department of Earth Sciences, Global Earth Observation and Data Analysis Center (GEODAC), National Cheng Kung University, No 1, Ta-Hsueh Road, Tainan City 700, Taiwan
Interests: remote sensing; ocean optics; geospatial information science; unmanned aerial vehicle; landslide susceptibility model
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Luo

E-Mail Website
Guest Editor
Department of Geographic and Atmospheric Sciences, Northern Illinois University, DeKalb, IL, USA
Interests: geomorphology (Earth and Mars); landslides susceptibility modeling; GIS applications; geoscience education
Dr. Yi-Chin Chen

E-Mail Website
Guest Editor
Department of Geography, National Changhua University of Education, No. 1, Jinde Road, Changhua City, Changhua County 500, Taiwan
Interests: geomorphology; natural hazards; UAV-photogrammetry; landslide erosion; sediment transport

Special Issue Information

Dear Colleagues,

Satellites orbiting around the Earth not only overcome the barrier of time and space but also provide a significant source of Earth observation. Nevertheless, the process of setting up, testing, launching, and operating satellites is a space science that requires large capital input and specialized technologies. It generally costs much to obtain satellite imagery, which restrains scientific research from reaching the full potential of satellite remote sensing.

The concept of open data is highly promoted by most governments as well as the scientific community around the world. Particularly open access satellite imagery, such as Landsat imagery made available by the United States Geological Survey and Sentinel imagery made available by the European Space Agency, has already been and will be the key source of data to meet the challenges of Earth observation for the next decade. In this Special Issue, we welcome studies on processing and applications of open access satellite imagery and presenting the most recent advances in:

  • Innovative and successful applications of open access satellite imagery;
  • Thematic or regional applications of open access satellite imagery;
  • Integration of multiple open access satellite imagery;
  • Processing of high-level products for open access satellite imagery;
  • Web-based system to facilitate the use of open access satellite imagery.

Dr. Cheng-Chien Liu
Dr. Wei Luo
Dr. Yi-Chin Chen
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.

Keywords

  • Open access
  • Open data
  • Big geospatial data
  • Image processing
  • Data integration
  • High-level product
  • Web-based system

Published Papers (3 papers)

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Research

27 pages, 8435 KiB  
Article
Discrimination of Rock Units in Karst Terrains Using Sentinel-2A Imagery
by Nikola Gizdavec, Mateo Gašparović, Slobodan Miko, Borna Lužar-Oberiter, Nikolina Ilijanić and Zoran Peh
Remote Sens. 2022, 14(20), 5169; https://doi.org/10.3390/rs14205169 - 15 Oct 2022
Cited by 2 | Viewed by 1973
Abstract
We explored the potential incorporation of Sentinel-2A imagery for rock unit determination in the Croatian karst region dominated by carbonate rocks. The various lithological units are potential sources of both stone aggregates and dimension stone, and their spatial distribution is of high importance [...] Read more.
We explored the potential incorporation of Sentinel-2A imagery for rock unit determination in the Croatian karst region dominated by carbonate rocks. The various lithological units are potential sources of both stone aggregates and dimension stone, and their spatial distribution is of high importance for mineral resource management. The presented approach included the preprocessing and processing of existing analog data (geological maps), Sentinel-2A satellite images and the United States Geological Survey spectral indices, all in combination with ground truth data. Geological mapping and digital processing of legacy maps using the K-means and random forest algorithm reduced the spatial error of the geometry of geological boundaries from 100 m and 300 m to below 100 m. The possibility of discriminating individual lithological units based on spectral analysis and discriminant function analysis was also examined, providing a tool for evaluating the geological potential for mineral resources. Despite the challenges posed by the lithological homogeneity of karst terrain, the results of this study show that the use of spectral signature data derived from Sentinel-2A satellite images can be successfully implemented in such terrains for the enhancement of existing geological maps and mineral resources exploration. Full article
(This article belongs to the Special Issue Open Access Satellite Imagery Processing and Applications)
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25 pages, 10286 KiB  
Article
Towards Streamlined Single-Image Super-Resolution: Demonstration with 10 m Sentinel-2 Colour and 10–60 m Multi-Spectral VNIR and SWIR Bands
by Yu Tao, Siting Xiong, Rui Song and Jan-Peter Muller
Remote Sens. 2021, 13(13), 2614; https://doi.org/10.3390/rs13132614 - 03 Jul 2021
Cited by 7 | Viewed by 3583
Abstract
Higher spatial resolution imaging data are considered desirable in many Earth observation applications. In this work, we propose and demonstrate the TARSGAN (learning Terrestrial image deblurring using Adaptive weighted dense Residual Super-resolution Generative Adversarial Network) system for Super-resolution Restoration (SRR) of 10 m/pixel [...] Read more.
Higher spatial resolution imaging data are considered desirable in many Earth observation applications. In this work, we propose and demonstrate the TARSGAN (learning Terrestrial image deblurring using Adaptive weighted dense Residual Super-resolution Generative Adversarial Network) system for Super-resolution Restoration (SRR) of 10 m/pixel Sentinel-2 “true” colour images as well as all the other multispectral bands. In parallel, the ELF (automated image Edge detection and measurements of edge spread function, Line spread function, and Full width at half maximum) system is proposed to achieve automated and precise assessments of the effective resolutions of the input and SRR images. Subsequent ELF measurements of the TARSGAN SRR results suggest an averaged effective resolution enhancement factor of about 2.91 times (equivalent to ~3.44 m/pixel for the 10 m/pixel bands) given a nominal SRR upscaling factor of 4 times. Several examples are provided for different types of scenes from urban landscapes to agricultural scenes and sea-ice floes. Full article
(This article belongs to the Special Issue Open Access Satellite Imagery Processing and Applications)
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23 pages, 8611 KiB  
Article
Application of High-Resolution Radar Rain Data to the Predictive Analysis of Landslide Susceptibility under Climate Change in the Laonong Watershed, Taiwan
by Chun-Wei Tseng, Cheng-En Song, Su-Fen Wang, Yi-Chin Chen, Jien-Yi Tu, Ci-Jian Yang and Chih-Wei Chuang
Remote Sens. 2020, 12(23), 3855; https://doi.org/10.3390/rs12233855 - 25 Nov 2020
Cited by 8 | Viewed by 2580
Abstract
Extreme rainfall has caused severe road damage and landslide disasters in mountainous areas. Rainfall forecasting derived from remote sensing data has been widely adopted for disaster prevention and early warning as a trend in recent years. By integrating high-resolution radar rain data, for [...] Read more.
Extreme rainfall has caused severe road damage and landslide disasters in mountainous areas. Rainfall forecasting derived from remote sensing data has been widely adopted for disaster prevention and early warning as a trend in recent years. By integrating high-resolution radar rain data, for example, the QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system provides a great opportunity to establish the extreme climate-based landslide susceptibility model, which would be helpful in the prevention of hillslope disasters under climate change. QPESUMS was adopted to obtain spatio-temporal rainfall patterns, and further, multi-temporal landslide inventories (2003–2018) would integrate with other explanatory factors and therefore, we can establish the logistic regression method for prediction of landslide susceptibility sites in the Laonong River watershed, which was devastated by Typhoon Morakot in 2009. Simulations of landslide susceptibility under the critical rainfall (300, 600, and 900 mm) were designed to verify the model’s sensitivity. Due to the orographic effect, rainfall was concentrated at the low mountainous and middle elevation areas in the southern Laonong River watershed. Landslide change analysis indicates that the landslide ratio increased from 1.5% to 7.0% after Typhoon Morakot in 2009. Subsequently, the landslide ratio fluctuated between 3.5% and 4.5% after 2012, which indicates that the recovery of landslide areas is still in progress. The validation results showed that the calibrated model of 2005 is preferred in the general period, with an accuracy of 78%. For extreme rainfall typhoons, the calibrated model of 2009 would perform better (72%). This study presented that the integration of multi-temporal landslide inventories in a logistic regression model is capable of predicting rainfall-triggered landslide risk under climate change. Full article
(This article belongs to the Special Issue Open Access Satellite Imagery Processing and Applications)
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