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Remote Sensing
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Remote Sensing of the Cryosphere II
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Remote Sensing of the Cryosphere II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 2047

Special Issue Editors

Prof. Dr. Ulrich Kamp

E-Mail Website
Guest Editor
Earth and Environment Discipline, Department of Natural Sciences, University of Michigan-Dearborn, 4901 Evergreen Rd., 211 Science Faculty Center, Dearborn, MI 48128, USA
Interests: cryosphere; environmental change; environmental hazards; human-environment interactions; mountain geography; quaternary geology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dmitry Ganyushkin

E-Mail Website
Guest Editor
Department of Physical Geography and Landscape Design, Saint-Petersburg State University, 199034 St. Petersburg, Russia
Interests: glaciology and glacial geomorphology; geocryology; palaeogeography of mountainous Eurasian countries in Pleistocene and Holocene; rhythms in landscape and space
Special Issues, Collections and Topics in MDPI journals
Dr. Bijeesh K. Veettil

E-Mail Website
Guest Editor
Laboratory of Ecology and Environmental Management, Science and Technology Advanced Institute, Van Lang University, Ho Chi Minh City 700000, Vietnam
Interests: environmental assessment and monitoring; remote sensing of the cryosphere; remote sensing of wetlands; Andes; Himalayas
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The first volume of the Special Issue “Remote Sensing of the Cryosphere” received positive feedback and includes 16 contributions (https://www.mdpi.com/journal/remotesensing/special_issues/RS_Cryosphere). The second volume “Remote Sensing of the Cryosphere II” aims to continue this success. The call remains identical as follows:

The cryosphere, the frozen water part of the Earth system, is sensitive to changes in global climate; hence, scientists monitor its state and changes, particularly with remote sensing. We welcome a broad spectrum of contributions to this Special Issue:

  • Frozen ground, glacial geomorphology, glaciers, ice caps and sheets, lake/river/sea ice, and snow cover;
  • Recent state of our cryosphere;
  • Changes in the cryosphere such as deglaciation;
  • Cryospheric hazards and risks;
  • Theories, methodologies, and applications;
  • Laboratory and field investigations;
  • Terrestrial and space measurements;
  • Local, regional, and global scales;
  • Extraterrestrial cryospheres;
  • Any other topic concerned with the cryosphere.

This Special Issue aims to represent the frontier in remote sensing research on the cryosphere. Cryospheric science is an interdisciplinary earth science, and we welcome authors from disciplines such as geology, hydrology, meteorology, and climatology, as well as from other disciplines such as biology, engineering, and environmental science.

Prof. Dr. Ulrich Kamp
Prof. Dr. Dmitry Ganyushkin
Dr. Bijeesh K. Veettil
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

  • cryosphere
  • GIS
  • glacier
  • ice
  • frozen ground
  • remote sensing
  • snow

Published Papers (2 papers)

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25 pages, 12362 KiB  
Article
Spatiotemporal Evolution of the Land Cover over Deception Island, Antarctica, Its Driving Mechanisms, and Its Impact on the Shortwave Albedo
by Javier F. Calleja, Rubén Muñiz, Jaime Otero, Francisco Navarro, Alejandro Corbea-Pérez, Carleen Reijmer, Miguel Ángel de Pablo and Susana Fernández
Remote Sens. 2024, 16(5), 915; https://doi.org/10.3390/rs16050915 - 05 Mar 2024
Viewed by 557
Abstract
The aim of this work is to provide a full description of how air temperature and solar radiation induce changes in the land cover over an Antarctic site. We use shortwave broadband albedo (albedo integrated in the range 300–3000 nm) from a spaceborne [...] Read more.
The aim of this work is to provide a full description of how air temperature and solar radiation induce changes in the land cover over an Antarctic site. We use shortwave broadband albedo (albedo integrated in the range 300–3000 nm) from a spaceborne sensor and from field surveys to calculate the monthly relative abundance of landscape units. Field albedo data were collected in January 2019 using a portable albedometer over seven landscape units: clean fresh snow; clean old snow; rugged landscape composed of dirty snow with disperse pyroclasts and rocky outcrops; dirty snow; stripes of bare soil and snow; shallow snow with small bare soil patches; and bare soil. The MODIS MCD43A3 daily albedo products were downloaded using the Google Earth Engine API from the 2000–2001 season to the 2020–2021 season. Each landscape unit was characterized by an albedo normal distribution. The monthly relative abundances of the landscape units were calculated by fitting a linear combination of the normal distributions to a histogram of the MODIS monthly mean albedo. The monthly relative abundance of the landscape unit consisting of rugged landscape composed of dirty snow with dispersed clasts and small rocky outcrops exhibits a high positive linear correlation with the monthly mean albedo (R2 = 0.87) and a high negative linear correlation with the monthly mean air temperature (R2 = 0.69). The increase in the solar radiation energy flux from September to December coincides with the decrease in the relative abundance of the landscape unit composed of dirty snow with dispersed clasts and small rocky outcrops. We propose a mechanism to describe the evolution of the landscape: uncovered pyroclasts act as melting centers favoring the melting of surrounding snow. Ash does not play a decisive role in the melting of the snow. The results also explain the observed decrease in the thaw depth of the permafrost on the island in the period 2006–2014, resulting from an increase in the snow cover over the whole island. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere II)
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17 pages, 3201 KiB  
Technical Note
Evaluating the Effects of UAS Flight Speed on Lidar Snow Depth Estimation in a Heterogeneous Landscape
by Franklin B. Sullivan, Adam G. Hunsaker, Michael W. Palace and Jennifer M. Jacobs
Remote Sens. 2023, 15(21), 5091; https://doi.org/10.3390/rs15215091 - 24 Oct 2023
Viewed by 745
Abstract
Recently, sensors deployed on unpiloted aerial systems (UAS) have provided snow depth estimates with high spatial resolution over watershed scales. While light detection and ranging (LiDAR) produces precise snow depth estimates for areas without vegetation cover, there has generally been poorer precision in [...] Read more.
Recently, sensors deployed on unpiloted aerial systems (UAS) have provided snow depth estimates with high spatial resolution over watershed scales. While light detection and ranging (LiDAR) produces precise snow depth estimates for areas without vegetation cover, there has generally been poorer precision in forested areas. At a constant flight speed, the poorest precision within forests is observed beneath tree canopies that retain foliage into or through winter. The precision of lidar-derived elevation products is improved by increasing the sample size of ground returns but doing so reduces the spatial coverage of a mission due to limitations of battery power. We address the influence of flight speed on ground return density for baseline and snow-covered conditions and the subsequent effect on precision of snow depth estimates across a mixed landscape, while evaluating trade-offs between precision and bias. Prior to and following a snow event in December 2020, UAS flights were conducted at four different flight speeds over a region consisting of three contrasting land types: (1) open field, (2) deciduous forest, (3) conifer forest. For all cover types, we observed significant improvements in precision as flight speeds were reduced to 2 m s−1, as well as increases in the area over which a 2 cm snow depth precision was achieved. On the other hand, snow depth estimate differences were minimized at baseline flight speeds of 2 m s−1 and 4 m s−1 and snow-on flight speeds of 6 m s−1 over open fields and between 2 and 4 m s−1 over forest areas. Here, with consideration to precision and estimate bias within each cover type, we make recommendations for ideal flight speeds based on survey ground conditions and vegetation cover. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere II)
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