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Remote Sensing
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State-of-the-Art Technology of Remote Sensing in Russia
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State-of-the-Art Technology of Remote Sensing in Russia

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 11067

Special Issue Editor

Prof. Sergey Pulinets

E-Mail Website
Guest Editor
Space Research Institute (IKI), Russian Academy of Sciences, Moscow, Russia
Interests: remote sensing of the ionosphere; space science instrumentation; geospheres interaction; natural hazards; lithosphere-atmosphere-ionosphere coupling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The market for remote sensing of the Earth (RS) is considered one of the fastest growing in the world. New companies, technologies and services appear every year. Great prospects are associated with the use active sounding: lidars, radars, HF radio sounding. The scale of RS satellites changes from the nanosatellites to the dedicated satellite constellations. The remote sensing information is used in many industries - agriculture, geological and hydrological research, forestry, environmental protection, site planning, educational, reconnaissance and military purposes. The Russian RS system consists from the 4 satellite constellations: Resours-P, Canopus-V, Meteor-M and Electro-L. The main purpose of this special issue is to present the modern state of the Russian RS technology, and close perspectives of its development. We invite the authors from the Russian Space industry and Space science institutions to take part in this endeavor to present their vision but the perspective of development of the satellite and scientific technology of remote sensing in Russia.

Prof. Sergey Pulinets
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 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

  • remote sensing
  • infrared spectrometers
  • radar
  • microwave spectrometers
  • satellite constellation

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Published Papers (3 papers)

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Research

19 pages, 5151 KiB  
Article
Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires
by Andrey Sirin and Maria Medvedeva
Remote Sens. 2022, 14(1), 194; https://doi.org/10.3390/rs14010194 - 2 Jan 2022
Cited by 15 | Viewed by 3786
Abstract
Peat fires differ from other wildfires in their duration, carbon losses, emissions of greenhouse gases and highly hazardous products of combustion and other environmental impacts. Moreover, it is difficult to identify peat fires using ground-based methods and to distinguish peat fires from forest [...] Read more.
Peat fires differ from other wildfires in their duration, carbon losses, emissions of greenhouse gases and highly hazardous products of combustion and other environmental impacts. Moreover, it is difficult to identify peat fires using ground-based methods and to distinguish peat fires from forest fires and other wildfires by remote sensing. Using the example of catastrophic fires in July–August 2010 in the Moscow region (the center of European Russia), in the present study, we consider the results of peat-fire detection using Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) hotspots, peat maps, and analysis of land cover pre- and post-fire according to Landsat-5 TM data. A comparison of specific (for detecting fires) and non-specific vegetation indices showed the difference index ΔNDMI (pre- and post-fire normalized difference moisture Index) to be the most effective for detecting burns in peatlands according to Landsat-5 TM data. In combination with classification (both unsupervised and supervised), this index offered 95% accuracy (by ground verification) in identifying burnt areas in peatlands. At the same time, most peatland fires were not detected by Terra/Aqua MODIS data. A comparison of peatland and other wildfires showed the clearest differences between them in terms of duration and the maximum value of the fire radiation power index. The present results may help in identifying peat (underground) fires and their burnt areas, as well as accounting for carbon losses and greenhouse gas emissions. Full article
(This article belongs to the Special Issue State-of-the-Art Technology of Remote Sensing in Russia)
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19 pages, 15272 KiB  
Article
Usage Experience and Capabilities of the VEGA-Science System
by Evgeny Loupian, Mikhail Burtsev, Andrey Proshin, Alexandr Kashnitskii, Ivan Balashov, Sergey Bartalev, Anna Konstantinova, Dmitriy Kobets, Maxim Radchenko, Vladimir Tolpin and Ivan Uvarov
Remote Sens. 2022, 14(1), 77; https://doi.org/10.3390/rs14010077 - 24 Dec 2021
Cited by 38 | Viewed by 3011
Abstract
Currently, when satellite data volumes grow rapidly and exceed petabyte values and their quality provides reliable analysis of long-term time series, traditional data handling methods assuming local storage and processing may be impossible to implement for small or distributed research teams. Thus, new [...] Read more.
Currently, when satellite data volumes grow rapidly and exceed petabyte values and their quality provides reliable analysis of long-term time series, traditional data handling methods assuming local storage and processing may be impossible to implement for small or distributed research teams. Thus, new methods based on modern web technologies providing access to very large distributed data archives are gaining increasing importance. Furthermore, these new data handling solutions should provide not just access but also analysis and processing features, similar to desktop solutions. This paper describes the VEGA-Science web GIS—an open-access novel tool for satellite data processing and analysis. The overview of its architecture and basic technical components is given, but most attention is paid to examples of actual system application for various applied and research tasks. In addition, an overview of projects using the system is given to illustrate its versatility and further development directions are considered. Full article
(This article belongs to the Special Issue State-of-the-Art Technology of Remote Sensing in Russia)
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20 pages, 6015 KiB  
Article
Fusion of SAR Interferometry and Polarimetry Methods for Landslide Reactivation Study, the Bureya River (Russia) Event Case Study
by Valery Bondur, Tumen Chimitdorzhiev, Aleksey Dmitriev and Pavel Dagurov
Remote Sens. 2021, 13(24), 5136; https://doi.org/10.3390/rs13245136 - 17 Dec 2021
Cited by 7 | Viewed by 2487
Abstract
In this paper, we demonstrate the estimation capabilities of landslide reactivation based on various SAR (Synthetic Aperture Radar) methods: Cloude-Pottier decomposition of Sentinel-1 dual polarimetry data, MT-InSAR (Multi-temporal Interferometric Synthetic Aperture Radar) techniques, and cloud computing of backscattering time series. The object of [...] Read more.
In this paper, we demonstrate the estimation capabilities of landslide reactivation based on various SAR (Synthetic Aperture Radar) methods: Cloude-Pottier decomposition of Sentinel-1 dual polarimetry data, MT-InSAR (Multi-temporal Interferometric Synthetic Aperture Radar) techniques, and cloud computing of backscattering time series. The object of the study is the landslide in the east of Russia that took place on 11 December 2018 on the Bureya River. H-α-A polarimetric decomposition of C-band radar images not detected significant transformations of scattering mechanisms for the surface of the rupture, whereas L-band radar data show changes in scattering mechanisms before and after the main landslide. The assessment of ground displacements along the surface of the rupture in the 2019–2021 snowless periods was carried out using MT-InSAR methods. These displacements were 40 mm/year along the line of sight. The SBAS-InSAR results have allowed us to reveal displacements of great area in 2020 and 2021 snowless periods that were 30–40 mm/year along the line-of-sight. In general, the results obtained by MT-InSAR methods showed, on the one hand, the continuation of displacements along the surface of the rupture and on the other hand, some stabilization of the rate of landslide processes. Full article
(This article belongs to the Special Issue State-of-the-Art Technology of Remote Sensing in Russia)
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