Special Issue "Remote Sensing in University of Warsaw: Celebrating 60th Anniversary on Remote Sensing"

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

Deadline for manuscript submissions: 31 December 2022 | Viewed by 933

Special Issue Editor

Dr. Bogdan Zagajewski
E-Mail Website
Guest Editor
Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, Warsaw University, Poland, ul. Krakowskie Przedmieście 30, 00-927 Warsaw, Poland
Interests: imaging spectroscopy; classification; algorithms; vegetation; natural and semi-natural ecosystems; high-mountain and Arctic monitoring; land cover mapping
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Special Issue Information

Dear Colleagues,

The beginnings of remote sensing at the University of Warsaw date back to 1962, when normal teaching program on photointerpretation of aerial photographs began. First, the course was addressed to students of geography. Over the years, appropriate equipment and new teaching programs were developed, allowing both staff members and students to engage in a wide range of national and international research programs focusing on the development of field, UAS, airborne, and satellite data acquisition, processing and analyzing in research on the natural environment, e.g., UNESCO MAB reserves, European Natura 2000, national parks, polar regions, as well as agricultural areas and urban space.

As an academic unit, we teach remote sensing at all university levels (undergraduate, graduate, and doctoral studies) in different fields of the University.

The Special Issue aims to celebrate contributions in remote searing area in the past 60 years. We would like to collect a wide spectrum of the newest ideas developed by University of Warsaw staff members and our colleagues, who were our students, colleagues, friends with whom we carried out research projects, who enriched our knowledge through their constructive comments, discussions, organization of scientific meetings, and implementation of different ideas, but also pleasant conversations in which remote sensing was the key topic. At the same time, we would like to thank our teachers and older colleagues who introduced us to remote sensing, creating a place that has become part of our research and teaching passion, allowing us to find a place to fulfill our professional dreams. We would like to thank our younger colleagues, who want to spend their free time supporting us, as well.

We are open to manuscripts oriented on the newest remote sensing applications in environmental studies, agriculture, land use and land cover and urban remote sensing, geophysical research, the newest sensors and methods of data acquisition, processing and analysis, as well as education and training.

The aim of this Special Issue is to collect articles that will contribute new ideas and methods.

Dr. Bogdan Zagajewski
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 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.


  • remote sensing of environment
  • new remote sensing sensors and data
  • new methods and algorithms
  • image processing
  • atmosphere correction
  • data mining
  • cloud computing
  • sensors
  • training & education
  • UAS
  • 3D
  • urban space

Published Papers (1 paper)

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Airborne HySpex Hyperspectral Versus Multitemporal Sentinel-2 Images for Mountain Plant Communities Mapping
Remote Sens. 2022, 14(5), 1209; https://doi.org/10.3390/rs14051209 - 01 Mar 2022
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Climate change and anthropopression significantly impact plant communities by leading to the spread of expansive and alien invasive plants, thus reducing their biodiversity. Due to significant elevation gradients, high-mountain plant communities in a small area allow for the monitoring of the most important [...] Read more.
Climate change and anthropopression significantly impact plant communities by leading to the spread of expansive and alien invasive plants, thus reducing their biodiversity. Due to significant elevation gradients, high-mountain plant communities in a small area allow for the monitoring of the most important environmental changes. Additionally, being a tourist attraction, they are exposed to direct human influence (e.g., trampling). Airborne hyperspectral remote sensing is one of the best data sources for vegetation mapping, but flight campaign costs limit the repeatability of surveys. A possible alternative approach is to use satellite data from the Copernicus Earth observation program. In our study, we compared multitemporal Sentinel-2 data with HySpex airborne hyperspectral images to map the plant communities on Tatra Mountains based on open-source R programing implementation of Random Forest and Support Vector Machine classifiers. As high-mountain ecosystems are adapted to topographic conditions, the input of Digital Elevation Model (DEM) derivatives on the classification accuracy was analyzed and the effect of the number of training pixels was tested to procure practical information for field campaign planning. For 13 classes (from rock scree communities and alpine grasslands to montane conifer and deciduous forests), we achieved results in the range of 76–90% F1-score depending on the data set. Topographic features: digital terrain model (DTM), normalized digital surface model (nDSM), and aspect and slope maps improved the accuracy of HySpex spectral images, transforming their minimum noise fraction (MNF) bands and Sentinel-2 data sets by 5–15% of the F1-score. Maps obtained on the basis of HySpex imagery (2 m; 430 bands) had a high similarity to maps obtained on the basis of multitemporal Sentinel-2 data (10 m; 132 bands; 11 acquisition dates), which was less than one percentage point for classifications based on 500–1000 pixels; for sets consisting of 50–100 pixels, Random Forest (RF) offered better accuracy. Full article
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