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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: closed (30 September 2023) | Viewed by 10891

Special Issue 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
Special Issues, Collections and Topics in MDPI journals

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 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 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 (6 papers)

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Research

Article
Testing Textural Information Base on LiDAR and Hyperspectral Data for Mapping Wetland Vegetation: A Case Study of Warta River Mouth National Park (Poland)
Remote Sens. 2023, 15(12), 3055; https://doi.org/10.3390/rs15123055 - 11 Jun 2023
Viewed by 925
Abstract
One of the key issues in wetland monitoring is mapping vegetation. Remote sensing and machine learning are used to acquire vegetation maps, which, due to the development of sensors and data processing and analysis methods, have increasingly high accuracy. The objectives of this [...] Read more.
One of the key issues in wetland monitoring is mapping vegetation. Remote sensing and machine learning are used to acquire vegetation maps, which, due to the development of sensors and data processing and analysis methods, have increasingly high accuracy. The objectives of this study were to test: (i) which of the textural information (TI) features have the highest information potential for identifying wetland communities; and (ii) whether the use of TI improves the accuracy of wetland communities mapping using hyperspectral (HS) and Airborne Laser Scanning (ALS) data. The analysis indicated that the mean and entropy features of the Gray Level Co-occurrence Matrix had the highest potential to differentiate between various wetland communities. Adding these features to the dataset resulted in a small increase (0.005) in average F1 accuracy based on HS data and 0.011 for HS and ALS scenarios in wetland communities classification, and adding TI improved the delineation of patch boundaries. A higher increase was noted for forest and scrub vegetation (by 0.019 for the HS scenario and 0.022 for the HS and ALS scenario) and rushes (only for the HS and ALS scenario 0.017). It can be concluded that it is reasonable to use textural information for mapping wetland communities, especially for areas with a high proportion of scrub and forest and rushes vegetation included in the analysis. Full article
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Article
Natura 2000 Grassland Habitats Mapping Based on Spectro-Temporal Dimension of Sentinel-2 Images with Machine Learning
Remote Sens. 2023, 15(5), 1388; https://doi.org/10.3390/rs15051388 - 01 Mar 2023
Cited by 1 | Viewed by 910
Abstract
Habitat mapping is essential for the management and monitoring of Natura 2000 sites. Time-consuming field surveys are still the most frequently used solution for the implementation of the European Habitats Directive, but the use of remote sensing tools for this is becoming more [...] Read more.
Habitat mapping is essential for the management and monitoring of Natura 2000 sites. Time-consuming field surveys are still the most frequently used solution for the implementation of the European Habitats Directive, but the use of remote sensing tools for this is becoming more common. The high temporal resolution of Sentinel-2 data, registering the visible, near, and shortwave infrared ranges of the electromagnetic spectrum, makes them valuable material in this context. In this study, we aimed to use multitemporal Sentinel-2 data for mapping three grassland Natura 2000 habitats in Poland. We performed the classification based on spectro-temporal features extracted from data collected from eight different terms within the year 2017 using Convolutional Neural Networks (CNNs), and we also tested other widely used machine learning algorithms for comparison, such as Random Forests (RFs) and Support Vector Machines (SVMs). Based on ground truth data, we randomly selected training and validation polygons and then performed the evaluation iteratively (100 times). The best resulting median F1 accuracies that we obtained for habitats were as follows: 6210, 0.85; 6410, 0.80; and 6510, 0.84 (with SVM). Finally, we concluded that the accuracy of the results was comparable, but we obtained the best results using SVM (median OA = 88%, with 86% for RF and 84% for CNNs). In this work, we confirmed the usefulness of the spectral dimension of Sentinel-2 time series data for mapping grassland habitats, and researchers of future work can further develop the use of CNNs for this purpose. Full article
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Article
Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery
Remote Sens. 2023, 15(3), 844; https://doi.org/10.3390/rs15030844 - 02 Feb 2023
Cited by 4 | Viewed by 1655
Abstract
Europe’s mountain forests, which are naturally valuable areas due to their high biodiversity and well-preserved natural characteristics, are experiencing major alterations, so an important component of monitoring is obtaining up-to-date information concerning species composition, extent, and location. An important aspect of mapping tree [...] Read more.
Europe’s mountain forests, which are naturally valuable areas due to their high biodiversity and well-preserved natural characteristics, are experiencing major alterations, so an important component of monitoring is obtaining up-to-date information concerning species composition, extent, and location. An important aspect of mapping tree stands is the selection of remote sensing data that vary in temporal, spectral, and spatial resolution, as well as in open and commercial access. For the Tatra Mountains area, which is a unique alpine ecosystem in central Europe, we classified 13 woody species by iterative machine learning methods using random forest (RF) and support vector machine (SVM) algorithms of more than 1000 polygons collected in the field. For this task, we used free Sentinel-2 multitemporal satellite data (10 m pixel size, 12 spectral bands, and 21 acquisition dates), commercial PlanetScope data (3 m pixel size, 8 spectral bands, and 3 acquisitions dates), and airborne HySpex hyperspectral data (2 m pixel size, 430 spectral bands, and a single acquisition) with fusion of the data of topographic derivatives based on Shuttle Radar Topography Mission (SRTM) and airborne laser scanning (ALS) data. The iterative classification method achieved the highest F1-score with HySpex (0.95 RF; 0.92 SVM) imagery, but the multitemporal Sentinel-2 data cube, which consisted of 21 scenes, offered comparable results (0.93 RF; 0.89 SVM). The three images of the high-resolution PlanetScope produced slightly less accurate results (0.89 RF; 0.87 SVM). Full article
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Article
Estimations of the Ground-Level NO2 Concentrations Based on the Sentinel-5P NO2 Tropospheric Column Number Density Product
Remote Sens. 2023, 15(2), 378; https://doi.org/10.3390/rs15020378 - 07 Jan 2023
Cited by 2 | Viewed by 1866
Abstract
The main objective of the presented study was to verify the potential of the Sentinel-5 Precursor (S-5P) Tropospheric NO2 Column Number Density (NO2 TVCD) to support air pollution monitoring in Poland. The secondary objective of this project was to establish a [...] Read more.
The main objective of the presented study was to verify the potential of the Sentinel-5 Precursor (S-5P) Tropospheric NO2 Column Number Density (NO2 TVCD) to support air pollution monitoring in Poland. The secondary objective of this project was to establish a relationship between air pollution and meteorological conditions. The ERA-5 data together with the NO2 TVCD product and auxiliary data were further assimilated into an artificial intelligence model in order to estimate surface NO2 concentrations. The results revealed that the random forest method was the most accurate method for estimating the surface NO2. The random forest model demonstrated MAE values of 3.4 μg/m3 (MAPE~37%) and 3.2 μg/m3 (MAPE~31%) for the hourly and weekly estimates, respectively. It was observed that the proposed model could be used for at least 120 days per year due to the cloud-free conditions. Further, it was found that the S-5P NO2 TVCD was the most important variable, which explained more than 50% of the predictions. Other important variables were the nightlights, solar radiation flux, road density, population, and planetary boundary layer height. The predictions obtained with the proposed model were better fitted to the actual surface NO2 concentrations than the CAMS median ensemble estimations (~15% better accuracy). Full article
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Article
Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia
Remote Sens. 2022, 14(21), 5531; https://doi.org/10.3390/rs14215531 - 02 Nov 2022
Cited by 2 | Viewed by 1471
Abstract
The Mediterranean ecosystem exhibits a particular geology and climate, which is characterized by mild, rainy winters and long, very hot summers with low precipitation; it has led to the emergence of resilient plant species. Such habitats contain a preponderance of shrubs, and collectively [...] Read more.
The Mediterranean ecosystem exhibits a particular geology and climate, which is characterized by mild, rainy winters and long, very hot summers with low precipitation; it has led to the emergence of resilient plant species. Such habitats contain a preponderance of shrubs, and collectively harbor 10% of the Earth’s species, thus containing some of the most unique shrubby formations protecting against environmental natural degradation. Due to shrub species diversity, initial phases of forestland, heterogenous grasses, bare ground and stones, the monitoring of such areas is difficult. For this reason, the aim of this paper is to assess semi-automatic classifications of the shrubby formations based on multispectral Sentinel-2 and visible and near infrared (VINR) AISA-EAGLE II hyperspectral airborne images with a support of Canopy High Model (CHM) as a three-dimensional information and field-verified patterns, based on Match-T/DSM and aerial photos. Support Vector Machine (SVM) and Random Forest (RF) classifiers have been tested on a few scenarios featuring different combinations of spectral and Minimum Noise Fraction (MNF) transformed bands and vegetation indices. Referring to the results, the average overall accuracy for the SVM and AISA images (all tested data sets) was 78.23%, and for the RF: 79.85%. In the case of Sentinel-2, the SVM classifier obtained an average value of 83.63%, while RF: 85.32%; however, in the case of the shrubland, we would like to recommend the RF classifier, because the highest mean value of F1-score achieved was 91.86% (SVM offered few-percent-point worse results), and the required training time was quicker than SVM. Commonly available Sentinel-2 data offered higher accuracies for shrubland monitoring than did the airborne VNIR data. Full article
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Article
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
Cited by 15 | Viewed by 2771
Abstract
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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