remotesensing-logo

Journal Browser

Journal Browser

Recent Advances in GIS Techniques for Remote Sensing

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 (30 June 2023) | Viewed by 33248

Special Issue Editor


E-Mail Website
Guest Editor
Institute of Earth and Environmental Sciences, Faculty of Earth Sciences and Spatial Management, Maria Curie-Sklodowska University in Lublin, Al. Krasnicka 2 D, 20-718 Lublin, Poland
Interests: contemporary spatial and temporal river valley development and analysis of the geomorphic responses to rapid climatic and environmental changes; fluvial geomorphology and application of remote sensing (LiDAR, photogrammetry) to differential (DoD) surface changes analysis; Polar Regions (Svalbard) and Central Europe
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geographic Information Systems (GIS) are becoming increasingly popular in earth and environmental sciences due to their capabilities for modern 2D and 3D mapping, presentation of phenomena complexity, flexibility of criteria selection and geodata management. Advanced remote sensing measurement techniques (multi- and hyperspectral imagery, LiDAR, photogrammetry) provide more and more accurate geospatial data, which can be used in studies on landscape evolution, contemporary geomorphic changes, land cover and land use changes or physical and chemical features of soils and surface waters. The repeatable data from satellites, airplanes, and unmanned aerial vehicles (UAVs) provided by various sensors allow for the detection of changes at different spatial scales and time steps. GIS uniquely provides the ability to compile and compare selected information (thematic layers) with environmental data, with user-directed definition of the boundaries of the study area. GIS and remote sensing play an important role in environmental monitoring and assessment. Flexibility in the selection criteria of information makes it possible to identify, separate, and determine the relationship of selected landforms or environmental elements to specific geographic conditions or climate change using GIS.

(2) Aim of the Special Issue and how the subject relates to the journal scope.

Within the framework of this research topic, we would like to invite you to submit original papers presenting innovative use of GIS techniques and a wide spectrum of geospatial data (i.e., remote sensing, global navigation satellite system (GNSS)) to solve complex research problems of earth and environmental sciences. Comprehensive reviews of this topic are also welcome. Potential topics include, but are not limited to, the following:

(3) Suggested themes and article types for submissions.

  • Original GIS methods or tools for remote sensing data analysis and interpretation;
  • Use of machine learning and novel GIS tools to process remote sensing data from a variety of sources;
  • Use of global navigation satellite system (GNSS) to enhance remote sensing data and GIS analysis;
  • GIS mapping using remote sensing and machine learning techniques;
  • Using remote sensing and GNSS to optimize GIS mapping.

Dr. Waldemar Kociuba
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

  • geographic information systems
  • remote sensing
  • photogrammetry
  • machine learning

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

17 pages, 3389 KiB  
Article
Similarity and Change Detection of Relief in a Proglacial River Valley (Scott River, SW Svalbard)
by Leszek Gawrysiak and Waldemar Kociuba
Remote Sens. 2023, 15(20), 5066; https://doi.org/10.3390/rs15205066 - 22 Oct 2023
Cited by 1 | Viewed by 978
Abstract
This study focuses on contemporary geomorphic changes in the proglacial valley floor of the Scott River catchment (northwest of Wedel Jarlsberg Land, southwestern Spitsbergen). The similarity and variability of landforms along the entire 3.3 km length of the unglaciated valley floor was assessed [...] Read more.
This study focuses on contemporary geomorphic changes in the proglacial valley floor of the Scott River catchment (northwest of Wedel Jarlsberg Land, southwestern Spitsbergen). The similarity and variability of landforms along the entire 3.3 km length of the unglaciated valley floor was assessed using precision terrestrial laser scanning (TLS) measurements made in July/August 2010–2013. Digital terrain models (DTMs) were generated from the high-resolution TLS survey data, followed by a geomorphon map, which was then used for a similarity and changes of morphology analysis performed with GeoPAT2 software. The study revealed a large spatial variation of contemporary processes shaping the valley floor and changes in its morphology. Their spatial distribution relates to the geologically determined split of the valley floor into three morphological zones separated by gorges. The upper gorge cuts the terminal moraine rampart, which limits the uppermost section of the valley floor, which is up to 700 m wide and is occupied by the outwash plain. The study showed that this is the area characterised by the greatest dynamics of contemporary geomorphic processes and relief changes. The similarity index value here is characterised by a large spatial variation that in some places reaches values close to 0. In the middle section stretching between the upper gorge (cutting the terminal moraine) and the lower gorge (cutting the elevated marine terraces), a much smaller variability of processes and landforms is observed, and the found changes of the valley floor relief mainly include the area of braided channel activity. Similarity index values in this zone do not fall below 0.65. The lowest section, the mouth of the alluvial fan, on the other hand, is characterised by considerable spatial differentiation. The southern part of the fan is stable, while the northern part is intensively re-shaped and has a similarity index that locally falls below 0.5. The most dynamic changes are found within the active channel system along the entire length of the unglaciated section of the Scott River. The peripheral areas, located in the outer zones of the valley floor, show great stability. Full article
(This article belongs to the Special Issue Recent Advances in GIS Techniques for Remote Sensing)
Show Figures

Figure 1

17 pages, 4146 KiB  
Article
LiDAR-Derived Relief Typology of Loess Patches (East Poland)
by Leszek Gawrysiak and Waldemar Kociuba
Remote Sens. 2023, 15(7), 1875; https://doi.org/10.3390/rs15071875 - 31 Mar 2023
Cited by 2 | Viewed by 1578
Abstract
The application of the automated analysis of remote sensing data processed into high-resolution digital terrain models (DTMs) using geographic information systems (GIS) tools provides a geomorphometric characterization of the diversity of the relief of loess patches over large areas. Herein, a quantitative classification [...] Read more.
The application of the automated analysis of remote sensing data processed into high-resolution digital terrain models (DTMs) using geographic information systems (GIS) tools provides a geomorphometric characterization of the diversity of the relief of loess patches over large areas. Herein, a quantitative classification of 79 loess patches with a total area of 3361 km2, distributed within the eastern part of the Polish Uplands belt, is carried out. A high-resolution 1 × 1 m DTM was generated from airborne laser scanning (ALS) data with densities ranging from 4 pts/m2 to 12 pts/m2, which was resampled to a resolution of 5 × 5 m for the study. This model was used to classify landform surfaces using the r.geomorphon (geomorphon algorithm) function in GRASS GIS software. By comparing the values in the neighborhood of each cell, a map of geomorphometric features (geomorphon) was obtained. The classification and typology of the relief of the studied loess patches was performed using GeoPAT2 (Geospatial Pattern Analysis Toolbox) software. Pattern signatures with a resolution of 100 × 100 m were extracted from the source data grid, and the similarity of geomorphological maps within the signatures was calculated and saved as a signature file and segment map using the spatial coincidence method. The distance matrix between each pair of segments was calculated, and the heterogeneity and isolation of the maps were generated. R system was used to classify the segments, which generated a dendrogram and a heat map based on the distance matrix. This made it possible to distinguish three main types and eight subtypes of relief. The morphometric approach used will contribute to a better understanding of the spatial variation in the relief of loess patches. Full article
(This article belongs to the Special Issue Recent Advances in GIS Techniques for Remote Sensing)
Show Figures

Figure 1

17 pages, 4992 KiB  
Article
Spectral Indices as a Tool to Assess the Moisture Status of Forest Habitats
by Adam Młynarczyk, Monika Konatowska, Sławomir Królewicz, Paweł Rutkowski, Jan Piekarczyk and Wojciech Kowalewski
Remote Sens. 2022, 14(17), 4267; https://doi.org/10.3390/rs14174267 - 30 Aug 2022
Cited by 6 | Viewed by 1995
Abstract
Measurement of water content in forest habitats is considered essential in ecological research on forests, climate change, or forest management. In the traditional forest habitat classification, two systems of habitat conditions analysis are found: single factor and multifactor methods. Both are laborious and [...] Read more.
Measurement of water content in forest habitats is considered essential in ecological research on forests, climate change, or forest management. In the traditional forest habitat classification, two systems of habitat conditions analysis are found: single factor and multifactor methods. Both are laborious and therefore costly. Remote sensing methods provide a low-cost alternative. The aim of the presented study was to find the relationship between the spectral indices obtained from satellite images and the forest habitats moisture indices used traditionally in the Polish forest habitats classification. The scientific hypothesis of the research is as follows: it is possible to assess the variation in the humidity of forest habitats on the basis of spectral indices. Using advanced geographic information system (GIS) technology, 923 research plots were tested, where habitat studies performed with the traditional methods were compared with the analysis of 191 spectral indices calculated for Sentinel-2 image data. The normalized difference vegetation index (NDVI) has proved to be the most useful to the assessing of moisture of forest habitats. The ranking of the most correlated indices was calculated as Eintg—the product of the absolute value of the slope and the mean square error complement, and for the top five indices was as follows: NDVI = 0.248619, EXG = 0.242112, OSAVI = 0.239412, DSWI-4 = 0.238784, and RDVI = 0.236995. The results also highlight the impact of water reservoirs on the humidity and trophicity of forest habitats, showing a decrease in the fertility of habitats with an increase in distance from the water reservoir. The results of the study can be used to preparing maps of the diversity of forest types, especially in hard-to-reach places, as well as to assess changes in the moisture status of habitats, which may be useful, for example, in the assessment of the fire risk of forest habitats. We have proved that NDVI can be used in applications for which it was not originally designed. Full article
(This article belongs to the Special Issue Recent Advances in GIS Techniques for Remote Sensing)
Show Figures

Figure 1

14 pages, 2848 KiB  
Article
Attempt to Combine Physicochemical Data with Thermal Remote Sensing to Determine the Extent of Water Mixing between River and Lake
by Remigiusz Tritt, Adam Młynarczyk and Jędrzej Proch
Remote Sens. 2022, 14(16), 4020; https://doi.org/10.3390/rs14164020 - 18 Aug 2022
Viewed by 1621
Abstract
The mixing of river and lake waters is important for the functioning of a reservoir, especially in the case of shallow polymictic reservoirs such as Lake Swarzędzkie. The extent of this mixing depends largely on the river flow rate. In lakes, which rivers [...] Read more.
The mixing of river and lake waters is important for the functioning of a reservoir, especially in the case of shallow polymictic reservoirs such as Lake Swarzędzkie. The extent of this mixing depends largely on the river flow rate. In lakes, which rivers with low flow values flow through, it should be expected that the flow currents only reach the narrow zone adjacent to the mouth of the river to the lake. The water of rivers generally has different chemical compositions and physicochemical parameters in relation to lake water. Therefore, to determine the range of the river in the lake and characterize the water mixing, measurements of temperature, electrolytic conductivity, and the concentrations of selected chemical elements were made in the estuary zone and at other points located on the lake and on the river near the tributary. In addition, the values and directions of horizontal currents were determined, and thermal photos were taken from a low-altitude ceiling. Full article
(This article belongs to the Special Issue Recent Advances in GIS Techniques for Remote Sensing)
Show Figures

Figure 1

16 pages, 4490 KiB  
Article
Experience Gained When Using the Yuneec E10T Thermal Camera in Environmental Research
by Adam Młynarczyk, Sławomir Królewicz, Monika Konatowska and Grzegorz Jankowiak
Remote Sens. 2022, 14(11), 2633; https://doi.org/10.3390/rs14112633 - 31 May 2022
Cited by 3 | Viewed by 2176
Abstract
Thermal imaging is an important source of information for geographic information systems (GIS) in various aspects of environmental research. This work contains a variety of experiences related to the use of the Yuneec E10T thermal imaging camera with a 320 × 240 pixel [...] Read more.
Thermal imaging is an important source of information for geographic information systems (GIS) in various aspects of environmental research. This work contains a variety of experiences related to the use of the Yuneec E10T thermal imaging camera with a 320 × 240 pixel matrix and 4.3 mm focal length dedicated to working with the Yuneec H520 UAV in obtaining data on the natural environment. Unfortunately, as a commercial product, the camera is available without radiometric characteristics. Using the heated bed of the Omni3d Factory 1.0 printer, radiometric calibration was performed in the range of 18–100 °C (high sensitivity range–high gain settings of the camera). The stability of the thermal camera operation was assessed using several sets of a large number of photos, acquired over three areas in the form of aerial blocks composed of parallel rows with a specific sidelap and longitudinal coverage. For these image sets, statistical parameters of thermal images such as the mean, minimum and maximum were calculated and then analyzed according to the order of registration. Analysis of photos taken every 10 m in vertical profiles up to 120 m above ground level (AGL) were also performed to show the changes in image temperature established within the reference surface. Using the established radiometric calibration, it was found that the camera maintains linearity between the observed temperature and the measured brightness temperature in the form of a digital number (DN). It was also found that the camera is sometimes unstable after being turned on, which indicates the necessity of adjusting the device’s operating conditions to external conditions for several minutes or taking photos over an area larger than the region of interest. Full article
(This article belongs to the Special Issue Recent Advances in GIS Techniques for Remote Sensing)
Show Figures

Figure 1

19 pages, 4345 KiB  
Article
Normalized Burn Ratio Plus (NBR+): A New Index for Sentinel-2 Imagery
by Emanuele Alcaras, Domenica Costantino, Francesca Guastaferro, Claudio Parente and Massimiliano Pepe
Remote Sens. 2022, 14(7), 1727; https://doi.org/10.3390/rs14071727 - 3 Apr 2022
Cited by 43 | Viewed by 20158
Abstract
The monitoring of burned areas can easily be performed using satellite multispectral images: several indices are available in the literature for highlighting the differences between healthy vegetation areas and burned areas, in consideration of their different signatures. However, these indices may have limitations [...] Read more.
The monitoring of burned areas can easily be performed using satellite multispectral images: several indices are available in the literature for highlighting the differences between healthy vegetation areas and burned areas, in consideration of their different signatures. However, these indices may have limitations determined, for example, by the presence of clouds or water bodies that produce false alarms. To avoid these inaccuracies and optimize the results, this work proposes a new index for detecting burned areas named Normalized Burn Ratio Plus (NBR+), based on the involvement of Sentinel-2 bands. The efficiency of this index is verified by comparing it with five other existing indices, all applied on an area with a surface of about 500 km2 and covering the north-eastern part of Sicily (Italy). To achieve this aim, both a uni-temporal approach (single date image) and a bi-temporal approach (two date images) are adopted. The maximum likelihood classifier (MLC) is applied to each resulting index map to define the threshold separating burned pixels from non-burned ones. To evaluate the efficiency of the indices, confusion matrices are constructed and compared with each other. The NBR+ shows excellent results, especially because it excludes a large part of the areas incorrectly classified as burned by other indices, despite being clouds or water bodies. Full article
(This article belongs to the Special Issue Recent Advances in GIS Techniques for Remote Sensing)
Show Figures

Figure 1

15 pages, 3715 KiB  
Article
Evaluation of the Influence of Processing Parameters in Structure-from-Motion Software on the Quality of Digital Elevation Models and Orthomosaics in the Context of Studies on Earth Surface Dynamics
by Szymon Śledź and Marek W. Ewertowski
Remote Sens. 2022, 14(6), 1312; https://doi.org/10.3390/rs14061312 - 9 Mar 2022
Cited by 12 | Viewed by 3099
Abstract
The fully automated Structure-from-Motion approach for developing digital elevation models and orthomosaics has been known and used in photogrammetry for at least 15 years. Years of practice and experience have allowed researchers to provide a solid description of the applicability and limitations of [...] Read more.
The fully automated Structure-from-Motion approach for developing digital elevation models and orthomosaics has been known and used in photogrammetry for at least 15 years. Years of practice and experience have allowed researchers to provide a solid description of the applicability and limitations of this method. That being said, the impact of input processing parameters in software on the quality of photogrammetric products has yet to be fully ascertained empirically. This study is aimed at identifying the most advantageous processing workflow to fill this research gap by testing 375 different setup variations in the Agisoft Metashape software for the same set of images acquired using an unmanned aerial vehicle in a proglacial area. The purpose of the experiment was to determine three workflows: (1) the fastest, which has the shortest calculation time; (2) the best quality, which is as accurate as possible, regardless of the time taken for the calculations; and (3) the optimal, which is a compromise between accuracy and calculation time. Each of the 375 processing setup variations was assessed based on final product accuracy, i.e., orthomosaics and digital elevation models. The three workflows were selected based on calculating the height differences between the digital elevation models and the control points that did not participate in their georeferencing. The analyses of the root mean square errors (RMSE) and standard deviations indicate that excluding some of the optimization parameters during the camera optimization stage results in high RMSE and an increase in the values of standard deviation errors. Furthermore, it was shown that increasing the detail of individual processing steps in software does not always positively affect the accuracy of the resulting models. The experiment resulted in the development of three different workflows in the form of Python scripts for Agisoft Metashape software, which will help users to process image sets efficiently in the context of earth surface dynamics studies. Full article
(This article belongs to the Special Issue Recent Advances in GIS Techniques for Remote Sensing)
Show Figures

Figure 1

Back to TopTop