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Applied Sciences
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New Trends of GIS Technology in Environmental Studies
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New Trends of GIS Technology in Environmental Studies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 8161

Special Issue Editors

Dr. Zdena Dobesova

E-Mail Website
Guest Editor
Department of Geoinformatics, Faculty of Science, Palacky University in Olomouc, 771 46 Olomouc, Czech Republic
Interests: visual programming for GIS; scripting in Python; database, data mining; digital cartography; evaluation of walkability by GIS
Special Issues, Collections and Topics in MDPI journals
Dr. Vilém Pechanec

E-Mail Website
Guest Editor
Department of Geoinformatics, Faculty of Science, Palacký University, 17th listopadu 50, 771 46 Olomouc, Czech Republic
Interests: environmental geoinformatics; geonformatics & landscape ecology; modelling of ecosystem functions/services; spatial decision support system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will present the potential and trends of new GIS technologies in environmental studies. GIS is a powerful tool for environmental data analysis and prediction. GIS technology allows better viewing and understanding of physical features and nature in their relationships that influence critical environmental conditions. Assessment of hazards and risks becomes the foundation for planning decisions and mitigation activities under global climate change.

This Special Issue is dedicated to exploring current GIS and GI technology trends.

Contributions can address the following topics:

  • case studies using new GIS technology for environmental analysis
  • planning and prediction of land use change
  • using artificial intelligence in environmental studies
  • monitoring of environmental problems using sensor networks
  • methods of deriving environmental biophysical data from RS data
  • spatial decision support system for landscape

Dr. Zdena Dobesova
Dr. Vilém Pechanec
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. Applied Sciences 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 2400 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

  • landscape ecology
  • land use/land cover analyses
  • RS data
  • machine learning
  • case study
  • prediction
  • natural hazards
  • mitigation activities

Published Papers (6 papers)

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Research

19 pages, 9091 KiB  
Article
Charactering Human-Caused Fires Using GIS-Based Dimensionality Reduction Techniques in Keelung City, Taiwan
by Cheng-Yu Ku, Hsueh-Chuan Lu, Yi-Tse Tu and Chih-Yu Liu
Appl. Sci. 2024, 14(5), 1930; https://doi.org/10.3390/app14051930 - 27 Feb 2024
Viewed by 443
Abstract
Fires resulting from human activities, encompassing arson, electrical problems, smoking, cooking mishaps, and industrial accidents, necessitate understanding to facilitate effective prevention. This study investigates human-caused fires in Keelung City, Taiwan, employing geographic information system (GIS)-based dimensionality reduction techniques. By analyzing eleven diverse factors, [...] Read more.
Fires resulting from human activities, encompassing arson, electrical problems, smoking, cooking mishaps, and industrial accidents, necessitate understanding to facilitate effective prevention. This study investigates human-caused fires in Keelung City, Taiwan, employing geographic information system (GIS)-based dimensionality reduction techniques. By analyzing eleven diverse factors, including fire incident density, population-related, building-related and economic-related features, valuable insights are gained for enhancing fire prevention. Utilizing principal component analysis (PCA), factor analysis (FA), and out-of-bag (OOB) predictor importance, our algorithm identifies key factors explaining dataset variance. Results from three approaches reveal a significant link between fire incidents and the elderly population, buildings over 40 years old, and the tertiary sector in the economy, contributing to developing effective measures for mitigating and managing fire occurrences. Full article
(This article belongs to the Special Issue New Trends of GIS Technology in Environmental Studies)
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23 pages, 11481 KiB  
Article
Landscape Analysis of the Arribes del Duero Natural Park (Spain): Cartography of Quality and Fragility
by Leticia Merchán, Antonio Miguel Martínez-Graña, Carlos E. Nieto and Marco Criado
Appl. Sci. 2023, 13(20), 11556; https://doi.org/10.3390/app132011556 - 22 Oct 2023
Viewed by 753
Abstract
The landscape is a resource to be considered in the planning and sustainable management of the territory of natural spaces, such as the Arribes del Duero Natural Park. It is conditioned by environmental factors. They are highly influential on the quality of life [...] Read more.
The landscape is a resource to be considered in the planning and sustainable management of the territory of natural spaces, such as the Arribes del Duero Natural Park. It is conditioned by environmental factors. They are highly influential on the quality of life of the people who live there. A historical analysis of the landscape was carried out with a qualitative and partially subjective character. In this work, we took advantage of current technologies, such as GIS techniques, to objectively and quantitatively calculate the variables. Firstly, it was necessary to draw up a map of landscape units, which is derived from the union of the abiotic (geomorphology and lithology) and biotic (vegetation) components in the background. Twelve homogeneous landscape units were identified by analyzing the quality and perceptual fragility of each one and considering intrinsic and extrinsic factors. The results obtained showed that the landscape quality presents areas with very high values in the fluvial canyon of the Duero river. The lowest values were found in very degraded and vegetated polygenic areas. On the other hand, the most fragile areas were those with some vulnerable character that prevents the development of human activities, such as areas with steep slopes. The procedure and results obtained constitute a useful tool for public administrations to carry out sustainable management of natural areas. Full article
(This article belongs to the Special Issue New Trends of GIS Technology in Environmental Studies)
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14 pages, 21742 KiB  
Article
Comparison of Automatic Classification Methods for Identification of Ice Surfaces from Unmanned-Aerial-Vehicle-Borne RGB Imagery
by Jakub Jech, Jitka Komárková and Devanjan Bhattacharya
Appl. Sci. 2023, 13(20), 11400; https://doi.org/10.3390/app132011400 - 17 Oct 2023
Viewed by 948
Abstract
This article describes a comparison of the pixel-based classification methods used to distinguish ice from other land cover types. The article focuses on processing RGB imagery, as these are very easy to obtained. The imagery was taken using UAVs and has a very [...] Read more.
This article describes a comparison of the pixel-based classification methods used to distinguish ice from other land cover types. The article focuses on processing RGB imagery, as these are very easy to obtained. The imagery was taken using UAVs and has a very high spatial resolution. Classical classification methods (ISODATA and Maximum Likelihood) and more modern approaches (support vector machines, random forests, deep learning) have been compared for image data classifications. Input datasets were created from two distinct areas: The Pond Skříň and the Baroch Nature Reserve. The images were classified into two classes: ice and all other land cover types. The accuracy of each classification was verified using a Cohen’s Kappa coefficient, with reference values obtained via manual surface identification. Deep learning and Maximum Likelihood were the best classifiers, with a classification accuracy of over 92% in the first area of interest. On average, the support vector machine was the best classifier for both areas of interest. A comparison of the selected methods, which were applied to highly detailed RGB images obtained with UAVs, demonstrates the potential of their utilization compared to imagery obtained using satellites or aerial technologies for remote sensing. Full article
(This article belongs to the Special Issue New Trends of GIS Technology in Environmental Studies)
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21 pages, 8743 KiB  
Article
Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
by Muhammad Akhtar, Iqbal Murtza, Muhammad Adnan and Ayesha Saadia
Appl. Sci. 2023, 13(13), 7882; https://doi.org/10.3390/app13137882 - 5 Jul 2023
Cited by 3 | Viewed by 1662
Abstract
Natural scene classification, which has potential applications in precision agriculture, environmental monitoring, and disaster management, poses significant challenges due to variations in the spatial resolution, spectral resolution, texture, and size of remotely sensed images of natural scenes on Earth. For such challenging problems, [...] Read more.
Natural scene classification, which has potential applications in precision agriculture, environmental monitoring, and disaster management, poses significant challenges due to variations in the spatial resolution, spectral resolution, texture, and size of remotely sensed images of natural scenes on Earth. For such challenging problems, deep-learning-based algorithms have demonstrated amazing performances in recent years. Among these methodologies, transfer learning is a useful technique which employs the learned features already extracted from the pre-trained models from large-scale datasets for the problem at hand, resulting in quicker and more accurate models. In this study, we deployed cross-domain transfer learning for the land-cover classification of remotely sensed images of natural scenes. We conducted extensive experiments to measure the performance of the proposed method and explored the factors that affect the performance of the models. Our findings suggest that fine-tuning the ResNet-50 model outperforms various other models in terms of the classification accuracy. The experimental results showed that the deployed cross-domain transfer-learning system achieved outstanding (99.5% and 99.1%) accurate performances compared to previous benchmarks on the NaSC-TG2 dataset with the final tuning of the whole structure and only the last three layers, respectively. Full article
(This article belongs to the Special Issue New Trends of GIS Technology in Environmental Studies)
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20 pages, 3769 KiB  
Article
Assessment and Spatial Distribution of Urban Ecosystem Functions Applied in Two Czech Cities
by Renata Včeláková, Marcela Prokopová, Vilém Pechanec, Lenka Štěrbová, Ondřej Cudlín, Ahmed Mohammed Ahmed Alhuseen, Jan Purkyt and Pavel Cudlín
Appl. Sci. 2023, 13(9), 5759; https://doi.org/10.3390/app13095759 - 6 May 2023
Cited by 1 | Viewed by 1734
Abstract
As urban areas expand worldwide, the importance of ecosystem services provided by urban and peri-urban areas (ESs) increases, especially those that mitigate the effects of ongoing climate change. We present a relatively simple method to assess the performance of three ecosystem functions (EFs: [...] Read more.
As urban areas expand worldwide, the importance of ecosystem services provided by urban and peri-urban areas (ESs) increases, especially those that mitigate the effects of ongoing climate change. We present a relatively simple method to assess the performance of three ecosystem functions (EFs: evapotranspiration, carbon production, and habitat- and landscape-level biodiversity) in urban and peri-urban areas, indicating their capacity to provide relevant regulative ESs. The method was applied to two Czech foothill cities, Liberec and Děčín, and the results showed that the EFs of both cities were at comparable or even higher levels than the average values for the whole Czech Republic. The peri-urban area showed surprisingly high values for all EFs and habitat connectivity. The urban–rural gradient of EFs also showed higher values for EFs in the peri-urban area than in the adjacent rural (forest and agricultural) landscape. The method can serve as a useful tool to quickly identify valuable urban habitats (strong ESs providers) to support their protection or to identify places with low functional values that should be considered and sorted in urban adaptation strategies to global climate change to support the creation of functional green infrastructure. Full article
(This article belongs to the Special Issue New Trends of GIS Technology in Environmental Studies)
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19 pages, 9275 KiB  
Article
GIS-Analysis for Active Tectonics Assessment of Wadi Al-Arish, Egypt
by Bashar Bashir, Abdullah Alsalman, Hussein Bachir and Mahmoud Elnobi
Appl. Sci. 2023, 13(4), 2659; https://doi.org/10.3390/app13042659 - 18 Feb 2023
Cited by 2 | Viewed by 2004
Abstract
In this paper, we apply an effective method to evaluate relative tectonic activity by applying several morph-tectonic indices that are useful in evaluating topography and tectonics. These indices include stream length-gradient, asymmetric factor, hypsometric index, hypsometric curves, valley floor width to valley height [...] Read more.
In this paper, we apply an effective method to evaluate relative tectonic activity by applying several morph-tectonic indices that are useful in evaluating topography and tectonics. These indices include stream length-gradient, asymmetric factor, hypsometric index, hypsometric curves, valley floor width to valley height ratio, drainage basin shape, and mountain front sinuosity. The study region of Wadi Al-Arish in northern Sinai Peninsula in northern Egypt is a natural laboratory to examine relative tectonic activity levels for calculating morpho-tectonic indices of several catchments and sub-catchments rather than an individual catchment. Northern Sinai, comprising the Waid Al-Arish area, is characterized by several large inversion anticline folds. The cumulative results extracted from morpho-tectonic indices ae presented as a new index, namely relative tectonic activity level (RTAL), which we classified into four levels: low, moderate, high, and very high relative tectonic activity. Therefore, the study region provides different levels of relative tectonic activity resulting from fault patterns affecting the northern Sinai inversion forms. The paper examines the concept that regions with various levels of tectonic activity are associated with specific values of RTAL. Full article
(This article belongs to the Special Issue New Trends of GIS Technology in Environmental Studies)
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