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Sensors
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Remote Sensing and Field Sensing for Geoenvironmental Applications
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Remote Sensing and Field Sensing for Geoenvironmental Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 6614

Special Issue Editors

Prof. Dr. Masashi Matsuoka

E-Mail Website
Guest Editor
Department of Architecture and Building Engineering, Tokyo Institute of Technology, Yokohama 226-8502, Japan
Interests: earthquake engineering; geomorphology; GIS and application of remote sensing technology to disaster management
Special Issues, Collections and Topics in MDPI journals
Dr. Hiroyuki Miura

E-Mail Website
Guest Editor
Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, Japan
Interests: earthquake engineering; geospatial analysis for damage assessment; remote sensing for disaster response; DEM analysis for geomorphology
Special Issues, Collections and Topics in MDPI journals
Dr. Bakhtiar Feizizadeh

E-Mail Website
Guest Editor
Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran
Interests: GIS Multicriteria Decsion Analysis (GIS-MCDA); Sensitivity and Uncertainty Analysis; Integration of Remote sensing and GIS; GIS database and SDI; GIS- Big Data; Remote Sensing and GIS application for geohazard monitoring and risk assessment; Land use/cover mapping; Object Based Image Anlalysis (OBIA); Thermal remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Hideomi Gokon

E-Mail Website
Guest Editor
School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi City 923-1292, Japan
Interests: knowledge science; decision making; disaster prevention; data analytics; remote sensing; tsunami numerical modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Sadra Karimzadeh

E-Mail Website
Guest Editor
Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166/15731, Iran
Interests: SAR remote sensing; damage assessment; site characterization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traditionally, field sensing provides helpful information about various phenomena, events and features. However, field data gathering might be a difficult, time-consuming and expensive approach. Recently, remote sensing technology has been emerging as an alternative tool for field sensing techniques, providing useful information for researchers, decision makers and managers to work effectively on a problem. Remote sensing data can cover larger areas and can be obtained from various sensors such as multispectral/hyperspectral remote sensors and synthetic aperture radar (SAR) remote sensors. Remote sensing datasets, coupled with machine learning and spatial analyses, can lead to paradigm shifts in different disciplines, such as geographic information science (GIS), civil engineering, earth and soil sciences, atmospheric sciences and disaster sciences.

The objective of this Special Issue is to provide a new perspective of joint use of remote sensing and field sensing datasets for readers to understand clearly how remote and field sensing are complementary tools for monitoring events (e.g., earthquake, flood, drought, etc.), exploring features (e.g., object extraction, object detection, etc.) and preparing GIS databases. We encourage manuscript submissions (review or original research articles) related, but not strictly limited, to the following:

  • Synthetic aperture radar (SAR) remote sensing and field sensing for monitoring of natural events;
  • Field and remote sensing techniques for built-up and environmental studies;
  • Machine learning techniques for field and remote sensing data;
  • Big remote sensing data for geographic information system (GIS);
  • Optical/hyperspectral remote sensing together with field sensing for monitoring of natural events;
  • Remote-sensing-based data driven approaches;
  • Spatiotemporal image analysis;
  • Feature extraction using remote sensing and field observations;
  • Time-series analysis of SAR interferometry (InSAR) for natural hazards supported by field sensing;
  • Field and remote sensing applications for agricultural studies

Prof. Dr. Masashi Matsuoka
Dr. Hiroyuki Miura
Dr. Bakhtiar Feizizadeh
Dr. Hideomi Gokon
Dr. Sadra Karimzadeh
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. Sensors 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 2600 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.

Published Papers (2 papers)

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Research

27 pages, 11267 KiB  
Article
Evaluation of Different Landslide Susceptibility Models for a Local Scale in the Chitral District, Northern Pakistan
by Bilal Aslam, Ahsen Maqsoom, Umer Khalil, Omid Ghorbanzadeh, Thomas Blaschke, Danish Farooq, Rana Faisal Tufail, Salman Ali Suhail and Pedram Ghamisi
Sensors 2022, 22(9), 3107; https://doi.org/10.3390/s22093107 - 19 Apr 2022
Cited by 17 | Viewed by 3099
Abstract
This work evaluates the performance of three machine learning (ML) techniques, namely logistic regression (LGR), linear regression (LR), and support vector machines (SVM), and two multi-criteria decision-making (MCDM) techniques, namely analytical hierarchy process (AHP) and the technique for order of preference by similarity [...] Read more.
This work evaluates the performance of three machine learning (ML) techniques, namely logistic regression (LGR), linear regression (LR), and support vector machines (SVM), and two multi-criteria decision-making (MCDM) techniques, namely analytical hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS), for mapping landslide susceptibility in the Chitral district, northern Pakistan. Moreover, we create landslide inventory maps from LANDSAT-8 satellite images through the change vector analysis (CVA) change detection method. The change detection yields more than 500 landslide spots. After some manual post-processing correction, the landslide inventory spots are randomly split into two sets with a 70/30 ratio for training and validating the performance of the ML techniques. Sixteen topographical, hydrological, and geological landslide-related factors of the study area are prepared as GIS layers. They are used to produce landslide susceptibility maps (LSMs) with weighted overlay techniques using different weights of landslide-related factors. The accuracy assessment shows that the ML techniques outperform the MCDM methods, while SVM yields the highest accuracy of 88% for the resulting LSM. Full article
(This article belongs to the Special Issue Remote Sensing and Field Sensing for Geoenvironmental Applications)
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28 pages, 21761 KiB  
Article
Remote Sensing Image Classification with a Graph-Based Pre-Trained Neighborhood Spatial Relationship
by Xudong Guan, Chong Huang, Juan Yang and Ainong Li
Sensors 2021, 21(16), 5602; https://doi.org/10.3390/s21165602 - 20 Aug 2021
Cited by 2 | Viewed by 2307
Abstract
Previous knowledge of the possible spatial relationships between land cover types is one factor that makes remote sensing image classification “smarter”. In recent years, knowledge graphs, which are based on a graph data structure, have been studied in the community of remote sensing [...] Read more.
Previous knowledge of the possible spatial relationships between land cover types is one factor that makes remote sensing image classification “smarter”. In recent years, knowledge graphs, which are based on a graph data structure, have been studied in the community of remote sensing for their ability to build extensible relationships between geographic entities. This paper implements a classification scheme considering the neighborhood relationship of land cover by extracting information from a graph. First, a graph representing the spatial relationships of land cover types was built based on an existing land cover map. Empirical probability distributions of the spatial relationships were then extracted using this graph. Second, an image was classified based on an object-based fuzzy classifier. Finally, the membership of objects and the attributes of their neighborhood objects were joined to decide the final classes. Two experiments were implemented. Overall accuracy of the two experiments increased by 5.2% and 0.6%, showing that this method has the ability to correct misclassified patches using the spatial relationship between geo-entities. However, two issues must be considered when applying spatial relationships to image classification. The first is the “siphonic effect” produced by neighborhood patches. Second, the use of global spatial relationships derived from a pre-trained graph loses local spatial relationship in-formation to some degree. Full article
(This article belongs to the Special Issue Remote Sensing and Field Sensing for Geoenvironmental Applications)
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