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Applied Sciences
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Applications of Remote Sensing and Geospatial Technologies to Earth Observations
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Applications of Remote Sensing and Geospatial Technologies to Earth Observations

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

Deadline for manuscript submissions: closed (1 June 2021) | Viewed by 21999

Special Issue Editors

Dr. Raffaele Albano

E-Mail Website
Guest Editor
School of Engineering, University of Basilicata, Potenza, Italy
Interests: floods; water resource management; soil moisture; hydrology; artificial intelligence; GIS; geomorphic method; DEM; geospatial analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Aurelia Sole

E-Mail Website
Guest Editor
School of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: 1D/2D hydraulic modeling; flood prone areas; GIS and remote sensing; water drainage network; landslide susceptibility and mapping; geomorphic modeling for flood-prone area mapping; decision making; risk communication
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ake Sivertun

E-Mail Website
Guest Editor
Research Institutes of Sweden (RISE), 581 83 Linköping, Sweden
Interests: geoinformatics; sensor data fusion; command, control, and communication for environment and emergency management

Special Issue Information

Dear Colleagues,

Earth Observation and modeling are highly resource- and data-intensive processes that are dominated by the complex interactions between the existing geographical conditions, technical installations and infrastructures, different—sometimes conflicting—human activities, as well as societal institutions and capacities.

Geomatics techniques and Geospatial tools are used increasingly in different phases of resources or risk management, due to the challenges posed by contemporary issues such as climate change and increasingly complex environmental and socio-economic interactions. Geospatial applications have the advantages of monitoring, assessing, and managing natural resources and risk (in this Special Issue, particularly, water-related) more comprehensively than ever before. These thanks, significantly, to finely tuned detection systems as satellite, unmanned (UAV) and Geographical Information Systems as well as algorithms for image and data processing and provision of a synoptic view of the region of interest in time series. In many situations these data sources have been brought to researchers, policymakers, and practitioners on cost-effective terms, that open use of the predictions for prevention and mitigation also at a regional and local level.

The purpose of this Special Issue is to provide an overview of the research advancements, scientific lessons learned, as well as operational issues and challenges in this rapidly evolving and expanding field.

Dr. Raffaele Albano
Prof. Aurelia Sole
Prof. Ake Sivertun
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

  • Earth observations
  • Artificial Intelligence
  • Machine learning
  • Geospatial applications
  • Geomatics
  • Crowdsourcing
  • Internet of Things
  • Remote sensing
  • Geographic Information Systems (GIS)
  • Big data
  • Hazard monitoring
  • Risk modeling
  • Risk mapping
  • Monitoring
  • Forecasting
  • Post-disaster recovery
  • Resilience
  • Risk reduction strategies
  • Decision making
  • Unmanned Aerial Vehicles (UAV)

Published Papers (9 papers)

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Research

23 pages, 5712 KiB  
Article
How to Quantify the Dynamics of Single (Straight or Sinuous) and Multiple (Anabranching) Channels from Imagery for River Restoration
by Gilles Arnaud-Fassetta, Gabriel Melun, Paul Passy, Guillaume Brousse and Olivier Theureaux
Appl. Sci. 2021, 11(17), 8075; https://0-doi-org.brum.beds.ac.uk/10.3390/app11178075 - 31 Aug 2021
Cited by 1 | Viewed by 1688
Abstract
Since the 2000s, European rivers have undergone restoration works to give them back a little more ‘freedom space’ and consolidate the hydro-sedimentary continuum and biological continuity as required by the Water Framework Directive (WFD). In high-energy rivers, suppression of lateral constraints (embankment removal) [...] Read more.
Since the 2000s, European rivers have undergone restoration works to give them back a little more ‘freedom space’ and consolidate the hydro-sedimentary continuum and biological continuity as required by the Water Framework Directive (WFD). In high-energy rivers, suppression of lateral constraints (embankment removal) leads to geomorphological readjustments in the modification of both the active-channel length and active-channel width. The article provides a new methodological development to overcome the shortcomings of traditional methods (based on diachronic cross-section analysis) unable to simultaneously take into account these geometric adjustments after active-channel restoration. It allows us to follow and precisely quantify the geomorphological changes of the active channel faced to the stakes (i.e., structures or urbanized, recreation or agricultural areas) in the floodplain. The methodology proposes three new indicators (distance from active channel to stakes or floodplain margins as indicator 1; distance from stakes to active channel as indicator 2; diachronic distance as indicator 3) and a metric analysis grid in the 2D Euclidean space. It is applied to the Clamoux River (order 4, Strahler; bankfull, specific stream power: 280 W/m2) in the Aude watershed (Mediterranean France). The paper shows the full potential of this methodological protocol to be able to meet managers’ expectations as closely as possible within the framework of the multi-annual active-channel monitoring. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Geospatial Technologies to Earth Observations)
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15 pages, 5869 KiB  
Article
On the Deployment of Out-of-the-Box Embedded Devices for Self-Powered River Surface Flow Velocity Monitoring at the Edge
by Arsal-Hanif Livoroi, Andrea Conti, Luca Foianesi, Fabio Tosi, Filippo Aleotti, Matteo Poggi, Flavia Tauro, Elena Toth, Salvatore Grimaldi and Stefano Mattoccia
Appl. Sci. 2021, 11(15), 7027; https://0-doi-org.brum.beds.ac.uk/10.3390/app11157027 - 29 Jul 2021
Cited by 5 | Viewed by 1987
Abstract
As reported in the recent image velocimetry literature, tracking the motion of sparse feature points floating on the river surface as done by the Optical Tracking Velocimetry (OTV) algorithm is a promising strategy to address surface flow monitoring. Moreover, the lightweight nature of [...] Read more.
As reported in the recent image velocimetry literature, tracking the motion of sparse feature points floating on the river surface as done by the Optical Tracking Velocimetry (OTV) algorithm is a promising strategy to address surface flow monitoring. Moreover, the lightweight nature of OTV coupled with computational optimizations makes it suited even for its deployment in situ to perform measurements at the edge with cheap embedded devices without the need to perform offload processing. Despite these notable achievements, the actual practical deployment of OTV in remote environments would require cheap and self-powered systems enabling continuous measurements without the need for cumbersome and expensive infrastructures rarely found in situ. Purposely, in this paper, we propose an additional simplification to the OTV algorithm to reduce even further its computational requirements, and we analyze self-powered off-the-shelf setups for in situ deployment. We assess the performance of such set-ups from different perspectives to determine the optimal solution to design a cost-effective self-powered measurement node. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Geospatial Technologies to Earth Observations)
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16 pages, 30792 KiB  
Article
Comparing Three Machine Learning Techniques for Building Extraction from a Digital Surface Model
by Nicla Maria Notarangelo, Arianna Mazzariello, Raffaele Albano and Aurelia Sole
Appl. Sci. 2021, 11(13), 6072; https://0-doi-org.brum.beds.ac.uk/10.3390/app11136072 - 30 Jun 2021
Cited by 4 | Viewed by 1966
Abstract
Automatic building extraction from high-resolution remotely sensed data is a major area of interest for an extensive range of fields (e.g., urban planning, environmental risk management) but challenging due to urban morphology complexity. Among the different methods proposed, the approaches based on supervised [...] Read more.
Automatic building extraction from high-resolution remotely sensed data is a major area of interest for an extensive range of fields (e.g., urban planning, environmental risk management) but challenging due to urban morphology complexity. Among the different methods proposed, the approaches based on supervised machine learning (ML) achieve the best results. This paper aims to investigate building footprint extraction using only high-resolution raster digital surface model (DSM) data by comparing the performance of three different popular supervised ML models on a benchmark dataset. The first two methods rely on a histogram of oriented gradients (HOG) feature descriptor and a classical ML (support vector machine (SVM)) or a shallow neural network (extreme learning machine (ELM)) classifier, and the third model is a fully convolutional network (FCN) based on deep learning with transfer learning. Used data were obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) and cover the urban areas of Vaihingen an der Enz, Potsdam, and Toronto. The results indicated that performances of models based on shallow ML (feature extraction and classifier training) are affected by the urban context investigated (F1 scores from 0.49 to 0.81), whereas the FCN-based model proved to be the most robust and best-performing method for building extraction from a high-resolution raster DSM (F1 scores from 0.80 to 0.86). Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Geospatial Technologies to Earth Observations)
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20 pages, 17698 KiB  
Article
Combined Geodetic and Seismological Study of the December 2020 Mw = 4.6 Thiva (Central Greece) Shallow Earthquake
by Panagiotis Elias, Ioannis Spingos, George Kaviris, Andreas Karavias, Theodoros Gatsios, Vassilis Sakkas and Issaak Parcharidis
Appl. Sci. 2021, 11(13), 5947; https://0-doi-org.brum.beds.ac.uk/10.3390/app11135947 - 26 Jun 2021
Cited by 10 | Viewed by 2677
Abstract
On 2 December 2020, a moderate and shallow Mw = 4.6 earthquake occurred in Boeotia (Central Greece) near the city of Thiva. Despite its magnitude, the co-seismic ground deformation field was detectable and measurable by Sentinel-1, ascending and descending, synthetic aperture interferometry [...] Read more.
On 2 December 2020, a moderate and shallow Mw = 4.6 earthquake occurred in Boeotia (Central Greece) near the city of Thiva. Despite its magnitude, the co-seismic ground deformation field was detectable and measurable by Sentinel-1, ascending and descending, synthetic aperture interferometry radar (InSAR) acquisitions. The closest available GNSS station to the epicenter, located 11 km west, measured no deformation, as expected. We proceeded to the inversion of the deformation source. Moreover, we reassessed seismological data to identify the activated zone, associated with the mainshock and the aftershock sequence. Additionally, we used the rupture plane information from InSAR to better determine the focal mechanism and the centroid location of the mainshock. We observed that the mainshock occurred at a shallower depth and the rupture then expanded downdip, as revealed by the aftershock distribution. Our geodetic inversion modelling indicated the activation of a normal fault with a small left-lateral component, length of 2.0 km, width of 1.7 km, average slip of 0.2 m, a low dip angle of 33°, and a SW dip-direction. The inferred fault top was buried at a depth of ~0.5 km, rooted at a depth of ~1.4 km, with its geodetic centroid buried at 1.0 km. It was aligned with the Kallithea fault. In addition, the dip-up projection of the modeled fault to the surface was located very close (~0.4 km SW) to the mapped (by existing geological observations) trace of the Kallithea fault. The ruptured area was settled in a transition zone. We suggest the installation of at least one GNSS and seismological station near Kallithea; as the activated zone (inferred by the aftershock sequence and InSAR results) could yield events with M ≥ 5.0, according to empirical laws relating to rupture zone dimensions and earthquake magnitude. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Geospatial Technologies to Earth Observations)
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38 pages, 13926 KiB  
Article
Linking Soil Erosion Modeling to Landscape Patterns and Geomorphometry: An Application in Crete, Greece
by Imen Brini, Dimitrios D. Alexakis and Chariton Kalaitzidis
Appl. Sci. 2021, 11(12), 5684; https://0-doi-org.brum.beds.ac.uk/10.3390/app11125684 - 19 Jun 2021
Cited by 13 | Viewed by 2474
Abstract
Soil erosion is a severe and continuous environmental problem caused mainly by natural factors, which can be enhanced by anthropogenic activities. The morphological relief with relatively steep slopes, the dense drainage network, and the Mediterranean climate are some of the factors that render [...] Read more.
Soil erosion is a severe and continuous environmental problem caused mainly by natural factors, which can be enhanced by anthropogenic activities. The morphological relief with relatively steep slopes, the dense drainage network, and the Mediterranean climate are some of the factors that render the Paleochora region (South Chania, Crete, Greece) particularly prone to soil erosion in cases of intense rainfall events. In this study, we aimed to assess the correlation between soil erosion rates estimated from the Revised Universal Soil Loss Equation (RUSLE) and the landscape patterns and to detect the most erosion-prone sub-basins based on an analysis of morphometric parameters, using geographic information system (GIS) and remote sensing technologies. The assessment of soil erosion rates was conducted using the RUSLE model. The landscape metrics analysis was carried out to correlate soil erosion and landscape patterns. The morphometric analysis helped us to prioritize erosion-prone areas at the sub-basin level. The estimated soil erosion rates were mapped, showing the spatial distribution of the soil loss for the study area in 2020. For instance, the landscape patterns seemed to highly impact the soil erosion rates. The morphometric parameter analysis is considered as a useful tool for delineating areas that are highly vulnerable to soil erosion. The integration of three approaches showed that there is are robust relationships between soil erosion modeling, landscape patterns, and morphometry. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Geospatial Technologies to Earth Observations)
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21 pages, 3000 KiB  
Article
Accurate Algorithms for Spatial Operations on the Spheroid in a Spatial Database Management System
by José Carlos Martínez-Llario, Sergio Baselga and Eloína Coll
Appl. Sci. 2021, 11(11), 5129; https://0-doi-org.brum.beds.ac.uk/10.3390/app11115129 - 31 May 2021
Cited by 5 | Viewed by 2391
Abstract
Some of the most powerful spatial analysis software solutions (Oracle, Google Earth Engine, PostgreSQL + PostGIS, etc.) are currently performing geometric calculations directly on the ellipsoid (a quadratic surface that models the earth shape), with a double purpose: to attain a high degree [...] Read more.
Some of the most powerful spatial analysis software solutions (Oracle, Google Earth Engine, PostgreSQL + PostGIS, etc.) are currently performing geometric calculations directly on the ellipsoid (a quadratic surface that models the earth shape), with a double purpose: to attain a high degree of accuracy and to allow the full management of large areas of territory (countries or even continents). It is well known that both objectives are impossible to achieve by means of the traditional approach using local mathematical projections and Cartesian coordinates. This paper demonstrates in a quantitative methodological way that most of the spatial analysis software products make important deviations in calculations regarding to geodesics, being the users unaware of the magnitude of these inaccuracies, which can easily reach meters depending on the distance. This is due to the use of ellipsoid calculations in an approximate way (e.g., using a sphere instead of an ellipsoid). This paper presents the implementation of two algorithms that solve with high accuracy (less than 100 nm) and efficiently (few iterations) two basic geometric calculations on the ellipsoid that are essential to build more complex spatial operators: the intersection of two geodesics and the minimum distance from a point to a geodesic. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Geospatial Technologies to Earth Observations)
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18 pages, 4994 KiB  
Article
Flow Duration Curves from Surface Reflectance in the Near Infrared Band
by Angelica Tarpanelli and Alessio Domeneghetti
Appl. Sci. 2021, 11(8), 3458; https://0-doi-org.brum.beds.ac.uk/10.3390/app11083458 - 12 Apr 2021
Cited by 5 | Viewed by 1727
Abstract
Flow duration curve (FDC) is a cumulative frequency curve that shows the percent of time a specific discharge has been equaled or exceeded during a particular period of time at a given river location, providing a comprehensive description of the hydrological regime of [...] Read more.
Flow duration curve (FDC) is a cumulative frequency curve that shows the percent of time a specific discharge has been equaled or exceeded during a particular period of time at a given river location, providing a comprehensive description of the hydrological regime of a catchment. Thus, relying on historical streamflow records, FDCs are typically constrained to gauged and updated ground stations. Earth Observations can support our monitoring capability and be considered as a valuable and additional source for the observation of the Earth’s physical parameters. Here, we investigated the potential of the surface reflectance in the Near Infrared (NIR) band of the MODIS 500 m and eight-day product, in providing reliable FDCs along the Mississippi River. Results highlight the capability of NIR bands to estimate the FDCs, enabling a realistic reconstruction of the flow regimes at different locations. Apart from a few exceptions, the relative Root Mean Square Error, rRMSE, of the discharge value in validation period ranges from 27–58% with higher error experienced for extremely high flows (low duration), mainly due to the limit of the sensor to penetrate the clouds during the flood events. Due to the spatial resolution of the satellite product higher errors are found at the stations where the river is narrow. In general, good performances are obtained for medium flows, encouraging the use of the satellite for the water resources management at ungauged river sites. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Geospatial Technologies to Earth Observations)
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14 pages, 4897 KiB  
Article
Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues
by Mohamad M. Al Rahhal, Yakoub Bazi, Taghreed Abdullah, Mohamed L. Mekhalfi and Mansour Zuair
Appl. Sci. 2020, 10(24), 8931; https://0-doi-org.brum.beds.ac.uk/10.3390/app10248931 - 14 Dec 2020
Cited by 14 | Viewed by 3614
Abstract
Compared to image-image retrieval, text-image retrieval has been less investigated in the remote sensing community, possibly because of the complexity of appropriately tying textual data to respective visual representations. Moreover, a single image may be described via multiple sentences according to the perception [...] Read more.
Compared to image-image retrieval, text-image retrieval has been less investigated in the remote sensing community, possibly because of the complexity of appropriately tying textual data to respective visual representations. Moreover, a single image may be described via multiple sentences according to the perception of the human labeler and the structure/body of the language they use, which magnifies the complexity even further. In this paper, we propose an unsupervised method for text-image retrieval in remote sensing imagery. In the method, image representation is obtained via visual Big Transfer (BiT) Models, while textual descriptions are encoded via a bidirectional Long Short-Term Memory (Bi-LSTM) network. The training of the proposed retrieval architecture is optimized using an unsupervised embedding loss, which aims to make the features of an image closest to its corresponding textual description and different from other image features and vise-versa. To demonstrate the performance of the proposed architecture, experiments are performed on two datasets, obtaining plausible text/image retrieval outcomes. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Geospatial Technologies to Earth Observations)
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23 pages, 24957 KiB  
Article
An Automatic Shadow Compensation Method via a New Model Combined Wallis Filter with LCC Model in High Resolution Remote Sensing Images
by Yuanwei Yang, Shuhao Ran, Xianjun Gao, Mingwei Wang and Xi Li
Appl. Sci. 2020, 10(17), 5799; https://0-doi-org.brum.beds.ac.uk/10.3390/app10175799 - 21 Aug 2020
Cited by 3 | Viewed by 2316
Abstract
Current automatic shadow compensation methods often suffer because their contrast improvement processes are not self-adaptive and, consequently, the results they produce do not adequately represent the real objects. The study presented in this paper designed a new automatic shadow compensation framework based on [...] Read more.
Current automatic shadow compensation methods often suffer because their contrast improvement processes are not self-adaptive and, consequently, the results they produce do not adequately represent the real objects. The study presented in this paper designed a new automatic shadow compensation framework based on improvements to the Wallis principle, which included an intensity coefficient and a stretching coefficient to enhance contrast and brightness more efficiently. An automatic parameter calculation strategy also is a part of this framework, which is based on searching for and matching similar feature points around shadow boundaries. Finally, a final compensation combination strategy combines the regional compensation with the local window compensation of the pixels in each shadow to improve the shaded information in a balanced way. All these strategies in our method work together to provide a better measurement for customizing suitable compensation depending on the condition of each region and pixel. The intensity component I also is automatically strengthened through the customized compensation model. Color correction is executed in a way that avoids the color bias caused by over-compensated component values, thereby better reflecting shaded information. Images with clouds shadows and ground objects shadows were utilized to test our method and six other state-of-the-art methods. The comparison results indicate that our method compensated for shaded information more effectively, accurately, and evenly than the other methods for customizing suitable models for each shadow and pixel with reasonable time-cost. Its brightness, contrast, and object color in shaded areas were approximately equalized with non-shaded regions to present a shadow-free image. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Geospatial Technologies to Earth Observations)
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