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Special Issue "Remote Sensing in Geomatics"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 31 December 2022 | Viewed by 3081

Special Issue Editors

Dr. Gino Dardanelli
E-Mail Website
Guest Editor
Department of Engineering, Università degli Studi di Palermo, 90100 Palermo, Italy
Interests: Galileo; GLONASS; GPS; GNSS; CORS; remote sensing; geomatics; photogrammetry; surveying; mapping; drones; cartography; topography; dam displacements; laser scanner; UAV
Special Issues, Collections and Topics in MDPI journals
Dr. Mauro Lo Brutto
E-Mail Website
Guest Editor
Associate Professor, Department of Engineering, University of Palermo, 90128 Palermo, Italy
Interests: geomatics disciplines with a specialization in photogrammetry; laser scanning; 3D modelling; HBIM; cultural heritage documentation and drones applications
Special Issues, Collections and Topics in MDPI journals
Dr. Antonino Maltese
E-Mail Website1 Website2
Guest Editor
Dipartimento di Ingegneria, University of Palermo, 90128 Palermo, Italy
Interests: geomatics disciplines with a specialization in GIS, thermography, interferometry, radiometry and surface energy balance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Allowing the observation and quantification of spatiotemporal processes of the Earth’s surface, remote sensing is one of the most widely used disciplines in geomatics. Moreover, remote sensing has an intrinsic interdisciplinary connotation, being interrelated with most disciplines in geomatics, including global satellite positioning techniques, photogrammetry, laser scanning, geostatistics, geographic information systems (GIS), decision support systems, WebGIS, and geomatics applications of artificial intelligence (AI).

Since these specialized fields are intimately interconnected, innovative research on complex contexts often relies on the integration of remote sensing with other geomatics disciplines.

The key question, therefore, is: To what extent does the study of Earth surface processes benefit from the synergy of remote sensing with and among geomatics disciplines?

We are seeking novel, hypothesis-driven, high-impact research on geomatics that interfaces remote sensing with GNSS, photogrammetry, LIDAR, GIS, geostatistics, and more.

Dr. Gino Dardanelli
Dr. Mauro Lo Brutto
Dr. Antonino Maltese
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. 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 2500 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
  • radar, thermal, optical, interferometry, hyperspectral
  • geostatistics
  • geodesy
  • cartography
  • GIS, WebGIS, DSS
  • GNSS
  • LIDAR
  • geometric and radiometric accuracy
  • photogrammetry
  • UAV

Published Papers (4 papers)

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Research

Article
Two-Dimensional InSAR Monitoring of the Co- and Post-Seismic Ground Deformation of the 2021 Mw 5.9 Arkalochori (Greece) Earthquake and Its Impact on the Deformations of the Heraklion City Wall Relic
Remote Sens. 2022, 14(20), 5212; https://doi.org/10.3390/rs14205212 - 18 Oct 2022
Viewed by 510
Abstract
Contributing to the United Nations 2030 Sustainable Development Goals (SDGs) within Target 11.4 “Strengthen efforts to protect and safeguard the world’s cultural and natural heritage”, it is critical to monitor the spatial and temporal stabilities of cultural heritages. The study of the interactive [...] Read more.
Contributing to the United Nations 2030 Sustainable Development Goals (SDGs) within Target 11.4 “Strengthen efforts to protect and safeguard the world’s cultural and natural heritage”, it is critical to monitor the spatial and temporal stabilities of cultural heritages. The study of the interactive relationship between earthquakes and the protection of cultural heritages needs to be strengthened. On 27 September 2021, the destructive Mw 5.9 Arkalochori earthquake occurred ~25 km away from the city of Heraklion (Greece) where the Heraklion City Wall (HCW), a representative cultural heritage of Greece and Europe, was located. This offered a proper case to investigate the shortcomings aforementioned. Here, we intend to set up and answer the following three questions (Whether, Where and What, 3Ws): Whether there were impacts on the HCW caused by the Arkalochori earthquake? Where did the maximum deformation occur? What was the relationship between seismic deformation between the epicenter and the HCW over time? We performed two-dimensional (2D) InSAR measurements for both co-seismic and post-seismic deformations using the ascending and descending Sentinel-1A SAR images. The spatial-temporal characteristics of Up–Down (UD) and East–West (EW) were revealed. The 2D co-seismic deformation field showed that the near-filed deformations were dominating compared with the deformations at the HCW, the UD deformation was mainly featured with subsidence with a maximum value of ~21 cm, the EW deformation was ~9 cm westward and ~10 cm eastward. The time-series measurements showed that: (1) temporally, the HCW responded quickly to the Arkalochori earthquake, and the accumulative deformations at the seven different bastions of the HCW showed the same trend as the near-field area over time. (2) Spatially, the closer to the Mw 5.9 epicenter, the larger the deformations that occurred. (3) The EW and UD deformation trends of the HCW that were consistent with the Mw 5.9 epicenter were interrupted at the middle time spot (22 January 2022), indicating the influence of another earthquake sequence consisting of eight earthquakes with magnitudes larger than 3.5 that happened on 16–18 January 2022. Respectively, to summarize and address the aforementioned 3Ws based on the post-seismic analysis accomplished by the MSBAS method, the Arkalochori earthquake did affect the HCW; besides, the influences of the ~13 km earthquake sequence were also detected; the nearest part to the epicenter suffered the most; the deformation trends of the HCW were approximately the same with the epicenter area of the Arkalochori earthquake both in the UD and EW directions. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
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Article
Ground Penetrating Radar in Coastal Hazard Mitigation Studies Using Deep Convolutional Neural Networks
Remote Sens. 2022, 14(19), 4899; https://doi.org/10.3390/rs14194899 - 30 Sep 2022
Viewed by 483
Abstract
There is a long history of coastal erosion caused by frequent storm surges in the coastal regions of Australia, which imposes great threats to communities and infrastructures alongside the beach. Old Bar Beach, New South Wales, Australia, is one such hotspot famous for [...] Read more.
There is a long history of coastal erosion caused by frequent storm surges in the coastal regions of Australia, which imposes great threats to communities and infrastructures alongside the beach. Old Bar Beach, New South Wales, Australia, is one such hotspot famous for its extreme coastal erosion. To apply remedial measures such as beach nourishment effectively and economically, estimating/reconstructing the subsurface hydrogeology over the coastal areas is essential. A geophysical tool such as a ground-penetrating radar (GPR) which works on the principle of reflecting electromagnetic (EM) waves, can be conveniently deployed to delineate the soil and rock profiling, water-table depth, bedrock depth, and the subsurface structural features. Here, DeepLabv3+ architecture based newly developed deep convolutional neural networks (DCNNs) were used to establish an inherent non-linear relationship between the GPR data and the EM wave velocity. The presented DCNNs have a lesser number of layers, a lesser number of trainable (learnable) parameters, a high convergence rate and, at the same time, achieve prediction accuracy comparable to that of well-established DeepLabv3+ networks, having high trainable parameters and a relatively low convergence rate. Here, firstly the DCNNs were trained and validated on small 1D datasets. Each dataset contains a 1D GPR trace and a corresponding EM velocity model. The DCNNs turned out to be quite promising in the 1D case, with training, validation, and testing accuracy of approximately 95%, 94%, and 95%, respectively. Secondly, 1D trained weights were applied to 2D synthetic GPR data for EM velocity prediction, and the accuracy of prediction achieved was approximately 95%. Seeing the excellent performance of the DCNNs in the 2D prediction case using 1D trained weights, a large amount of 1D synthetic datasets (approximately 1.2 million) were generated and gaussian noise was added to it to replicate the real field scenario. Thirdly, topographically corrected GPR data acquired over the Old Bar Beach were inverted using the DCNNs trained on 1.2 million 1D synthetic datasets to obtain the subsurface high-resolution, high-precision EM velocity, and εr distribution information to understand the hydrogeology over the beach. The findings presented in this paper agree well with the previous hydrogeological studies carried out using GPR. Our findings show that DCNNs, along with GPR, can be successfully used in coastal environments for the quick and accurate hydrogeological investigation required for the implementation of coastal erosion mitigation methods such as beach nourishment. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
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Article
Evaluation of Simulated AVIRIS-NG Imagery Using a Spectral Reconstruction Method for the Retrieval of Leaf Chlorophyll Content
Remote Sens. 2022, 14(15), 3560; https://doi.org/10.3390/rs14153560 - 25 Jul 2022
Viewed by 389
Abstract
The leaf chlorophyll content (LCC) is a vital parameter that indicates plant production, stress, and nutrient availability. It is critically needed for precision farming. There are several multispectral images available freely, but their applicability is restricted due to their low spectral resolution, whereas [...] Read more.
The leaf chlorophyll content (LCC) is a vital parameter that indicates plant production, stress, and nutrient availability. It is critically needed for precision farming. There are several multispectral images available freely, but their applicability is restricted due to their low spectral resolution, whereas hyperspectral images which have high spectral resolution are very limited in availability. In this work, hyperspectral imagery (AVIRIS-NG) is simulated using a multispectral image (Sentinel-2) and a spectral reconstruction method, namely, the universal pattern decomposition method (UPDM). UPDM is a linear unmixing technique, which assumes that every pixel of an image can be decomposed as a linear composition of different classes present in that pixel. The simulated AVIRIS-NG was very similar to the original image, and its applicability in estimating LCC was further verified by using the ground based measurements, which showed a good correlation value (R = 0.65). The simulated image was further classified using a spectral angle mapper (SAM), and an accuracy of 87.4% was obtained, moreover a receiver operating characteristic (ROC) curve for the classifier was also plotted, and the area under the curve (AUC) was calculated with values greater than 0.9. The obtained results suggest that simulated AVIRIS-NG is quite useful and could be used for vegetation parameter retrieval. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
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Article
Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling
Remote Sens. 2022, 14(13), 3005; https://doi.org/10.3390/rs14133005 - 23 Jun 2022
Cited by 2 | Viewed by 754
Abstract
Globally, estimating crop acreage and yield is one of the most critical issues that policy and decision makers need for assessing annual crop productivity and food supply. Nowadays, satellite remote sensing and geographic information system (GIS) can enable the estimation of these crop [...] Read more.
Globally, estimating crop acreage and yield is one of the most critical issues that policy and decision makers need for assessing annual crop productivity and food supply. Nowadays, satellite remote sensing and geographic information system (GIS) can enable the estimation of these crop production parameters over large geographic areas. The present work aims to estimate the wheat (Triticum aestivum) acreage and yield of Maharajganj, Uttar Pradesh, India, using satellite-based data products and the Carnegie-Ames-Stanford Approach (CASA) model. Uttar Pradesh is the largest wheat-producing state in India, and this district is well known for its quality organic wheat. India is the leader in wheat grain export, and, hence, its monitoring of growth and yield is one of the top economic priorities of the country. For the calculation of wheat acreage, we performed supervised classification using the Random Forest (RF) and Support Vector Machine classifiers and compared their classification accuracy based on ground-truthing. We found that RF performed a significantly accurate acreage assessment (kappa coefficient 0.84) compared to SVM (0.68). The CASA model was then used to calculate the winter crop (Rabi, winter-sown, and summer harvested) wheat net primary productivity (NPP) in the study area for the 2020–2021 growth season using the RF-based acreage product. The model used for wheat NPP-yield conversion (CASA) showed 3100.27 to 5000.44 kg/ha over 148,866 ha of the total wheat area. The results showed that in the 2020–2021 growing season, all the districts of Uttar Pradesh had similar wheat growth trends. A total of 30 observational data points were used to verify the CASA model-based estimates of wheat yield. Field-based verification shows that the estimated yield correlates well with the observed yield (R2 = 0.554, RMSE = 3.36 Q/ha, MAE −0.56 t ha−1, and MRE = −4.61%). Such an accuracy for assessing regional wheat yield can prove to be one of the promising methods for calculating the whole region’s agricultural yield. The study concludes that RF classifier-based yield estimation has shown more accurate results and can meet the requirements of a regional-scale wheat grain yield estimation and, thus, can prove highly beneficial in policy and decision making. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
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