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Remote Sensing in Geomatics and Environmental Sciences
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Remote Sensing in Geomatics and Environmental Sciences

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

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 4024

Special Issue Editors

Prof. Dr. Eva Savina Malinverni

E-Mail Website
Guest Editor
Edile e dell’ Architettura (DICEA), Dipartimento di Ingegneria Civile, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy
Interests: geomatics; GIS; remote sensing; photogrammetry; surveying; mapping; HBIM
Special Issues, Collections and Topics in MDPI journals
Dr. Roberto Pierdicca

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Civile, Edile e dell’ Architettura, Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: geospacial artificial intelligence; photogrammetry; remote sensing; extended reality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing techniques have proven to be very useful in environmental monitoring and environmental management. The field of geomatics offers various tools and methods that are capable of capturing and understanding the environment in land surface at different scales, i.e., geometric shapes, quantitative analysis, the enrichment of semantic knowledge, the application of different technologies, and multi-scale management.

This Special Issue aims to collect research articles that propose innovative solutions on the use of remote sensing for the following (including but not limited to) application domains:

  • GIS data collection and storage;
  • geomatic data processing;
  • analysis and management/monitoring;
  • multi image spherical photogrammetry;
  • GIS and (H)BIM;
  • remote sensing solutions;
  • geospatial applications;
  • precision farming;
  • change detection;
  • land cover/land use;
  • risks and hazards;
  • topographic survey;
  • UAV surveying;
  • laser scanner;
  • SLAM.

Prof. Dr. Eva Savina Malinverni
Dr. Roberto Pierdicca
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.

Keywords

  • geomatics
  • remote sensing
  • mapping
  • environmental
  • risks
  • GIS
  • ML

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Published Papers (3 papers)

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Research

18 pages, 30556 KiB  
Article
Assessment of Panorama Photogrammetry as a Tool for Long-Range Deformation Monitoring
by Peyman Javadi, Luis García-Asenjo, Raquel Luján and José Luis Lerma
Sensors 2024, 24(11), 3298; https://doi.org/10.3390/s24113298 - 22 May 2024
Viewed by 961
Abstract
This study investigates panorama photogrammetry (PPh) as a potential method to collect massive 3D information for long-range deformation monitoring. Particularly, this study focuses on areas with measuring restrictions, i.e., inaccessible objects and distances above 0.6 km. Under these particular conditions, geodetic techniques based [...] Read more.
This study investigates panorama photogrammetry (PPh) as a potential method to collect massive 3D information for long-range deformation monitoring. Particularly, this study focuses on areas with measuring restrictions, i.e., inaccessible objects and distances above 0.6 km. Under these particular conditions, geodetic techniques based on Electromagnetic Distance Meters (EDMs) or Total Stations (TSs) can provide coordinates with a precision better than 1 cm, but only for a limited number of discrete points. For mass capture, Terrestrial Laser Scanning (TLS) is normally the preferred solution, but long-range instruments are expensive, and drawbacks such as weak return signals and non-automatic target recognition appear. As an alternative, PPh is investigated in the well-controlled area of La Muela in Cortes de Pallas, where images are automatically captured from geodetic pillars using a GigaPan device, processed, and then rigorously compared to TLS point clouds. The results obtained after integrating both techniques into a high-accuracy geodetic reference frame show that PPh and TLS provide similar precision to within approximately 4 cm in the range of 0.6–1.0 km. Therefore, considering cost-effectiveness and ease of use, the proposed method can be considered a low-cost alternative to TLS for long-range deformation monitoring. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics and Environmental Sciences)
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18 pages, 8062 KiB  
Article
A High-Precision Remote Sensing Identification Method for Land Desertification Based on ENVINet5
by Jingyi Yang, Qinjun Wang, Dingkun Chang, Wentao Xu and Boqi Yuan
Sensors 2023, 23(22), 9173; https://doi.org/10.3390/s23229173 - 14 Nov 2023
Viewed by 1006
Abstract
Land desertification is one of the serious ecological and environmental problems facing mankind today, which threatens the survival and development of human society. China is one of the countries with the most serious land desertification problems in the world. Therefore, it is of [...] Read more.
Land desertification is one of the serious ecological and environmental problems facing mankind today, which threatens the survival and development of human society. China is one of the countries with the most serious land desertification problems in the world. Therefore, it is of great theoretical value and practical significance to carry out accurate identification and monitoring of land desertification and its influencing factors in ecologically fragile areas of China. This is conducive to curbing land desertification and ensuring regional ecological security. Minqin County, Gansu Province, located in northwestern China, is one of the most serious areas of land desertification, which is also one of the four sandstorm sources in China. Based on ENVINet5, this paper constructs a high-precision land desertification identification method with an accuracy of 93.71%, which analyzes the trend and reasons of land desertification in this area, provides suggestions for disaster prevention in Minqin County. and provides a reference for other similar areas to make corresponding desertification control policies. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics and Environmental Sciences)
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24 pages, 14621 KiB  
Article
Multi-Source Soil Moisture Data Fusion Based on Spherical Cap Harmonic Analysis and Helmert Variance Component Estimation in the Western U.S.
by Hao Chen, Peng Chen, Rong Wang, Liangcai Qiu, Fucai Tang and Mingzhu Xiong
Sensors 2023, 23(19), 8019; https://doi.org/10.3390/s23198019 - 22 Sep 2023
Cited by 1 | Viewed by 1217
Abstract
Soil moisture (SM) is a vital climate variable in the interaction process between the Earth’s atmosphere and land. However, global soil moisture products from various satellite missions and land surface models are affected by inherently discontinuous observations and coarse spatial resolution, which limits [...] Read more.
Soil moisture (SM) is a vital climate variable in the interaction process between the Earth’s atmosphere and land. However, global soil moisture products from various satellite missions and land surface models are affected by inherently discontinuous observations and coarse spatial resolution, which limits their application at fine spatial scales. To address this problem, this paper integrates three diverse types of datasets from in situ, satellites, and models through Spherical cap harmonic analysis (SCHA) and Helmert variance component estimation (HVCE) to produce 1 km of spatio-temporally continuous SM products with high accuracy. First, this paper eliminates the bias between different datasets and in situ sites and resamples the datasets before data fusion. Then, multi-source SM data fusion is performed based on the SCHA and HVCE methods. Finally, this paper evaluates the fused products from three aspects, including the performance of representative sites under different climate types, the overall performance of validation sites, and the comparison with other products. The results show that the fused products have better performance than other SM products. In the representative sites, the minimal correlation coefficient (R) of the fused products is above 0.85, and the largest root mean square error (RMSE) is below 0.040 m3 m−3. For all validation sites, the R and RMSE of the fused products are 0.889 and 0.036 m3 m−3, respectively, while the R for other products is below 0.75 and the RMSE is above 0.06 m3 m−3. In comparison to other SM products, the fused products exhibit superior performance, generally align more closely with in situ measurements, and possess the ability to accurately and finely capture the spatial and temporal variability of surface SM. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics and Environmental Sciences)
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