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Applied Sciences
Special Issues
Geospatial Technology: Modern Applications and Their Impact
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Geospatial Technology: Modern Applications and Their Impact

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

Deadline for manuscript submissions: 20 November 2024 | Viewed by 1267

Special Issue Editor

Dr. Laramie Potts

E-Mail Website
Guest Editor
School of Applied Engineering and Technology, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: planetary surfaces; geophysics; geodesy; geographic information science; remote sensing; geoAI; autonomous mobile mapping systems; point cloud processing; ionospheric dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geospatial technology, including sensors, cameras, lasers, telescopes, unmanned aerial vehicles, autonomous mobile mapping robots, geographic information sciences/systems, remote sensing, and global navigation satellite systems, is growing at a rapid pace and is now informing decision makers on topics such as industrial engineering, biodiversity conservation, climate change, ecological and agricultural monitoring, humanitarian relief, and much more. Geospatial technology, which takes the possibilities of humankind to all-new levels of advancement, usually generates large and complex datasets for reality capture that call for all areas of artificial intelligence/augmented intelligence (AI) as well as innovative and straightforward visualization platforms to reveal meaningful information. The scope of geospatial technology applications embraces every sphere or industry where location-based data play a crucial role in answering major social questions related, but not limited to, the environment, climate change, crisis management, sustainable development, civil infrastructure/assets mapping, and structural health monitoring. The goal of the Special Issue “Geospatial Technology: Modern Applications and their Impact” is to provide a special forum for disseminating theories and innovative applications of geospatial data/imagery analysis and visualization in several diverse fields, including geophysics, geography, agriculture, ecology, law enforcement, mapping, engineering, marine science, meteorology, as well as those of an interdisciplinary nature. Lastly, papers describing theoretical results will also expand on their practical utility.

Dr. Laramie Potts
Guest Editor

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

  • reality capture
  • AI
  • machine learning
  • visualization
  • multi-criteria decision analysis
  • geoAI
  • big data

Published Papers (2 papers)

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Research

19 pages, 3993 KiB  
Article
Optimal Timing of Carrot Crop Monitoring and Yield Assessment Using Sentinel-2 Images: A Machine-Learning Approach
by Rangaswamy Madugundu, Khalid A. Al-Gaadi, ElKamil Tola, Mohamed K. Edrris, Haroon F. Edrees and Ahmed A. Alameen
Appl. Sci. 2024, 14(9), 3636; https://doi.org/10.3390/app14093636 - 25 Apr 2024
Viewed by 319
Abstract
Remotely sensed images provide effective sources for monitoring crop growth and the early prediction of crop productivity. To monitor carrot crop growth and yield estimation, three 27 ha center-pivot irrigated fields were studied to develop yield prediction models using crop biophysical parameters and [...] Read more.
Remotely sensed images provide effective sources for monitoring crop growth and the early prediction of crop productivity. To monitor carrot crop growth and yield estimation, three 27 ha center-pivot irrigated fields were studied to develop yield prediction models using crop biophysical parameters and vegetation indices (VIs) extracted from Sentinel-2A (S2) multi-temporal satellite data. A machine learning (ML)-based image classification technique, the random forest (RF) algorithm, was used for carrot crop monitoring and yield analysis. The VIs (NDVI, RDVI, GNDVI, SIPI, and GLI), extracted from S2 satellite data for the crop ages of 30, 45, 60, 75, 90, 105, and 120 days after plantation (DAP), and the chlorophyll content, SPAD (Soil Plant Analysis Development) meter readings, were incorporated as predictors for the RF algorithm. The RMSE of the five RF scenarios studied ranged from 7.8 t ha−1 (R2 ≥ 0.82 with Scenario 5) to 26.2 t ha−1 (R2 ≤ 0.46 with Scenario 1). The optimal window for monitoring the carrot crop for yield prediction with the use of S2 images could be achieved between the 60 DAP and 75 DAP with an RMSE of 8.6 t ha−1 (i.e., 12.4%) and 11.4 t ha−1 (16.2%), respectively. The developed RF algorithm can be utilized in carrot crop yield monitoring and decision-making processes for the self-sustainability of carrot production. Full article
(This article belongs to the Special Issue Geospatial Technology: Modern Applications and Their Impact)
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20 pages, 6824 KiB  
Article
Hierarchical Fuzzy MCDA Multi-Risk Model for Detecting Critical Urban Areas in Climate Scenarios
by Barbara Cardone, Valeria D’Ambrosio, Ferdinando Di Martino and Vittorio Miraglia
Appl. Sci. 2024, 14(7), 3066; https://doi.org/10.3390/app14073066 - 5 Apr 2024
Viewed by 525
Abstract
One of the issues of greatest interest in urban planning today concerns the evaluation of the most vulnerable urban areas in the presence of different types of climate hazards. In this research, a hierarchical fuzzy MCDA model is implemented on a GIS-based platform [...] Read more.
One of the issues of greatest interest in urban planning today concerns the evaluation of the most vulnerable urban areas in the presence of different types of climate hazards. In this research, a hierarchical fuzzy MCDA model is implemented on a GIS-based platform aimed at detecting the urban areas most at risk in the presence of heatwave and pluvial flooding scenarios. The proposed model aims to detect the urban areas most vulnerable to both the two climatic phenomena and the two types of hazards as independent events; it partitions the physical component of an urban settlement into two subsystems: buildings and open spaces, and it determines the criticality of a subzone of the urban area of study by evaluating the vulnerabilities of the two subsystems to the two phenomena. The use of a hierarchical fuzzy MCDA model facilitates the modeling of the two subsystems and the assessment of their vulnerability to the two phenomena, and it provides a computationally fast tool for detecting critical urban areas. The model was tested on a study area made up of the districts of the central-eastern area of the city of Naples (Italy); it was divided into subzones made up of individual census areas. The most critical areas are represented by the subzones with criticality values higher than a specific threshold. Full article
(This article belongs to the Special Issue Geospatial Technology: Modern Applications and Their Impact)
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