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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 6215

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

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Keywords

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

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Published Papers (5 papers)

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Research

13 pages, 3905 KiB  
Article
Spatial Data Infrastructure and Mobile Big Data for Urban Planning Based on the Example of Mikolajki Town in Poland
by Agnieszka Zwirowicz-Rutkowska and Anna Michalik
Appl. Sci. 2024, 14(19), 9117; https://doi.org/10.3390/app14199117 - 9 Oct 2024
Viewed by 644
Abstract
Spatial Data Infrastructure (SDI) is a decision-making tool that is often used in the area of urban planning. At the same time, many other data sources with great utility potential, such as Big Data, can be identified. The aim of the paper is [...] Read more.
Spatial Data Infrastructure (SDI) is a decision-making tool that is often used in the area of urban planning. At the same time, many other data sources with great utility potential, such as Big Data, can be identified. The aim of the paper is to present the possibility of using mobile Big Data collections with data from Polish SDI, for the purposes of local spatial planning on the example of the tourist town, Mikolajki in Poland. The publication also focuses on assessing the quality of data, as well as the decision-making process supported by these sources. The draft of the local spatial development plan was verified based on integrated data sources. The results showed that the visualization of Big Data as a heat map may be used in urban tasks and as the thematic layer integrated with vector and raster data sets from the SDI in the geographic information system software. The contribution is the practical example how information about users of mobile devices and some information from behavioral profiles may be analyzed for the purposes of verifying planned land use. Full article
(This article belongs to the Special Issue Geospatial Technology: Modern Applications and Their Impact)
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15 pages, 5443 KiB  
Article
Identification of Pasture Degradation Using Remote Sensing Data and Machine Learning: A Case Study of Obichnik
by Boris Evstatiev, Irena Valova, Tsvetelina Kaneva, Nikolay Valov, Atanas Sevov, Georgi Stanchev, Georgi Komitov, Tsenka Zhelyazkova, Mariya Gerdzhikova, Mima Todorova, Neli Grozeva, Durhan Saliev and Iliyan Damyanov
Appl. Sci. 2024, 14(17), 7599; https://doi.org/10.3390/app14177599 - 28 Aug 2024
Viewed by 716
Abstract
The degradation of pastures and meadows is a global problem with a wide range of impacts. It affects farmers in different ways, such as decreases in cattle production, milk yield, and forage quality. Still, it also has other side effects, such as loss [...] Read more.
The degradation of pastures and meadows is a global problem with a wide range of impacts. It affects farmers in different ways, such as decreases in cattle production, milk yield, and forage quality. Still, it also has other side effects, such as loss of biodiversity, loss of resources, etc. In this study, the degradation of a semi-natural pasture near the village of Obichnik, Bulgaria, was evaluated using machine learning algorithms, and an unmanned aerial vehicle (UAV) obtained visual spectrum images. A high-quality (HQ) orthomosaic of the area was created and numerous regions of interest were manually marked for training and validation purposes. Three machine learning algorithms were used—Maximum likelihood, Random trees (RT), and Support Vector Machine (SVM). Furthermore, object-based and pixel-based approaches were utilized. The obtained results indicate that the object-based RT and SVM models provide significantly better accuracy, with their Cohen’s Kappa reaching 0.86 and 0.82, respectively. The performed classification showed that approximately 61% of the investigated pasture area is covered with grass, which indicates light-to-medium degradation. Full article
(This article belongs to the Special Issue Geospatial Technology: Modern Applications and Their Impact)
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16 pages, 3608 KiB  
Article
A Spatial Analysis of Urban Tree Canopy Using High-Resolution Land Cover Data for Chattanooga, Tennessee
by Charles Mix, Nyssa Hunt, William Stuart, A.K.M. Azad Hossain and Bradley Wade Bishop
Appl. Sci. 2024, 14(11), 4861; https://doi.org/10.3390/app14114861 - 4 Jun 2024
Viewed by 1686
Abstract
Urban tree canopy (UTC) provides urban residents with numerous benefits, including positive mental and physical health, the mitigation and prevention of urban heat islands, and a sense of place. Numerous studies have shown that as the wealth of a community decreases, so does [...] Read more.
Urban tree canopy (UTC) provides urban residents with numerous benefits, including positive mental and physical health, the mitigation and prevention of urban heat islands, and a sense of place. Numerous studies have shown that as the wealth of a community decreases, so does the amount of UTC found in the community; thus, wealthier communities are more likely to enjoy the benefits that urban forests provide. Emerging technologies in remote sensing and GIS are allowing for new opportunities to study and understand the relationships between urban neighborhoods and UTC. In this study, land cover data for Chattanooga, Tennessee were derived from high-resolution (50 cm) multispectral imagery to assess the previously unknown extent and distribution of UTC and to measure the extent of UTC by neighborhood and census block group level. Using exploratory regression analysis, variables representing income, population density, race, educational attainment, and urban heat islands were analyzed to investigate the influence of UTC on neighborhood characteristics. This study found that UTC represented half of the total land cover composition, the tree equity was not as profound as shown in other cities, and the lack of UTC likely influences the prevalence of urban heat islands. This study also shows the importance and utility of using high-resolution imagery and land cover to assess and understand the impact and distribution of UTC in urban environments. Full article
(This article belongs to the Special Issue Geospatial Technology: Modern Applications and Their Impact)
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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
Cited by 4 | Viewed by 995
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
Cited by 1 | Viewed by 1117
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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