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Sustainability in Geospatial Analysis and Geographic Information Science Application

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 6764

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Special Issue Editors

Dr. Hui Wang

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Guest Editor

Department of Geosciences, Mississippi State University, Mississippi State, MS 39762, USA
Interests: GIS; geospatial analysis; climate change; hydrology; land use and land cover; health geography; machine learning

Dr. Xiang Que

E-Mail Website
Guest Editor

Computer and Information College, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
Interests: land use/cover change; geoinformatics; machine learning

Special Issue Information

Dear Colleagues,

Geographic Information Science (GIS) has been rapidly and efficiently applied to human activities and environmental protection in our modern society. Over the past few years, sustainability has been a key goal. It allows for the maximization of development, while causing the least harm to the next generation. One of the benefits of implementing GIS is that it can bring disparate and complex data sets together, thus making visualization more straightforward. Yet, certain relevant limitations still need to be further studied, such as inconsistent data and a lack of standardization, in applications of sustainability.

The major themes of this Special Issue mainly focus on interdisciplinary collaborations between GIS applications and geospatial analysis in the realm of human–environment activities. Any original research articles as well as in-depth reviews are especially welcome. Research themes may include (but are not limited to) the following:

(1) Watershed environmental science;

(2) Land use/cover change;

(3) Climate change issues;

(4) GIS modeling of physical environment;

(5) Remote sensing applications;

(6) Geospatial analysis;

(7) Machine (deep) learning in GIS applications;

(8) Health–environmental problems.

We look forward to receiving your contributions.

Dr. Hui Wang
Dr. Xiang Que
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. Sustainability 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

GIS
geospatial analysis
climate change
hydrology
sustainable land use and land cover
health geography and sustainability
machine learning

Published Papers (4 papers)

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Research

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18 pages, 3292 KiB

Open AccessArticle

Spatiotemporal Heterogeneous Responses of Ecosystem Services to Landscape Patterns in Urban–Suburban Areas

by Xinyan Zou, Chen Wang, Xiang Que, Xiaogang Ma, Zhe Wang, Quanli Fu, Yuting Lai and Xinhan Zhuang

Sustainability 2024, 16(8), 3260; https://doi.org/10.3390/su16083260 - 13 Apr 2024

Viewed by 617

Abstract

With the acceleration of urbanization, the ecosystem around cities is facing severe challenges. The drastic changes in the landscape pattern, especially in urban–suburban areas, are usually regarded as one of the main drivers. However, the spatiotemporal heterogeneous impacts of landscape patterns on the ecosystem services in this region remain unclear. To address this issue, we propose a novel framework integrating the InVEST-based ecosystem service assessment and spatiotemporal weighted regression (STWR)-based analysis of the spatiotemporal heterogeneity in urban–suburban areas, and apply it to the empirical study of Fuzhou City from 2000 to 2020. It first utilized the InVEST model to build a comprehensive ecosystem service index (CES) from five aspects (i.e., habitat quality, carbon storage, water yield, soil retention, and water purification capacity). Then, four landscape pattern indices (LPIs) (i.e., patch density (PD), area-weighted mean fractal dimension (FRAC_AM), splitting (SPLIT), and Shannon’s diversity (SHDI) index) were selected to build the STWR model. We compared and analyzed the differences in the spatial coefficient surfaces and significance tests generated by the STWR model in urban, urban–suburban, and rural areas. Results show that the following: (1) The CES in Fuzhou shows an upward trend from the urban area to the urban–suburban and rural areas, with significant gradient differences. (2) Compared with other areas, the LPIs in urban–suburban areas show more fragmentation, discreteness, and diversity, indicating more socioeconomic activities. (3) Although LPIs’ impacts on CES change over time (increasing from 2005 to 2010 and 2020 but decreasing in 2015), their effects are relatively low in urban–suburban areas, significantly lower than in urban areas. (4) Interestingly, the LPI coefficients near the urban–suburban boundary seem more significant. (5) This framework can effectively reveal the spatiotemporal heterogeneous relationships between various LPIs and CES, thus guiding concrete policies and measures that support decision-making for improving the ecosystem services surrounding cities through shaping landscape patterns. Full article

(This article belongs to the Special Issue Sustainability in Geospatial Analysis and Geographic Information Science Application)

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25 pages, 21953 KiB

Open AccessArticle

Geospatial Artificial Intelligence (GeoAI) and Satellite Imagery Fusion for Soil Physical Property Predicting

by Fatemeh Sadat Hosseini, Myoung Bae Seo, Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Mohammad Jamshidi and Soo-Mi Choi

Sustainability 2023, 15(19), 14125; https://doi.org/10.3390/su151914125 - 24 Sep 2023

Viewed by 2888

Abstract

This study aims to predict vital soil physical properties, including clay, sand, and silt, which are essential for agricultural management and environmental protection. Precision distribution of soil texture is crucial for effective land resource management and precision agriculture. To achieve this, we propose an innovative approach that combines Geospatial Artificial Intelligence (GeoAI) with the fusion of satellite imagery to predict soil physical properties. We collected 317 soil samples from Iran’s Golestan province for dependent data. The independent dataset encompasses 14 parameters from Landsat-8 satellite images, seven topographic parameters from the Shuttle Radar Topography Mission (SRTM) DEM, and two meteorological parameters. Using the Random Forest (RF) algorithm, we conducted feature importance analysis. We employed a Convolutional Neural Network (CNN), RF, and our hybrid CNN-RF model to predict soil properties, comparing their performance with various metrics. This hybrid CNN-RF network combines the strengths of CNN networks and the RF algorithm for improved soil texture prediction. The hybrid CNN-RF model demonstrated superior performance across metrics, excelling in predicting sand (MSE: 0.00003%, RMSE: 0.006%), silt (MSE: 0.00004%, RMSE: 0.006%), and clay (MSE: 0.00005%, RMSE: 0.007%). Moreover, the hybrid model exhibited improved precision in predicting clay (R²: 0.995), sand (R²: 0.992), and silt (R²: 0.987), as indicated by the R² index. The RF algorithm identified MRVBF, LST, and B7 as the most influential parameters for clay, sand, and silt prediction, respectively, underscoring the significance of remote sensing, topography, and climate. Our integrated GeoAI-satellite imagery approach provides valuable tools for monitoring soil degradation, optimizing agricultural irrigation, and assessing soil quality. This methodology has significant potential to advance precision agriculture and land resource management practices. Full article

(This article belongs to the Special Issue Sustainability in Geospatial Analysis and Geographic Information Science Application)

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20 pages, 14580 KiB

Open AccessArticle

Spatio-Temporal Modeling of COVID-19 Spread in Relation to Urban Land Uses: An Agent-Based Approach

by Mohammad Tabasi, Ali Asghar Alesheikh, Mohsen Kalantari, Abolfazl Mollalo and Javad Hatamiafkoueieh

Sustainability 2023, 15(18), 13827; https://doi.org/10.3390/su151813827 - 16 Sep 2023

Cited by 1 | Viewed by 1059

Abstract

This study aims to address the existing gaps in evidence regarding spatio-temporal modeling of COVID-19 spread, specifically focusing on the impact of different urban land uses in a geospatial information system framework. It employs an agent-based model at the individual level in Gorgan, northeast Iran, characterized by diverse spatial and demographic features. The interactions between human agents and their environment were considered by incorporating social activities based on different urban land uses. The proposed model was integrated with the susceptible–asymptomatic–symptomatic–on treatment–aggravated infection–recovered–dead epidemic model to better understand the disease transmission at the micro-level. The effect of various intervention scenarios, such as social distancing, complete and partial lockdowns, restriction of social gatherings, and vaccination was investigated. The model was evaluated in three modes of cases, deaths, and the spatial distribution of COVID-19. The results show that the disease was more concentrated in central areas with a high population density and dense urban land use. The proposed model predicted the distribution of disease cases and mortality for different age groups, achieving 72% and 71% accuracy, respectively. Additionally, the model was able to predict the spatial distribution of disease cases at the neighborhood level with 86% accuracy. Moreover, findings demonstrated that early implementation of control scenarios, such as social distancing and vaccination, can effectively reduce the transmission of COVID-19 spread and control the epidemic. In conclusion, the proposed model can serve as a valuable tool for health policymakers and urban planners. This spatio-temporal model not only advances our understanding of COVID-19 dynamics but also provides practical tools for addressing future pandemics and urban health challenges. Full article

(This article belongs to the Special Issue Sustainability in Geospatial Analysis and Geographic Information Science Application)

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Review

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32 pages, 12746 KiB

Open AccessReview

Radar Interferometry for Urban Infrastructure Stability Monitoring: From Techniques to Applications

by Songbo Wu, Bochen Zhang, Xiaoli Ding, Lei Zhang, Zhijie Zhang and Zeyu Zhang

Sustainability 2023, 15(19), 14654; https://doi.org/10.3390/su151914654 - 9 Oct 2023

Viewed by 1313

Abstract

Urban infrastructure is an important part of supporting the daily operation of a city. The stability of infrastructure is subject to various deformations related to disasters, engineering activities, and loadings. Regular monitoring of such deformations is critical to identify potential risks to infrastructure and take timely remedial actions. Among the advanced geodetic technologies available, radar interferometry has been widely used for infrastructure stability monitoring due to its extensive coverage, high spatial resolution, and accurate deformation measurements. Specifically, spaceborne InSAR and ground-based radar interferometry have become increasingly utilized in this field. This paper presents a comprehensive review of both technologies for monitoring urban infrastructures. The review begins by introducing the principles and their technical development. Then, a bibliometric analysis and the major advancements and applications of urban infrastructure monitoring are introduced. Finally, the paper identifies several challenges associated with those two radar interferometry technologies for monitoring urban infrastructure. These challenges include the inconsistent in the distribution of selected measurements from different methods, obstacles arising from rapid urbanization and geometric distortion, specialized monitoring techniques for distinct urban features, long-term deformation monitoring, and accurate interpretation of deformation. It is important to carry out further research to tackle these challenges effectively. Full article

(This article belongs to the Special Issue Sustainability in Geospatial Analysis and Geographic Information Science Application)

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