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Sustainability
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Sustainable Development: Role of Geospatial Modeling, AI and Remote Sensing
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Sustainable Development: Role of Geospatial Modeling, AI and Remote Sensing

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 9868

Special Issue Editors

Dr. Minxuan Lan

E-Mail Website
Guest Editor
Department of Geography and Planning, The University of Toledo, Toledo, OH 43606, USA
Interests: spatial-temporal analysis and its applications to crime, public health, and human sustainability; data mining, natural language processing (NLP), and their applications on social media big data; substance abuse epidemic and drug problem
Dr. Song Shu

E-Mail Website
Guest Editor
Department of Geography and Planning, Appalachian State University, Boone, NC 28608, USA
Interests: remote sensing applications applied to Arctic snow, lake hydrology, water resources, cryospheric processes, and global climate change; using a variety of advanced remote sensing technologies and geospatial analysis methods to quantify and analyze rapid environmental changes in the hydrosphere and cryosphere within the context of global climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Yang

E-Mail Website
Guest Editor
Department of Urban and Regional Planning, San José State University, San José, CA 95192, USA
Interests: cutting-edge technologies in GIS and remote sensing, including machine learning and AI in GIS, UAV remote sensing, coastal ecosystem monitoring, wildfire mapping, urban heat, urban crime, and urban transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2030 Agenda for Sustainable Development of the United Nations proposes 17 goals to improve the lives and prospects of everyone, everywhere, for now and in the future. The aim of this is to ensure a sustainable world and society where we preserve the health and vitality of the natural environment and human society so that future generations can inhabit happily and bountifully. The advancement in geographic information science and technology (GIS&T) will be a vital part of this process. GIS&T can be an important tool in the various stages of sustainable development: scope exploration, project design, data collection, data analysis, results exhibition, strategy proposition, policy implementation, and more. Meanwhile, GIS&T also has a scientific significance as it can discover and create new knowledge to help build a sustainable world. Thus, multiple disciplines related to sustainability are using GIS&T, for instance, geography, geology, community planning, environmental studies, agriculture, economics, engineering, ecology, sociology, emergency management, public safety and health, history, political science, etc.

In this Special Issue, we cordially invite your expertise and innovations through the submission of papers that demonstrate applications of GIS&T, e.g., a geographic information system (GIS), remote sensing, database development and management, crowdsourcing, geo-visualization, spatio-temporal modeling, quantitative/qualitative analysis, programming, machine learning, artificial intelligence (AI), etc., on building a sustainable society and making it a better world. We welcome the submission of original research articles and reviews that address the following issues using GIS&T:

  • Environmental sustainability: climate change, clean water, clean energy, habitats, deforestation, desertification, pollution, soil erosion, land sliding, coastal monitoring and management, etc.;
  • Human sustainability: crime, safety, public health, traffic congestion, housing, overcrowding, urban heat island, knowledge and education, etc.;
  • Social and economic sustainability: poverty, unemployment, well-being, equality, accessibility, etc.

Dr. Minxuan Lan
Dr. Song Shu
Dr. Bo Yang
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
  • remote sensing (RS)
  • spatial analysis
  • artificial intelligence (AI)
  • environment
  • human society
  • sustainable development
  • climate change
  • public safety and health

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Published Papers (4 papers)

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Research

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23 pages, 17413 KiB  
Article
Unveiling the Patterns and Drivers of Ecological Efficiency in Chinese Cities: A Comprehensive Study Using Super-Efficiency Slacks-Based Measure and Geographically Weighted Regression Approaches
by Jiantao Peng, Yihua Liu, Chong Xu and Debao Chen
Sustainability 2024, 16(8), 3112; https://doi.org/10.3390/su16083112 - 9 Apr 2024
Viewed by 1007
Abstract
Urban ecological efficiency stands as a pivotal indicator that mirrors the level of sustainable development within cities. To unravel the sustainable development status of Chinese cities and illuminate the factors impacting the diverse developments among them, this study leveraged the super-efficiency SBM (slacks-based [...] Read more.
Urban ecological efficiency stands as a pivotal indicator that mirrors the level of sustainable development within cities. To unravel the sustainable development status of Chinese cities and illuminate the factors impacting the diverse developments among them, this study leveraged the super-efficiency SBM (slacks-based measure) model to assess the ecological efficiency of 284 prefectural-level and above cities across China in 2019, divulging their spatial distribution. Furthermore, a GWR (geographically weighted regression) model was also employed to scrutinize the factors influencing the ecological efficiency of these cities. Key findings include: (1) The mean ecological efficiency of Chinese cities in 2019 stood at 0.555, signaling moderate urban sustainability, with southern cities outperforming their northern counterparts. (2) A pronounced spatial clustering of ecological efficiency was evident, featuring positive spillover effects around high-efficiency cities and conversely, negative spillover effects around low-efficiency cities. (3) Economic development and population density positively influenced urban ecological efficiency, while urbanization levels exhibited a negative impact. The influences of industrial structure, technological level, and opening-up level varied, showcasing both positive and negative impacts contingent upon the spatial disposition of the cities. Hence, policymakers are advised to recognize the spatial nuances in the impacts of distinct factors on urban ecological efficiency and tailor measures accordingly to fortify urban sustainability. Full article
(This article belongs to the Special Issue Sustainable Development: Role of Geospatial Modeling, AI and Remote Sensing)
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17 pages, 2261 KiB  
Article
A Broadscale Assessment of Sentinel-2 Imagery and the Google Earth Engine for the Nationwide Mapping of Chlorophyll a
by Richard A. Johansen, Molly K. Reif, Christina L. Saltus and Kaytee L. Pokrzywinski
Sustainability 2024, 16(5), 2090; https://doi.org/10.3390/su16052090 - 2 Mar 2024
Viewed by 1464
Abstract
Harmful algal blooms are a global phenomenon that degrade water quality and can result in adverse health impacts to both humans and wildlife. Monitoring algal blooms at scale is extremely difficult due to the lack of coincident data across space and time. Additionally, [...] Read more.
Harmful algal blooms are a global phenomenon that degrade water quality and can result in adverse health impacts to both humans and wildlife. Monitoring algal blooms at scale is extremely difficult due to the lack of coincident data across space and time. Additionally, traditional field collection methods tend to be labor- and cost-prohibitive, resulting in disparate data collection not capable of capturing the physical and biological variations within waterbodies or regions. This research attempts to help alleviate this issue by leveraging large, public, water quality databases coupled with open-access Google Earth Engine-derived Sentinel-2 imagery to evaluate the practical usability of four common chlorophyll a algorithms as a proxy for detecting and mapping algal blooms nationwide. Chlorophyll a data were aggregated from spatially diverse sites across the continental United States between 2019 and 2022. Data were aggregated via a field method and matched to coincident Sentinel-2 imagery using k-folds cross-validation to evaluate the performance of the band ratio algorithms at the nationwide scale. Additionally, the dataset was portioned to evaluate the influence of temporal windows and annual consistency on algorithm performance. The 2BDA and the NDCI algorithms were the most viable for broadscale mapping of chlorophyll a, which performed moderately well (R2 > 0.5) across the entire continental united states, encompassing highly diverse spatial, temporal, and physical conditions. Algorithms’ performances were consistent across different field methods, temporal windows, and annually. The most compatible field data acquisition method was the chlorophyll a, water, trichromatic method, uncorrected with R2 values of 0.63, 0.62, and 0.41 and RMSE values of 15.89, 16.2, and 23.30 for 2BDA, NDCI, and MCI, respectively. These results indicate the feasibility of utilizing band ratio algorithms for broadscale detection and mapping of chlorophyll a as a proxy for HABs, which is especially valuable when coincident data are unavailable or limited. Full article
(This article belongs to the Special Issue Sustainable Development: Role of Geospatial Modeling, AI and Remote Sensing)
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14 pages, 6761 KiB  
Article
Remote Sensing-Based Revegetation Assessment at Post-Closure Mine Sites in Canada
by Sam Gordon, Xiaoyong Xu and Yanyu Wang
Sustainability 2023, 15(14), 11287; https://doi.org/10.3390/su151411287 - 20 Jul 2023
Viewed by 1419
Abstract
The environmental legacy of post-closure mine sites poses a significant risk to the sustainability of mining operations and natural resource development. This study aims to advance the understanding of sustainable mine site reclamation behavior in Canada by using multi-temporal Landsat satellite images to [...] Read more.
The environmental legacy of post-closure mine sites poses a significant risk to the sustainability of mining operations and natural resource development. This study aims to advance the understanding of sustainable mine site reclamation behavior in Canada by using multi-temporal Landsat satellite images to examine the long-term land cover changes at post-closure mine sites. Six representative post-closure mine sites were selected for the evaluation and comparison. The Normalized Difference Vegetation Index (NDVI) analysis, Landsat image classification, post-classification change detection, and Regrowth Index (RI) analysis were conducted to assess the speed and extent of landscape and vegetation recovery at the target mine sites. A significant vegetation recovery was quantified for the mine sites that have experienced active reclamation activities. In contrast, the post-closure mine area undergoing only passive revegetation typically demonstrated a slow and minor increase in vegetation over time. The actively revegetated mine sites can typically be restored to a vegetation cover level that equals or is better than the pre-mining situation. This work confirms that active reclamation and revegetation at post-closure mine sites is critically important in sustainable mining. The quantified mine site reclamation behavior and the relevant sustainable practices would be useful for evidence-based sustainable resource management in Canada. Full article
(This article belongs to the Special Issue Sustainable Development: Role of Geospatial Modeling, AI and Remote Sensing)
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Review

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23 pages, 641 KiB  
Review
Artificial Intelligence Techniques in Crop Yield Estimation Based on Sentinel-2 Data: A Comprehensive Survey
by Muhammet Fatih Aslan, Kadir Sabanci and Busra Aslan
Sustainability 2024, 16(18), 8277; https://doi.org/10.3390/su16188277 - 23 Sep 2024
Viewed by 5036
Abstract
This review explores the integration of Artificial Intelligence (AI) with Sentinel-2 satellite data in the context of precision agriculture, specifically for crop yield estimation. The rapid advancements in remote sensing technology, particularly through Sentinel-2’s high-resolution multispectral imagery, have transformed agricultural monitoring by providing [...] Read more.
This review explores the integration of Artificial Intelligence (AI) with Sentinel-2 satellite data in the context of precision agriculture, specifically for crop yield estimation. The rapid advancements in remote sensing technology, particularly through Sentinel-2’s high-resolution multispectral imagery, have transformed agricultural monitoring by providing critical data on plant health, soil moisture, and growth patterns. By leveraging Vegetation Indices (VIs) derived from these images, AI algorithms, including Machine Learning (ML) and Deep Learning (DL) models, can now predict crop yields with high accuracy. This paper reviews studies from the past five years that utilize Sentinel-2 and AI techniques to estimate yields for crops like wheat, maize, rice, and others. Various AI approaches are discussed, including Random Forests, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and ensemble methods, all contributing to refined yield forecasts. The review identifies a notable gap in the standardization of methodologies, with researchers using different VIs and AI techniques for similar crops, leading to varied results. As such, this study emphasizes the need for comprehensive comparisons and more consistent methodologies in future research. The work underscores the significant role of Sentinel-2 and AI in advancing precision agriculture, offering valuable insights for future studies that aim to enhance sustainability and efficiency in crop management through advanced predictive models. Full article
(This article belongs to the Special Issue Sustainable Development: Role of Geospatial Modeling, AI and Remote Sensing)
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