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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: closed (31 August 2023) | Viewed by 15582

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Special Issue Editors

Dr. Shi Shen

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Guest Editor

Faculty of Geographical Science, Beijing Normal University, Beijing 10010, China
Interests: GIS; sustainability; LUCC; land use simulation; GeoAI
Special Issues, Collections and Topics in MDPI journals

Dr. Peichao Gao

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Guest Editor

Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: sustainable land use; geographic trade-offs; geographic complexity; spatial entropy as applied in landscapes and image analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Shaohua Wang

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Guest Editor

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: spatiotemporal big data analysis; GeoAI; remote sensing AI; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The COVID-19 pandemic has threatened the achievement of the United Nations’ 2030 Agenda for the Sustainable Development Goals (SDGs). Meanwhile, the Glasgow climate summit called for more intensive actions in fighting climate change. Geographical information science (GIScience) bridges multidisciplinary and interdisciplinary data, methods, and knowledge from geography, remote sensing, sustainability science, landscape ecology, environmental science, and planning. The broad ground ensures that GIScience can and will play an essential role in addressing societal and environmental issues for achieving regional sustainable development.

This Special Issue calls for and encourages more intellectual efforts and contributions to GIScience theories, tools, and practices regarding sustainable development. We invite all researchers to share research articles, reviews, and case studies tackling challenges at local, regional and global scale with GIScience.

The scope of the Special Issue includes but is not limited to the following:

GIScience theories or frameworks for sustainable development;
Novel geographical information system (GIS) tools, models or methods for sustainable development;
Big data analytics for the SDGs;
Applications of GIScience or GIS for sustainable development;
GeoAI models and their applications

Dr. Shi Shen
Dr. Peichao Gao
Dr. Shaohua Wang
Guest Editors

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Keywords

geographical information science
sustainability
Sustainable Development Goals
GeoAI
high-quality development
urban informatics

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Published Papers (8 papers)

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Research

19 pages, 6986 KiB

Open AccessArticle

Flood Risk Assessment Based on a Cloud Model in Sichuan Province, China

by Jian Liu, Kangjie Wang, Shan Lv, Xiangtao Fan and Haixia He

Sustainability 2023, 15(20), 14714; https://doi.org/10.3390/su152014714 - 10 Oct 2023

Cited by 1 | Viewed by 1852

Abstract

Floods are serious threats to the safety of people’s lives and property. This paper systematically introduces the basic theories and methods of flood risk assessment, takes Sichuan Province as the study area, and establishes a flood risk assessment index system with 14 indicators in four aspects—disaster-causing factors, disaster-forming environment, disaster-bearing body, and regional disaster resilience capacity—from the causes of disaster losses and flood formation mechanisms. Furthermore, this paper constructs a flood disaster risk assessment model for Sichuan Province based on a cloud model, entropy value, and GIS technology. The model is validated by taking the July–August 2023 flood disaster as an example, and the results show that the distribution of the disaster was consistent with the flood risk assessment results of this paper, which verifies that the selected indicators are appropriate and the model is accurate and valid. Full article

(This article belongs to the Special Issue Geographic Information Science for the Sustainable Development)

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21 pages, 6865 KiB

Open AccessArticle

Analysis of Spatial Correlation and Influencing Factors of Building a Carbon Emission Reduction Potential Network Based on the Coordination of Equity and Efficiency

by Sensen Zhang and Zhenggang Huo

Sustainability 2023, 15(15), 11616; https://doi.org/10.3390/su151511616 - 27 Jul 2023

Cited by 2 | Viewed by 1204

Abstract

Collaborative promotion of carbon emission reduction has become one of the most significant strategies for China to realize the dual-carbon goal. The purpose of this study is to utilize “relational data” to investigate overall and regional building carbon emission reduction networks based on the coordination of equity and efficiency. Specifically, the difference in importance between equity and efficiency principles is measured by an improved Markov chain. The spatial correlation network is constructed under the principle of coordinating equity and efficiency, and the network is analyzed using the modified gravity model and social network analysis. The results indicate that (1) the long-term “low-efficiency” problem of building carbon emissions is more serious than the long-term “low-equity” problem, and (2) the efficiency principle should be given greater weight in calculating carbon emission reduction potential. (3) The strength of network spatial association is increasing, and the spillover effect is significant, but the network form remains unstable. (4) The network is significantly impacted by geographic proximity, environmental regulations, energy consumption intensity, and the development level of the construction industry. The main achievement will assist developing countries in promoting sustainable development and collaborative carbon emission reduction in the construction sector. Full article

(This article belongs to the Special Issue Geographic Information Science for the Sustainable Development)

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22 pages, 8334 KiB

Open AccessArticle

Using the DTFM Method to Analyse the Degradation Process of Bilateral Trade Relations between China and Australia

by Xiaoyang Han, Sijing Ye, Shuyi Ren and Changqing Song

Sustainability 2023, 15(9), 7297; https://doi.org/10.3390/su15097297 - 27 Apr 2023

Viewed by 1460

Abstract

Quantitative assessment and visual analysis of the multidimensional features of international bilateral product trade are crucial for global trade research. However, current methods face poor salience and expression issues when analysing the characteristics of China—Australia bilateral trade from 1998 to 2019. To address this, we propose a new perspective that involves period division, feature extraction, construction of product space, and spatiotemporal analysis by selecting the display competitive advantage index using the digital trade feature map (DTFM) method. Our results reveal that the distribution of product importance in China—Australia bilateral trade is heavy-tailed, and that the number of essential products has decreased by 68% over time. The proportion of products in which China dominates increased from 71% to 77%. Furthermore, Australia consistently maintains dominance in the most crucial development in trade, and the supremacy of the head product is becoming stronger. Based on these findings, the stability of bilateral trade between Australia and China is declining, and the pattern of polarisation in the importance of traded products is worsening. This paper proposes a novel method for studying Sino—Australian trade support. The analytical approach presented can be extended to analyse the features of bilateral trade between other countries. Full article

(This article belongs to the Special Issue Geographic Information Science for the Sustainable Development)

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19 pages, 3355 KiB

Open AccessArticle

A Study on the Deployment of Mesoscale Chemical Hazard Area Monitoring Points by Combining Weighting and Fireworks Algorithms

by Yimeng Shi, Hongyuan Zhang, Zheng Chen, Yueyue Sun, Xuecheng Liu and Jin Gu

Sustainability 2023, 15(7), 5779; https://doi.org/10.3390/su15075779 - 27 Mar 2023

Viewed by 1319

Abstract

In order to address the problems of redundancy and waste of resources in the deployment of monitoring points in mesoscale chemical hazard areas, we propose a method for the deployment of monitoring points in mesoscale chemical hazard areas by combining weight and fireworks algorithms. Taking the mesoscale chemical hazard monitoring area as the research background, we take the probabilistic sensing model of telemetry sensor nodes as the research object, make a reasonable grid division of the mesoscale monitoring area, calculate the importance of each grid and perform clustering, utilize the diversity of the fireworks algorithm and the rapidity of the solution to solve the monitoring point deployment model and discuss the relevant factors affecting the deployment scheme. The simulation results show that the proposed algorithm can achieve the optimal coverage monitoring for monitoring areas with different importance and reduce the number of monitoring nodes and redundancy; meanwhile, the relevant factors such as the grid edge length, the number of clusters, and the average importance of monitoring areas have different degrees of influence on the complexity of the algorithm and the deployment scheme. Full article

(This article belongs to the Special Issue Geographic Information Science for the Sustainable Development)

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17 pages, 8488 KiB

Open AccessArticle

An Evaluation of Possible Sugarcane Plantations Expansion Areas in Lamongan, East Java, Indonesia

by Salis Deris Artikanur, Widiatmaka, Yudi Setiawan and Marimin

Sustainability 2023, 15(6), 5390; https://doi.org/10.3390/su15065390 - 17 Mar 2023

Viewed by 2067

Abstract

Sugar is a significant commodity for Indonesia because the need for sugar reaches 7 million tons. Meanwhile, imports from Thailand, Australia, and Brazil were approximately 5.54 million tons in 2020. Sugarcane and sugar production in East Java province is also supported by Lamongan Regency. Therefore, this study aims to evaluate the possible sugarcane plantation expansion areas in Lamongan. The evaluation process carried out in this study was an analysis of land suitability using the analytic network process (ANP) and land availability using an overlay analysis of several policy maps. Three parameters with the highest weight of the ANP were soil drainage (0.181), cation exchange capacity and base saturation (0.134), and rainfall (0.133). The total possible area for sugarcane plantations expansion in Lamongan was 32,552.37 ha and the largest class was Possible Area 2 (65.67%). The three sub-districts with the highest possible areas include Solokuro, Ngimbang, and Mantup. We recommend that the government and stakeholders extend the area allocated to sugarcane plantations in Lamongan because the possible expansion areas are still more than 30 ha, while in the 2011–2031 spatial plan they were only 8927 ha. Expansion plans must take into consideration other uses such as residence, industry, food crops, and protected areas. Full article

(This article belongs to the Special Issue Geographic Information Science for the Sustainable Development)

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22 pages, 10964 KiB

Open AccessArticle

Multi-Objective Optimization of Land Use in the Beijing–Tianjin–Hebei Region of China Based on the GMOP-PLUS Coupling Model

by Fandi Meng, Zhi Zhou and Pengtao Zhang

Sustainability 2023, 15(5), 3977; https://doi.org/10.3390/su15053977 - 22 Feb 2023

Cited by 11 | Viewed by 2198

Abstract

The changeable patterns and contractions of land use have become increasingly significant in recent years as the economy and society have rapidly developed. Subsequently, land use change simulation has become a focal point in the study of land use patterns and change processes. Four development scenarios in 2030, including business-as-usual, ecological protection, economic development, and sustainable development scenarios, are proposed to realize the sustainable development of land use in Beijing–Tianjin–Hebei in the context of a low-carbon economy and ecological security. Then, a feasible multi-objective land use optimization scheme suitable for the region’s long-term development was identified through comparative analysis. The GMOP-PLUS model analyzed changes in ecological and economic benefits and carbon emissions by optimizing the quantitative structure and spatial layout of land use in different scenarios. The cultivated land area in the four scenarios decreased, while the construction land area increased for all scenarios other than the ecological protection and sustainable development scenarios. Moreover, the target development of the sustainable development scenario was the most balanced, with carbon emissions and economic benefits reduced by 49.77 million tons and CNY 0.73 billion compared with the business-as-usual scenario, respectively. Meanwhile, the ecological benefits increased by CNY 0.03 billion, and the economic benefits increased by 1.54 times compared with those in 2020. Therefore, the sustainable development scenario was more in line with the needs of Beijing, Tianjin, and Hebei for high-quality economic and ecological development, aiming towards a low-carbon goal. This work provides a theoretical basis for Beijing–Tianjin–Hebei territorial spatial planning and more perspectives for the study of sustainable land use through the obtained results. Full article

(This article belongs to the Special Issue Geographic Information Science for the Sustainable Development)

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23 pages, 6014 KiB

Open AccessArticle

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

by Lele Sun and Baoxuan Jin

Sustainability 2023, 15(3), 2442; https://doi.org/10.3390/su15032442 - 30 Jan 2023

Cited by 2 | Viewed by 2605

Abstract

Geospatial databases are basic tools to collect, index, and manage georeferenced data indicators in sustainability research for efficient, long-term analysis. NoSQL databases are increasingly applied to manage the ever-growing massive spatial vector data (SVD) with their changeable data schemas, agile scalability, and fast query response time. Spatial queries are basic operations in geospatial databases. According to Green information technology, an efficient spatial index can accelerate query processing and save power consumption for ubiquitous spatial applications. Current solutions tend to pursue it by indexing spatial objects with space-filling curves or geohash on NoSQL databases. As for the performance-wise R-tree family, they are mainly used in slow disk-based spatial access methods on NoSQL databases that incur high loading and searching costs. Therefore, performing spatial queries efficiently with the R-tree family on NoSQL databases remains a challenge. In this paper, an in-memory balanced and distributed R*-tree index named the BDRST index is proposed and implemented on HBase for efficient spatial-query processing of massive SVD. The BDRST index stores and distributes serialized R*-trees to HBase regions in association with SVD partitions in the same table. Moreover, an efficient optimized server-side parallel processing framework is presented for real-time R*-tree instantiation and query processing. Through extensive experiments on real-world land-use data sets, the performance of our method is tested, including index building, index quality, spatial queries, and applications. Our proposed method outperforms other state-of-the-art solutions, saving between 27.36% and 95.94% on average execution time for the above operations. Experimental results show the capability of the BDRST index to support spatial queries over large-scale SVD, and our method provides a solution for efficient sustainability research that involves massive georeferenced data. Full article

(This article belongs to the Special Issue Geographic Information Science for the Sustainable Development)

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13 pages, 3586 KiB

Open AccessArticle

A Study on the Propagation Trend of Underground Coal Fires Based on Night-Time Thermal Infrared Remote Sensing Technology

by Xiaomin Du, Dongqi Sun, Feng Li and Jing Tong

Sustainability 2022, 14(22), 14741; https://doi.org/10.3390/su142214741 - 9 Nov 2022

Cited by 4 | Viewed by 1770

Abstract

Underground coal fires in coal fields endanger the mine surface ecological environment, endanger coal resources, threaten mine safety and workers’ health, and cause geological disasters. The study of methods by which to monitor the laws that determine the way underground coal fires spread is helpful in the safe production of coal and the smooth execution of fire extinguishing projects. Based on night-time ASTER thermal infrared images of 2002, 2003, 2005 and 2007 in Huangbaici and Wuhushan mining areas in the Wuda coalfield, an adaptive-edge-threshold algorithm was used to extract time-series for underground coal fire areas. A method of time-series dynamic analysis for geometric centers of underground coal fire areas was proposed to study the propagation law and development trend of underground coal fires. The results indicate that, due to the effective prevention of the external influences of solar irradiance, topographic relief and land cover, the identification accuracy of coal fires via the use of a night-time ASTER thermal infrared image was higher by 7.70%, 13.19% and 14.51% than that of the daytime Landsat thermal infrared image in terms of producer accuracy, user accuracy and overall accuracy, respectively. The propagation direction of the geometric center of the time-series coal fire areas can be used to represent the propagation direction of underground coal fires. There exists a linear regression relationship between the migration distance of the geometric center of coal fire areas and the variable-area of coal fires in adjacent years, with the correlation coefficient reaching 0.705, which indicates that the migration distance of the geometric center of a coal fire area can be used to represent the intensity variation of underground coal fires. This method can be applied to the analysis of the trends of underground coal fires under both natural conditions and human intervention. The experimental results show that the Wuda underground coal fires spread to the southeast and that the area of the coal fires increased by 0.71 km² during the period of 2002–2003. From 2003 to 2005, Wuda’s underground coal fires spread to the northwest under natural conditions, and the area of coal fires decreased by 0.30 km² due to the closure of some small coal mines. From 2005 to 2007, due to increased mining activities, underground coal fires in Wuda spread to the east, south, west and north, and the area of coal fires increased dramatically by 1.76 km². Full article

(This article belongs to the Special Issue Geographic Information Science for the Sustainable Development)

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