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Application of Geoinformatics Technologies for Environmental Sustainability
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Application of Geoinformatics Technologies for Environmental Sustainability

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 July 2022) | Viewed by 25243

Special Issue Editor

Dr. Christos Polykretis

E-Mail Website
Guest Editor
Lab of Geophysical - Satellite Remote Sensing and Archaeo-environment (GeoSat ReSeArch), Institute for Mediterranean Studies (IMS), Foundation for Research and Technology Hellas (FORTH), 74100 Rethymno, Crete, Greece
Interests: GIS; remote sensing; spatial analysis; (geo)statistical analysis; environmental modeling; natural hazard assessment; landslides; soil erosion; land use/land cover monitoring; social sciences; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The "2030 Agenda for Sustainable Development", adopted by United Nations Member States in 2015, highlights the sustainability as one of the major concerns worldwide. According to this Agenda, several global challenges are required to be addressed in order to achieve a better and more sustainable future for all the humanity. Among the different components of sustainability, the environmental sustainability is considered of great importance. Generally, it reflects the creation and maintenance of the conditions under which humans and nature can exist in productive harmony and permit fulfilling the social, economic and environmental needs of present and future generations. Environmental sustainability refers to a variety of environmental issues related to natural resources (water, air and soil), landscape ecology, climatic–atmospheric changes, natural hazards, and human activities. The acquisition of knowledge on how to identify and manage these issues caused by natural processes and the interaction between environmental and anthropogenic factors is crucial to achieve sustainable development.

The transformation from traditional to sustainable environments involves interdisciplinary approaches in order to combine technologies and the abovementioned knowledge. The "Future We Want" outcome document endorsed by the United Nations Conference on Sustainable Development in 2012 recognized the contribution of Geoinformatics geoinformatics technologies and reliable geospatial information data to sustainable development policy-making, programming and project operations (paragraph 274). The continuous development of these technologies and the increasing availability of relative data over recent decades have allowed for the exploration of the environment and its sustainability in a cost–time effective manner. Geoinformatics technologies, such as Geographic Information Systems (GIS) and Remote Sensing, have the capabilities to process various geospatial data by space-based visualization, spatial analysis and modelling for natural resource and hazard assessment, climate and land cover change monitoring, agricultural development, urban expansion, etc. Their integration can lead to a deeper and more comprehensive understanding of these environemntal issues.

This Special Issue will benefit scientists, decision makers and other stakeholders with an interest in sustainable environment development. In this line, it welcomes original and high-quality research efforts describing cutting-edge application of Geoinformatics technologies for studying environmental sustainability-related issues. The studies submitted in this Special Issue are expected to address the use of Geoinformatics technologies for among others:

  • Natural resource (water, air and soil) assessment and management;
  • Deterioration and pollution of environment;
  • Climate change impacts;
  • Natural hazard (floods, landslides, soil erosion, etc.) monitoring and assessment;
  • Land cover/Landscape monitoring and change detection;
  • Urban and human activity expansion.

Dr. Christos Polykretis
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. 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

  • geoinformatics
  • GIS
  • remote sensing
  • environment
  • sustainability

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Published Papers (6 papers)

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Research

25 pages, 14570 KiB  
Article
Geoinformatic Analysis of Rainfall-Triggered Landslides in Crete (Greece) Based on Spatial Detection and Hazard Mapping
by Athanasios V. Argyriou, Christos Polykretis, Richard M. Teeuw and Nikos Papadopoulos
Sustainability 2022, 14(7), 3956; https://doi.org/10.3390/su14073956 - 27 Mar 2022
Cited by 13 | Viewed by 3501
Abstract
Among several natural and anthropogenic conditioning factors that control slope instability, heavy rainfall is a key factor in terms of triggering landslide events. In the Mediterranean region, Crete suffers the frequent occurrence of heavy rainstorms that act as a triggering mechanism for landslides. [...] Read more.
Among several natural and anthropogenic conditioning factors that control slope instability, heavy rainfall is a key factor in terms of triggering landslide events. In the Mediterranean region, Crete suffers the frequent occurrence of heavy rainstorms that act as a triggering mechanism for landslides. The Mediterranean island of Crete suffers from frequent occurrences of heavy rainstorms, which often trigger landslides. Therefore, the spatial and temporal study of recent storm/landslide events and the projection of potential future events is crucial for long-term sustainable land use in Crete and Mediterranean landscapes with similar geomorphological settings, especially with climate change likely to produce bigger and more frequent storms in this region. Geoinformatic technologies, mainly represented by remote sensing (RS) and Geographic Information Systems (GIS), can be valuable tools towards the analysis of such events. Considering an administrative unit of Crete (municipality of Rethymnon) for investigation, the present study focused on using RS and GIS-based approaches to: (i) detect landslides triggered by heavy rainstorms during February 2019; (ii) determine the interaction between the triggering factor of rainfall and other conditioning factors; and (iii) estimate the spatial component of a hazard map by spatially indicating the possibility for rainfall-triggered landslides when similar rainstorms take place in the future. Both landslide detection and hazard mapping outputs were validated by field surveys and empirical analysis, respectively. Based on the validation results, geoinformatic technologies can provide an ideal methodological framework for the acquisition of landslide-related knowledge, being particularly beneficial to land-use planning and decision making, as well as the organization of emergency actions by local authorities. Full article
(This article belongs to the Special Issue Application of Geoinformatics Technologies for Environmental Sustainability)
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22 pages, 48997 KiB  
Article
Landslide Susceptibility Mapping with Deep Learning Algorithms
by Jules Maurice Habumugisha, Ningsheng Chen, Mahfuzur Rahman, Md Monirul Islam, Hilal Ahmad, Ahmed Elbeltagi, Gitika Sharma, Sharmina Naznin Liza and Ashraf Dewan
Sustainability 2022, 14(3), 1734; https://doi.org/10.3390/su14031734 - 2 Feb 2022
Cited by 65 | Viewed by 6595
Abstract
Among natural hazards, landslides are devastating in China. However, little is known regarding potential landslide-prone areas in Maoxian County. The goal of this study was to apply four deep learning algorithms, the convolutional neural network (CNN), deep neural network (DNN), long short-term memory [...] Read more.
Among natural hazards, landslides are devastating in China. However, little is known regarding potential landslide-prone areas in Maoxian County. The goal of this study was to apply four deep learning algorithms, the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) networks, and recurrent neural network (RNN) in evaluating the possibility of landslides throughout Maoxian County, Sichuan, China. A total of 1290 landslide records was developed using historical records, field observations, and remote sensing techniques. The landslide susceptibility maps showed that most susceptible areas were along the Minjiang River and in some parts of the southeastern portion of the study area. Slope, rainfall, and distance to faults were the most influential factors affecting landslide occurrence. Results revealed that proportion of landslide susceptible areas in Maoxian County was as follows: identified landslides (13.65–23.71%) and non-landslides (76.29–86.35%). The resultant maps were tested against known landslide locations using the area under the curve (AUC). This study indicated that the DNN algorithm performed better than LSTM, CNN, and RNN in identifying landslides in Maoxian County, with AUC values (for prediction accuracy) of 87.30%, 86.50%, 85.60%, and 82.90%, respectively. The results of this study are useful for future landslide risk reduction along with devising sustainable land use planning in the study area. Full article
(This article belongs to the Special Issue Application of Geoinformatics Technologies for Environmental Sustainability)
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23 pages, 6526 KiB  
Article
On the Issues of Spatial Modeling of Non-Standard Profiles by the Example of Electromagnetic Emission Measurement Data
by Emiliia Iakovleva, Margarita Belova, Amilcar Soares and Anton Rassõlkin
Sustainability 2022, 14(1), 574; https://doi.org/10.3390/su14010574 - 5 Jan 2022
Cited by 9 | Viewed by 2476
Abstract
This paper examines the possibility of the spatial modelling of the Earth’s natural pulsed-electromagnetic-field measured values, which form a closed profile without the data inside. This geophysical method allows us to map active tectonic movement which breaches the integrity of pipes. During the [...] Read more.
This paper examines the possibility of the spatial modelling of the Earth’s natural pulsed-electromagnetic-field measured values, which form a closed profile without the data inside. This geophysical method allows us to map active tectonic movement which breaches the integrity of pipes. During the experiment, 4.5 km of profiles were measured in the Admiralteysky district of St. Petersburg, Russia. Regular electromotive force (EMF) values and anomalous EMF values were obtained, ranging from 0 to 900 µV and above 900 µV, respectively. The anomalous values are associated with tectonic faults in the bedrock. The data obtained are characterized by complex spatial anisotropy associated with the development of two groups of tectonic faults of different orientations. The authors have considered the problems of the spatial modeling of the data obtained. The main problems, the solutions to which should allow the obtaining of adequate models, have been identified. Based on the analysis of the measurement results, geological features of the studied areas, as well as variography, the following possible solutions were proposed: changing the measurement technique; dividing the data array according to the main directions of anisotropy; the need to introduce additional correction coefficients. The problem revealed in this article requires further research on the basis of the obtained results, which will reduce the cost and timing of such studies, and, as a result, give an opportunity to take into account active tectonic disturbances during the construction and scheduled maintenance of underground utilities, which is especially important within the framework of the concept of sustainable development. Full article
(This article belongs to the Special Issue Application of Geoinformatics Technologies for Environmental Sustainability)
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14 pages, 2230 KiB  
Article
Classification of Recreation Opportunity Spectrum Using Night Lights for Evidence of Humans and POI Data for Social Setting
by Wenjing Zeng, Yongde Zhong, Dali Li and Jinyang Deng
Sustainability 2021, 13(14), 7782; https://doi.org/10.3390/su13147782 - 12 Jul 2021
Cited by 9 | Viewed by 4148
Abstract
The recreation opportunity spectrum (ROS) has been widely recognized as an effective tool for the inventory and planning of outdoor recreational resources. However, its applications have been primarily focused on forest-dominated settings with few studies being conducted on all land types at a [...] Read more.
The recreation opportunity spectrum (ROS) has been widely recognized as an effective tool for the inventory and planning of outdoor recreational resources. However, its applications have been primarily focused on forest-dominated settings with few studies being conducted on all land types at a regional scale. The creation of a ROS is based on physical, social, and managerial settings, with the physical setting being measured by three criteria: remoteness, size, and evidence of humans. One challenge to extending the ROS to all land types on a large scale is the difficulty of quantifying the evidence of humans and social settings. Thus, this study, for the first time, developed an innovative approach that used night lights as a proxy for evidence of humans and points of interest (POI) for social settings to generate an automatic ROS for Hunan Province using Geographic Information System (GIS) spatial analysis. The whole province was classified as primitive (2.51%), semi-primitive non-motorized (21.33%), semi-primitive motorized (38.60%), semi-developed natural (30.99%), developed natural (5.61%), and highly developed (0.96%), which was further divided into three subclasses: large-natural (0.63%), small natural (0.27%), and facilities (0.06%). In order to implement the management and utilization of natural recreational resources in Hunan Province at the county (city, district) level, the province’s 122 counties (cities, districts) were categorized into five levels based on the ROS factor dominance calculated at the county and provincial levels. These five levels include key natural recreational counties (cities, districts), general natural recreational counties (cities, districts), rural counties (cities, districts), general metropolitan counties (cities, districts), and key metropolitan counties (cities, districts), with the corresponding numbers being 8, 21, 50, 24, and 19, respectively. Full article
(This article belongs to the Special Issue Application of Geoinformatics Technologies for Environmental Sustainability)
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15 pages, 8048 KiB  
Article
Dengue Transmission Mapping with Weather-Based Predictive Model in Three Southernmost Provinces of Thailand
by Teerawad Sriklin, Siriwan Kajornkasirat and Supattra Puttinaovarat
Sustainability 2021, 13(12), 6754; https://doi.org/10.3390/su13126754 - 15 Jun 2021
Cited by 6 | Viewed by 3061
Abstract
This study aimed to show maps and analyses that display dengue cases and weather-related factors on dengue transmission in the three southernmost provinces of Thailand, namely Pattani, Yala, and Narathiwat provinces. Data on the number of dengue cases and weather variables including rainfall, [...] Read more.
This study aimed to show maps and analyses that display dengue cases and weather-related factors on dengue transmission in the three southernmost provinces of Thailand, namely Pattani, Yala, and Narathiwat provinces. Data on the number of dengue cases and weather variables including rainfall, rainy day, mean temperature, min temperature, max temperature, relative humidity, and air pressure for the period from January 2015 to December 2019 were obtained from the Bureau of Epidemiology, Ministry of Public Health and the Meteorological Department of Southern Thailand, respectively. Spearman rank correlation test was performed at lags from zero to two months and the predictive modeling used time series Poisson regression analysis. The distribution of dengue cases showed that in Pattani and Yala provinces the most dengue cases occurred in June. Narathiwat province had the most dengue cases occurring in August. The air pressure, relative humidity, rainfall, rainy day, and min temperature are the main predictors in Pattani province, while air pressure, rainy day, and max/mean temperature seem to play important roles in the number of dengue cases in Yala and Narathiwat provinces. The goodness-of-fit analyses reveal that the model fits the data reasonably well. The results provide scientific information for creating effective dengue control programs in the community, and the predictive model can support decision making in public health organizations and for management of the environmental risk area. Full article
(This article belongs to the Special Issue Application of Geoinformatics Technologies for Environmental Sustainability)
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11 pages, 2853 KiB  
Article
Online Analytics for Shrimp Farm Management to Control Water Quality Parameters and Growth Performance
by Siriwan Kajornkasirat, Jareeporn Ruangsri, Charuwan Sumat and Pete Intaramontri
Sustainability 2021, 13(11), 5839; https://doi.org/10.3390/su13115839 - 22 May 2021
Cited by 4 | Viewed by 3573
Abstract
An online analytic service system was designed as a web and a mobile application for shrimp farmers and shrimp farm managers to manage the growth performance of shrimp. The MySQL database management system was used to manage the shrimp data. The Apache Web [...] Read more.
An online analytic service system was designed as a web and a mobile application for shrimp farmers and shrimp farm managers to manage the growth performance of shrimp. The MySQL database management system was used to manage the shrimp data. The Apache Web Server was used for contacting the shrimp database, and the web content displays were implemented with PHP script, JavaScript, and HTML5. Additionally, the program was linked with Google Charts to display data in various graphs, such as bar graphs and scatter diagrams, and Google Maps API was used to display water quality factors that are related to shrimp growth as spatial data. To test the system, field survey data from a shrimp farm in southern Thailand were used. Growth performance of shrimp and water quality data were collected from 13 earthen ponds in southern peninsular Thailand, located in the Surat Thani, Krabi, Phuket, and Satun provinces. The results show that the system allowed administrators to manage shrimp and farm data from the field sites. Both mobile and web applications were accessed by the users to manage the water quality factors and shrimp data. The system also provided the data analysis tool required to select a parameter from a list box and shows the association between water quality factors and shrimp data with a scatter diagram. Furthermore, the system generated a report of shrimp growth for the different farms with a line graph overlay on Google Maps™ in the data entry suite via mobile application. Online analytics for the growth performance of shrimp as provided by this system could be useful as decision support tools for effective shrimp farming. Full article
(This article belongs to the Special Issue Application of Geoinformatics Technologies for Environmental Sustainability)
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