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Applications of GIS and Remote Sensing for Sustainable Spatial Planning—2nd...
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Applications of GIS and Remote Sensing for Sustainable Spatial Planning—2nd Edition

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3551

Special Issue Editors

Dr. Lia Bárbara Cunha Barata Duarte

E-Mail Website
Guest Editor
1. Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
2. Institute of Earth Sciences, University of Porto, 4169-007 Porto, Portugal
Interests: GIS; GIS open-source applications; spatial management; land use planning; spatial analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Ana Cláudia Teodoro

E-Mail Website
Guest Editor
1. Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
2. Institute of Earth Sciences, University of Porto, 4169-007 Porto, Portugal
Interests: remote sensing; image processing; environmental applications; geologic applications; GIS
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Beniamino Murgante

E-Mail Website
Guest Editor
School of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: urban planning; spatial analysis; computational intelligence; e-learning; environment; sustainable development; sustainability; mapping; urban sustainability; modeling; simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The study of sustainable development in any area depends on the knowledge about resource management and the hazards in that area. Remote sensing (RS) technology has been enhanced significantly in terms of its data acquisition time, sensor resolution, and accessibility over the past few years with, for instance, the emergence of the Google Earth Engine platform. This form of technology has also been widely applied to address challenges in sustainability. Geographical information systems (GISs) also provide essential tools for implementing sustainable processes at different scales. In recent years, GIS technologies combined with satellite RS data have emerged as relevant geospatial tools for the sustainable monitoring and management of natural resources on Earth, providing the required support with a focus on natural resource management and the assessment of natural hazards.

This Special Issue of Sustainability will address the implementation and use of GISs combined with RS data/techniques for sustainable management through geospatial analyses in several areas, such as climate change, the environment, geology, agriculture, forestry, ecology, and coastal ecosystems, among others. It provides a platform for researchers that aimed to publish high-quality original research papers and reviews that focus on sustainable environments.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Spatial planning systems;
  • Urban planning;
  • Spatial analysis and modeling of natural hazards;
  • Environmental management;
  • Spatial and landscape planning;
  • Sustainable planning;
  • Ecosystem service analyses;
  • Performance-based planning.

We look forward to receiving your contributions.

Dr. Lia Bárbara Cunha Barata Duarte
Dr. Ana Cláudia Teodoro
Prof. Dr. Beniamino Murgante
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
  • environmental sustainability
  • sustainable planning
  • hazard assessment
  • geospatial analysis

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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22 pages, 8715 KiB  
Article
Study on Landslide Susceptibility Based on Multi-Model Coupling: A Case Study of Sichuan Province, China
by Jinming Zhang, Jianxi Qian, Yuefeng Lu, Xueyuan Li and Zhenqi Song
Sustainability 2024, 16(16), 6803; https://doi.org/10.3390/su16166803 - 8 Aug 2024
Cited by 2 | Viewed by 1406
Abstract
Landslides are among the most prevalent geological hazards and are characterized by their high frequency, significant destructive potential, and considerable incident rate. Annually, these events lead to substantial casualties and property losses. Thus, conducting landslide susceptibility assessments in the regions vulnerable to such [...] Read more.
Landslides are among the most prevalent geological hazards and are characterized by their high frequency, significant destructive potential, and considerable incident rate. Annually, these events lead to substantial casualties and property losses. Thus, conducting landslide susceptibility assessments in the regions vulnerable to such hazards has become crucial. In recent years, the coupling of traditional statistical methods with machine learning techniques has shown significant advantages in assessing landslide risk. This study focused on Sichuan Province, China, a region characterized by its vast area and diverse climatic and geological conditions. We selected 13 influencing factors for the analysis: elevation, slope, aspect, plan curve, profile curve, valley depth, precipitation, the stream power index (SPI), the topographic wetness index (TWI), the topographic position index (TPI), surface roughness, fractional vegetation cover (FVC), and slope height. This study incorporated the certainty factor method (CF), the information value method (IV), and their coupling with the decision tree C5.0 model (DT) and a logistic regression model (LR) as follows: IV-LR, IV-DT, CF-LR, and CF-DT. The results, validated by an ROC curve analysis, demonstrate that the evaluation accuracy of all six models exceeded 0.750 (AUC > 0.750). The IV-LR model exhibited the highest accuracy, with an AUC of 0.848. When comparing the accuracy among the models, it is evident that the coupling models outperformed the individual statistical models. Based on the results of the six models, a landslide susceptibility map was generated, categorized into five levels. High and very high landslide risk zones are mainly concentrated in the eastern and southeastern regions, covering nearly half of Sichuan Province. Medium-risk areas form linear distributions from northeast to southwest, occupying a smaller proportion of the area. Extremely low- and low-risk zones are predominantly located in the western and northwestern regions. The density of the landslide points increases with higher risk levels across the regions. This further validates the suitability of this research methodology for landslide susceptibility studies on a large scale. Consequently, this methodology can provide crucial insights for landslide prevention and mitigation efforts in this region. Full article
(This article belongs to the Special Issue Applications of GIS and Remote Sensing for Sustainable Spatial Planning—2nd Edition)
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16 pages, 29595 KiB  
Article
Zagros Grass Index—A New Vegetation Index to Enhance Fire Fuel Mapping: A Case Study in the Zagros Mountains
by Iraj Rahimi, Lia Duarte and Ana Cláudia Teodoro
Sustainability 2024, 16(10), 3900; https://doi.org/10.3390/su16103900 - 7 May 2024
Cited by 1 | Viewed by 1542
Abstract
Annually, the oak forests of the Zagros Mountains chains in western Iran and northeastern Iraq face recurring challenges posed by forest fires, particularly in the Kurdo–Zagrosian forests in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation conditions. Integrating in [...] Read more.
Annually, the oak forests of the Zagros Mountains chains in western Iran and northeastern Iraq face recurring challenges posed by forest fires, particularly in the Kurdo–Zagrosian forests in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation conditions. Integrating in situ data, Remote Sensing (RS) data, and Geographical Information Systems (GIS) integration presents a cost-effective and precise approach to capturing environmental conditions before, during, and after fire events, minimizing the need for extensive fieldwork. This study refines and applies the Zagros Grass Index (ZGI), a local vegetation index tailored to discern between grass-covered surfaces and tree canopies in Zagros forests, identifying the grass masses as the most flammable fuel type. Utilizing the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as input from 2013 to 2022, the ZGI aims to mitigate the influence of tree canopies by isolating NDVI values solely attributable to grass cover. By incorporating phenological characteristics of forest trees and grass species, the ZGI outperforms NDVI in mapping grass-covered areas crucial for the study region’s fire susceptibility assessment. Results demonstrate a substantial overlap between ZGI-based maps and recorded fire occurrences, validating the efficacy of the index in fire susceptibility estimation. Full article
(This article belongs to the Special Issue Applications of GIS and Remote Sensing for Sustainable Spatial Planning—2nd Edition)
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