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Special Issues
GIS and Spatial Analysis in Environmental Assessment under Uncertainty
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GIS and Spatial Analysis in Environmental Assessment under Uncertainty

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 14431

Special Issue Editor

Prof. Dr. John Radke

E-Mail Website
Guest Editor
Associate Professor of City & Regional Planning, Landscape Architecture & Environmental Planning and Urban Design, UC Berkeley, CA, USA
Interests: geographic information systems; database design and construction; spatial analysis; pattern recognition; computational morphology; environmental assessment; landscape characterization; potential modeling

Special Issue Information

Dear Colleagues,

Environmental assessment (EA) is a proactive and iterative process which seeks to reduce the negative outcomes of uncertainties. From Carson’s silent spring, which initiated the modern environmental movement, and McHarg’s spatial overlay approach, which emphasized the incorporation of environmental concerns into design in the mid-20th century, to Steinitz’s Framework for Geodesign and Integrated Earth System models, the theoretical and technological scope of EA continues to broaden and evolve. Today’s challenges include: (1) how to integrate voluminous and multiple sources of the same geographic facts in ways that reflect their various properties and uncertainties (Goodchild, 2018), (2) how to expand frontiers with interdisciplinary practices to detect patterns based on statistical learning frameworks that offer robust prediction methods, and (3) how to tweak policies to more practical and achievable outcomes by bringing EA to the neighborhood and human scale.

EA is challenged to effectively align new paradigms of the environment as complex systems with iterative inclusive assessments integrating geospatial data with their intrinsic uncertainties at meaningful scales for stakeholders. It is necessary to reflect upon deficiencies in the existing EA regime and move toward a process-based approach to the development of effective policy.

This Special Issue seeks to further investigate and advance the paradigms of spatial methods for EA under uncertainty. The Guest Editors invite submissions of original research from communities utilizing forward-looking and policy-informative concepts, theories, or practices of EA with strong geospatial perspectives.

Prof. Dr. John Radke
Guest Editor


Citations:

Goodchild, M. F. 2018. Reimagining the history of GIS. Annals of GIS, 24, 1-8. https://doi.org/10.1080/19475683.2018.1424737.

Research questions/ topics include but are not limited to:

  • Spatial data collection, cleaning, analysis, and integration across scales
  • Incorporating Artificial Intelligence to expand existing frontiers
  • Downscaling new metrics for EA to enhance understanding of climate change, both globally and locally
  • Earth system modeling and climate science in support of dynamically driven policy
  • Network science and complex systems modeling for EA
  • Challenges and opportunities in big data for EA
  • New directions of environmental risk management through GIS
  • Critical views of GIScience and its role for EA efficacy and uncertainty management.
  • Understanding, implementing, and transforming governance mechanisms within EA


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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1700 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

  • Process-based solutions
  • Data integration
  • GIScience
  • Climate change
  • Scale mismatch
  • Uncertainty management
  • Spatial analysis
  • Artificial Intelligence (machine learning/deep learning)

Published Papers (5 papers)

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Research

30 pages, 6908 KiB  
Article
A Novel Composite Index to Measure Environmental Benefits in Urban Land Use Optimization Problems
by Md. Mostafizur Rahman and György Szabó
ISPRS Int. J. Geo-Inf. 2022, 11(4), 220; https://doi.org/10.3390/ijgi11040220 - 23 Mar 2022
Cited by 7 | Viewed by 2761
Abstract
In urban land use optimization problems, different conflicting objectives are applied. One of the most significant goals in urban land use optimization problems is to maximize environmental benefits. To quantify environmental benefits in land use optimization, many researchers have employed a variety of [...] Read more.
In urban land use optimization problems, different conflicting objectives are applied. One of the most significant goals in urban land use optimization problems is to maximize environmental benefits. To quantify environmental benefits in land use optimization, many researchers have employed a variety of methodologies. According to previous studies, there is no standard approach for calculating environmental benefits in urban land use allocation problems. Against this background, this study aims to (a) identify indicators of environmental benefits and (b) propose a novel composite index to measure environmental benefits in urban land use optimization problems. This study identified four indicators as a measure of environmental benefits based on a literature assessment and expert opinion. These are spatial compactness, land surface temperature, carbon storage, and ecosystem service value. In this work, we proposed a novel composite environmental benefits index (EBI) to quantify environmental benefits in urban land use allocation problems using an ordered weighted averaging (OWA) method. The study results showed that land surface temperature (LST) is the most influential indicator of environmental benefit while carbon storage is the least important factor. Finally, the proposed method was applied in Rajshahi city in Bangladesh. This study identified that, in an average-risk decision, most of the land (64.55%) of the study area falls within the low-environmental-benefit zone due to a lack of vegetated land cover. The result suggests the potential of using EBI in the land use allocation problem to ensure environmental benefits. Full article
(This article belongs to the Special Issue GIS and Spatial Analysis in Environmental Assessment under Uncertainty)
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19 pages, 6982 KiB  
Article
Intelligent Mining of Urban Ventilated Corridor Based on Digital Surface Model under the Guidance of K-Means
by Chaoxiang Chen, Shiping Ye, Zhican Bai, Juan Wang, Alexander Nedzved and Sergey Ablameyko
ISPRS Int. J. Geo-Inf. 2022, 11(4), 216; https://doi.org/10.3390/ijgi11040216 - 22 Mar 2022
Cited by 2 | Viewed by 2026
Abstract
With the acceleration of urbanization, climate problems affecting human health and safe operation of cities have intensified, such as heat island effect, haze, and acid rain. Using high-resolution remote sensing mapping image data to design scientific and efficient algorithms to excavate and plan [...] Read more.
With the acceleration of urbanization, climate problems affecting human health and safe operation of cities have intensified, such as heat island effect, haze, and acid rain. Using high-resolution remote sensing mapping image data to design scientific and efficient algorithms to excavate and plan urban ventilation corridors and improve urban ventilation environment is an effective way to solve these problems. In this paper, we use unmanned aerial vehicle (UAV) tilt photography technology to obtain high-precision remote sensing image digital elevation model (DEM) and digital surface model (DSM) data, count the city’s dominant wind direction in each season using long-term meteorological data, and use building height to calculate the dominant wind direction. The projection algorithm calculates the windward area density of this dominant direction. Under the guidance of K-means, the binarized windward area density map is used to determine each area and boundary of potential ventilation corridors within the threshold range, and the length and angle of each area’s fitted elliptical long axis are calculated to extract the ventilation corridors that meet the criteria. On the basis of high-precision stereo remote sensing data from UAV, the paper uses image classification, segmentation, fitting, and fusion algorithms to intelligently mine potential urban ventilation corridors, and the effectiveness of the proposed method is demonstrated through a case study in Zhuji City, Zhejiang Province. Full article
(This article belongs to the Special Issue GIS and Spatial Analysis in Environmental Assessment under Uncertainty)
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28 pages, 22074 KiB  
Article
Diffuse Anthropization Impacts in Vulnerable Protected Areas: Comparative Analysis of the Spatial Correlation between Land Transformation and Ecological Deterioration of Three Wetlands in Spain
by Salvador Garcia-Ayllon and John Radke
ISPRS Int. J. Geo-Inf. 2021, 10(9), 630; https://doi.org/10.3390/ijgi10090630 - 21 Sep 2021
Cited by 8 | Viewed by 2884
Abstract
The management and conservation of wetlands and vulnerable protected areas of high ecological value dependent on the existence of water is complex and generally depends on the climate and rainfall in semi-arid territories such as southeastern Spain. However, one variable that is not [...] Read more.
The management and conservation of wetlands and vulnerable protected areas of high ecological value dependent on the existence of water is complex and generally depends on the climate and rainfall in semi-arid territories such as southeastern Spain. However, one variable that is not usually considered sufficiently rigorously in this field of research is the environmental impact of the transformation of the surrounding territory due to anthropic diffuse issues. This phenomenon is not easy to appreciate, since it does not necessarily occur in the environment directly closest to protected areas and it is always difficult to measure and analyze. This study proposes an innovative spatiotemporal methodological framework to evaluate all these phenomena of diffuse anthropization whose indirect impacts on protected areas dependent on the existence of water are currently full of unknowns. Using GIS indicators, a geostatistical analysis based on the concept of the area of influence of diffuse anthropization (AIDA) is proposed to assess the spatial correlation between the anthropic transformation of the territory and the degradation of protected areas over time. The proposal has been applied with a comparative approach to three case studies located in Spain between 2000 and 2020, obtaining clarifying results on the existing spatial correlation patterns between both questions. Full article
(This article belongs to the Special Issue GIS and Spatial Analysis in Environmental Assessment under Uncertainty)
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21 pages, 12279 KiB  
Article
Towards Resilient Critical Infrastructures: Understanding the Impact of Coastal Flooding on the Fuel Transportation Network in the San Francisco Bay
by Yiyi He, Sarah Lindbergh, Yang Ju, Marta Gonzalez and John Radke
ISPRS Int. J. Geo-Inf. 2021, 10(9), 573; https://doi.org/10.3390/ijgi10090573 - 24 Aug 2021
Cited by 4 | Viewed by 3225
Abstract
Sea level rise (SLR) and storm surge inundation are major concerns along the coast of the San Francisco Bay (the Bay Area), impacting both coastal communities and critical infrastructure networks. The oil industry comprises a complex and critical infrastructure network located in the [...] Read more.
Sea level rise (SLR) and storm surge inundation are major concerns along the coast of the San Francisco Bay (the Bay Area), impacting both coastal communities and critical infrastructure networks. The oil industry comprises a complex and critical infrastructure network located in the Bay Area. There is an urgent need to assess consequences and identify risk-based solutions to increase the resilience of this industrial network in the Bay Area to SLR and storm surge. In this study, a comprehensive multi-modal network model representing the fuel supply system was built. A total of 120 coastal flooding scenarios, including four General Circulation Models, two Representative Concentration Pathways, three percentiles of future SLR estimates, and five planning horizons (20 year intervals from 2000 to 2100) were considered. The impact of coastal flooding on fuel transportation networks was studied at two different scales: regional and local. At the regional scale, basic network properties and network efficiency were analyzed across multiple flooding scenarios. At the local scale, cascading effects of individual node disruptions were simulated. Based on this research, smarter and more holistic risk-based adaptation strategies can be established which could lead to a more resilient fuel transportation network system. Full article
(This article belongs to the Special Issue GIS and Spatial Analysis in Environmental Assessment under Uncertainty)
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17 pages, 25807 KiB  
Article
Rurality and Origin–Destination Trajectories of Medical School Application and Matriculation in the United States
by Lan Mu, Yusi Liu, Donglan Zhang, Yong Gao, Michelle Nuss, Janani Rajbhandari-Thapa, Zhuo Chen, José A. Pagán, Yan Li, Gang Li and Heejung Son
ISPRS Int. J. Geo-Inf. 2021, 10(6), 417; https://doi.org/10.3390/ijgi10060417 - 16 Jun 2021
Viewed by 2311
Abstract
Physician shortages are more pronounced in rural than in urban areas. The geography of medical school application and matriculation could provide insights into geographic differences in physician availability. Using data from the Association of American Medical Colleges (AAMC), we conducted geospatial analyses, and [...] Read more.
Physician shortages are more pronounced in rural than in urban areas. The geography of medical school application and matriculation could provide insights into geographic differences in physician availability. Using data from the Association of American Medical Colleges (AAMC), we conducted geospatial analyses, and developed origin–destination (O–D) trajectories and conceptual graphs to understand the root cause of rural physician shortages. Geographic disparities exist at a significant level in medical school applications in the US. The total number of medical school applications increased by 38% from 2001 to 2015, but the number had decreased by 2% in completely rural counties. Most counties with no medical school applicants were in rural areas (88%). Rurality had a significant negative association with the application rate and explained 15.3% of the variation at the county level. The number of medical school applications in a county was disproportional to the population by rurality. Applicants from completely rural counties (2% of the US population) represented less than 1% of the total medical school applications. Our results can inform recruitment strategies for new medical school students, elucidate location decisions of new medical schools, provide recommendations to close the rural–urban gap in medical school applications, and reduce physician shortages in rural areas. Full article
(This article belongs to the Special Issue GIS and Spatial Analysis in Environmental Assessment under Uncertainty)
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