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Modeling and Simulation in Geographic Information Science: A Useful Tool for Land System Science Studies

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 2023) | Viewed by 16470

Special Issue Editors

Dr. Shafique Matin

E-Mail Website
Guest Editor
Madhya Pradesh Agency For Promotion of Information Technology, Bhopal 462011, India
Interests: satellite remote sensing and GIS; landscape modelling; land system studies and desertification
Dr. Gabriela Mihaela Afrasinei

E-Mail Website
Guest Editor
School of Surveying and Construction Management, College of Engineering and Built Environment, Technological University Dublin, Bolton Street, D01 K82 Dublin2, Ireland
Interests: environmental sciences and land essential climate change variables; land degradation and desertification dynamics; remote sensing and GIS
Dr. Pulakesh Das

E-Mail Website
Guest Editor
Sustainable Landscapes & Restoration, World Resources Institute India (WRI India), New Delhi 110016, India
Interests: forest resilience; lulc modelling; forest cover and shifting cultivation mapping; plant biophysical characterization
Dr. Mohd Yawar Ali Khan

E-Mail Website
Guest Editor
Department of Hydrogeology, Faculty of Earth Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Interests: hydrology and water resources; water quality; application of remote sensing

Special Issue Information

Dear Colleagues,

Studying land system science involves the past, current, and projected state and dynamics of land use caused by natural and human-induced drivers of change. Land system science does not just signify land systems as drivers of change or a result of global environmental change, but also offers solutions to global changes through adaptation and mitigation, playing a key role in achieving a sustainable environment. Around 80% of the ice-free land of our planet shows significant evidence of land use-induced alterations of many environmental processes, such as primary production, the water cycle, biogeochemical cycles, the climate system, and biodiversity. On the other hand, the land provides vital socioeconomic resources to society, such as food, fuel, fibers, and many other ecosystem services that support production functions, regulate risks of natural hazards, or provide cultural and spiritual services. Land system changes are the direct result of human decision-making at multiple levels, with far-reaching consequences for the Earth that then have knock-on effects on human wellbeing and decision making. Thus, land system change is both a cause and consequence of socio-ecological processes that encompass a huge range of spatiotemporal scales.

This Special Issue, entitled Modeling and Simulation in Geographic Information Science: A Useful Tool for Land System Science Studies, provides an overview of recent advances in land system science while at the same time setting the research agenda for the land system science community. The Special Issue will focus on the different approaches to land systems science and their dynamics, current trends, research topics, and suggested ways forward. As reported by the Web of Science database, the use of geographical information system (GIS) based spatial analysis modeling for different aspects of land system science began in the early 1990s and has continued since then, with significant growth in the 21st century. GIS has been extensively used for various land system studies like to understand long-term urbanization trends, deforestation and its drivers, agricultural intensification studyies, land degradation and desertification, the impact of agricultural land change on landscape and urban services, and artificialization of land surface.

Dr. Shafique Matin
Dr. Gabriela Mihaela Afrasinei
Dr. Pulakesh Das
Dr. Mohd Yawar Ali Khan
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

  • human-environment systems
  • land use change, resilience
  • conservation
  • satellite remote sensing

Published Papers (5 papers)

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Research

24 pages, 8391 KiB  
Article
Crop Water Requirements with Changing Climate in an Arid Region of Saudi Arabia
by Mohd Anul Haq and Mohd Yawar Ali Khan
Sustainability 2022, 14(20), 13554; https://doi.org/10.3390/su142013554 - 20 Oct 2022
Cited by 13 | Viewed by 2152
Abstract
Agriculture is critical for a country’s population growth and economic expansion. In Saudi Arabia (SA), agriculture relies on groundwater, seasonal water, desalinated water, and recycled water due to a lack of surface water resources, a dry environment, and scanty rainfall. Estimating water consumption [...] Read more.
Agriculture is critical for a country’s population growth and economic expansion. In Saudi Arabia (SA), agriculture relies on groundwater, seasonal water, desalinated water, and recycled water due to a lack of surface water resources, a dry environment, and scanty rainfall. Estimating water consumption to plan crop water requirements (CWR) in changing environments is difficult due to a lack of micro-level data on water consumption, particularly in agricultural systems. High-resolution satellite data combined with environmental data provides a valuable tool for computing the CWR. This study aimed to estimate the CWR with a greater spatial and temporal resolution and localized field data and environmental variables. Obtaining this at the field level is appropriate, but geospatial technology can produce repeatable, time-series phenomena and align with environmental data for wider coverage regions. The CWR in the study area has been investigated through two methods: firstly, based on the high-resolution PlanetScope (PS) data, and secondly, using the FAO CROPWAT model v8.0. The analysis revealed that evapotranspiration (ETo) showed a minimum response of 2.22 mm/day in January to a maximum of 6.13 mm/day in July, with high temperatures (42.8). The humidity reaches a peak of 51%, falling to a minimum in June of 15%. Annual CWR values (in mm) for seven crops studied in the present investigation, including date palm, wheat, citrus, maize, barley, clover, and vegetables, were 1377, 296, 964, 275, 259, 1077, 214, respectively. The monthly averaged CWR derived using PS showed a higher correlation (r = 0.83) with CROPWAT model results. The study was promising and highlighted that such analysis is decisive and can be implemented in any region by using Machine Learning and Deep Learning for in-depth insights. Full article
(This article belongs to the Special Issue Modeling and Simulation in Geographic Information Science: A Useful Tool for Land System Science Studies)
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20 pages, 8278 KiB  
Article
Analysis of Decadal Land Use Changes and Its Impacts on Urban Heat Island (UHI) Using Remote Sensing-Based Approach: A Smart City Perspective
by Sashikanta Sahoo, Atin Majumder, Sabyasachi Swain, Gareema, Brijendra Pateriya and Nadhir Al-Ansari
Sustainability 2022, 14(19), 11892; https://doi.org/10.3390/su141911892 - 21 Sep 2022
Cited by 14 | Viewed by 2880
Abstract
The land surface temperature (LST) pattern is regarded as one of the most important indicators of the environmental consequences of land use/land cover change. The possible contribution of land surface to the warming phenomenon is being investigated by scientists across the world. This [...] Read more.
The land surface temperature (LST) pattern is regarded as one of the most important indicators of the environmental consequences of land use/land cover change. The possible contribution of land surface to the warming phenomenon is being investigated by scientists across the world. This research focuses on variations in surface temperature and urban heat islands (UHIs) over the course of two seasons, i.e., winter and summer. Using remotely sensed datasets and geospatial techniques, an attempt was made to analyze the spatiotemporal variation in urban heat islands (UHIs) and its association with LULC over Chandigarh from 2000 to 2020. The Enhanced Built-up and Bareness Index (EBBI), Dry Built-up Index (DBI), and Dry Bare-Soil Index (DBSI) were used to identify built-up areas in the city. The results revealed an increase of 10.08% in BA, whereas the vegetation decreased by 4.5% over the study period, which is in close agreement with the EBBI, DBI, and DBSI assessments. From 2000 to 2020, the UHI intensities increased steadily in both the summer and winter seasons. Dense built-up areas such as the industrial unit of the city possessed the highest UHIindex (>0.7) values. Full article
(This article belongs to the Special Issue Modeling and Simulation in Geographic Information Science: A Useful Tool for Land System Science Studies)
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24 pages, 13034 KiB  
Article
Flash Flood Assessment and Management for Sustainable Development Using Geospatial Technology and WMS Models in Abha City, Aseer Region, Saudi Arabia
by Mohd Yawar Ali Khan, Mohamed ElKashouty, Ali M. Subyani and Fuqiang Tian
Sustainability 2022, 14(16), 10430; https://doi.org/10.3390/su141610430 - 22 Aug 2022
Cited by 11 | Viewed by 2806
Abstract
Abha city is distinguished by urbanization, infrastructure, deepening watercourses, and changes in runoff flow which encourage flash floods in the urban zones of many villages in the region. AlMahalah village is prone to flash flooding due to its geographic location near the outlet [...] Read more.
Abha city is distinguished by urbanization, infrastructure, deepening watercourses, and changes in runoff flow which encourage flash floods in the urban zones of many villages in the region. AlMahalah village is prone to flash flooding due to its geographic location near the outlet of convergence streams of significant flow. The Geographic Information System (GIS), Remote Sensing (RS), Water Modeling System (WMS), and Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) were used to assess the effects of flash floods on AlMahala village. Precipitation data from 1978 to 2020 was statistically processed and analysed to provide more information about flash flood hazards. With a 3-h lag time in both watersheds, the higher peak discharge in Wadi Abha than in Wadi Al Akkas indicates that flooding was a primary concern in Wadi Abha. With an average yearly rainfall of 520 mm, the hydrograph simulation from 1 to 5 April 2020 would contribute to the junction (outlet) point of AlMahala village with a peak discharge rate of 474.14 m3/s. The vegetation cover increased by 243 km2 in 2020 compared to 2016. The HEC-RAS model was used to calculate the water depth, velocity, and elevation of the water surface with and without dam installation. The study provides the administration with practical and reasonable procedures for avoiding flash flood destruction in urban areas. Full article
(This article belongs to the Special Issue Modeling and Simulation in Geographic Information Science: A Useful Tool for Land System Science Studies)
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16 pages, 5734 KiB  
Article
Multi-Decadal Mapping and Climate Modelling Indicates Eastward Rubber Plantation Expansion in India
by Pulakesh Das, Rajendra Mohan Panda, Padmanava Dash, Anustup Jana, Avijit Jana, Debabrata Ray, Poonam Tripathi and Venkatesh Kolluru
Sustainability 2022, 14(13), 7923; https://doi.org/10.3390/su14137923 - 29 Jun 2022
Cited by 1 | Viewed by 2274
Abstract
Automated long-term mapping and climate niche modeling are important for developing adaptation and management strategies for rubber plantations (RP). Landsat imageries at the defoliation and refoliation stages were employed for RP mapping in the Indian state of Tripura. A decision tree classifier was [...] Read more.
Automated long-term mapping and climate niche modeling are important for developing adaptation and management strategies for rubber plantations (RP). Landsat imageries at the defoliation and refoliation stages were employed for RP mapping in the Indian state of Tripura. A decision tree classifier was applied to Landsat image-derived vegetation indices (Normalized Difference Vegetation Index and Difference Vegetation Index) for mapping RPs at two-three years intervals from 1990 to 2017. A comparison with actual plantation data indicated more than 91% mapping accuracy, with most RPs able to be identified within six years of plantation, while several patches were detected after six years of plantations. The RP patches identified in 1990 and before 2000 were used for training the Maxent species distribution model, wherein bioclimatic variables for 1960–1990 and 1970–2000 were used as predictor variables, respectively. The model-estimated suitability maps were validated using the successive plantation sites. Moreover, the RPs identified before 2017 and the Shared Socioeconomic Pathways (SSP) climate projections (SSP126 and SSP245) were used to predict the habitat suitability for 2041–2060. The past climatic changes (decrease in temperature and a minor reduction in precipitation) and identified RP patches indicated an eastward expansion in the Indian state of Tripura. The projected increase in temperature and a minor reduction in the driest quarter precipitation will contribute to more energy and sufficient water availability, which may facilitate the further eastward expansion of RPs. Systematic multi-temporal stand age mapping would help to identify less productive RP patches, and accurate monitoring could help to develop improved management practices. In addition, the existing RP patches, their expansion, and the projected habitat suitability maps could benefit resource managers in adapting climate change measures and better landscape management. Full article
(This article belongs to the Special Issue Modeling and Simulation in Geographic Information Science: A Useful Tool for Land System Science Studies)
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17 pages, 4266 KiB  
Article
Agroforestry Suitability for Planning Site-Specific Interventions Using Machine Learning Approaches
by Raj Kumar Singh, Mukunda Dev Behera, Pulakesh Das, Javed Rizvi, Shiv Kumar Dhyani and Çhandrashekhar M. Biradar
Sustainability 2022, 14(9), 5189; https://doi.org/10.3390/su14095189 - 25 Apr 2022
Cited by 9 | Viewed by 4822
Abstract
Agroforestry in the form of intercropping, boundary plantation, and home garden are parts of traditional land management systems in India. Systematic implementation of agroforestry may help achieve various ecosystem benefits, such as reducing soil erosion, maintaining biodiversity and microclimates, mitigating climate change, and [...] Read more.
Agroforestry in the form of intercropping, boundary plantation, and home garden are parts of traditional land management systems in India. Systematic implementation of agroforestry may help achieve various ecosystem benefits, such as reducing soil erosion, maintaining biodiversity and microclimates, mitigating climate change, and providing food fodder and livelihood. The current study collected ground data for agroforestry patches in the Belpada block, Bolangir district, Odisha state, India. The agroforestry site-suitability analysis employed 15 variables on climate, soil, topography, and proximity, wherein the land use land cover (LULC) map was referred to prescribe the appropriate interventions. The random forest (RF) machine learning model was applied to estimate the relative weight of the determinant variables. The results indicated high accuracy (average suitability >0.87 as indicated by the validation data) and highlighted the dominant influence of the socioeconomic variables compared to soil and climate variables. The results show that >90% of the agricultural land in the study area is suitable for various agroforestry interventions, such as bund plantation and intercropping, based on the cropping intensity. The settlement and wastelands were found to be ideal for home gardens and bamboo block plantations, respectively. The spatially explicit data on agroforestry suitability may provide a baseline map and help the managers and planners. Moreover, the adopted approach can be hosted in cloud-based platforms and applied in the different agro-ecological zones of India, employing the local ground data on various agroforestry interventions. The regional and national scale agroforestry suitability and appropriate interventions map would help the agriculture managers to implement and develop policies. Full article
(This article belongs to the Special Issue Modeling and Simulation in Geographic Information Science: A Useful Tool for Land System Science Studies)
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