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Geospatial Data in Land Suitability Assessment: 2nd Edition
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Geospatial Data in Land Suitability Assessment: 2nd Edition

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Systems and Global Change".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 5300

Special Issue Editors

Dr. Ivan Plaščak

E-Mail Website
Guest Editor
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: GIS; precision agriculture; variable rate technology; multicriteria decision making; farming and cropping systems; agricultural land management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mladen Jurišić

E-Mail Website
Guest Editor
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: crop production; GIS; multicriteria decision making; inventarization of natural resources; agroecosystems and the environment; farming and cropping systems
Special Issues, Collections and Topics in MDPI journals
Dr. Dorijan Radočaj

E-Mail Website
Guest Editor
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: cropland suitability; land suitability; remote sensing; GIS; predictive mapping; digital soil mapping; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue of Land, entitled “Geospatial Data in Land Suitability Assessment: 2nd Edition”.

The need for improved spatial approaches and models has increased along with the need for effective and ecologically responsible land-use management. Even if multicriteria analysis techniques lead to excellent management and marketing outcomes, the integration of geographic information systems (GISs) with already used techniques greatly expands the potential applications of multicriteria analysis. Using GIS-based multicriteria analysis, it is possible to determine a land’s ideal suitability based on a variety of natural factors (such as topography, soil, and climate), as well as numerous socioeconomic and economic factors. Due to this combination, GIS-based multicriteria analysis for suitability studies in land management, environmental sciences and protection, landscape design, agriculture, urban planning, and many other crucial areas of land use may be carried out with great efficiency and flexibility. The best land-use alternatives are chosen using these suitability results, and the usage of costly and potentially harmful environmental inputs is minimized. This allows for efficient decision making in land use management.

It gives us great pleasure to extend this invitation to you for the Special Issue on “Geospatial Data in Land Suitability Assessment”, which aims to bring together various interdisciplinary fields with the cutting-edge, effective, and adaptable techniques found in GIS-based multicriteria analysis to identify the best possible land suitability.

This Special Issue seeks to further current understanding of the assessment of land suitability using GIS-based multicriteria analysis in a number of areas that are crucial for effective land use management. Submissions should include a wide variety of subjects that are envisioned as the foundation for effective land use management, with GIS-based multicriteria analysis as the main tool. Urban planning, agriculture, environmental sciences, and other multidisciplinary fields with a direct connection to land use management are a few examples of potential themes. Case studies are also encouraged for submission due to the versatility of GIS-based multicriteria analysis, which is useful for professionals from all over the world to assess their own technique and criteria evaluation in their chosen disciplines.

We look forward to receiving your contributions.

Dr. Ivan Plaščak
Prof. Dr. Mladen Jurišić
Dr. Dorijan Radočaj
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. Land 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 2600 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

  • land management
  • environmental modeling
  • geographic information system (gis)
  • remote sensing
  • satellite missions
  • multicriteria decision making

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Related Special Issue

Published Papers (5 papers)

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Research

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14 pages, 2233 KiB  
Article
Spatial Prediction of Soil Total Phosphorus in a Karst Area: Comparing GWR and Residual-Centered Kriging
by Laimou Lu, Penghui Li, Liang Zhong, Mingbao Luo, Liyuan Xing and Chunlai Zhang
Land 2024, 13(12), 2204; https://doi.org/10.3390/land13122204 - 17 Dec 2024
Viewed by 490
Abstract
Accurate soil total phosphorus (TP) prediction is essential to support sustainable agricultural practices and formulate ecological conservation protection policies, particularly in complex karst landscapes with high spatial variability and high phosphorus and cadmium content and interactions, complicating nutrient management. This study uses GIS [...] Read more.
Accurate soil total phosphorus (TP) prediction is essential to support sustainable agricultural practices and formulate ecological conservation protection policies, particularly in complex karst landscapes with high spatial variability and high phosphorus and cadmium content and interactions, complicating nutrient management. This study uses GIS and geostatistical methods to analyze the spatial distribution, influencing factors, and predictive modeling of soil TP in the karst region of northern Mashan County, Guangxi, China. Using 427 surface soil samples, we developed five predictive models: ordinary kriging (OK), regression kriging (RK) and geographically weighted regression kriging (GWRK) combined with environmental variables such as land uses, soil types, and topographic factors; residual mean-centered kriging (MM_OK), and residual median-centered kriging (MC_OK). Our results indicate that higher TP levels were observed in agricultural lands (paddy fields and dry land, at 766 and 913 mg·kg−1, respectively) may due to fertilization, while forests and shrublands showed lower TP levels (383 and 686 mg·kg−1, respectively), reflecting natural phosphorus cycling. The high-value areas of soil TP concentration are in the karst areas in the west and east of the study area, and the low-value area is in the Hongshui River valley in the north of Mashan. The spatial distribution of soil TP is affected by land use, soil type, and topography. The GWRK model exhibited superior accuracy (80.6%), with predicted concentration of TP closely aligning with observed TP values, effectively capturing fine spatial variations, and showing the lowest mean standardized error, average standard error, and mean absolute error. GWRK also achieved the highest R2 (0.67), demonstrating robust predictive capability. MM_OK and MC_OK models performed well and showed smoother spatial transitions, while the OK model displayed the lowest predictive accuracy (62%). By utilizing spatially adaptive weighting, GWRK and its residual-centered kriging method improve soil TP’s prediction accuracy and smoothness in karst areas, providing a reference for targeted soil conservation and sustainable agricultural practices in spatially complex karst environments. Full article
(This article belongs to the Special Issue Geospatial Data in Land Suitability Assessment: 2nd Edition)
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20 pages, 10246 KiB  
Article
Investigation into the Mechanism of the Impact of Sunlight Exposure Area of Urban Artificial Structures and Human Activities on Land Surface Temperature Based on Point of Interest Data
by Yuchen Wang, Yu Zhang and Nan Ding
Land 2024, 13(11), 1879; https://doi.org/10.3390/land13111879 - 10 Nov 2024
Viewed by 764
Abstract
With rapid urbanization, the urban heat island (UHI) effect has intensified, posing challenges to human health and ecosystems. This study explores the impact of sunlight exposure areas of artificial structures and human activities on land surface temperature (LST) in Hefei and Xuzhou, using [...] Read more.
With rapid urbanization, the urban heat island (UHI) effect has intensified, posing challenges to human health and ecosystems. This study explores the impact of sunlight exposure areas of artificial structures and human activities on land surface temperature (LST) in Hefei and Xuzhou, using Landsat 9 data, Google imagery, nighttime light data, and Point of Interest (POI) data. Building shadow distributions and urban road surface areas were derived, and geospatial analysis methods were applied to assess their impact on LST. The results indicate that the sunlight exposure areas of roofs and roads are the primary factors affecting LST, with a more pronounced effect in Xuzhou, while anthropogenic heat plays a more prominent role in Hefei. The influence of sunlight exposure on building facades is relatively weak, and population density shows a limited impact on LST. The geographical detector model reveals that interactions between roof and road sunlight exposure and anthropogenic heat are key drivers of LST increases. Based on these findings, urban planning should focus on optimizing building layouts and heights, enhancing greening on roofs and roads, and reducing the sunlight exposure areas of artificial structures. Additionally, strategically utilizing building shadows and minimizing anthropogenic heat emissions can help lower local temperatures and improve the urban thermal environment. Full article
(This article belongs to the Special Issue Geospatial Data in Land Suitability Assessment: 2nd Edition)
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25 pages, 5738 KiB  
Article
How Accurately Is Topsoil Texture Shown on Agricultural Soil Maps? A Case Study of Eleven Fields Located in Poland
by Michał Stępień, Dariusz Gozdowski and Stanisław Samborski
Land 2024, 13(11), 1852; https://doi.org/10.3390/land13111852 - 6 Nov 2024
Viewed by 574
Abstract
Agricultural soil maps (ASMs) showing the agricultural land of Poland were prepared at a 1:5000 scale in the 1960s and 1970s. These maps show land suitability groups, soil type, and soil texture (ST) to a depth of 150 cm. Nowadays, these maps are [...] Read more.
Agricultural soil maps (ASMs) showing the agricultural land of Poland were prepared at a 1:5000 scale in the 1960s and 1970s. These maps show land suitability groups, soil type, and soil texture (ST) to a depth of 150 cm. Nowadays, these maps are being digitalized and might be a basis for the preparation of modern soil maps at the local, regional, national, and international levels. The agreement between the ST of the topsoil derived from ASMs and the recently evaluated one for eleven fields located in three voivodeships (regions) of Poland was studied. This study considered the examination of soil profiles or augerings and the laboratory analysis of the ST. The agreement between the ST status in the field and that according to the ASMs was field-specific. A complete agreement (purity) within the field was assessed for 5–79% of ST classes and for 23–100% of agronomic categories (ACs), i.e., groupings of similar ST classes. However, the averaged agreement, which treated adjacent ST classes as having a partial agreement, varied from 37 to 88% for ST classes and from 61 to 100% for the ACs among studied fields. These results indicate the variable quality of the information shown on ASMs and the necessity of improving these maps. Full article
(This article belongs to the Special Issue Geospatial Data in Land Suitability Assessment: 2nd Edition)
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30 pages, 18624 KiB  
Article
Harnessing Machine Learning Algorithms to Model the Association between Land Use/Land Cover Change and Heatwave Dynamics for Enhanced Environmental Management
by Kumar Ashwini, Briti Sundar Sil, Abdulla Al Kafy, Hamad Ahmed Altuwaijri, Hrithik Nath and Zullyadini A. Rahaman
Land 2024, 13(8), 1273; https://doi.org/10.3390/land13081273 - 12 Aug 2024
Cited by 2 | Viewed by 2207
Abstract
As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages [...] Read more.
As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km2. The increase in PM2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning. Full article
(This article belongs to the Special Issue Geospatial Data in Land Suitability Assessment: 2nd Edition)
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Review

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26 pages, 6384 KiB  
Review
Research Overview on Urban Heat Islands Driven by Computational Intelligence
by Chao Liu, Siyu Lu, Jiawei Tian, Lirong Yin, Lei Wang and Wenfeng Zheng
Land 2024, 13(12), 2176; https://doi.org/10.3390/land13122176 - 13 Dec 2024
Viewed by 647
Abstract
In recent years, the intensification of the urban heat island (UHI) effect has become a significant concern as urbanization accelerates. This survey comprehensively explores the current status of surface UHI research, emphasizing the role of land use and land cover changes (LULC) in [...] Read more.
In recent years, the intensification of the urban heat island (UHI) effect has become a significant concern as urbanization accelerates. This survey comprehensively explores the current status of surface UHI research, emphasizing the role of land use and land cover changes (LULC) in urban environments. We conducted a systematic review of 8260 journal articles from the Web of Science database, employing bibliometric analysis and keyword co-occurrence analysis using CiteSpace to identify research hotspots and trends. Our investigation reveals that vegetation cover and land use types are the two most critical factors influencing UHI intensity. We analyze various computational intelligence techniques, including machine learning algorithms, cellular automata, and artificial neural networks, used for simulating urban expansion and predicting UHI effects. The study also examines numerical modeling methods, including the Weather Research and Forecasting (WRF) model, while examining the application of Computational Fluid Dynamics (CFD) in urban microclimate research. Furthermore, we evaluate potential mitigation strategies, considering urban planning approaches, green infrastructure solutions, and the use of high-albedo materials. This comprehensive survey not only highlights the critical relationship between land use dynamics and UHIs but also provides a direction for future research in computational intelligence-driven urban climate studies. Full article
(This article belongs to the Special Issue Geospatial Data in Land Suitability Assessment: 2nd Edition)
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