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Land
Special Issues
Smart Land Management
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Smart Land Management

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Socio-Economic and Political Issues".

Deadline for manuscript submissions: 23 June 2025 | Viewed by 7249

Special Issue Editors

Prof. Dr. Hossein Azadi

E-Mail Website
Guest Editor
Department of Economics and Rural Development, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
Interests: land and food policies; land governance; food security; agrarian change; resilient agriculture; sustainable livelihood; vulnerability
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guy M Robinson

E-Mail Website
Guest Editor
Department of Geography, Environment and Population, School of Social Sciences, University of Adelaide, Adelaide, SA 5005, Australia
Interests: human geography; environmental management

Special Issue Information

Dear Colleagues,

Land management is “smart” if it relies on passive and active information sensors before, during, and after the decision-making processes with regard to land. Smartness, in other words, is the combination of both smart citizens, who can use information and communication technologies to advocate for and pursue their interests, as well as smart information processing, i.e., facilities that can combine data into all types of sources and platforms. The assumption is that smarter citizens are more capable of collectively shaping and deciding their own future and as a result produce more intelligent, sustainable, and participative cities and societies. The governance of cities is also changing because decision-making processes and the abundance of information as a basis for decision making are changing in parallel. However, it is not clear whether smart land management directly leads to responsible land management.

This Special Issue (SI) will try to answer the following questions: To what extent are smart technologies changing the scale and type of governance along with increasing/decreasing disparities between cities and regions? What factors can contribute to a stronger sense of responsibility in smart land management? The SI is looking for smart land management as a type of land management based on land-related agreements and change processes derived from and built on smart technologies. This SI attempts to show how smart land management will be developed through learning and adaptive paradigms, built together with citizens and land-related actors. The SI will further focus on studies investigating smart land use planning; remote sensing, blockchain, and meta-universe and other advanced technologies; challenging approaches for smart land use planning methods; smart land use planning and sustainable development; urban governance; smart land use planning experience and best practices around the world. We are inviting contributions of academics and practitioners describing their practical experiences and insights in the field of land use planning and smart land management. All types of scientific contributions, including empirical studies, research articles, and critical reviews, are welcome for publication.

Prof. Dr. Hossein Azadi
Prof. Dr. Guy M Robinson
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

  • technology innovation
  • land management
  • land-use planning
  • land administration
  • artificial intelligence

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Published Papers (3 papers)

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Research

15 pages, 3430 KiB  
Article
Study on Intelligent Classing of Public Welfare Forestland in Kunyu City
by Meng Sha, Hua Yang, Jianwei Wu and Jianning Qi
Land 2025, 14(1), 89; https://doi.org/10.3390/land14010089 - 5 Jan 2025
Viewed by 363
Abstract
Manual forestland classification methods, which rely on predetermined scoring criteria and subjective interpretation, are commonly used but suffer from limitations such as high labor costs, complexity, and lack of scalability. This study proposes an innovative machine learning-based approach to forestland classification, utilizing a [...] Read more.
Manual forestland classification methods, which rely on predetermined scoring criteria and subjective interpretation, are commonly used but suffer from limitations such as high labor costs, complexity, and lack of scalability. This study proposes an innovative machine learning-based approach to forestland classification, utilizing a Support Vector Machine (SVM) model to automate the classification process and enhance both efficiency and accuracy. The main contributions of this work are as follows: A machine learning model was developed using integrated data from the Third National Land Survey of China, including forestry, grassland, and wetland datasets. Unlike previous approaches, the SVM model is optimized with Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to automatically determine classification parameters, overcoming the limitations of manual rule-based methods. The performance of the SVM model was evaluated using confusion matrices, classification accuracy, and Matthews Correlation Coefficient (MCC). A comprehensive comparison under different optimization techniques revealed significant improvements in classification accuracy and generalization ability over manual classification systems. The experimental results demonstrated that the GA-SVM model achieved classification accuracies of 98.83% (test set) and 99.65% (overall sample), with MCC values of 0.9796 and 0.990, respectively, outpacing other optimization algorithms, including Grid Search (GS) and Particle Swarm Optimization (PSO). The GA-SVM model was applied to classify public welfare forestland in Kunyu City, yielding detailed classifications across various forestland categories. This result provides a more efficient and accurate method for large-scale forestland management, with significant implications for future land use assessments. The findings underscore the advantages of the GA-SVM model in forestland classification: it is efficient, accurate, and easy to operate. This study not only presents a more reliable alternative to conventional rule-based and manual scoring methods but also sets a precedent for using machine learning to automate and optimize forestland classification in future applications. Full article
(This article belongs to the Special Issue Smart Land Management)
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23 pages, 2368 KiB  
Article
Investigation of the Transition to Environmental Remote Sensing and Factors Influencing Effective Decision-Making on Soil Preparation and Sowing Timing: A Case Study
by Yevhen Kononets, Roman Rabenseifer, Petr Bartos, Pavel Olsan, Martin Filip, Roman Bumbalek, Ales Hermanek and Pavel Kriz
Land 2024, 13(10), 1676; https://doi.org/10.3390/land13101676 - 14 Oct 2024
Viewed by 887
Abstract
The advancement of smart metering technology is progressing steadily and inevitably across various key economic sectors. The utilizatio.n of remote sensors in agriculture presents unique characteristics and specific challenges. In this study, an on-site experiment was carried out on a Slovakian production farm [...] Read more.
The advancement of smart metering technology is progressing steadily and inevitably across various key economic sectors. The utilizatio.n of remote sensors in agriculture presents unique characteristics and specific challenges. In this study, an on-site experiment was carried out on a Slovakian production farm to analyze the transition from traditional measurement methods to smart meters, focusing on timing decisions related to soil preparation and sowing and their relation to scientifically justified dates. Consequently, a clear distinction was observed in terms of the timing decisions made regarding agricultural activities during traditional, combined, and scientifically based approaches in meteorological data readings. This study contrasts these three scenarios and deliberates on the factors that need to be carefully evaluated before incorporating remote sensors into agricultural processes. This study serves as a valuable resource for individuals involved in the adoption of smart metering practices in the Eastern European agricultural sector and promotes an improved understanding of the interactions within smart-sensing, scientific developments, and land management that contribute to the goal of land-system sustainability. Full article
(This article belongs to the Special Issue Smart Land Management)
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20 pages, 1344 KiB  
Article
Smart Land Governance: Towards a Conceptual Framework
by Hossein Azadi, Guy Robinson, Ali Akbar Barati, Imaneh Goli, Saghi Movahhed Moghaddam, Narges Siamian, Rando Värnik, Rong Tan and Kristina Janečková
Land 2023, 12(3), 600; https://doi.org/10.3390/land12030600 - 3 Mar 2023
Cited by 5 | Viewed by 4217
Abstract
Global environmental governance (GEG) is one of the world’s major attempts to address climate change issues through mitigation and adaptation strategies. Despite a significant improvement in GEG’s structural, human, and financial capital, the global commons are decaying at an unprecedented pace. Among the [...] Read more.
Global environmental governance (GEG) is one of the world’s major attempts to address climate change issues through mitigation and adaptation strategies. Despite a significant improvement in GEG’s structural, human, and financial capital, the global commons are decaying at an unprecedented pace. Among the global commons, land has the largest share in GEG. Land use change, which is rooted in increasing populations and urbanization, has a significant role in greenhouse gas (GHG) emissions. As a response, land governance and, consequently, good land governance, have arisen as normative concepts emerging from a series of success factors (notably economic development, environmental conservation, and social justice) to achieve greater sustainability. However, global land governance has shown little success in helping GEG due to the lack of intellectual and flexible thinking over governing the land sector. Consequently, reforming land governance “in a smart way” is one of the most critical actions that could contribute to achieving GEG goals. Hence, we propose a smart land governance (SLG) system that will be well addressed, understood, and modeled in a systemic and dynamic way. A smart system may be smart enough to adapt to different contexts and intellectual responses in a timely fashion. Accordingly, SLG is able to promote shared growth and solve many land sector problems by considering all principles of good land governance. Therefore, in order to enhance adaptive land governance systems, efficient land administration and management are required. This study’s outcomes will raise the comprehension of the problems of land management, providing an excellent framework to help land planners and policy-makers, as well as the development of strategic principles with respect to the principal multidimensional components of SLG. Full article
(This article belongs to the Special Issue Smart Land Management)
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