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Land
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Advances in Land Use and Land Cover Mapping (Second Edition)
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Advances in Land Use and Land Cover Mapping (Second Edition)

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land – Observation and Monitoring".

Deadline for manuscript submissions: 3 July 2025 | Viewed by 5183

Special Issue Editors

Dr. Kathryn Sheffield

E-Mail Website
Guest Editor
Agriculture Victoria Research, Department of Energy, Environment and Climate Action, Bundoora 3083, Australia
Interests: land use and land cover mapping; validation; remote sensing; biosecurity; agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Mohammad Abuzar

E-Mail Website
Guest Editor
Agriculture Victoria Research, Department of Energy, Environment and Climate Action, Bundoora 3083, Australia
Interests: remote sensing; land cover; crop water use; irrigation benchmarking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land use and land cover (LULC) information underpins our understanding of the earth, and our impact on it. While often referred to interchangeably, land use and cover are two distinct concepts. Land cover refers to the physical surface of the earth, while land use refers to the purpose to which the land is committed. Whilst distinct, the two components of land information are intrinsically linked, and can be mapped and analyzed both separately and together in order to highlight linkages between land use, cover and management, as well as land transition.

LULC data support policy making, strategic planning, and monitoring, with an increasing focus on climate change and sustainability. LULC change plays a critical role in the global cycle of greenhouse gases. Given the applied nature of LULC information, and the increasing need for research methodologies to be translated and applied in governmental policy and decision making, it is critical to evaluate the reliability of such data (and the means and technologies used to create it) in order to ensure that strong evidence-based decisions are made.

There have been many recent advances in the production of spatial LULC information, including data integration approaches, the application of machine learning and artificial intelligence, and advanced analytics. The enhanced availability and accessibility of spatial LULC data creates its own challenges of interpretation, presentation and communication of diverse datasets, with new approaches required in an ever-evolving technology landscape.

This Special Issue seeks to focus on innovative approaches to LULC mapping, including, but not limited to, the following:

  • Mapping and monitoring LULC at variety of spatial and temporal scales;
  • Spatial LULC data analytics, including change detection;
  • Validation of LULC information;
  • Spatial LULC data reporting and visualization/communication approaches;
  • Dataset development to support climate change and sustainability applications;
  • Evidence-based decision making based on critically evaluated LULC information.

Dr. Kathryn Sheffield
Dr. Mohammad Abuzar
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 use mapping
  • land cover mapping
  • climate change
  • sustainability
  • validation
  • data communication

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Related Special Issue

Published Papers (5 papers)

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Research

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21 pages, 6169 KiB  
Article
Automated Global Method to Detect Rapid and Future Urban Areas
by Heather S. Sussman and Sarah J. Becker
Land 2025, 14(5), 1061; https://doi.org/10.3390/land14051061 - 13 May 2025
Viewed by 189
Abstract
As many areas of the world continue to grow, it is important to detect areas that are urbanizing at paces above the norm and predict future urban areas, so that optimal city planning can occur. However, methods to detect rapid urbanization are currently [...] Read more.
As many areas of the world continue to grow, it is important to detect areas that are urbanizing at paces above the norm and predict future urban areas, so that optimal city planning can occur. However, methods to detect rapid urbanization are currently absent. Additionally, methods that predict future urban areas often rely on deep learning algorithms, which can be computationally expensive and require a large data volume. Furthermore, prediction methods are typically developed in a single location and are not evaluated across diverse geographies. In this study, rapid and future urbanization algorithms are developed, which are based on methods that use an ensemble of built-up spectral indices and a random forest classifier to detect built-up land cover in Sentinel-2 imagery, across ten sites that vary in their climate and population. Results show that the rapid urbanization algorithm can highlight anomalous urban growth. The future urbanization algorithm had an average overall accuracy of 0.66 (±0.11) and an average F1-score of 0.46 (±0.23). However, the method performed well in areas without seasonal vegetation changes and bare ground surroundings with overall accuracy values and F1-scores near or over 0.80. Overall, these methods provide an automated global approach to identifying rapid and future urban areas with minimal data and computational resources needed, which can enable urban planners to obtain information quickly so that decision making for city planning can be completed faster. Full article
(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping (Second Edition))
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14 pages, 1092 KiB  
Article
The Potential of the Copernicus Product “Imperviousness Classified Change” to Assess Soil Sealing in Agricultural Areas in Poland and Norway
by Wendy Fjellstad, Agata Hościło, Svein Olav Krøgli, Jonathan Rizzi and Milena Chmielewska
Land 2025, 14(4), 794; https://doi.org/10.3390/land14040794 - 7 Apr 2025
Viewed by 236
Abstract
Many countries have goals to reduce soil sealing of agricultural land to preserve food production capacity. To monitor progress, reliable data are needed to quantify soil sealing and changes over time. We examined the potential of the Imperviousness Classified Change (IMCC) 2015–2018 product [...] Read more.
Many countries have goals to reduce soil sealing of agricultural land to preserve food production capacity. To monitor progress, reliable data are needed to quantify soil sealing and changes over time. We examined the potential of the Imperviousness Classified Change (IMCC) 2015–2018 product provided by the Copernicus Land Monitoring Service (CLMS) to assess soil sealing in agricultural areas in Poland and Norway. We found very high overall accuracy due to the dominance of the area with no change. When we focused on areas classified as change, we found low user accuracy, with over-estimation of soil sealing. The producer accuracy was generally much higher, meaning that real cases of soil sealing were captured. This is better than under-estimation of soil sealing because it highlights areas where sealing may have occurred, allowing the user to carry out further control of this much smaller area, without having to assess the great expanse of unchanged area. We concluded that the datasets provide useful information for Europe. They are standardized and comparable across countries, which can enable comparison of the effects of policies intended to prevent soil sealing. Some distinctions between classes are not reliable, but the general information about increase or decrease is useful. Full article
(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping (Second Edition))
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16 pages, 5280 KiB  
Article
Land Use and Land Cover Changes: A Case Study in Nigeria
by Olanrewaju H. Ologunde, Mordiyah O. Kelani, Moges K. Biru, Abdullahi B. Olayemi and Márcio R. Nunes
Land 2025, 14(2), 389; https://doi.org/10.3390/land14020389 - 13 Feb 2025
Cited by 2 | Viewed by 1742
Abstract
Land Use and Land Cover (LULC) assessment is vital for achieving sustainable ecosystems. This study quantified and mapped the spatiotemporal LULC changes in Ado-Odo Ota Local Government Area of Ogun State, Nigeria, between 2015 and 2023. The LULC was classified into water, forest [...] Read more.
Land Use and Land Cover (LULC) assessment is vital for achieving sustainable ecosystems. This study quantified and mapped the spatiotemporal LULC changes in Ado-Odo Ota Local Government Area of Ogun State, Nigeria, between 2015 and 2023. The LULC was classified into water, forest or thick bush, sparse vegetation, built-up, and bare land using Landsat images. Processing, classification, and image analysis were done using the ESRI ArcGIS Pro 3.3. LULC changed from 2015 to 2023, with built-up areas and sparse vegetation increasing by 138.2 km2 and 28.7 km2, respectively. In contrast, forest or thick bush, which had the greatest change among the LULC classes, decreased by 153.7 km2 over this period while bare land and water bodies decreased by 9.5 km2 and 3.8 km2, respectively. Forest or thick bush (201.0 km2) was converted to sparse vegetation, which reflects an increase in agricultural activities in the region. The conversion of about 109.8 km2 of vegetation and 3.7 km2 of water bodies to built-up areas highlights considerable urbanization. Overall, the increase in the built-up area highlights the need for sustainable land use practices to balance urban growth with ecological preservation, achievable through effective management and policy frameworks. Full article
(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping (Second Edition))
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23 pages, 14074 KiB  
Article
Comprehensive Representations of Subpixel Land Use and Cover Shares by Fusing Multiple Geospatial Datasets and Statistical Data with Machine-Learning Methods
by Yuxuan Chen, Rongping Li, Yuwei Tu, Xiaochen Lu and Guangsheng Chen
Land 2024, 13(11), 1814; https://doi.org/10.3390/land13111814 - 1 Nov 2024
Cited by 1 | Viewed by 1394
Abstract
Land use and cover change (LUCC) is a key factor influencing global environmental and socioeconomic systems. Many long-term geospatial LUCC datasets have been developed at various scales during the recent decades owing to the availability of long-term satellite data, statistical data and computational [...] Read more.
Land use and cover change (LUCC) is a key factor influencing global environmental and socioeconomic systems. Many long-term geospatial LUCC datasets have been developed at various scales during the recent decades owing to the availability of long-term satellite data, statistical data and computational techniques. However, most existing LUCC products cannot accurately reflect the spatiotemporal change patterns of LUCC at the regional scale in China. Based on these geospatial LUCC products, normalized difference vegetation index (NDVI), socioeconomic data and statistical data, we developed multiple procedures to represent both the spatial and temporal changes of the major LUC types by applying machine-learning, regular decision-tree and hierarchical assignment methods using northeastern China (NEC) as a case study. In this approach, each individual LUC type was developed in sequence under different schemes and methods. The accuracy evaluation using sampling plots indicated that our approach can accurately reflect the actual spatiotemporal patterns of LUC shares in NEC, with an overall accuracy of 82%, Kappa coefficient of 0.77 and regression coefficient of 0.82. Further comparisons with existing LUCC datasets and statistical data also indicated the accuracy of our approach and datasets. Our approach unfolded the mixed-pixel issue of LUC types and integrated the strengths of existing LUCC products through multiple fusion processes. The analysis based on our developed dataset indicated that forest, cropland and built-up land area increased by 17.11 × 104 km2, 15.19 × 104 km2 and 2.85 × 104 km2, respectively, during 1980–2020, while grassland, wetland, shrubland and bare land decreased by 26.06 × 104 km2, 4.24 × 104 km2, 3.97 × 104 km2 and 0.92 × 104 km2, respectively, in NEC. Our developed approach accurately reconstructed the shares and spatiotemporal patterns of all LUC types during 1980–2020 in NEC. This approach can be further applied to the entirety of China, and worldwide, and our products can provide accurate data supports for studying LUCC consequences and making effective land use policies. Full article
(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping (Second Edition))
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Review

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28 pages, 2953 KiB  
Review
Synergies Between Land Use/Land Cover Mapping and Urban Morphology: A Review of Advances and Methodologies
by Aleksandra Milovanović, Nikola Cvetković, Uroš Šošević, Stefan Janković and Mladen Pešić
Land 2024, 13(12), 2205; https://doi.org/10.3390/land13122205 - 17 Dec 2024
Viewed by 1120
Abstract
This study aims to bridge the fields of urban morphology and land use/land cover (LULC) mapping through a systematic analysis of their integration in recent research. The research employs systematic literature review (SLR) methodology combining quantitative and qualitative methods through four methodological steps: [...] Read more.
This study aims to bridge the fields of urban morphology and land use/land cover (LULC) mapping through a systematic analysis of their integration in recent research. The research employs systematic literature review (SLR) methodology combining quantitative and qualitative methods through four methodological steps: data search, data selection, data analysis, and data clustering. The analysis performed three distinct clustering patterns: (1) methods and tools, (2) data types, and (3) urban morphology aspects. The results reveal five distinct methodological approaches—Data-Driven Typological Decoding Approach, Quantitative Structural Metrics Approach, Predictive Spatiotemporal Transition Approach, Temporal Change Detection and Performance Approach, and Spatial Configuration and Density Analysis Approach—each contributing unique insights to urban form analysis. The findings demonstrate the multidimensional nature of urban form analysis, incorporating both social and temporal dimensions, while highlighting the essential role of change detection in understanding urban pattern evolution. This systematic review establishes a comprehensive framework for understanding the relationship between urban morphology and LULC mapping, providing valuable insights for future research integration. Full article
(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping (Second Edition))
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