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Land
Special Issues
Land Use and Land Cover Change Analysis in Dynamic Landscapes
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Land Use and Land Cover Change Analysis in Dynamic Landscapes

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: 30 November 2026 | Viewed by 3909

Special Issue Editors

Dr. Sifiso Xulu

E-Mail Website
Guest Editor
Department of Geography and Environmental Studies, University of Zululand, KwaDlangezwa 3886, South Africa
Interests: land use analysis; remote sensing; geographic information system
Dr. Romano Lottering

E-Mail Website
Guest Editor
School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Durban 4041, South Africa
Interests: forest health; invasive species; classification; climate change; drought; image texture; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Mahlatse Kganyago

E-Mail Website
Guest Editor
Department of Geography, Environmental Management and Energy Studies, University of Johannesburg, Johannesburg 2000, South Africa
Interests: optical remote sensing; environmental management
Special Issues, Collections and Topics in MDPI journals
Dr. Humphrey Kgabo Thamaga

E-Mail Website
Guest Editor
Department of Geography, University of South Africa, Florida 1709, South Africa
Interests: geospatial big data analytics; hydrological modeling and water resource management; cloud computing and geospatial artificial intelligence (Geo-AI); synthetic aperture radar (SAR) applications; land use land cover analysis and future prediction; disaster risk management; drone technology; rangeland ecology and management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land use and land cover change (LULCC) are both the main drivers and consequences of global environmental change, especially across the rapidly changing landscapes of the world. As landscapes change due to urbanization, agricultural intensification, climate variability, and other socio-economic forces, it is essential that reliable analytical geospatial technologies are used to capture the extent, causes, and implications of these changes. This Special Issue aims, therefore, to advance understanding of LULCC processes and their implications in a variety of dynamic environments, and contributions are invited that address innovative methods of remote sensing, machine learning, and geospatial modelling for the detection, monitoring, and prediction of LULCCs. Case studies from dynamic regions are particularly welcome, including urban and coastal areas, wetlands, and arid and mountainous areas. Studies examining the relationship between land cover change and ecosystem services, biodiversity loss, water resources, and the resilience of the socio-economic situation, as well as on governance and landscape management, will be supported. This Special Issue also aims to foster interdisciplinary dialogue and to contribute to evidence-based land management strategies to promote sustainability and climate adaptation and protection. Bringing together research from different geographic contexts and thematic areas, the goal is to better understand how dynamic landscapes respond to and shape LULCC. Researchers, practitioners, and policy analysts are invited to share their insights, which will not only map the changes but also provide actionable solutions in the face of growing environmental uncertainty.

We look forward to receiving your original research articles and reviews.

Dr. Sifiso Xulu
Dr. Romano Lottering
Dr. Mahlatse Kganyago
Dr. Humphrey Kgabo Thamaga
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 250 words) can be sent to the Editorial Office for assessment.

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 and land cover change
  • remote sensing
  • GIS
  • machine learning
  • modelling

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Published Papers (2 papers)

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Research

41 pages, 35748 KB  
Article
A Remote Sensing Baseline and Time Sequence of Land Cover Change for the Conservation of Rainbowfish (Melanotaenia spp.) from the Bird’s Head Peninsula, Western New Guinea
by Margaret Kalacska, Oliver Lucanus, Hans Georg Evers and Juan Pablo Arroyo-Mora
Land 2026, 15(2), 332; https://doi.org/10.3390/land15020332 - 15 Feb 2026
Viewed by 2452
Abstract
Rainbowfish of the genus Melanotaenia are highly endemic freshwater fishes found only in Australia and New Guinea. Although widespread, most species have narrow geographic ranges, making them particularly vulnerable to environmental change. Currently, 43 described (and many undescribed) Melanotaenia species occur in the [...] Read more.
Rainbowfish of the genus Melanotaenia are highly endemic freshwater fishes found only in Australia and New Guinea. Although widespread, most species have narrow geographic ranges, making them particularly vulnerable to environmental change. Currently, 43 described (and many undescribed) Melanotaenia species occur in the Bird’s Head and Bird’s Neck region of Western New Guinea, 29 of which are currently classified as critically endangered, endangered, or vulnerable by the IUCN Red List, including two that may be extinct in the wild. We generated a high-spatial-resolution baseline land cover classification of rainbowfish habitats using low-cloud Planet Labs quarterly basemap mosaics and compared it with a moderate-resolution Landsat 8 OLI-derived classification to assess how spatial resolution influences land cover classification. Using the full 40-year Landsat archive, we quantified decadal land cover change around species type localities and identified localized disturbance events that may affect rainbowfish habitats. For species described from large rivers and lakes, changes in water-body extent over time were quantified. Deforestation varied widely, ranging from little or no detectable change in remote, difficult-to-access locations (e.g., M. misoolensis, M. sneideri), to landscapes heavily modified by logging, urbanization, mining, and agriculture (e.g., M. boesemani, M. arfakensis). Around the type localities, from the high-resolution imagery, we detected ~2939 ha of cleared land, whereas from the Landsat classification we identified only 31 ha of clearing, indicating that most of the fine-scale deforestation was not resolved at the Landsat scale. Time-sequence analyses indicate that over one-third of type localities experienced one or more localized disturbance events over the last 40 years. Land cover change in this region is highly dynamic and differs from commonly studied frontier deforestation patterns elsewhere. It also underscores a critical conservation challenge where rainbowfish species are being discovered in landscapes that are simultaneously undergoing rapid, spatially heterogeneous change. The same infrastructure that enables biological exploration also accelerates habitat modification. These changes threaten the persistence of highly endemic rainbowfish and underscore the value of multi-scale spatial and temporal remote sensing approaches for assessing habitat change in remote, biodiverse regions. The framework presented here is also broadly applicable to other narrowly distributed endemic taxa. Full article
(This article belongs to the Special Issue Land Use and Land Cover Change Analysis in Dynamic Landscapes)
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19 pages, 11009 KB  
Article
The Application of CA–MLP–ANN in Assessing Urbanisation in Quaternary Catchment X22J of Mpumalanga, South Africa
by Mary Nkosi and Fhumulani I. Mathivha
Land 2025, 14(11), 2099; https://doi.org/10.3390/land14112099 - 22 Oct 2025
Viewed by 710
Abstract
Quaternary catchment X22J boasts ecological biodiversity, making ecotourism one of the thriving industries in the catchment. However, recent population growth and the migration from rural areas to urban areas have increased urbanisation. Therefore, this study aimed to assess and predict the trajectory of [...] Read more.
Quaternary catchment X22J boasts ecological biodiversity, making ecotourism one of the thriving industries in the catchment. However, recent population growth and the migration from rural areas to urban areas have increased urbanisation. Therefore, this study aimed to assess and predict the trajectory of urban growth. Through the random forest algorithm in Google Earth Engine, this study analysed urban use in 1990, 2007 and 2024. The classification achieved an overall score of 0.89, 0.96 and 0.91 for 1990, 2007 and 2024, respectively. In addition, the Kappa coefficient varied between 0.85, 0.83 and 0.87 for 1990, 2007 and 2024. The CA–MLP–ANN algorithm was applied for the prediction of 2040 urban changes, leading to the model achieving a score of an overall Kappa coefficient of 0.52 and 74% correctness. Overall, the study predicted an increase of 4.01% in built-up areas from 2024 to 2040, maintaining the increasing trend from 1990. Consequently, a loss of 11% was observed in agricultural lands and a loss of 0.17 in waterbodies by 2040. Full article
(This article belongs to the Special Issue Land Use and Land Cover Change Analysis in Dynamic Landscapes)
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