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Remote Sensing
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Remote Sensing Measurements of Land Use and Land Cover
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Remote Sensing Measurements of Land Use and Land Cover

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 15 January 2026 | Viewed by 3346

Special Issue Editor

Prof. Dr. Victor Mesev

E-Mail Website
Guest Editor
Department of Geography, Florida State University, Tallahassee, FL 32306, USA
Interests: image classification; urban morphology; dasymetric mapping; population
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing is now an established scientific process for mapping and monitoring changes on the Earth’s surface and atmosphere. This Special Issue will gather research on a variety of land uses and land covers. Examples include urban sprawl, agriculture, forestry, deserts, coasts, hydrology, and glaciers. Changes in these and other land uses and land covers will contribute to debates on global processes, such as population growth, food output, deforestation, aridity, water erosion, and glacier retreat. Remote sensing continues to play a critical role in monitoring global change and ascribing policies on how to alleviate health problems.

This Special Issue will report research using high-spatial-resolution and global remotely sensed data, obtained via Space Imaging, AVHRR, Lidar, and Landsat technologies, among others. Articles will report cutting-edge research, innovative methodologies, and empirical applications.

We invite articles on the following themes: image classification, spatial pattern recognition, change detection analysis, and demographic and environmental land use and land cover.

Prof. Dr. Victor Mesev
Guest Editor

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. Remote Sensing 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 2700 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

  • image classification
  • change detection
  • image processing
  • urban sprawl
  • environmental science

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Published Papers (4 papers)

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Research

18 pages, 4428 KB  
Article
Integrating Unsupervised Land Cover Analysis with Socioeconomic Change for Post-Industrial Cities: A Case Study of Ponca City, Oklahoma
by Jaryd Hinch and Joni Downs
Remote Sens. 2025, 17(17), 2957; https://doi.org/10.3390/rs17172957 - 26 Aug 2025
Viewed by 575
Abstract
Urban centers shaped by industrial histories often exhibit complex patterns of land cover change that are not well-captured by standard classification techniques. This study investigates post-industrial urban change in Ponca City, Oklahoma, using remote sensing, unsupervised machine learning, and socioeconomic contextualization. Using a [...] Read more.
Urban centers shaped by industrial histories often exhibit complex patterns of land cover change that are not well-captured by standard classification techniques. This study investigates post-industrial urban change in Ponca City, Oklahoma, using remote sensing, unsupervised machine learning, and socioeconomic contextualization. Using a Jupyter Notebook version 7.0.8 environment for Python libraries, Landsat imagery from 1990 to 2020 was analyzed to detect shifts in land cover patterns across a relatively small, heterogeneous landscape. Principal component analysis (PCA) was applied to reduce dimensionality and enhance pixel distinction across multiband reflectance data. Socioeconomic data and historical context were incorporated to interpret changes in land use alongside patterns of industrial reduction and urban redevelopment. Results revealed changes in five distinct land cover classes of urban, vegetative, and industrial land uses, with observable trends aligning with key periods of economic and infrastructural transition. The trends also aligned with socioeconomic changes of the city, with a larger reduction in industrial and commercial land cover than in residential and vegetation cover types. These findings demonstrate the utility of machine learning classification in small-scale, heterogeneous environments and provide a replicable methodological framework for smaller city municipalities to monitor urban change. Full article
(This article belongs to the Special Issue Remote Sensing Measurements of Land Use and Land Cover)
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19 pages, 8198 KB  
Article
Spatiotemporal Evolution and Differentiated Spatial Governance of Slope-Classified Cultivated Land Fragmentation in Rapid Urbanization: Machine Learning-Driven Insights from Guangdong Province
by Mengyuan Su, Nuo Cheng, Yajuan Wang and Yu Cao
Remote Sens. 2025, 17(16), 2855; https://doi.org/10.3390/rs17162855 - 16 Aug 2025
Viewed by 416
Abstract
Rapid urbanization exerts immense pressure on cultivated land. Among these, slope-classified cultivated land (referring to cropland categorized by slope gradients) is especially vulnerable to fragmentation due to its ecological fragility, challenging utilization, and critical role in soil conservation and sustainable agriculture. This study [...] Read more.
Rapid urbanization exerts immense pressure on cultivated land. Among these, slope-classified cultivated land (referring to cropland categorized by slope gradients) is especially vulnerable to fragmentation due to its ecological fragility, challenging utilization, and critical role in soil conservation and sustainable agriculture. This study explores the spatiotemporal dynamics and driving mechanisms of slope-classified cultivated land fragmentation (SCLF) in Guangdong Province, China, from 2000 to 2020. Using multi-temporal geospatial data, machine learning interpretation, and socioeconomic datasets, this research quantifies the spatiotemporal changes in SCLF, identifies key drivers and their interactions, and proposes differentiated protection strategies. The results reveal the following: (1) The SCLF decreased in the Pearl River Delta, exhibited “U-shaped” fluctuations in the west and east, and increased steadily in northern Guangdong. (2) The machine learning interpretation highlights significantly amplified synergistic effects among drivers, with socioeconomic factors, particularly agricultural mechanization and non-farm employment rates, exerting dominant influences on fragmentation patterns. (3) A “core–transitional–marginal” protection framework is proposed, intensifying the land use efficiency and ecological resilience in core areas, coupling land consolidation with green infrastructure in transitional zones, and promoting agroecological diversification in marginal regions. This research proposed a novel framework for SCLF, contributing to cultivated land protection and informing differentiated spatial governance in rapidly urbanizing regions. Full article
(This article belongs to the Special Issue Remote Sensing Measurements of Land Use and Land Cover)
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20 pages, 2466 KB  
Article
Quantifying the Drivers of the Spatial Distribution of Urban Surfaces in Bangladesh: A Multi-Method Geospatial Analysis
by Kazi Jihadur Rashid, Rajsree Das Tuli, Weibo Liu and Victor Mesev
Remote Sens. 2025, 17(12), 2050; https://doi.org/10.3390/rs17122050 - 13 Jun 2025
Cited by 1 | Viewed by 925
Abstract
Urban expansion threatens sustainable development in densely populated countries like Bangladesh. This study aims to quantitatively identify and evaluate the key drivers influencing the spatial distribution of urban surfaces (SDUS) in Chattogram City, providing insights into urban growth patterns over 30 years. Using [...] Read more.
Urban expansion threatens sustainable development in densely populated countries like Bangladesh. This study aims to quantitatively identify and evaluate the key drivers influencing the spatial distribution of urban surfaces (SDUS) in Chattogram City, providing insights into urban growth patterns over 30 years. Using Landsat 5 and 9 imageries, the Normalized Difference Built-up Index (NDBI) was computed for 1993 and 2023 to map urban surface changes. A total of 16 geospatial variables representing potential drivers were analyzed. Four statistical and machine learning methods, including GeoDetector, Distributed Random Forest (DRF), global Geographically Weighted Random Forest (GWRF), and local GWRF, were employed to quantify individual and interactive influences on SDUS. The Geodetector analysis identified the central business district (CBD) as the most influential driver of urban surface distribution, with a q statistic of 0.22, followed by river proximity (q = 0.14) and administrative boundaries (q = 0.13). Across all models, CBD consistently ranked as a dominant factor. In the Distributed Random Forest (DRF) model, CBD showed the highest importance score (0.57), followed by coastlines (0.35) and rivers (0.35). The DRF model achieved the highest performance (R2 = 0.612), outperforming the global GWRF (R2 = 0.59) and local GWRF (R2 = 0.529). Although variables like the proximity of administrative location and forests have low individual impacts, they show a stronger coupled influence. This industrial port-based economy expanded, facing challenges of uncontrolled urbanization, poor governance, and environmental issues. Promoting mixed land use planning, decentralizing urban governance, and improving coordination among implementing agencies may better resolve these issues. This work may help planners and policymakers in planning future cities and developing policies to promote sustainable urban growth. Full article
(This article belongs to the Special Issue Remote Sensing Measurements of Land Use and Land Cover)
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Graphical abstract

19 pages, 4025 KB  
Article
Study on Class Imbalance in Land Use Classification for Soil Erosion in Dry–Hot Valley Regions
by Yuzhuang Deng, Guokun Chen, Bohui Tang, Xingwu Duan, Lijun Zuo and Haijuan Zhao
Remote Sens. 2025, 17(9), 1628; https://doi.org/10.3390/rs17091628 - 4 May 2025
Cited by 1 | Viewed by 654
Abstract
The inherent spatial heterogeneity of land types often leads to a class imbalance in remote sensing-based classification, reducing the accuracy of minority class detection. Consequently, current land use datasets are often inadequate for the specific needs of soil erosion studies. In response to [...] Read more.
The inherent spatial heterogeneity of land types often leads to a class imbalance in remote sensing-based classification, reducing the accuracy of minority class detection. Consequently, current land use datasets are often inadequate for the specific needs of soil erosion studies. In response to the need for soil conservation in dry–hot valley regions, this study integrated multi-source remote sensing imagery and constructed three high-precision imbalanced sample datasets on the Google Earth Engine (GEE) platform to perform land use classification. The degree of class imbalance was quantified using the imbalance ratio (IR), and the impact of sample imbalance on the classification accuracy of different land use types in a typical dry–hot valley was analyzed. The results show that (1) Feature selection significantly improved both classification accuracy and computational efficiency. The period from February to April each year, between 2018 and 2023, was identified as the optimal time window for land use classification in dry–hot valleys. (2) Constructing composite images over longer time scales enhanced classification performance: using a 2020 annual composite image combined with a Gradient Tree Boosting classifier yielded the highest accuracy, indicating that longer temporal synthesis improves classification results. (3) The effect of class imbalance on classification accuracy varied by land type: woodland (the majority class) was least affected by imbalance, whereas minority classes such as cultivated land, garden plantations, and grassland were highly sensitive to imbalance. In imbalanced scenarios, minority classes are prone to omission errors, leading to notable accuracy declines; producer’s accuracy (PA) decreased by 46%, 42%, and 25% for cultivated land, garden plantations, and grassland, respectively, as IR increased (with PA dropping faster than user’s accuracy, UA). Cultivated land was especially sensitive and frequently overlooked under high imbalance conditions compared to gardens and grasslands. Despite overall accuracy improving with higher IR, the accuracy of these minority classes dropped significantly, underscoring the importance of addressing the class imbalance in land use classification for erosion-prone areas. Full article
(This article belongs to the Special Issue Remote Sensing Measurements of Land Use and Land Cover)
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