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Remote Sensing
Special Issues
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: 28 June 2025 | Viewed by 662

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

19 pages, 4025 KiB  
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
Viewed by 278
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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