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Applied Sciences
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Innovations in Remote Sensing Technology for Resource and Environmental Monitoring
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Innovations in Remote Sensing Technology for Resource and Environmental Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 411

Special Issue Editors

Prof. Dr. George Johannes Chirima

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Guest Editor
1. Agricultural Research Council–Natural Resources and Engineering–South Africa, 600 Belvedere Street, Arcadia, Pretoria 0083, South Africa
2. Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South Africa
Interests: data science; artificial intelligence; global change; regime shifts; non-linear drivers; modelling tools
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. John Odindi

E-Mail Website
Guest Editor
Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
Interests: adoption of remotely sensed datasets in understanding urban land use; land covers; urban green spaces; urban ecosystem services; urban heat islands; climate change and urban transformation
Special Issues, Collections and Topics in MDPI journals
Dr. Cletah Shoko

E-Mail Website
Guest Editor
Division of Geography, School of Geography, Archaeology and Environmental Studies, University of Witwatersrand, Johannesburg, South Africa
Interests: remote sensing; GIS; geospatial modelling; surface hydrology; spatial ecology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paidamwoyo Mhangara

E-Mail Website
Guest Editor
School of Geography, Archaeological & Environmental Studies, Faculty of Science, University of the Witwatersrand, Johannesburg 2000, South Africa
Interests: earth observation; remote sensing; GIS; machine learning; land cover and land use classification; management; leadership; strategic planning

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight the latest innovations and applications of remote sensing technology in monitoring natural resources and environmental changes. Articles which explore cutting-edge developments in sensor technology, data processing algorithms, and their integration with artificial intelligence and machine learning will feature. Key focus areas include high-resolution satellite imaging, LiDAR, UAV-based sensors, and hyperspectral imaging, which have significantly improved the accuracy and efficiency of resource assessment and environmental monitoring. Furthermore, the Issue emphasizes the role of remote sensing in addressing critical challenges, such as deforestation, water resource management, human settlement expansion, and climate change impacts.

Prof. Dr. George Johannes Chirima
Prof. Dr. John Odindi
Dr. Cletah Shoko
Prof. Dr. Paidamwoyo Mhangara
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. Applied Sciences 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 2400 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

  • remote sensing
  • environmental changes
  • resource assessment
  • sensor technology
  • high-resolution imaging
  • LiDAR
  • hyperspectral imaging
  • artificial intelligence
  • machine learning

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Published Papers (1 paper)

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Research

21 pages, 13517 KiB  
Article
A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
by Qi Wang, Guanghu Xu and Donglin Jing
Appl. Sci. 2025, 15(15), 8727; https://doi.org/10.3390/app15158727 - 7 Aug 2025
Viewed by 245
Abstract
Remote sensing targets typically exhibit characteristics of gradual scale changes and diverse orientations. Most existing remote sensing detectors adapt to these differences by adding multi-level structures for feature fusion. However, this approach leads to incomplete coverage of the overall target by the extracted [...] Read more.
Remote sensing targets typically exhibit characteristics of gradual scale changes and diverse orientations. Most existing remote sensing detectors adapt to these differences by adding multi-level structures for feature fusion. However, this approach leads to incomplete coverage of the overall target by the extracted local features, resulting in the loss of critical directional information and an increase in computational complexity which affect the detector’s performance. To address this issue, this paper proposes a Rotation Target Detection Network based on Multi-kernel Interaction and Hierarchical Expansion (MIHE-Net) as a systematic solution. Specifically, we first refine scale modeling through the Multi-kernel Context Interaction (MCI) module and Hierarchical Expansion Attention (HEA) mechanism, achieving sufficient extraction of local features and global information for targets of different scales. Additionally, the Midpoint Offset Loss Function is employed to mitigate the impact of gradual scale changes on target direction perception, enabling precise regression for targets across various scales. We conducted comparative experiments on three commonly used remote sensing target datasets (DOTA, HRSC2016, and UCAS-AOD), with mean average precision (mAP) as the core evaluation metric. The mAP values of the method in this paper on the three datasets reached 81.72%, 92.43%, and 91.86% respectively, which were 0.65%, 1.93%, and 1.87% higher than those of the optimal method, significantly outperforming existing one-stage and two-stage detectors. Through multi-scale feature interaction and direction-aware optimization, MIHE-Net effectively addresses the challenges posed by scale gradation and direction diversity in remote sensing target detection, providing an efficient and feasible solution for high-precision remote sensing target detection. Full article
(This article belongs to the Special Issue Innovations in Remote Sensing Technology for Resource and Environmental Monitoring)
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