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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: 20 November 2026 | Viewed by 2277

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 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-anonymized 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 (2 papers)

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Research

34 pages, 9762 KB  
Article
Apple Tree Distance and Volume Measurement Using LiDAR and RGB-D Imaging
by Md Rejaul Karim, Md Nasim Reza, Arnab Majumder, Dae-Hyun Lee and Sun-Ok Chung
Appl. Sci. 2026, 16(16), 7931; https://doi.org/10.3390/app16167931 - 9 Aug 2026
Viewed by 374
Abstract
LiDAR (Light Detection and Ranging) and RGB-D camera imaging have emerged as essential tools in agricultural applications, particularly for plant size and distance measurements, enabling non-destructive, cost-effective, and precise estimation. The objective of this study was to measure the plant canopy dimensions and [...] Read more.
LiDAR (Light Detection and Ranging) and RGB-D camera imaging have emerged as essential tools in agricultural applications, particularly for plant size and distance measurements, enabling non-destructive, cost-effective, and precise estimation. The objective of this study was to measure the plant canopy dimensions and distance between apples using commercial LiDAR, and an RGB-D camera with a speed sprayer platform was used to determine whether LiDAR provides a higher measurement accuracy under field conditions. Data were collected in an apple orchard in Muju, Republic of Korea. Commercial 3D LiDAR, a terminal box, an RGB-D camera, a microcontroller, a power supply, and individual display monitors were integrated into a customized data acquisition (DAQ) box for LiDAR point cloud (PCD), RGB, and depth imagery data collection. Commercial software was used for data acquisition, data conversion (pcap to PCD), segmentation of regions of interest (ROI), and pre-processing of data. PCD processing and measurement consisted of data frame selection, data conversion, outlier removal, downsampling, denoising, ground point removal by filtering, voxelization, and density map generation using an open access programming language script. Depth image processing included importing raw data, shaping metadata using intrinsic camera parameters, visualizing depth images, extracting depth points, and measuring the plant canopy at the pixel level. RGB image analysis involved grayscale conversion, thresholding, segmentation of ROI, contour preparation, noise removal, and binary masking for eliminating the background. Estimated results were compared to measured results. LiDAR measurements showed the closest agreement with the measured results for plant height, canopy volume, plant spacing, and row distance, outperforming both RGB and depth imaging. Under field conditions, plant spacing and row distance were estimated with accuracies of 97.5% and 94.7%, respectively, exhibiting higher measurement accuracies than RGB and depth imagery data results. Despite some discrepancies due to complex plant geometry and dynamic data collection, the results support data collection strategies critical for precision horticulture. Full article
(This article belongs to the Special Issue Innovations in Remote Sensing Technology for Resource and Environmental Monitoring)
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21 pages, 13517 KB  
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
Cited by 1 | Viewed by 1206
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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