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Remote Sensing Image Processing, Analysis and Application
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Remote Sensing Image Processing, Analysis and Application

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 1 June 2025 | Viewed by 978

Special Issue Editors

Dr. Yilong Han

E-Mail Website
Guest Editor
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Interests: photogrammetry; image processing; 3D computer vision; image matching; geospatial analysis
Dr. Qi Chen

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing; machine learning; building integrated photovoltaics
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiling Guo

E-Mail Website
Guest Editor
Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
Interests: computer vision; remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Hanchao Zhang

E-Mail Website
Guest Editor
Chinese Academy of Surveying and Mapping, Beijing 100036, China
Interests: deep learning; change detection; urban monitoring

Special Issue Information

Dear Colleagues,

The evolution of remote sensing technologies, alongside substantial advancements in computational methodologies, has greatly enhanced our ability to extract meaningful insights from complex remote sensing datasets. This Special Issue aims to compile and disseminate cutting-edge research that focuses on advancements in remote sensing image processing, analytical methodologies, and practical applications across various domains.

This Special Issue seeks to explore innovative approaches, including but not limited to machine learning and advanced statistical techniques, and to improve image processing and analysis. Contributions that address the progress and implementation of advanced photogrammetry techniques, alongside the integration of remote sensing data with other geospatial datasets, are particularly encouraged. Topics may include comprehensive analyses in 3D real scene generation, environmental monitoring, urbanization, agricultural practices, and disaster management. This Special Issue intends to foster interdisciplinary dialogue and collaboration among researchers, practitioners, and policy-makers in the field of remote sensing.

We cordially invite researchers, scholars, and practitioners to contribute to this Special Issue entitled "Remote Sensing Image Processing, Analysis, and Applications." We welcome original research articles, comprehensive reviews, and insightful case studies that highlight contemporary advancements and applications in remote sensing. This Special Issue presents a valuable opportunity to share your research with a diverse audience and to contribute to the evolving discourse surrounding remote sensing methodologies and applications.

Dr. Yilong Han
Dr. Qi Chen
Dr. Zhiling Guo
Dr. Hanchao Zhang
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. Sensors 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 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

  • remote sensing
  • image processing
  • photogrammetry
  • geospatial analysis
  • meachine learning
  • environmental monitoring
  • satellite imagery
  • agricultural practices

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Published Papers (2 papers)

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Research

15 pages, 4689 KiB  
Article
The Applicability of a Complete Archive of Keyhole Imagery for Land-Use Change Detection in China (1960–1984)
by Hao Li, Tao Wang and Jinyu Sun
Sensors 2025, 25(10), 3147; https://doi.org/10.3390/s25103147 - 16 May 2025
Viewed by 37
Abstract
Declassified Keyhole imagery partially provides multi-temporal coverage that can support land-use change analysis. However, the volume of commercial (paid) Keyhole data is much larger than that of free imagery, and the extent to which commercial data can enhance the application of Keyhole imagery [...] Read more.
Declassified Keyhole imagery partially provides multi-temporal coverage that can support land-use change analysis. However, the volume of commercial (paid) Keyhole data is much larger than that of free imagery, and the extent to which commercial data can enhance the application of Keyhole imagery for land-use change analysis remains unknown. In this work, the full archive of Keyhole images for China was obtained from the USGS to identify regions with repeated coverage automatically by using the ArcPy library in Python. The years from 1960 to 1984 were divided into five 5-year periods (T1, 1960~1964; T2, 1965~1969; T3, 1970~1974; T4, 1975~1979; and T5, 1980~1984). The Keyhole images’ metadata, including resolution, acquisition time, and image extent, were utilized to classify the images into meter level (C1), five-meter level (C2), and ten-meter level (C3). The spatial distributions of combinations of imagery at different resolutions for each period and the repeated coverage of imagery at each resolution across the five periods were investigated to extract repeated-coverage regions. The coverage proportions were nearly 100% for C1 imagery for the T3, T4, and T5 periods; C2 for T1 and T2; and C3 for T1 and T3. The T3 period featured extensive coverage at all three resolutions (66%). The T1 period was mainly covered by C2/C3 (93%), and T4 had C1/C3 coverage (68%). In contrast, T2 relied primarily on C2 imagery (100%), and T5 was only covered by C1 (96%). For C1 imagery, land-use changes in almost all areas in China in the T3/T4/T5 time span could be detected, and for C2 and C3 images, the corresponding time spans were T1/T2 and T1/T3. Although this study focused on repeated-coverage area detection within China, the methodology and Python codes provided allow for the implementation of an automated process for land-use change detection from the 1960s to the 1980s in other regions worldwide. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing, Analysis and Application)
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22 pages, 21322 KiB  
Article
Detecting Burn Severity and Vegetation Recovery After Fire Using dNBR and dNDVI Indices: Insight from the Bosco Difesa Grande, Gravina in Southern Italy
by Somayeh Zahabnazouri, Patrick Belmont, Scott David, Peter E. Wigand, Mario Elia and Domenico Capolongo
Sensors 2025, 25(10), 3097; https://doi.org/10.3390/s25103097 - 14 May 2025
Viewed by 436
Abstract
Wildfires serve a paradoxical role in landscapes—supporting biodiversity and nutrient cycling while also threatening ecosystems and economies, especially as climate change intensifies their frequency and severity. This study investigates the impact of wildfires and vegetation recovery in the Bosco Difesa Grande forest in [...] Read more.
Wildfires serve a paradoxical role in landscapes—supporting biodiversity and nutrient cycling while also threatening ecosystems and economies, especially as climate change intensifies their frequency and severity. This study investigates the impact of wildfires and vegetation recovery in the Bosco Difesa Grande forest in southern Italy, focusing on the 2017 and 2021 fire events. Using Google Earth Engine (GEE) accessed in January 2025, we applied remote sensing techniques to assess burn severity and post-fire regrowth. Sentinel-2 imagery was used to compute the Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI); burn severity was derived from differenced NBR (dNBR), and vegetation recovery was monitored via differenced NDVI (dNDVI) and multi-year NDVI time series. We uniquely compare recovery across four zones with different fire histories—unburned, single-burn (2017 or 2021), and repeated-burn (2017 and 2021)—providing a novel perspective on post-fire dynamics in Mediterranean ecosystems. Results show that low-severity zones recovered more quickly than high-severity areas. Repeated-burn zones experienced the slowest and least complete recovery, while unburned areas remained stable. These findings suggest that repeated fires may shift vegetation from forest to shrubland. This study highlights the importance of remote sensing for post-fire assessment and supports adaptive land management to enhance long-term ecological resilience. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing, Analysis and Application)
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