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Remote Sensing in Mangroves IV

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1536

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Special Issue Editor

Dr. Chandra Giri

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Guest Editor

Office of Research and Development, United States Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA
Interests: mangrove forests mapping and monitoring using high-resolution satellite data; global and continental land cover mapping and monitoring using multi-spectral, multi-temporal, and multi-platform remotely sensed data; image pre-processing, classification, and validation using cloud computing
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Special Issue Information

Dear Colleagues,

Due to overwhelming support and interest from all of you, we are introducing the 4th edition of the Special Issue on “Remote Sensing in Mangroves”. I would like to thank all the authors and co-authors in the previous editions, who made Volumes 1, 2, and 3 a grand success. Two books on Volume 1 and Volume 2 have been published, and a book on Volume 3 is being finalized and will be published soon.

Mangrove forests are in constant flux due to both natural and anthropogenic forces. The changing mangroves will have important consequences for coastal communities. The conversion of mangroves to other land uses is the dominant factor responsible for the change; however, sea level rise and natural disasters such as hurricanes are becoming increasingly dominant. Observation and monitoring of the distribution and dynamics of mangroves are central to a wide range of scientific investigations conducted in terrestrial and marine ecosystems.

Recent advancements in remote sensing data availability, image-processing methodologies, computing and information technology, and human resources development have provided an opportunity to regularly observe and monitor mangroves from local to global scales. The spectral, spatial, and temporal resolution of remote sensing data and their availability have improved, making it possible to observe and monitor mangroves in unprecedented spatial, thematic, and temporal detail. Novel remote sensing platforms such as unmanned aerial vehicles, and emerging sensors such as Fourier transform infrared spectroscopy and Lidar can now be used for mangrove monitoring. Furthermore, it is now possible to store and analyze large volumes of data using cloud computing.

The journal Remote Sensing announces a Special Issue dedicated to observing and monitoring mangroves using local and global remote sensing. The Special Issue will broadly cover the application of remote sensing using optical (multi-spectral and hyperspectral), radar, and Lidar data obtained from multiple platforms, including ground, air, and space. Research papers are expected to use the latest techniques to acquire, manage, exploit, process, and analyze various remote sensing data for mangrove forest applications. Both research papers and innovative review papers are invited.

High-quality contributions emphasizing (but not limited to) the topic areas listed below are solicited for the Special Issue:

Application of aerial and ground remote sensing, photography, multi-spectral, multi-temporal, multi-resolution, satellite data, synthetic aperture radar (SAR) data, hyperspectral data, and Lidar data.
Application of advanced image pre-processing for geometric, radiometric, and atmospheric correction, cloud removal, and image mosaicking.
Application of advanced image classification and validation techniques, including supervised and unsupervised classification.
Application of advanced image storage, retrieval, processing, and distribution techniques such as networked data transmission and distributed computing.
Application of remote sensing to derive spatio-temporal information on mangrove forests distribution, species discrimination, forest density, forest health, mangrove expansion and contraction, and other ongoing changes in mangrove ecosystems.

Dr. Chandra Giri
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

mangrove distribution
mangrove expansion and squeeze
deforestation and afforestation
species discrimination
stand density
forest health
forest disturbance
multi-platform, multi-spectral, and multi-resolution data
image processing
image classification
results validation
change detection
cloud computing

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Related Special Issues

Remote Sensing in Mangroves in Remote Sensing (13 articles)
Remote Sensing in Mangroves II in Remote Sensing (13 articles)
Remote Sensing in Mangroves III in Remote Sensing (12 articles)

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22 pages, 3162 KiB

Open AccessArticle

Assessing Mangrove Forest Recovery in the British Virgin Islands After Hurricanes Irma and Maria with Sentinel-2 Imagery and Google Earth Engine

by Michael R. Routhier, Gregg E. Moore, Barrett N. Rock, Stanley Glidden, Matthew Duckett and Susan Zaluski

Remote Sens. 2025, 17(14), 2485; https://doi.org/10.3390/rs17142485 - 17 Jul 2025

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Abstract

Mangroves form the dominant coastal plant community of low-energy tropical intertidal habitats and provide critical ecosystem services to humans and the environment. However, more frequent and increasingly powerful hurricanes and storm surges are creating additional pressure on the natural resilience of these threatened coastal ecosystems. Advances in remote sensing techniques and approaches are critical to providing robust quantitative monitoring of post-storm mangrove forest recovery to better prioritize the often-limited resources available for the restoration of these storm-damaged habitats. Here, we build on previously utilized spatial and temporal ranges of European Space Agency (ESA) Sentinel satellite imagery to monitor and map the recovery of the mangrove forests of the British Virgin Islands (BVI) since the occurrence of back-to-back category 5 hurricanes, Irma and Maria, on September 6 and 19 of 2017, respectively. Pre- to post-storm changes in coastal mangrove forest health were assessed annually using the normalized difference vegetation index (NDVI) and moisture stress index (MSI) from 2016 to 2023 using Google Earth Engine. Results reveal a steady trajectory towards forest health recovery on many of the Territory’s islands since the storms’ impacts in 2017. However, some mangrove patches are slower to recover, such as those on the islands of Virgin Gorda and Jost Van Dyke, and, in some cases, have shown a continued decline (e.g., Prickly Pear Island). Our work also uses a linear ANCOVA model to assess a variety of geospatial, environmental, and anthropogenic drivers for mangrove recovery as a function of NDVI pre-storm and post-storm conditions. The model suggests that roughly 58% of the variability in the 7-year difference (2016 to 2023) in NDVI may be related by a positive linear relationship with the variable of population within 0.5 km and a negative linear relationship with the variables of northwest aspect vs. southwest aspect, island size, temperature, and slope. Full article

(This article belongs to the Special Issue Remote Sensing in Mangroves IV)

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