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Remote Sensing in Mangroves (Fourth Edition)
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Remote Sensing in Mangroves (Fourth Edition)

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 9825

Special Issue Editor

Dr. Chandra Giri

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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 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-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

Published Papers (6 papers)

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Research

31 pages, 21235 KB  
Article
Historical Mangrove Changes on Bangka Island Derived from Thirty Years of Landsat Data
by Suci Puspita Sari, Nico Koedam, Tom Van der Stocken and Frieke Van Coillie
Remote Sens. 2026, 18(6), 947; https://doi.org/10.3390/rs18060947 - 20 Mar 2026
Viewed by 574
Abstract
Bangka’s mangroves contribute to Indonesia’s species-rich coastal ecosystems, yet they have experienced substantial degradation, largely driven by human activities such as tin mining. Establishing long-term records of mangrove extent is essential for understanding distribution dynamics, assessing impacts, and guiding conservation strategies. In this [...] Read more.
Bangka’s mangroves contribute to Indonesia’s species-rich coastal ecosystems, yet they have experienced substantial degradation, largely driven by human activities such as tin mining. Establishing long-term records of mangrove extent is essential for understanding distribution dynamics, assessing impacts, and guiding conservation strategies. In this study, we applied change detection techniques, a random forest classifier, and the LandTrendr algorithm to analyze Landsat time-series data from 1994 to 2023 across Bangka Island. We quantified multi-decadal changes in mangrove extent, periods of disturbance and recovery, and discrepancies between local and global datasets. Mangrove dynamics were spatially heterogeneous, with both expansion and loss observed across regions in landward and seaward settings. Over the 30-year period, total gains reached 4956.39 ha (10.30% of the baseline), yet the net change indicated an overall loss of 1055.85 ha. LandTrendr analysis further revealed sustained mangrove expansion since 1989. Observed changes reflect the combined influence of natural processes, including accretion and erosion, and human pressures, particularly tin mining. Although net area loss aligns with national trends, the drivers in this mining-dominated region differ from those elsewhere, and some mangrove areas remain absent from global datasets. These findings emphasize the need to better capture local gain–loss dynamics to support effective management and conservation. Full article
(This article belongs to the Special Issue Remote Sensing in Mangroves (Fourth Edition))
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20 pages, 6317 KB  
Article
A Method for Mangrove Extraction Integrating Multi-Source Remote Sensing Data with Topographic Mechanism Correction
by Yi Li, Wandong Ma, Shuguo Lv, Qiwei Wang, Chuanhui Fu, Yuanli Shi, Zhihua Ren and Yuhuan Zhang
Remote Sens. 2026, 18(4), 567; https://doi.org/10.3390/rs18040567 - 11 Feb 2026
Viewed by 395
Abstract
(1) Background: The accurate remote sensing extraction of mangroves is often impeded by spectral confusion, particularly the misclassification of stagnant water bodies as mangroves in flat coastal regions. (2) Methods: To overcome this challenge, we propose a novel “spectral-spatial-terrain” stepwise correction framework. This [...] Read more.
(1) Background: The accurate remote sensing extraction of mangroves is often impeded by spectral confusion, particularly the misclassification of stagnant water bodies as mangroves in flat coastal regions. (2) Methods: To overcome this challenge, we propose a novel “spectral-spatial-terrain” stepwise correction framework. This approach integrates multi-source data: Sentinel-2 imagery for spectral pre-screening, Gaofen-2 (GF-2) imagery for geometric refinement, and a newly developed Potential Waterlogging Index (PWI), derived from a digital elevation model (DEM), for topographic correction. The framework was applied to evaluate mangrove damage following Typhoon Yagi (2024) in the East Harbour National Nature Reserve. (3) Results: The method achieved high extraction accuracy, with a Kappa coefficient of 0.97. The remote sensing-based damage assessment revealed that 48.2% of the mangrove area was affected, with a significantly higher damage rate of 63.0% observed within the PWI-identified potential waterlogging zones. (4) Conclusions: The high classification accuracy confirms the effectiveness of the proposed framework. More importantly, the spatially consistent damage pattern provides strong ecological evidence supporting the mechanistic rationale behind the terrain-based correction. This study presents a reliable and transferable remote sensing methodology for high-precision, dynamic monitoring and assessment of mangrove ecosystem after disaster. Full article
(This article belongs to the Special Issue Remote Sensing in Mangroves (Fourth Edition))
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23 pages, 4942 KB  
Article
Provincial-Scale Monitoring of Mangrove Area and Spartina alterniflora Invasion in Subtropical China Using UAV Imagery and Machine Learning Methods
by Qiliang Lv, Peng Zhou, Sheng Yang, Yongjun Shi, Jiangming Ma, Jiangcheng Yang and Guangsheng Chen
Remote Sens. 2026, 18(2), 345; https://doi.org/10.3390/rs18020345 - 20 Jan 2026
Viewed by 504
Abstract
The survival and growth of mangroves along coastal China is threatened by invasive smooth cordgrass (Spartina alterniflora). Due to the high mortality and frequent replanting of mangrove trees and the impacts of invasive smooth cordgrass, the exact mangrove forest area in [...] Read more.
The survival and growth of mangroves along coastal China is threatened by invasive smooth cordgrass (Spartina alterniflora). Due to the high mortality and frequent replanting of mangrove trees and the impacts of invasive smooth cordgrass, the exact mangrove forest area in Zhejiang Province, China, is still unclear. Based on provincial-scale fine-resolution Unmanned Aerial Vehicle (UAV) imagery and a large number of field survey plots, this study mapped the distribution of mangroves and smooth cordgrass in 2023 using three machine learning classifiers, including Classification and Regression Tree (CART), Convolutional Neural Networks (CNNs), and Support Vector Machine (SVM). The accuracy assessment indicated that the CNN algorithm was superior to the other two algorithms and yielded an overall accuracy and Kappa coefficient of 97% and 0.96, respectively. The total areas of mangrove forest and smooth cordgrass were 140.83 ha and 52.95 ha, respectively, in 2023 in Zhejiang Province. The mangrove forest area was mostly concentrated in Yuhuan, Dongtou, Yueqing, and Longgang districts. The mean canopy coverage of mangrove trees was only 36.41%, with lower than 20% coverage in all northern and some central districts. At the spatial scale, the mangrove trees showed a scattered distribution pattern, and over 70.04% of the planting area had canopy coverage lower than 20%. Smooth cordgrass has widely invaded all 11 districts, accounting for about 13.7% of the total planting area of mangrove trees. Over 67.3% and 85.4% of the planting areas have been occupied by smooth cordgrass in Wenling and Jiaoxiang districts, respectively, which necessitates an intensive anthropogenic intervention to control its spread in these districts. Our study provides more accurate monitoring of the mangrove and smooth cordgrass distribution areas at a provincial scale. The findings will help guide the replanting and management activities of mangrove trees, control planning for smooth cordgrass, and provide a data basis for the accurate estimation of carbon stock for mangrove forests in Zhejiang Province. Full article
(This article belongs to the Special Issue Remote Sensing in Mangroves (Fourth Edition))
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19 pages, 2742 KB  
Article
Cloud-Based Solutions for Monitoring Coastal Ecosystems and the Prioritization of Restoration Efforts Across Belize
by Christine Evans, Lauren Carey, Florencia Guerra, Emil A. Cherrington, Edgar Correa and Diego Quintero
Remote Sens. 2025, 17(20), 3396; https://doi.org/10.3390/rs17203396 - 10 Oct 2025
Viewed by 1777
Abstract
In recent years, the availability of automated change detection algorithms in Google Earth Engine has permitted the cloud-based processing of large quantities of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of [...] Read more.
In recent years, the availability of automated change detection algorithms in Google Earth Engine has permitted the cloud-based processing of large quantities of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) allow users to exploit decades of Earth Observations (EOs), leveraging the Landsat archive and data from other sensors to detect disturbances in forest ecosystems. Despite the wide adoption of these methods, robust documentation, and a growing community of users, little research has systematically detailed their tuning process in mangrove environments. This work aims to identify the best practices for applying these models to monitor changes within mangrove forest cover, which has been declining gradually in Belize the last several decades. Partnering directly with the Belizean Forest Department, our team developed a replicable, efficient methodology to annually update the country’s mangrove extent, employing EO-based change detection. We ran a series of model variations in both CCDC-SMA and LandTrendr to identify the parameterizations best suited to identifying change in Belizean mangroves. Applying the best performing model run to the starting 2017 mangrove extent, we estimated a total loss of 540 hectares in mangrove coverage by 2024. Overall accuracy across thirty variations in model runs of LandTrendr and CCDC-SMA ranged from 0.67 to 0.75. While CCDC-SMA generally detected more disturbances and had higher precision for true changes, LandTrendr runs tended to have higher recall. Our results suggest LandTrendr offered more flexibility in balancing precision and recall for true changes compared to CCDC-SMA, due to its greater variety of adjustable parameters. Full article
(This article belongs to the Special Issue Remote Sensing in Mangroves (Fourth Edition))
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20 pages, 6431 KB  
Article
Characterizing Role of Spatial Features in Improving Mangrove Classification—A Case Study over the Mesoamerican Reef Region
by Suvarna M. Punalekar, A. Justin Nowakowski, Steven W. J. Canty, Craig Fergus, Qiongyu Huang, Melissa Songer and Grant M. Connette
Remote Sens. 2025, 17(16), 2837; https://doi.org/10.3390/rs17162837 - 15 Aug 2025
Viewed by 1988
Abstract
Mangrove forests are among the world’s most vital coastal ecosystems. Mapping mangrove cover from local to global scales using spectral data and machine learning models is a well-established method. While non-spectral contextual datasets (spatial features) have also been incorporated into such models, the [...] Read more.
Mangrove forests are among the world’s most vital coastal ecosystems. Mapping mangrove cover from local to global scales using spectral data and machine learning models is a well-established method. While non-spectral contextual datasets (spatial features) have also been incorporated into such models, the contribution of these additional features to improving mangrove mapping remains underexplored. Using the Mesoamerican Reef Region as a case study, we evaluate the effectiveness of incorporating spatial features in binary mangrove classification to enhance mapping accuracy. We compared an aspatial model that includes only spectral data with three spatial models: two included features such as geographic coordinates, elevation, and proximity to coastlines and streams, while the third integrated a geostatistical approach using Inverse Distance Weighted (IDW) interpolation. Spectral inputs included bands and indices derived from Sentinel-1 and Sentinel-2, and all models were implemented using the Random Forest algorithm in Google Earth Engine. Results show that spatial features reduced omission errors without increasing commission errors, enhancing the model’s ability to capture spatial variability. Models using geographic coordinates and elevation performed comparably to those with additional environmental variables, with storm frequency and distance to streams emerging as important predictors in the Mesoamerican Reef region. In contrast, the IDW-based model underperformed, likely due to overfitting and limited representation of local spectral variation. Spatial analyses show that models incorporating spatial features produced more continuous mangrove patches and removed some false positives in non-mangrove areas. These findings highlight the value of spatial features in improving classification accuracy, especially in regions with ecologically diverse mangroves across varied environments. By integrating spatial context, these models support more accurate, locally relevant mangrove maps that are essential for effective conservation and management. Full article
(This article belongs to the Special Issue Remote Sensing in Mangroves (Fourth Edition))
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22 pages, 3162 KB  
Article
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
Cited by 2 | Viewed by 2482
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 [...] Read more.
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 (Fourth Edition))
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