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Remote Sensing in Mangroves III

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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: closed (30 April 2025) | Viewed by 34521

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Special Issue Editor

Dr. Chandra Giri

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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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Dear Colleagues,

Due to overwhelming support and interest from all of you, we are introducing the 3rd edition of the Special Issue on “Remote Sensing in Mangroves” https://www.mdpi.com/journal/remotesensing/special_issues/remote_sensing_mangroves. I would like to thank all the authors and co-authors in the previous editions, who made Volumes 1 and 2 a grand success.

Mangrove forests are in constant flux due to both natural and anthropogenic forces. The changing mangroves will have important consequences to coastal communities. At present, 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 is central to a wide range of scientific investigations conducted in both 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 observe and monitor mangroves from local to global scales on a regular basis. 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 the observation and monitoring of mangroves using remote sensing from local to global scales. 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 a wide variety of 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 and 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

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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.

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Keywords

mangrove forests
mangrove change
mapping
monitoring
remote sensing
image processing

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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 IV in Remote Sensing

Published Papers (11 papers)

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Research

20 pages, 9191 KiB

Open AccessArticle

Can Synthetic Aperture Radar Enhance the Quality of Satellite-Based Mangrove Detection? A Focus on the Denpasar Region of Indonesia

by Soohyun Kwon, Hyeon Kwon Ahn and Chul-Hee Lim

Remote Sens. 2025, 17(11), 1812; https://doi.org/10.3390/rs17111812 - 22 May 2025

Abstract

Mangrove forests are vital ecosystems with the highest global carbon absorption capacity, playing a crucial role in climate change mitigation. Therefore, their conservation and management are essential. However, as mangroves are primarily found in tropical regions, frequent cloud cover and limited accessibility pose significant challenges to effective monitoring using optical satellite imagery. In addition, many developing countries with extensive mangrove coverage face challenges in conducting precise monitoring due to limited technological infrastructure. To overcome these limitations, this study integrated open-access synthetic aperture radar (SAR) data with optical imagery to enhance the classification accuracy of mangrove forests in the Bali Denpasar–Badung region. The Sentinel-1 and Sentinel-2 datasets were used, and the U-Net deep learning model was employed for training and classification. A digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) was applied to exclude areas higher than 10 m above sea level, thereby improving the classification accuracy. Additionally, a time-series analysis was performed to assess changes in the mangrove distribution over the past decade, revealing a consistent increase in mangrove extent in the study area. The classification performance was evaluated using a confusion matrix, demonstrating that the combined SAR-optical model outperformed single-source models across all key metrics including precision, accuracy, recall, and F1-score. The findings highlight the effectiveness of integrating SAR and optical data for capturing the complex ecological and geographical characteristics of mangrove forests. Notably, SAR imagery, which is resistant to cloud cover, shows considerable potential for independent application in tropical mangrove monitoring, warranting further research to explore its capabilities in greater depth. Full article

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

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19 pages, 2501 KiB

Open AccessArticle

Mapping Tropical Forested Wetlands Biomass with LiDAR: A Machine Learning Comparison

by Jonathan V. Solórzano, Candelario Peralta-Carreta and J. Alberto Gallardo-Cruz

Remote Sens. 2025, 17(6), 1076; https://doi.org/10.3390/rs17061076 - 19 Mar 2025

Viewed by 631

Abstract

Mangroves and tropical swamp forests are ecosystems that play a critical role in carbon sequestration, coastal protection, and biodiversity support. Accurately estimating aboveground biomass (AGB) in these forests is crucial for global carbon management and conservation efforts. This study evaluates the potential of LiDAR-derived metrics to model the AGB of an area with mangroves and tropical swamp forests in Southeast Mexico. The study area, located in the Pantanos de Centla Protected Area, encompasses a gradient of seasonal waterlogged conditions, from saline to freshwater. Data were collected from 25 1250-m² plots, and three modeling approaches—linear regression, random forest, and XGBoost—were employed to estimate the AGB. The data were divided into training and test sets using an 80:20 ratio. The results indicate that the random forest model outperformed the others, achieving the lowest root mean squared error (RMSE = 20.25 Mg/ha, rRMSE = 12.25%, R² = 0.88). The most influential variables in this model were mean height (zmean), the 35th percentile of height (zq35), and the fourth percentile of returns (p4th), all positively correlated with the AGB. The model’s robustness and uncertainty were evaluated through bootstrapping and spatial prediction across the study area, with higher AGB values concentrated near the main water channels. This study underscores the effectiveness of LiDAR-derived metrics for AGB estimation in complex forested environments. Full article

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

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18 pages, 18618 KiB

Open AccessArticle

Extraction of Mangrove Community of Kandelia obovata in China Based on Google Earth Engine and Dense Sentinel-1/2 Time Series Data

by Chen Lin, Jiali Zheng, Luojia Hu and Luzhen Chen

Remote Sens. 2025, 17(5), 898; https://doi.org/10.3390/rs17050898 - 4 Mar 2025

Viewed by 617

Abstract

Although significant progress has been made in the remote sensing extraction of mangroves, research at the species level remains relatively limited. Kandelia obovata is a dominant mangrove species and is frequently used in ecological restoration projects in China. However, owing to the fragmented distribution of K. obovata within mixed mangrove communities and the significant spectral and textural similarities among mangrove species, accurately extracting large-scale K. obovata-based remote sensing data remains a challenging task. In this study, we conducted extensive field surveys and developed a comprehensive sampling database covering K. obovata and other mangrove species across mangrove-distributing areas in China. We identified the optimal bands for extracting K. obovata by utilizing time-series remote sensing data from Sentinel-1 and Sentinel-2, along with the Google Earth Engine (GEE), and proposed a method for extracting K. obovata communities. The main conclusions are as follows: (1) The spectral-temporal variability characteristics of the blue and red-edge bands play a crucial role in the identification of K. obovata communities. The 90th percentile metric of the blue wavelength band ranks first in importance, while the 75th percentile metric of the blue wavelength band ranks second; (2) This method of remote sensing extraction using spectral-temporal variability metrics with time-series optical and radar remote sensing data offers significant advantages in identifying the K. obovata species, achieving a producer’s accuracy of up to 94.6%; (3) In 2018, the total area of pure K. obovata communities in China was 4825.97 ha; (4) In the southern provinces of China, Guangdong Province has the largest K. obovata community area, while Macau has the smallest. This research contributes to the understanding of mangrove ecosystems and provides a methodological framework for monitoring K. obovata and other coastal vegetation using advanced remote sensing technologies. Full article

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

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28 pages, 32933 KiB

Open AccessArticle

The Change Detection of Mangrove Forests Using Deep Learning with Medium-Resolution Satellite Imagery: A Case Study of Wunbaik Mangrove Forest in Myanmar

by Kyaw Soe Win and Jun Sasaki

Remote Sens. 2024, 16(21), 4077; https://doi.org/10.3390/rs16214077 - 31 Oct 2024

Cited by 2 | Viewed by 2675

Abstract

This paper presents the development of a U-Net model using four basic optical bands and SRTM data to analyze changes in mangrove forests from 1990 to 2024, with an emphasis on the impact of restoration programs. The model, which employed supervised learning for binary classification by fusing multi-temporal Landsat 8 and Sentinel-2 imagery, achieved a superior accuracy of 99.73% for the 2020 image classification. It was applied to predict the long-term mangrove maps in Wunbaik Mangrove Forest (WMF) and to detect the changes at five-year intervals. The change detection results revealed significant changes in the mangrove forests, with 29.3% deforestation, 5.75% reforestation, and −224.52 ha/yr of annual rate of changes over 34 years. The large areas of mangrove forests have increased since 2010, primarily due to naturally recovered and artificially planted mangroves. Approximately 30% of the increased mangroves from 2015 to 2024 were attributed to mangrove plantations implemented by the government. This study contributes to developing a deep learning model with multi-temporal and multi-source imagery for long-term mangrove monitoring by providing accurate performance and valuable information for effective conservation strategies and restoration programs. Full article

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

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20 pages, 6119 KiB

Open AccessArticle

Integrating SAR, Optical, and Machine Learning for Enhanced Coastal Mangrove Monitoring in Guyana

by Kim Chan-Bagot, Kelsey E. Herndon, Andréa Puzzi Nicolau, Vanesa Martín-Arias, Christine Evans, Helen Parache, Kene Mosely, Zola Narine and Brian Zutta

Remote Sens. 2024, 16(3), 542; https://doi.org/10.3390/rs16030542 - 31 Jan 2024

Cited by 5 | Viewed by 4388

Abstract

Mangrove forests are a biodiverse ecosystem known for a wide variety of crucial ecological services, including carbon sequestration, coastal erosion control, and prevention of saltwater intrusion. Given the ecological importance of mangrove forests, a comprehensive and up-to-date mangrove extent mapping at broad geographic scales is needed to define mangrove forest changes, assess their implications, and support restoration activities and decision making. The main objective of this study is to evaluate mangrove classifications derived from a combination of Landsat-8 OLI, Sentinel-2, and Sentinel-1 observations using a random forest (RF) machine learning (ML) algorithm to identify the best approach for monitoring Guyana’s mangrove forests on an annual basis. Algorithm accuracy was tested using high-resolution planet imagery in Collect Earth Online. Results varied widely across the different combinations of input data (overall accuracy, 88–95%; producer’s accuracy for mangroves, 50–87%; user’s accuracy for mangroves, 13–69%). The combined optical–radar classification demonstrated the best performance with an overall accuracy of 95%. Area estimates of mangrove extent ranged from 908.4 to 3645.0 hectares. A ground-based validation exercise confirmed the extent of several large, previously undocumented areas of mangrove forest loss. The results establish that a data fusion approach combining optical and radar data performs marginally better than optical-only approaches to mangrove classification. This ML approach, which leverages free and open data and a cloud-based analytics platform, can be applied to mapping other areas of mangrove forests in Guyana. This approach can also support the operational monitoring of mangrove restoration areas managed by Guyana’s National Agricultural and Research Extension Institute (NAREI). Full article

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

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21 pages, 12733 KiB

Open AccessArticle

Improving Land Use and Land Cover Information of Wunbaik Mangrove Area in Myanmar Using U-Net Model with Multisource Remote Sensing Datasets

by Win Sithu Maung, Satoshi Tsuyuki and Zhiling Guo

Remote Sens. 2024, 16(1), 76; https://doi.org/10.3390/rs16010076 - 24 Dec 2023

Cited by 9 | Viewed by 4952

Abstract

Information regarding land use and land cover (LULC) is essential for regional land and forest management. The contribution of reliable LULC information remains a challenge depending on the use of remote sensing data and classification methods. This study conducted a multiclass LULC classification of an intricate mangrove ecosystem using the U-Net model with PlanetScope and Sentinel-2 imagery and compared it with an artificial neural network model. We mainly used the blue, green, red, and near-infrared bands, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) of each satellite image. The Digital Elevation Model (DEM) and Canopy Height Model (CHM) were also integrated to leverage the model performance in mixed ecosystems of mangrove and non-mangrove forest areas. Through a labeled image created from field ground truth points, the models were trained and evaluated using the metrics of overall accuracy, Intersection over Union, F1 score, precision, and recall of each class. The results demonstrated that the combination of PlanetScope bands, spectral indices, DEM, and CHM yielded superior performance for both the U-Net and ANN models, achieving a higher overall accuracy (94.05% and 92.82%), mean IoU (0.82 and 0.79), mean F1 scores (0.94 and 0.93), recall (0.94 and 0.93), and precision (0.94). In contrast, models utilizing the Sentinel-2 dataset showed lower overall accuracy (86.94% and 82.08%), mean IoU (0.71 and 0.63), mean F1 scores (0.87 and 0.81), recall (0.87 and 0.82), and precision (0.87 and 0.81). The best-classified image, which was produced by U-Net using the PlanetScope dataset, was exported to create an LULC map of the Wunbaik Mangrove Area in Myanmar. Full article

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

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22 pages, 7460 KiB

Open AccessArticle

Using Multisource High-Resolution Remote Sensing Data (2 m) with a Habitat–Tide–Semantic Segmentation Approach for Mangrove Mapping

by Ziyu Sun, Weiguo Jiang, Ziyan Ling, Shiquan Zhong, Ze Zhang, Jie Song and Zhijie Xiao

Remote Sens. 2023, 15(22), 5271; https://doi.org/10.3390/rs15225271 - 7 Nov 2023

Cited by 10 | Viewed by 3044

Abstract

Mangrove wetlands are hotspots of global biodiversity and blue carbon reserves in coastal wetlands, with unique ecological functions and significant socioeconomic value. Annual fine-scale monitoring of mangroves is crucial for evaluating national conservation programs and implementing sustainable mangrove management strategies. However, annual fine-scale mapping of mangroves over large areas using remote sensing remains a challenge due to spectral similarities with coastal vegetation, tidal periodic fluctuations, and the need for consistent and dependable samples across different years. In previous research, there has been a lack of strategies that simultaneously consider spatial, temporal, and methodological aspects of mangrove extraction. Therefore, based on an approach that considers mangrove habitat, tides, and a semantic segmentation approach, we propose a method for fine-scale mangrove mapping suitable for long time-series data. This is an optimized hybrid model that integrates spatial, temporal, and methodological considerations. The model uses five sensors (GF-1, GF-2, GF-6, ZY-301, ZY-302) to combine deep learning U-Net models with mangrove habitat information and algorithms during low-tide periods. This method produces a mangrove map with a spatial resolution of 2 m. We applied this algorithm to three typical mangrove regions in the Beibu Gulf of Guangxi Province. The results showed the following: (1) The model scored above 0.9 in terms of its F1-score in all three study areas at the time of training, with an average accuracy of 92.54% for mangrove extraction. (2) The average overall accuracy (OA) for the extraction of mangrove distribution in three typical areas in the Beibu Gulf was 93.29%. When comparing the validation of different regions and years, the overall OA accuracy exceeded 89.84% and the Kappa coefficient exceeded 0.74. (3) The model results are reliable for extracting sparse and slow-growing young mangroves and narrow mangrove belts along roadsides. In some areas where tidal flooding occurs, the existing dataset underestimates mangrove extraction to a certain extent. The fine-scale mangrove extraction method provides a foundation for the implementation of fine-scale management of mangrove ecosystems, support for species diversity conservation, blue carbon recovery, and sustainable development goals related to coastal development. Full article

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

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16 pages, 6679 KiB

Open AccessArticle

Mangrove Forest Cover Change in the Conterminous United States from 1980–2020

by Chandra Giri, Jordan Long and Prapti Poudel

Remote Sens. 2023, 15(20), 5018; https://doi.org/10.3390/rs15205018 - 18 Oct 2023

Cited by 3 | Viewed by 3753

Abstract

Mangrove forests in developed and developing countries are experiencing substantial transformations driven by natural and anthropogenic factors. This study focuses on the conterminous United States, including Florida, Texas, and Louisiana, where coastal development, urbanization, hydrological pattern alterations, global warming, sea level rise, and natural disasters such as hurricanes contribute to mangrove forest changes. Using time-series Landsat data and image-processing techniques in a cloud computing platform, we analyzed the dynamics of mangrove forests every five years from 1980 to 2020. Each thematic product was independently derived using a region of interest (ROI) suitable for local conditions. The analysis was performed using consistent data sources and a unified classification methodology. Our results revealed that the total mangrove area in the conterminous United States (CONUS) in 2020 was 266,179 ha. with 98.0% of the mangrove area in Florida, 0.6% in Louisiana, and 1.4% in Texas. Approximately 85% of the CONUS mangrove area was found between 24.5° and 26.0° latitude. Overall, mangrove forests in the CONUS increased by 13.5% from 1980 to 2020. However, the quinquennial variation in aerial coverage fluctuated substantially. The validation of 2020 using a statistical sample of reference data confirmed the high accuracy of 95%. Our results can aid policymakers and conservationists in developing targeted strategies for preserving the ecological and socio-economic value of mangrove forests in the conterminous United States. Additionally, all the datasets generated from this study have been released to the public. Full article

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

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22 pages, 5690 KiB

Open AccessArticle

Uncovering Dynamics of Global Mangrove Gains and Losses

by Valeria Contessa, Karen Dyson, Pedro Pablo Vivar Mulas, Adolfo Kindgard, Tianchi Liu, David Saah, Karis Tenneson and Anssi Pekkarinen

Remote Sens. 2023, 15(15), 3872; https://doi.org/10.3390/rs15153872 - 4 Aug 2023

Cited by 10 | Viewed by 3905

Abstract

Supporting successful global mangrove conservation and policy requires accurate identification of anthropogenic and biophysical drivers of mangrove extent, yet such studies are scarce. We apply a hybrid methodology, combining existing remote sensing mangrove maps with local expert knowledge of vegetation and land use dynamics. We conducted stratified random sampling in eight subregions, and local experts visually interpreted over 20,900 plots using high-resolution imagery in Collect Earth Online. Similar to previous estimates, we found 147,771 km² (±1.4%) of mangroves globally in 2020 and that rates of mangrove loss have decreased from 2000–2010 to 2010–2020, largely driven by South and Southeast Asia. Anthropogenic drivers of loss have shifted across subregions, with oil palm cultivation emerging in South and Southeast Asia and aquaculture in South America and Western and Central Africa, highlighting the need for ongoing monitoring and adaptable conservation efforts. Natural expansion outpaced natural retraction in both periods. This is the first global study uncovering land use drivers of mangrove decline and recovery, only made possible by collaboration with local experts. Key breakthroughs include successfully discerning spectrally similar anthropogenic from biophysical drivers, such as aquaculture from natural retraction, and creating data collection approaches that streamline visual interpretation efforts. Full article

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

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24 pages, 12260 KiB

Open AccessArticle

Extent, Severity, and Temporal Patterns of Damage to Cuba’s Ecosystems following Hurricane Irma: MODIS and Sentinel-2 Hurricane Disturbance Vegetation Anomaly (HDVA)

by Hannah C. Turner, Gillian L. Galford, Norgis Hernandez Lopez, Armando Falcón Méndez, Daily Yanetsy Borroto-Escuela, Idania Hernández Ramos and Patricia González-Díaz

Remote Sens. 2023, 15(10), 2495; https://doi.org/10.3390/rs15102495 - 9 May 2023

Cited by 4 | Viewed by 3058

Abstract

Mangrove forests provide a range of ecosystem services but may be increasingly threatened by climate change in the North Atlantic due to high-intensity storms. Hurricane Irma (Category 5) hit the northern coast of Cuba in September 2017, causing widespread damage to mangroves; losses have not yet been extensively documented due to financial and logistical constraints for local scientists. Our team estimated Irma’s impacts on Cuban ecosystems in a coastal and upland study area spanning over 1.7 million ha. We developed a multi-resolution time series “vegetation anomaly” approach, where post-disturbance observations in photosynthetically active vegetation (Enhanced Vegetation Index, EVI) were normalized to the reference period (dry season mean over a historical time series). The Hurricane Disturbance Vegetation Anomaly (HDVA) was used to estimate the extent, severity, and temporal patterns of ecological changes with Sentinel-2 and MODIS data and used vicarious validation with microsatellite interpretation (Planet). HDVA values were classed to convey qualitative labels useful for local scientists: (1) Catastrophic, (2) Severe, (3) Moderate, (4) Mild, and (5) No Loss. Sentinel-2 had a limited reference period (2015–2017) compared to MODIS (2000–2017), yet the HDVA patterns were similar. Mangrove and wetlands (>265,000 ha) sustained widespread damages, with a staggering 78% showing damage, largely severe to catastrophic (0–0.81 HDVA; >207,000 ha). The damaged area is 24 times greater than impacts from Irma as documented elsewhere. Caguanes National Park (>8400 ha, excluding marine zones) experienced concentrated, severe mangrove and wetland damages (nearly 4000 ha). The phenological declines from Irma’s impacts took up to 17 months to fully actualize, a much longer period than previously suggested. In contrast, dry forests saw rapid green flushes post-hurricane. With the increase of high-intensity storm events and other threats to ecosystems, the HDVA methods outlined here can be used to assess intense to low-level damages. Full article

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

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26 pages, 73332 KiB

Open AccessArticle

Global Mangrove Watch: Monthly Alerts of Mangrove Loss for Africa

by Pete Bunting, Lammert Hilarides, Ake Rosenqvist, Richard M. Lucas, Edmond Kuto, Yakhya Gueye and Laye Ndiaye

Remote Sens. 2023, 15(8), 2050; https://doi.org/10.3390/rs15082050 - 12 Apr 2023

Cited by 12 | Viewed by 5153

Abstract

Current mangrove mapping efforts, such as the Global Mangrove Watch (GMW), have focused on providing one-off or annual maps of mangrove forests, while such maps may be most useful for reporting regional, national and sub-national extent of mangrove forests, they may be of more limited use for the day-to-day management of mangroves and for supporting the Global Mangrove Alliance (GMA) goal of halting global mangrove loss. To this end, a prototype change mangrove loss alert system has been developed to identify mangrove losses on a monthly basis. Implemented on the Microsoft Planetary Computer, the Global Mangrove Watch v3.0 mangrove baseline extent map for 2018 was refined and used to define the mangrove extent mask under which potential losses would be identified. The study period was from 2018 to 2022 due to the availability of Sentinel-2 imagery used for the study. The mangrove loss alert system is based on optimised normalised difference vegetation index (NDVI) thresholds used to identify mangrove losses and a temporal scoring system to filter false positives. The mangrove loss alert system was found to have an estimated overall accuracy of 92.1%, with the alert commission and omission estimated to be 10.4% and 20.6%, respectively. Africa was selected for the mangrove loss alert system prototype, where significant losses were identified in the study period, with 90% of the mangrove loss alerts identified in Nigeria, Guinea-Bissau, Madagascar, Mozambique and Guinea. The primary drivers of these losses ranged from economic activities that dominated West Africa and Northern East Africa (mainly agricultural conversion and infrastructure development) to climatic in Southern East Africa (primarily storm frequency and intensity). The production of the monthly mangrove loss alerts for Africa will be continued as part of the wider Global Mangrove Watch project, and the spatial coverage is expected to be expanded to other regions over the coming months and years. The mangrove loss alerts will be published on the Global Mangrove Watch online portal and updated monthly. Full article

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

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