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

Deadline for manuscript submissions: closed (25 July 2019) | Viewed by 141554

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Special Issue Editors

Prof. Dr. Seung Kuk Lee

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Guest Editor

Department of Earth and Environmental Sciences, Pukyong National University, Busan, Republic of Korea
Interests: Pol-InSAR inversion; retrieval of 3D forest structure parameters; mangrove carbon; digital beamforming SAR sensors; Lidar and InSAR fusion for forest applications; GEDI

Dr. David Lagomasino

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Guest Editor

NASA Goddard Space Flight Center, USA / Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Interests: coastal ecology; mangrove ecology; wetland processes; land cover/land-use change; lidar; Landsat; ecosystem services

Special Issue Information

Dear Colleagues,

Mangrove forests provide a wide range of ecological, biogeochemical, social and economic services along the intertidal zones of the subtropics and tropics. Mangrove ecosystems also contain significantly high carbon stocks in different pools, including living vegetation (tree and root), dead trees, and soil sediments. These carbon-dense forests play an important role in mitigating global climate change through sequestering atmospheric CO₂ on the ground. Therefore, the conversion or restoration of mangroves to/from other land uses at local, regional, and global scales is vital for understanding mangrove carbon stocks and dynamics and valuing other ecosystem services. Recent advancements in time-series analysis of remote sensing data, new methodologies for 3D forest structure parameter retrieval, computing and information technology, as well as compact and portable sensors have provided a high potential to provide valuable information for mangrove ecosystem carbon stocks and carbon dynamics.

This Special Issue calls for submissions presenting advancements in remote sensing approaches addressing mangrove 3D forest structure, carbon stock and fluxes from multiple remote sensing data sources, for example; terrestrial laser scanner, structure-from-motion, drone photography, as well as novel studies using traditional air- and space-borne sensors. High quality contributions emphasizing various elements of the mangrove carbon balance are solicited for the Special Issue, but we will also consider contributions that improve the monitoring and valuation of mangrove ecosystem services. Review papers presenting the status and progress, as well as papers describing new measurement concepts/sensors and new remote sensing approaches/techniques are welcomed.

Dr. Seung Kuk Lee
Dr. David Lagomasino
Guest Editors

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Keywords

3D forest structure
Forest density and species mapping
Above and belowground carbon
Soil carbon
Change in extent
Carbon emission and sequestration
Ecosystem services

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Published Papers (12 papers)

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17 pages, 7198 KiB

Open AccessArticle

A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery

by Mingming Jia, Zongming Wang, Chao Wang, Dehua Mao and Yuanzhi Zhang

Remote Sens. 2019, 11(17), 2043; https://doi.org/10.3390/rs11172043 - 29 Aug 2019

Cited by 129 | Viewed by 12906

Abstract

Mangrove forests are tropical trees and shrubs that grow in sheltered intertidal zones. Accurate mapping of mangrove forests is a great challenge for remote sensing because mangroves are periodically submerged by tidal floods. Traditionally, multi-tides images were needed to remove the influence of water; however, such images are often unavailable due to rainy climates and uncertain local tidal conditions. Therefore, extracting mangrove forests from a single-tide imagery is of great importance. In this study, reflectance of red-edge bands in Sentinel-2 imagery were utilized to establish a new vegetation index that is sensitive to submerged mangrove forests. Specifically, red and short-wave near infrared bands were used to build a linear baseline; the average reflectance value of four red-edge bands above the baseline is defined as the Mangrove Forest Index (MFI). To evaluate MFI, capabilities of detecting mangrove forests were quantitatively assessed between MFI and four widely used vegetation indices (VIs). Additionally, the practical roles of MFI were validated by applying it to three mangrove forest sites globally. Results showed that: (1) theoretically, Jensen–Shannon divergence demonstrated that a submerged mangrove forest and water pixels have the largest distance in MFI compared to other VIs. In addition, the boxplot showed that all submerged mangrove forests could be separated from the water background in the MFI image. Furthermore, in the MFI image, to separate mangrove forests and water, the threshold is a constant that is equal to zero. (2) Practically, after applying the MFI to three global sites, 99–102% of submerged mangrove forests were successfully extracted by MFI. Although there are still some uncertainties and limitations, the MFI offers great benefits in accurately mapping mangrove forests as well as other coastal and aquatic vegetation worldwide. Full article

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

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19 pages, 6222 KiB

Open AccessArticle

Brazilian Mangrove Status: Three Decades of Satellite Data Analysis

by Cesar Diniz, Luiz Cortinhas, Gilberto Nerino, Jhonatan Rodrigues, Luís Sadeck, Marcos Adami and Pedro Walfir M. Souza-Filho

Remote Sens. 2019, 11(7), 808; https://doi.org/10.3390/rs11070808 - 4 Apr 2019

Cited by 144 | Viewed by 17104

Abstract

Since the 1980s, mangrove cover mapping has become a common scientific task. However, the systematic and continuous identification of vegetation cover, whether on a global or regional scale, demands large storage and processing capacities. This manuscript presents a Google Earth Engine (GEE)-managed pipeline to compute the annual status of Brazilian mangroves from 1985 to 2018, along with a new spectral index, the Modular Mangrove Recognition Index (MMRI), which has been specifically designed to better discriminate mangrove forests from the surrounding vegetation. If compared separately, the periods from 1985 to 1998 and 1999 to 2018 show distinct mangrove area trends. The first period, from 1985 to 1998, shows an upward trend, which seems to be related more to the uneven distribution of Landsat data than to a regeneration of Brazilian mangroves. In the second period, from 1999 to 2018, a trend of mangrove area loss was registered, reaching up to 2% of the mangrove forest. On a regional scale, ~85% of Brazil’s mangrove cover is in the states of Maranhão, Pará, Amapá and Bahia. In terms of persistence, ~75% of the Brazilian mangroves remained unchanged for two decades or more. Full article

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

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18 pages, 9028 KiB

Open AccessArticle

An Analysis of the Early Regeneration of Mangrove Forests using Landsat Time Series in the Matang Mangrove Forest Reserve, Peninsular Malaysia

by Viviana Otero, Ruben Van De Kerchove, Behara Satyanarayana, Husain Mohd-Lokman, Richard Lucas and Farid Dahdouh-Guebas

Remote Sens. 2019, 11(7), 774; https://doi.org/10.3390/rs11070774 - 31 Mar 2019

Cited by 46 | Viewed by 8726

Abstract

Time series of satellite sensor data have been used to quantify mangrove cover changes at regional and global levels. Although mangrove forests have been monitored using remote sensing techniques, the use of time series to quantify the regeneration of these forests still remains limited. In this study, we focus on the Matang Mangrove Forest Reserve (MMFR) located in Peninsular Malaysia, which has been under silvicultural management since 1902 and provided the opportunity to investigate the use of Landsat annual time series (1988–2015) for (i) detecting clear-felling events that take place in the reserve as part of the local management, and (ii) tracing back and quantifying the early regeneration of mangrove forest patches after clear-felling. Clear-felling events were detected for each year using the Normalized Difference Moisture Index (NDMI) derived from single date (cloud-free) or multi-date composites of Landsat sensor data. From this series, we found that the average period for the NDMI to recover to values observed prior to the clear-felling event between 1988 and 2015 was 5.9 ± 2.7 years. The maps created in this study can be used to guide the replantation strategies, the clear-felling planning, and the management and monitoring activities of the MMFR. Full article

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

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27 pages, 3659 KiB

Open AccessArticle

Identifying Mangrove Deforestation Hotspots in South Asia, Southeast Asia and Asia-Pacific

by Samir Gandhi and Trevor Gareth Jones

Remote Sens. 2019, 11(6), 728; https://doi.org/10.3390/rs11060728 - 26 Mar 2019

Cited by 71 | Viewed by 14604

Abstract

Mangroves inhabit highly productive inter-tidal ecosystems in >120 countries in the tropics and subtropics providing critical goods and services to coastal communities and contributing to global climate change mitigation owing to substantial carbon stocks. Despite their importance, global mangrove distribution continues to decline primarily due to anthropogenic drivers which vary by region/country. South Asia, Southeast Asia and Asia-Pacific contain approximately 46% of the world’s mangrove ecosystems, including the most biodiverse mangrove forests. This region also exhibits the highest global rates of mangrove loss. Remotely sensed data provides timely and accurate information on mangrove distribution and dynamics critical for targeting loss hotspots and guiding intervention. This report inventories, describes and compares all known single- and multi-date remotely sensed datasets with regional coverage and provides areal mangrove extents by country. Multi-date datasets were used to estimate dynamics and identify loss hotspots (i.e., countries that exhibit greatest proportional loss). Results indicate Myanmar is the primary mangrove loss hotspot, exhibiting 35% loss from 1975–2005 and 28% between 2000–2014. Rates of loss in Myanmar were four times the global average from 2000–2012. The Philippines is additionally identified as a loss hotspot, with secondary hotspots including Malaysia, Cambodia and Indonesia. This information helps inform and guide mangrove conservation, restoration and managed-use within the region. Full article

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

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21 pages, 16509 KiB

Open AccessArticle

Mapping Pure Mangrove Patches in Small Corridors and Sandbanks Using Airborne Hyperspectral Imagery

by Cheng-Chien Liu, Tsai-Wen Hsu, Hui-Lin Wen and Kung-Hwa Wang

Remote Sens. 2019, 11(5), 592; https://doi.org/10.3390/rs11050592 - 12 Mar 2019

Cited by 9 | Viewed by 4701

Abstract

Taijiang National Park (TNP) of Taiwan is the northernmost geographical position of mangrove habitats in the Northern Hemisphere. Instead of occupying a vast region with a single species, the mangroves in TNP are usually mingled with other plants in a narrow corridor along the water or in groups on a small sandbank. The multi-spectral images acquired from the spaceborne platforms are therefore limited in mapping the abundance and distribution of the mangrove species in TNP. We report the work of mapping pure mangrove patches in small corridors and sandbanks in TNP using airborne Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery. Bu considering the similarity of spectral reflectance among three species of mangrove and other plants, we followed the concept of supervised classification to select a few training areas with known mangrove trees, where the training areas are determined from the detailed map of mangrove distribution derived from the field investigation. The Hourglass hyperspectral analysis technique was employed to identify the endmembers of pure mangrove in the training areas. The results are consistent with the current distribution of mangrove trees, and the remarkable feature of a “mangrove desert” highlights a fact that biodiversity can be easily and quickly destroyed if no protection is provided. Some remnant patches located by this research are very important to the management of mangrove trees. Full article

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

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17 pages, 10036 KiB

Open AccessArticle

Mapping the Mangrove Forest Canopy Using Spectral Unmixing of Very High Spatial Resolution Satellite Images

by Florent Taureau, Marc Robin, Christophe Proisy, François Fromard, Daniel Imbert and Françoise Debaine

Remote Sens. 2019, 11(3), 367; https://doi.org/10.3390/rs11030367 - 12 Feb 2019

Cited by 35 | Viewed by 9723

Abstract

Despite the low tree diversity and scarcity of the understory vegetation, the high morphological plasticity of mangrove trees induces, at the stand level, a very large variability of forest structures that need to be mapped for assessing the functioning of such complex ecosystems. Fully constrained linear spectral unmixing (FCLSU) of very high spatial resolution (VHSR) multispectral images was tested to fine-scale map mangrove zonations in terms of horizontal variation of forest structure. The study was carried out on three Pleiades-1A satellite images covering French island territories located in the Atlantic, Indian, and Pacific Oceans, namely Guadeloupe, Mayotte, and New Caledonia archipelagos. In each image, FCLSU was trained from the delineation of areas exclusively related to four components including either pure vegetation, soil (ferns included), water, or shadows. It was then applied to the whole mangrove cover imaged for each island and yielded the respective contributions of those four components for each image pixel. On the forest stand scale, the results interestingly indicated a close correlation between FCLSU-derived vegetation fractions and canopy closure estimated from hemispherical photographs (R² = 0.95) and a weak relation with the Normalized Difference Vegetation Index (R² = 0.29). Classification of these fractions also offered the opportunity to detect and map horizontal patterns of mangrove structure in a given site. K-means classifications of fraction indeed showed a global view of mangrove structure organization in the three sites, complementary to the outputs obtained from spectral data analysis. Our findings suggest that the pixel intensity decomposition applied to VHSR multispectral satellite images can be a simple but valuable approach for (i) mangrove canopy monitoring and (ii) mangrove forest structure analysis in the perspective of assessing mangrove dynamics and productivity. As with Lidar-based surveys, these potential new mapping capabilities deserve further physically based interpretation of sunlight scattering mechanisms within forest canopy. Full article

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

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17 pages, 3595 KiB

Open AccessArticle

Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species

by Yi Xu, Junjie Wang, Anquan Xia, Kangyong Zhang, Xuanyan Dong, Kaipeng Wu and Guofeng Wu

Remote Sens. 2019, 11(3), 254; https://doi.org/10.3390/rs11030254 - 27 Jan 2019

Cited by 31 | Viewed by 4557

Abstract

Due to continuous degradation of mangrove forests, the accurate monitoring of spatial distribution and species composition of mangroves is essential for restoration, conservation and management of coastal ecosystems. With leaf hyperspectral reflectance, this study aimed to explore the potential of continuous wavelet analysis (CWA) combined with different sample subset partition (stratified random sampling (STRAT), Kennard-Stone sampling algorithm (KS), and sample subset partition based on joint X-Y distances (SPXY)) and feature extraction methods (principal component analysis (PCA), successive projections algorithm (SPA), and vegetation index (VI)) in mangrove species classification. A total of 301 mangrove leaf samples with four species (Avicennia marina, Bruguiera gymnorrhiza, Kandelia obovate and Aegiceras corniculatum) were collected across six different regions. The smoothed reflectance (Smth) and first derivative reflectance (Der) spectra were subjected to CWA using different wavelet scales, and a total of 270 random forest classification models were established and compared. Among the 120 models with CWA of Smth, 88.3% of models increased the overall accuracy (OA) values with an improvement of 0.2–28.6% compared to the model with the Smth spectra; among the 120 models with CWA of Der, 25.8% of models increased the OA values with an improvement of 0.1–11.4% compared to the model with the Der spectra. The model with CWA of Der at the scale of 2³ coupling with STRAT and SPA achieved the best classification result (OA = 98.0%), while the best model with Smth and Der alone had OA values of 86.3% and 93.0%, respectively. Moreover, the models using STRAT outperformed those using KS and SPXY, and the models using PCA and SPA had better performances than those using VIs. We have concluded that CWA with suitable scales holds great potential in improving the classification accuracy of mangrove species, and that STRAT combined with the PCA or SPA method is also recommended to improve classification performance. These results may lay the foundation for further studies with UAV-acquired or satellite hyperspectral data, and the encouraging performance of CWA for mangrove species classification can also be extended to other plant species. Full article

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

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23 pages, 3729 KiB

Open AccessArticle

Integration of UAV, Sentinel-1, and Sentinel-2 Data for Mangrove Plantation Aboveground Biomass Monitoring in Senegal

by José Antonio Navarro, Nur Algeet, Alfredo Fernández-Landa, Jessica Esteban, Pablo Rodríguez-Noriega and María Luz Guillén-Climent

Remote Sens. 2019, 11(1), 77; https://doi.org/10.3390/rs11010077 - 3 Jan 2019

Cited by 142 | Viewed by 14522

Abstract

Due to the increasing importance of mangroves in climate change mitigation projects, more accurate and cost-effective aboveground biomass (AGB) monitoring methods are required. However, field measurements of AGB may be a challenge because of their remote location and the difficulty to walk in these areas. This study is based on the Livelihoods Fund Oceanium project that monitors 10,000 ha of mangrove plantations. In a first step, the possibility of replacing traditional field measurements of sample plots in a young mangrove plantation by a semiautomatic processing of UAV-based photogrammetric point clouds was assessed. In a second step, Sentinel-1 radar and Sentinel-2 optical imagery were used as auxiliary information to estimate AGB and its variance for the entire study area under a model-assisted framework. AGB was measured using UAV imagery in a total of 95 sample plots. UAV plot data was used in combination with non-parametric support vector regression (SVR) models for the estimation of the study area AGB using model-assisted estimators. Purely UAV-based AGB estimates and their associated standard error (SE) were compared with model-assisted estimates using (1) Sentinel-1, (2) Sentinel-2, and (3) a combination of Sentinel-1 and Sentinel-2 data as auxiliary information. The validation of the UAV-based individual tree height and crown diameter measurements showed a root mean square error (RMSE) of 0.21 m and 0.32 m, respectively. Relative efficiency of the three model-assisted scenarios ranged between 1.61 and 2.15. Although all SVR models improved the efficiency of the monitoring over UAV-based estimates, the best results were achieved when a combination of Sentinel-1 and Sentinel-2 data was used. Results indicated that the methodology used in this research can provide accurate and cost-effective estimates of AGB in young mangrove plantations. Full article

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

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23 pages, 27919 KiB

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Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, China

by Chunyan Lu, Jinfu Liu, Mingming Jia, Mingyue Liu, Weidong Man, Weiwei Fu, Lianxiu Zhong, Xiaoqing Lin, Ying Su and Yibin Gao

Remote Sens. 2018, 10(12), 2020; https://doi.org/10.3390/rs10122020 - 12 Dec 2018

Cited by 34 | Viewed by 6889

Abstract

Mangrove forests are important coastal ecosystems and are crucial for the equilibrium of the global carbon cycle. Monitoring and mapping of mangrove forests are essential for framing knowledge-based conservation policies and funding decisions by governments and managers. The purpose of this study was to monitor mangrove forest dynamics in the Quanzhou Bay Estuary Wetland Nature Reserve. To achieve this goal, we compared and analyzed the spectral discrimination among mangrove forests, mudflats and Spartina using multi-seasonal Landsat images from 1990, 1997, 2005, 2010, and 2017. We identified the spatio-temporal distribution of mangrove forests by combining an optimal segmentation scale model based on object-oriented classification, decision tree and visual interpretation. In addition, mangrove forest dynamics were determined by combining the annual land change area, centroid migration and overlay analysis. The results showed that there were advantages in the approaches used in this study for monitoring mangrove forests. From 1990 to 2017, the extent of mangrove forests increased by 2.48 km², which was mostly converted from mudflats and Spartina. Environmental threats including climate change and sea-level rise, aquaculture development and Spartina invasion, pose potential and direct threats to the existence and expansion of mangrove forests. However, the implementation of reforestation projects and Spartina control plays a substantial role in the expansion of mangrove forests. It has been demonstrated that conservation activities can be beneficial for the restoration and succession of mangrove forests. This study provides an example of how the application of an optimal segmentation scale model and multi-seasonal images to mangrove forest monitoring can facilitate government policies that ensure the effective protection of mangrove forests. Full article

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

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14 pages, 3218 KiB

Open AccessArticle

Vegetation Horizontal Occlusion Index (VHOI) from TLS and UAV Image to Better Measure Mangrove LAI

by Xianxian Guo, Le Wang, Jinyan Tian, Dameng Yin, Chen Shi and Sheng Nie

Remote Sens. 2018, 10(11), 1739; https://doi.org/10.3390/rs10111739 - 3 Nov 2018

Cited by 14 | Viewed by 5119

Abstract

Accurate measurement of the field leaf area index (LAI) is crucial for assessing forest growth and health status. Three-dimensional (3-D) structural information of trees from terrestrial laser scanning (TLS) have information loss to various extents because of the occlusion by canopy parts. The data with higher loss, regarded as poor-quality data, heavily hampers the estimation accuracy of LAI. Multi-location scanning, which proved effective in reducing the occlusion effects in other forests, is hard to carry out in the mangrove forest due to the difficulty of moving between mangrove trees. As a result, the quality of point cloud data (PCD) varies among plots in mangrove forests. To improve retrieval accuracy of mangrove LAI, it is essential to select only the high-quality data. Several previous studies have evaluated the regions of occlusion through the consideration of laser pulses trajectories. However, the model is highly susceptible to the indeterminate profile of complete vegetation object and computationally intensive. Therefore, this study developed a new index (vegetation horizontal occlusion index, VHOI) by combining unmanned aerial vehicle (UAV) imagery and TLS data to quantify TLS data quality. VHOI is asymptotic to 0.0 with increasing data quality. In order to test our new index, the VHOI values of 102 plots with a radius of 5 m were calculated with TLS data and UAV image. The results showed that VHOI had a strong linear relationship with estimation accuracy of LAI (R² = 0.72, RMSE = 0.137). In addition, as TLS data were selected by VHOI less than different thresholds (1.0, 0.9, …, 0.1), the number of remaining plots decreased while the agreement between LAI derived from TLS and field-measured LAI was improved. When the VHOI threshold is 0.3, the optimal trade-off is reached between the number of plots and LAI measurement accuracy (R² = 0.67). To sum up, VHOI can be used as an index to select high-quality data for accurately measuring mangrove LAI and the suggested threshold is 0.30. Full article

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

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20 pages, 6161 KiB

Open AccessArticle

Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery

by Qing Xia, Cheng-Zhi Qin, He Li, Chong Huang and Fen-Zhen Su

Remote Sens. 2018, 10(9), 1343; https://doi.org/10.3390/rs10091343 - 23 Aug 2018

Cited by 64 | Viewed by 10916

Abstract

Mangrove forests, which are essential for stabilizing coastal ecosystems, have been suffering from a dramatic decline over the past several decades. Mapping mangrove forests using satellite imagery is an efficient way to provide key data for mangrove forest conservation. Since mangrove forests are periodically submerged by tides, current methods of mapping mangrove forests, which are normally based on single-date, remote-sensing imagery, often underestimate the spatial distribution of mangrove forests, especially when the images used were recorded during high-tide periods. In this paper, we propose a new method of mapping mangrove forests based on multi-tide, high-resolution satellite imagery. In the proposed method, a submerged mangrove recognition index (SMRI), which is based on the differential spectral signature of mangroves under high and low tides from multi-tide, high-resolution satellite imagery, is designed to identify submerged mangrove forests. The proposed method applies the SMRI values, together with textural features extracted from high-resolution imagery and geographical features of mangrove forests, to an object-based support vector machine (SVM) to map mangrove forests. The proposed method was evaluated via a case study with GF-1 images (high-resolution satellites launched by China) in Yulin City, Guangxi Zhuang Autonomous Region of China. The results show that our proposed method achieves satisfactory performance, with a kappa coefficient of 0.86 and an overall accuracy of 94%, which is better than results obtained from object-based SVMs that use only single-date, remote sensing imagery. Full article

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

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Review

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24 pages, 1524 KiB

Open AccessReview

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

by Tien Dat Pham, Naoto Yokoya, Dieu Tien Bui, Kunihiko Yoshino and Daniel A. Friess

Remote Sens. 2019, 11(3), 230; https://doi.org/10.3390/rs11030230 - 22 Jan 2019

Cited by 237 | Viewed by 29741

Abstract

The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial carbon stock losses. Additionally, some aspects of the mangrove ecosystem remain poorly characterized compared to other forest ecosystems due to practical difficulties in measuring and monitoring mangrove biomass and their carbon stocks. Without a quantitative method for effectively monitoring biophysical parameters and carbon stocks in mangroves, robust policies and actions for sustainably conserving mangroves in the context of climate change mitigation and adaptation are more difficult. In this context, remote sensing provides an important tool for monitoring mangroves and identifying attributes such as species, biomass, and carbon stocks. A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data. Remote sensing approaches have been proven effective for mapping mangrove species, estimating their biomass, and assessing changes in their extent. This review provides an overview of the techniques that are currently being used to map various attributes of mangroves, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies. We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and carbon stocks. Full article

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

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