Special Issue "Remote Sensing in Mangroves"

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

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editor

Dr. Chandra Giri
Website
Guest Editor
Remote Sensing and Spatial Analysis Branch, 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,

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, conversion of mangroves to other land uses is the dominant factor responsible for the change; however, sea level rise and natural disaster such as hurricane 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 advancement 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. Spectral, spatial, and temporal resolution of remote sensing data and their availability has improved making it possible to observe and monitor mangroves with unprecedented spatial thematic, and temporal details. 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 volume of data using cloud computing.

The “Remote Sensing” journal announces a special issue dedicated to observation and monitoring of mangroves using remote sensing from local to global scales. The issue will broadly cover 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 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 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, 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 papers will be 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 2200 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 forests
  • Mangrove change
  • Mapping
  • Monitoring
  • Remote Sensing
  • Image processing

Published Papers (5 papers)

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Research

Open AccessArticle
Delineation of Tree Patches in a Mangrove-Marsh Transition Zone by Watershed Segmentation of Aerial Photographs
Remote Sens. 2020, 12(13), 2086; https://doi.org/10.3390/rs12132086 - 29 Jun 2020
Abstract
Mangrove migration, or transgression in response to global climatic changes or sea-level rise, is a slow process; to capture it, understanding both the present distribution of mangroves at individual patch (single- or clumped trees) scale, and their rates of change are essential. In [...] Read more.
Mangrove migration, or transgression in response to global climatic changes or sea-level rise, is a slow process; to capture it, understanding both the present distribution of mangroves at individual patch (single- or clumped trees) scale, and their rates of change are essential. In this study, a new method was developed to delineate individual patches and to estimate mangrove cover from very high-resolution (0.08 m spatial resolution) true color (Red (R), Green (G), and Blue (B) spectral channels) aerial photography. The method utilizes marker-based watershed segmentation, where markers are detected using a vegetation index and Otsu’s automatic thresholding. Fourteen commonly used vegetation indices were tested, and shadows were removed from the segmented images to determine their effect on the accuracy of tree detection, cover estimation, and patch delineation. According to point-based accuracy analysis, we obtained adjusted overall accuracies >90% in tree detection using seven vegetation indices. Likewise, using an object-based approach, the highest overlap accuracy between predicted and reference data was 95%. The vegetation index Excess Green (ExG) without shadow removal produced the most accurate mangrove maps by separating tree patches from shadows and background marsh vegetation and detecting more individual trees. The method provides high precision delineation of mangrove trees and patches, and the opportunity to analyze mangrove migration patterns at the scale of isolated individuals and patches. Full article
(This article belongs to the Special Issue Remote Sensing in Mangroves)
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Open AccessArticle
Integration of GF2 Optical, GF3 SAR, and UAV Data for Estimating Aboveground Biomass of China’s Largest Artificially Planted Mangroves
Remote Sens. 2020, 12(12), 2039; https://doi.org/10.3390/rs12122039 - 25 Jun 2020
Abstract
Accurate methods to estimate the aboveground biomass (AGB) of mangroves are required to monitor the subtle changes over time and assess their carbon sequestration. The AGB of forests is a function of canopy-related information (canopy density, vegetation status), structures, and tree heights. However, [...] Read more.
Accurate methods to estimate the aboveground biomass (AGB) of mangroves are required to monitor the subtle changes over time and assess their carbon sequestration. The AGB of forests is a function of canopy-related information (canopy density, vegetation status), structures, and tree heights. However, few studies have attended to integrating these factors to build models of the AGB of mangrove plantations. The objective of this study was to develop an accurate and robust biomass estimation of mangrove plantations using Chinese satellite optical, SAR, and Unmanned Aerial Vehicle (UAV) data based digital surface models (DSM). This paper chose Qi’ao Island, which forms the largest contiguous area of mangrove plantation in China, as the study area. Several field visits collected 127 AGB samples. The models for AGB estimation were developed using the random forest algorithm and integrating images from multiple sources: optical images from Gaofen-2 (GF-2), synthetic aperture radar (SAR) images from Gaofen-3 (GF-3), and UAV-based digital surface model (DSM) data. The performance of the models was assessed using the root-mean-square error (RMSE) and relative RMSE (RMSEr), based on five-fold cross-validation and stratified random sampling approach. The results showed that images from the GF-2 optical (RMSE = 33.49 t/ha, RMSEr = 21.55%) or GF-3 SAR (RMSE = 35.32 t/ha, RMSEr = 22.72%) can be used appropriately to monitor the AGB of the mangrove plantation. The AGB models derived from a combination of the GF-2 and GF-3 datasets yielded a higher accuracy (RMSE = 29.89 t/ha, RMSEr = 19.23%) than models that used only one of them. The model that used both datasets showed a reduction of 2.32% and 3.49% in RMSEr over the GF-2 and GF-3 models, respectively. On the DSM dataset, the proposed model yielded the highest accuracy of AGB (RMSE = 25.69 t/ha, RMSEr = 16.53%). The DSM data were identified as the most important variable, due to mitigating the saturation effect observed in the optical and SAR images for a dense AGB estimation of the mangroves. The resulting map, derived from the most accurate model, was consistent with the results of field investigations and the mangrove plantation sequences. Our results indicated that the AGB can be accurately measured by integrating images from the optical, SAR, and DSM datasets to adequately represent canopy-related information, forest structures, and tree heights. Full article
(This article belongs to the Special Issue Remote Sensing in Mangroves)
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Open AccessArticle
Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data
Remote Sens. 2020, 12(10), 1690; https://doi.org/10.3390/rs12101690 - 25 May 2020
Abstract
Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts [...] Read more.
Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass. Full article
(This article belongs to the Special Issue Remote Sensing in Mangroves)
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Open AccessArticle
Estimating Mangrove Above-Ground Biomass Using Extreme Gradient Boosting Decision Trees Algorithm with Fused Sentinel-2 and ALOS-2 PALSAR-2 Data in Can Gio Biosphere Reserve, Vietnam
Remote Sens. 2020, 12(5), 777; https://doi.org/10.3390/rs12050777 - 29 Feb 2020
Cited by 1
Abstract
This study investigates the effectiveness of gradient boosting decision trees techniques in estimating mangrove above-ground biomass (AGB) at the Can Gio biosphere reserve (Vietnam). For this purpose, we employed a novel gradient-boosting regression technique called the extreme gradient boosting regression (XGBR) algorithm implemented [...] Read more.
This study investigates the effectiveness of gradient boosting decision trees techniques in estimating mangrove above-ground biomass (AGB) at the Can Gio biosphere reserve (Vietnam). For this purpose, we employed a novel gradient-boosting regression technique called the extreme gradient boosting regression (XGBR) algorithm implemented and verified a mangrove AGB model using data from a field survey of 121 sampling plots conducted during the dry season. The dataset fuses the data of the Sentinel-2 multispectral instrument (MSI) and the dual polarimetric (HH, HV) data of ALOS-2 PALSAR-2. The performance standards of the proposed model (root-mean-square error (RMSE) and coefficient of determination (R2)) were compared with those of other machine learning techniques, namely gradient boosting regression (GBR), support vector regression (SVR), Gaussian process regression (GPR), and random forests regression (RFR). The XGBR model obtained a promising result with R2 = 0.805, RMSE = 28.13 Mg ha−1, and the model yielded the highest predictive performance among the five machine learning models. In the XGBR model, the estimated mangrove AGB ranged from 11 to 293 Mg ha−1 (average = 106.93 Mg ha−1). This work demonstrates that XGBR with the combined Sentinel-2 and ALOS-2 PALSAR-2 data can accurately estimate the mangrove AGB in the Can Gio biosphere reserve. The general applicability of the XGBR model combined with multiple sourced optical and SAR data should be further tested and compared in a large-scale study of forest AGBs in different geographical and climatic ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing in Mangroves)
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Open AccessArticle
Remote Sensing of Mangroves and Estuarine Communities in Central Queensland, Australia
Remote Sens. 2020, 12(1), 197; https://doi.org/10.3390/rs12010197 - 06 Jan 2020
Abstract
Great Barrier Reef catchments are under pressure from the effects of climate change, landscape modifications, and hydrology alterations. With the use of remote sensing datasets covering large areas, conventional methods of change detection can expose broad transitions, whereas workflows that excerpt data for [...] Read more.
Great Barrier Reef catchments are under pressure from the effects of climate change, landscape modifications, and hydrology alterations. With the use of remote sensing datasets covering large areas, conventional methods of change detection can expose broad transitions, whereas workflows that excerpt data for time-series trends divulge more subtle transformations of land cover modification. Here, we combine both these approaches to investigate change and trends in a large estuarine region of Central Queensland, Australia, that encompasses a national park and is adjacent to the Great Barrier Reef World Heritage site. Nine information classes were compiled in a maximum likelihood post classification change analysis in 2004–2017. Mangroves decreased (1146 hectares), as was the case with estuarine wetland (1495 hectares), and saltmarsh grass (1546 hectares). The overall classification accuracies and Kappa coefficient for 2004, 2006, 2009, 2013, 2015, and 2017 land cover maps were 85%, 88%, 88%, 89%, 81%, and 92%, respectively. The cumulative area of open forest, estuarine wetland, and saltmarsh grass (1628 hectares) was converted to pasture in a thematic change analysis showing the “from–to” change. We generated linear regression relationships to examine trends in pixel values across the time series. Our findings from a trend analysis showed a decreasing trend (p value range = 0.001–0.099) in the vegetation extent of open forest, fringing mangroves, estuarine wetlands, saltmarsh grass, and grazing areas, but this was inconsistent across the study site. Similar to reports from tropical regions elsewhere, saltmarsh grass is poorly represented in the national park. A severe tropical cyclone preceding the capture of the 2017 Landsat 8 Operational Land Imager (OLI) image was likely the main driver for reduced areas of shoreline and stream vegetation. Our research contributes to the body of knowledge on coastal ecosystem dynamics to enable planning to achieve more effective conservation outcomes. Full article
(This article belongs to the Special Issue Remote Sensing in Mangroves)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Application of integrated GIS models for detection of mangrove vulnerability and coastal erosion changes in tropical coastal protected area
Authors: Fernando Morgado
Affiliation: Departament of Biology, University of Aveiro
Abstract: Currently there is a wide variety of models that support the planning and management of coastal areas (and coastal municipalities and cities), which include applications for the calculation of vulnerability of coastal erosion regarding Sustainable Infrastructures. Being these models supported by Geographical Information Systems and a set of parameters that best describes the study area, it becomes possible to identify vulnerable areas to erosion (affecting Infrastructures safety). This system features a large number of areas with strong evidence of erosion, highlighting geological and geomorphological areas with high vulnerability. Approximately 1221 Km2 have been classified in this work, and about 16% of the total present high and very high vulnerability. Other relevant aspects, were the identification and georeferencing sites that showed strong evidence of erosion, and thus having huge influence on the final results. This work led to the development of a multidisciplinary approach through the application of a prediction and description model that resulted from the adaptation of the study system from a set of implemented models for coastal regions, in order to determine the erosion vulnerability in the mangroves (and associated localities, municipalities and communities) wellbeing of Cananeia.

Title: Effect of multiple stressors on the Quelimane mangroves, Mozambique: human impacts and climate change
Authors: Fernando Morgado
Affiliation: Departament of Biology, University of Aveiro
Abstract: Mangroves are the only forests situated at the confluence of the terrestrial and marine environment in subtropical and tropical regions of the world. They have exceptional adaptations to environmental conditions and play a key role in the sustainability of the natural and human environment, and can create conditions for the development of wildlife-friendly habitats, while also contributing to the maintenance of biodiversity. The study aimed to characterize the horizontal structure of the Quelimane mangrove forest, to assess its current conservation status, to show temporal evolution and trends of the mangrove forest over the past sixteen (16) years (2002-2018) and to analyse the influence of anthropogenic and climatic factors involved. Data collection was carried out in Icídua and Inhangome neigh bourhoods in the year 2018 in August and September. In each neighbourhood, 18 gridsof 10x10m were marked out at 4m from the bank of the Bons Sinais River. In each grid was done the characterization of the mangrove and all regenerating juveniles individuals. Through satellite images the evolution of the mangrove forest was analysed and the transect factors, grids, species and locations and the salinity parameter were studied as this is one of the abiotic factors with significant influence on the dynamics of this forest. Most of the variables analysed showed significant differences between transects, grids, species, locations and selected interactions (P <0.05). However, after the interaction between the factors were done, significant differences were found mainly in the PAD. Five species of mangrove were identified among them Avicennia marina, Rhizophora mucronata, Ceriops tagal, Xylocarpus granatum e Bruguierra gymnorhiza. These species all occur in Inhangome and Icidua only two (Avicennia marina and Ceriopstagal).The Avicennia marina was the most abundant species in both study points with a relative density of 75% and 95% respectively. Quelimane mangrove still raises some conservation concerns, justified by the percentage of intact species in Inhangome and Icidua being 47% and 8% respectively and considering the importance of this ecosystem in the face of climate change. Regeneration levels are of little concern with 18% propagules, 45% intermediates and 35% shrubs for Inhangome and 77% propagules, 21% intermediates and 1% shrubs for Icidua. The conservation status of the forest is worrying due to anthropogenic and natural disturbances that have a direct impact on forest area reduction in recent years.

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