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Remote Sensing of Mangroves: Observation and Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 August 2015) | Viewed by 115471

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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,

Mangrove forests are in constant flux due to both natural and anthropogenic forces. The changing mangroves could serve as an indicator of climate change. At present, conversion of mangroves to other land uses is the dominant factor responsible for the change; however, climate change (e.g., sea level rise) is 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 and spatial resolution of remote sensing data and their availability has improved making it possible to observe and monitor mangroves with unprecedented spatial and thematic 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 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

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Keywords

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

Published Papers (10 papers)

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Editorial

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1741 KiB

Open AccessEditorial

Observation and Monitoring of Mangrove Forests Using Remote Sensing: Opportunities and Challenges

by Chandra Giri

Remote Sens. 2016, 8(9), 783; https://doi.org/10.3390/rs8090783 - 21 Sep 2016

Cited by 105 | Viewed by 15836

Abstract

Mangrove forests, distributed in the tropical and subtropical regions of the world, are in a constant flux. They provide important ecosystem goods and services to nature and society. In recent years, the carbon sequestration potential and protective role of mangrove forests from natural disasters is being highlighted as an effective option for climate change adaptation and mitigation. The forests are under threat from both natural and anthropogenic forces. However, accurate, reliable, and timely information of the distribution and dynamics of mangrove forests of the world is not readily available. Recent developments in the availability and accessibility of remotely sensed data, advancement in image pre-processing and classification algorithms, significant improvement in computing, availability of expertise in handling remotely sensed data, and an increasing awareness of the applicability of remote sensing products has greatly improved our scientific understanding of changing mangrove forest cover attributes. As reported in this special issue, the use of both optical and radar satellite data at various spatial resolutions (i.e., 1 m to 30 m) to derive meaningful forest cover attributes (e.g., species discrimination, above ground biomass) is on the rise. This multi-sensor trend is likely to continue into the future providing a more complete inventory of global mangrove forest distributions and attribute inventories at enhanced temporal frequency. The papers presented in this “Special Issue” provide important remote sensing monitoring advancements needed to meet future scientific objectives for global mangrove forest monitoring from local to global scales. Full article

(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)

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Research

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2735 KiB

Open AccessArticle

L-Band Polarimetric Target Decomposition of Mangroves of the Rufiji Delta, Tanzania

by Ian Brown, Simon Mwansasu and Lars-Ove Westerberg

Remote Sens. 2016, 8(2), 140; https://doi.org/10.3390/rs8020140 - 09 Feb 2016

Cited by 23 | Viewed by 7885

Abstract

The mangroves of the Rufiji Delta are an important habitat and resource. The mangrove forest reserve is home to an indigenous population and has been under pressure from an influx of migrants from the landward side of the delta. Timely and effective forest management is needed to preserve the delta and mangrove forest. Here, we investigate the potential of polarimetric target decomposition for mangrove forest monitoring and analysis. Using three ALOS PALSAR images, we show that L-band polarimetry is capable of mapping mangrove dynamics and is sensitive to stand structure and the hydro-geomorphology of stands. Entropy-alpha-anisotropy and incoherent target decompositions provided valuable measures of scattering behavior related to forest structure. Little difference was found between Yamaguchi and Arii decompositions, despite the conceptual differences between these models. Using these models, we were able to differentiate the scattering behavior of the four main species found in the delta, though classification was impractical due to the lack of pure stands. Scattering differences related to season were attributed primarily to differences in ground moisture or inundation. This is the first time mangrove species have been identified by their scattering behavior in L-band polarimetric data. These results suggest higher resolution L-band quad-polarized imagery, such as from PALSAR-2, may be a powerful tool for mangrove species mapping. Full article

(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)

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6333 KiB

Open AccessArticle

Madagascar’s Mangroves: Quantifying Nation-Wide and Ecosystem Specific Dynamics, and Detailed Contemporary Mapping of Distinct Ecosystems

by Trevor G. Jones, Leah Glass, Samir Gandhi, Lalao Ravaoarinorotsihoarana, Aude Carro, Lisa Benson, Harifidy Rakoto Ratsimba, Chandra Giri, Dannick Randriamanatena and Garth Cripps

Remote Sens. 2016, 8(2), 106; https://doi.org/10.3390/rs8020106 - 30 Jan 2016

Cited by 47 | Viewed by 13931

Abstract

Mangrove ecosystems help mitigate climate change, are highly biodiverse, and provide critical goods and services to coastal communities. Despite their importance, anthropogenic activities are rapidly degrading and deforesting mangroves world-wide. Madagascar contains 2% of the world’s mangroves, many of which have undergone or are starting to exhibit signs of widespread degradation and deforestation. Remotely sensed data can be used to quantify mangrove loss and characterize remaining distributions, providing detailed, accurate, timely and updateable information. We use USGS maps produced from Landsat data to calculate nation-wide dynamics for Madagascar’s mangroves from 1990 to 2010, and examine change more closely by partitioning the national distribution in to primary (i.e., >1000 ha) ecosystems; with focus on four Areas of Interest (AOIs): Ambaro-Ambanja Bays (AAB), Mahajamba Bay (MHJ), Tsiribihina Manombolo Delta (TMD) and Bay des Assassins (BdA). Results indicate a nation–wide net-loss of 21% (i.e., 57,359 ha) from 1990 to 2010, with dynamics varying considerably among primary mangrove ecosystems. Given the limitations of national-level maps for certain localized applications (e.g., carbon stock inventories), building on two previous studies for AAB and MHJ, we employ Landsat data to produce detailed, contemporary mangrove maps for TMD and BdA. These contemporary, AOI-specific maps provide improved detail and accuracy over the USGS national-level maps, and are being applied to conservation and restoration initiatives through the Blue Ventures’ Blue Forests programme and WWF Madagascar West Indian Ocean Programme Office’s work in the region. Full article

(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)

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5667 KiB

Open AccessArticle

Radarsat-2 Backscattering for the Modeling of Biophysical Parameters of Regenerating Mangrove Forests

by Michele F. Cougo, Pedro W. M. Souza-Filho, Arnaldo Q. Silva, Marcus E. B. Fernandes, João R. dos Santos, Maria R. S. Abreu, Wilson R. Nascimento and Marc Simard

Remote Sens. 2015, 7(12), 17097-17112; https://doi.org/10.3390/rs71215873 - 17 Dec 2015

Cited by 40 | Viewed by 7939

Abstract

The aim of this study is to understand the relationship between radar backscattering (σ°, β° and γ) of a multi-polarized Radarsat-2 C-band image with the structural attributes of regenerating mangrove vegetation located at the mouth of the Amazon River. CBH (circumference at breast height), height and species data were collected to characterize vegetation structure and above-ground biomass (AGB) at 17 plots with a total of 3090 measured individuals. Significant relationships between the linear σ° in VH (vertical transmit, horizontal receive) cross-polarization produced r² values of 0.63 for the average height, 0.53 for the DBH, 0.46 for the basal area (BA) and 0.52 for the AGB. Using co-polarized HH (horizontal transmit, horizontal receive) and VV (vertical transmit, vertical receive), r² values increased to 0.81, 0.79, 0.67 and 0.79, respectively. Vegetation attribute maps of average canopy height, DBH and AGB were generated for the study area. We conclude that multi-polarized Radarsat-2 images were adequate for characterization of vegetation attributes in areas of mangrove regeneration. Full article

(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)

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3975 KiB

Open AccessArticle

The Mangroves of the Zambezi Delta: Increase in Extent Observed via Satellite from 1994 to 2013

by Aurélie C. Shapiro, Carl C. Trettin, Helga Küchly, Sadroddin Alavinapanah and Salomão Bandeira

Remote Sens. 2015, 7(12), 16504-16518; https://doi.org/10.3390/rs71215838 - 05 Dec 2015

Cited by 53 | Viewed by 10851

Abstract

Mangroves are recognized for their valued ecosystem services provision while having the highest carbon density among forested ecosystems. Yet they are increasingly threatened by deforestation, conversion to agriculture and development, reducing the benefits they provide for local livelihoods, coastal protection and climate change mitigation. Accordingly, accurate estimates of mangrove area and change are fundamental for developing strategies for sustainable use, conservation and Reducing Emissions from Deforestation and Degradation (REDD+). The Zambezi River Delta in Mozambique contains one of the largest mangrove forests in Africa, and deforestation has been reported to be substantial, however these estimates vary widely. We used Landsat imagery from 1994, 2000 and 2013, to estimate a total current mangrove area of 37,034 ha, which is a net increase of 3723 ha over 19 years. The land cover change assessment was also used to provide perspective on ecosystem carbon stocks, showing that the Zambezi Delta mangrove ecosystem acts as a large carbon sink. Our findings reinforce the importance of conducting land cover change assessments using coherent data and analytical models, coupled with field validation. Broader application of our approach could help quantify the rates of natural change from erosion and land aggradation contrasted with anthropogenic causes. Full article

(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)

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3291 KiB

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Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

by Julio Pastor-Guzman, Peter M. Atkinson, Jadunandan Dash and Rodolfo Rioja-Nieto

Remote Sens. 2015, 7(11), 14530-14558; https://doi.org/10.3390/rs71114530 - 04 Nov 2015

Cited by 61 | Viewed by 11804

Abstract

There is a need to develop indicators of mangrove condition using remotely sensed data. However, remote estimation of leaf and canopy biochemical properties and vegetation condition remains challenging. In this paper, we (i) tested the performance of selected hyperspectral and broad band indices to predict chlorophyll concentration (CC) on mangrove leaves and (ii) showed the potential of Landsat 8 for estimation of mangrove CC at the landscape level. Relative leaf CC and leaf spectral response were measured at 12 Elementary Sampling Units (ESU) distributed along the northwest coast of the Yucatan Peninsula, Mexico. Linear regression models and coefficients of determination were computed to measure the association between CC and spectral response. At leaf level, the narrow band indices with the largest correlation with CC were Vogelmann indices and the MTCI (R²> 0.5). Indices with spectral bands around the red edge (705–753 nm) were more sensitive to mangrove leaf CC. At the ESU level Landsat 8 NDVI green, which uses the green band in its formulation explained most of the variation in CC (R² > 0.8). Accuracy assessment between estimated CC and observed CC using the leave-one-out cross-validation (LOOCV) method yielded a root mean squared error (RMSE) = 15 mg·cm⁻², and R² = 0.703. CC maps showing the spatiotemporal variation of CC at landscape scale were created using the linear model. Our results indicate that Landsat 8 NDVI green can be employed to estimate CC in large mangrove areas where ground networks cannot be applied, and mapping techniques based on satellite data, are necessary. Furthermore, using upcoming technologies that will include two bands around the red edge such as Sentinel 2 will improve mangrove monitoring at higher spatial and temporal resolutions. Full article

(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)

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1665 KiB

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Satellite Images for Monitoring Mangrove Cover Changes in a Fast Growing Economic Region in Southern Peninsular Malaysia

by Kasturi Devi Kanniah, Afsaneh Sheikhi, Arthur P. Cracknell, Hong Ching Goh, Kian Pang Tan, Chin Siong Ho and Fateen Nabilla Rasli

Remote Sens. 2015, 7(11), 14360-14385; https://doi.org/10.3390/rs71114360 - 29 Oct 2015

Cited by 121 | Viewed by 12900

Abstract

Effective monitoring is necessary to conserve mangroves from further loss in Malaysia. In this context, remote sensing is capable of providing information on mangrove status and changes over a large spatial extent and in a continuous manner. In this study we used Landsat satellite images to analyze the changes over a period of 25 years of mangrove areas in Iskandar Malaysia (IM), the fastest growing national special economic region located in southern Johor, Malaysia. We tested the use of two widely used digital classification techniques to classify mangrove areas. The Maximum Likelihood Classification (MLC) technique provided significantly higher user, producer and overall accuracies and less “salt and pepper effects” compared to the Support Vector Machine (SVM) technique. The classified satellite images using the MLC technique showed that IM lost 6740 ha of mangrove areas from 1989 to 2014. Nevertheless, a gain of 710 ha of mangroves was observed in this region, resulting in a net loss of 6030 ha or 33%. The loss of about 241 ha per year of mangroves was associated with a steady increase in urban land use (1225 ha per year) from 1989 until 2014. Action is necessary to protect the existing mangrove cover from further loss. Gazetting of the remaining mangrove sites as protected areas or forest reserves and introducing tourism activities in mangrove areas can ensure the continued survival of mangroves in IM. Full article

(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)

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2960 KiB

Open AccessArticle

Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

by Yuanhui Zhu, Kai Liu, Lin Liu, Shugong Wang and Hongxing Liu

Remote Sens. 2015, 7(9), 12192-12214; https://doi.org/10.3390/rs70912192 - 21 Sep 2015

Cited by 101 | Viewed by 8385

Abstract

Previous research studies have demonstrated that the relationship between remote sensing-derived parameters and aboveground biomass (AGB) could vary across different species types. However, there are few studies that calibrate reliable statistical models for mangrove AGB. This study quantifies the differences of accuracy in AGB estimation between the results obtained with and without the consideration of species types using Worldview-2 images and field surveys. A Back Propagation Artificial Neural Network (BP ANN) based model is developed for the accurate estimation of uneven-aged and dense mangrove forest biomass. The contributions of the input variables are further quantified using a “Weights” method based on BP ANN model. Two types of mangrove species, Sonneratia apetala (S. apetala) and Kandelia candel (K. candel), are examined in this study. Results show that the species type information is the most important variable for AGB estimation, and the red edge band and the associated vegetation indices from WorldView-2 images are more sensitive to mangrove AGB than other bands and vegetation indices. The RMSE of biomass estimation at the incorporation of species as a dummy variable is 19.17% lower than that of the mixed species level. The results demonstrate that species type information obtained from the WorldView-2 images can significantly improve of the accuracy of the biomass estimation. Full article

(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)

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2317 KiB

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Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets

by Muhammad Kamal, Stuart Phinn and Kasper Johansen

Remote Sens. 2015, 7(4), 4753-4783; https://doi.org/10.3390/rs70404753 - 17 Apr 2015

Cited by 146 | Viewed by 14270

Abstract

Providing accurate maps of mangroves, where the spatial scales of the mapped features correspond to the ecological structures and processes, as opposed to pixel sizes and mapping approaches, is a major challenge for remote sensing. This study developed and evaluated an object-based approach to understand what types of mangrove information can be mapped using different image datasets (Landsat TM, ALOS AVNIR-2, WorldView-2, and LiDAR). We compared and contrasted the ability of these images to map five levels of mangrove features, including vegetation boundary, mangrove stands, mangrove zonations, individual tree crowns, and species communities. We used the Moreton Bay site in Australia as the primary site to develop the classification rule sets and Karimunjawa Island in Indonesia to test the applicability of the rule sets. The results demonstrated the effectiveness of a conceptual hierarchical model for mapping specific mangrove features at discrete spatial scales. However, the rule sets developed in this study require modification to map similar mangrove features at different locations or when using image data acquired by different sensors. Across the hierarchical levels, smaller object sizes (i.e., tree crowns) required more complex classification rule sets. Incorporation of contextual information (e.g., distance and elevation) increased the overall mapping accuracy at the mangrove stand level (from 85% to 94%) and mangrove zonation level (from 53% to 59%). We found that higher image spatial resolution, larger object size, and fewer land-cover classes result in higher mapping accuracies. This study highlights the potential of selected images and mapping techniques to map mangrove features, and provides guidance for how to do this effectively through multi-scale mangrove composition mapping. Full article

(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)

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Other

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2746 KiB

Open AccessTechnical Note

Textural–Spectral Feature-Based Species Classification of Mangroves in Mai Po Nature Reserve from Worldview-3 Imagery

by Ting Wang, Hongsheng Zhang, Hui Lin and Chaoyang Fang

Remote Sens. 2016, 8(1), 24; https://doi.org/10.3390/rs8010024 - 31 Dec 2015

Cited by 121 | Viewed by 9093

Abstract

The identification of species within an ecosystem plays a key role in formulating an inventory for use in the development of conservation management plans. The classification of mangrove species typically involves intensive field surveys, whereas remote sensing techniques represent a cost-efficient means of mapping and monitoring mangrove forests at large scales. However, the coarse spectral resolution of remote sensing technology has up until recently restricted the ability to identify individual species. The more recent development of very high-resolution spatial optical remote sensing sensors and techniques has thus provided new opportunities for the accurate mapping of species within mangrove forests over large areas. When dealing with the complex problems associated with discriminating among species, classifier performance could be enhanced through the adoption of more intrinsic features; such as textural and differential spectral features. This study explored the effectiveness of textural and differential spectral features in mapping mangrove inter-species obtained from WorldView-3 high-spatial-resolution imagery for mangrove species in Hong Kong. Due to the different arrangement of leaves, the branch density, and the average height and size of plants, we found that the differential spectral features could aid in reducing inner-species variability and increasing intra-species separation. Using a combination of textural and differential spectral features thus represents a promising tool for discriminating among mangrove species. Experimental results suggest that combining these features can greatly improve mapping accuracy, thereby providing more reliable mapping results. Full article

(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)

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