A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018

Abstract

Blue carbon (BC) ecosystems are an important coastal resource, as they provide a range of goods and services to the environment. They play a vital role in the global carbon cycle by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, there has been a large reduction in the global BC ecosystems due to their conversion to agriculture and aquaculture, overexploitation, and removal for human settlements. Effectively monitoring BC ecosystems at large scales remains a challenge owing to practical difficulties in monitoring and the time-consuming field measurement approaches used. As a result, sensible policies and actions for the sustainability and conservation of BC ecosystems can be hard to implement. In this context, remote sensing provides a useful tool for mapping and monitoring BC ecosystems faster and at larger scales. Numerous studies have been carried out on various sensors based on optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), aerial photographs (APs), and multispectral data. Remote sensing-based approaches have been proven effective for mapping and monitoring BC ecosystems by a large number of studies. However, to the best of our knowledge, this is the first comprehensive review on the applications of remote sensing techniques for mapping and monitoring BC ecosystems. The main goal of this review is to provide an overview and summary of the key studies undertaken from 2010 onwards on remote sensing applications for mapping and monitoring BC ecosystems. Our review showed that optical imagery, such as multispectral and hyper-spectral data, is the most common for mapping BC ecosystems, while the Landsat time-series are the most widely-used data for monitoring their changes on larger scales. We investigate the limitations of current studies and suggest several key aspects for future applications of remote sensing combined with state-of-the-art machine learning techniques for mapping coastal vegetation and monitoring their extents and changes.

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

“…In recent years, remote sensing-based approaches have been widely used to retrieve mangrove AGB and map carbon stocks using various sensors ranging from optical and synthetic aperture radar (SAR) to light detection and ranging (LiDAR) data [10][11][12] because such sensors provide a large number of benefits compared to traditional field-based methods such as lower cost, faster speed, easier repeatability, and the coverage of wider areas [13,14]. However, to date, there have been no attempts to investigate an integration of optical and SAR sensors, such as L-band, X-band, and C-band data, to retrieve the mangrove AGB using novel machine-learning (ML) techniques in tropical regions.…”

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

“…The mapping of coastal and marine habitats is a preliminary step for studies of ecosystems. Pham et al, 2019 [227] presented a comprehensive review of RS data used for mapping and monitoring blue carbon ecosystems (mangroves, seagrasses, and salt marshes), including high spatial resolution and medium/low spatial resolution with different sensors from SRS, ARS, UAV, and USV. The review covers studies undertaken from 2010 to 2018.…”

“…Ecological RS applications with the different sensors have proven to be useful, such as the monitoring of blue carbon ecosystems [227,283], habitat [284], and valuation of ecosystem services [285,286]. For instance, coral reef restoration as a measure could be addressed by using RS technologies as they provide information on various abiotic conditions and other site characteristics [287].…”

A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

“…More recent studies used different machine learning algorithms in predicting and zoning the flash flooding areas [19][20][21]. However, only a few studies integrated remotely sensed data and spatial data in machine learning techniques for improving the accuracy of spatial prediction of flash floods, despite the fact that air-borne remote sensing data provide a number of benefits such as easier repeatability, low cost, and wider area coverage [21,22], resulting in a lack of cost-effective, precise, and timely models for the susceptibility mapping of flash floods. Thus, this study aims at developing a state-of-the-art model incorporating Sentinel-1 C band free-of-charge data and an advanced machine learning algorithm using the decision tree-based random subspace optimized by hybrid swarm intelligence, namely the HFPS-RSTree model, for the spatial prediction of flash floods in a mountainous area in Northwestern Vietnam.…”