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.
Highlights
Mangrove forests are found in the intertidal zone along tropical and subtropical coasts, and play a vital role in the coastal zone by providing a range of different ecosystem services to coastal populations [1]
This review highlighted the recent trends for the use of remote sensing approaches for the analysis of mangroves, and showed the advantages of using machine learning techniques for discriminating mangrove species, characterizing biophysical parameters, and estimating mangrove biomass
Machine learning approaches have generally been proved to be effective for estimating mangrove biophysical parameters, i.e., leaf area index (LAI), tree height and leaf pigments, classifying mangrove communities, and provide a better overall accuracy in estimating mangrove biomass using various remotely sensed data in comparison to parametric approaches
Summary
Mangrove forests are found in the intertidal zone along tropical and subtropical coasts, and play a vital role in the coastal zone by providing a range of different ecosystem services to coastal populations [1]. Standardized remote sensing techniques offer a way to reduce uncertainty in estimates of mangrove ecosystem service loss, and are needed for the monitoring, reporting, and verification (MRV) of international conservation programs that protect carbon, such as Reducing Emissions from Deforestation and forest Degradation (REDD+). The recent application of machine learning algorithms and data integration to mangrove mapping has contributed to a number of new publications that shed light on various aspects of the mangrove ecosystem, especially those that have data with high dimensionality [19] As this is an emerging field, machine learning techniques for discriminating mangrove species, monitoring mangrove structures, and estimating mangrove biomass are not well documented, and the current literature does not critically analyze the advantages and disadvantages of these approaches. This review updates previous reviews of mangrove remote sensing by Heumann [20], Kuenzer et al [21], and Lucas et al [22], but importantly, it focuses on Remote Sens. 2019, 11, 230 the methodologies that discriminate mangrove species, quantify biophysical parameters and structure, and estimate mangrove biomass
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