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

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.

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

“…This difference is likely attributable to the mixed mangrove species planted in the CGBRS and the number of plots. To archive a more accurate forest AGB map, we should exploit the advantages of various novel GBDT algorithms with multi-sensor data integration [74]. In more intensive works, novel boosting decision tree techniques should exploit the full capability of multi-source EO data in different mangrove communities occupying tropical intertidal areas at different geographical locations, particularly those of biosphere reserves.…”

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, ML algorithms such as Gaussian process regression, multi-layer perception neural networks, SVR, and RFR techniques have been employed to retrieve mangrove AGB, as reported in a number of published case studies [8,[20][21][22]. ML techniques often achieve better predictive performances compared to conventional parametric methods for mangrove AGB retrievals [20,81]. However, to date, an estimation of mangrove AGB including shrub and small mangrove patches has not been reported in the literature, indicating a need to propose an alternative and new approach to mangrove AGB estimation to support MRV and blue carbon projects.…”

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

“…A noteworthy study was conducted by Mauya et al [72]; these authors assessed the multiple linear regression (MLR) models built by Sentinel-1, Sentinel-2, and ALOS PALSAR-2 images to predict the FSV, and found that Sentinel-2 images performed best with an RMSEr = 42.03% and a pseudo-R 2 = 0.63. For predicting forest variables, Pham et al showed that machine learning algorithms were likely to become more attractive in remote sensing [73]. These authors suggest that future studies using more methods, large areas, and Sentinel-2 data to predict the FSV should be conducted.…”