The State of the World's Mangrove Forests: Past, Present, and Future

Friess, Daniel A.; Rogers, Kerrylee; Lovelock, Catherine E.; Krauss, Ken W.; Hamilton, Stuart E.; Lee, Joe; Lucas, Richard; Primavera, Jurgenne H.; Rajkaran, Anusha; Shi, Suhua

doi:10.1146/annurev-environ-101718-033302

Cited by 427 publications

(243 citation statements)

References 133 publications

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“…Firstly the CGMFC-21 is the product of two robust and peer-reviewed datasets, and as such is considered a reliable source for shifts in mangrove habitats that have been used in several other studies e.g. 5,11,46 . Secondly, the methodology for creating the CGMFC-21 was similar to the methodology utilised when identifying land-use changes in Southeast Asia 45 , thus the CGMFC-21 was extremely appropriate for working between these two datasets.…”

Section: Discussionmentioning

confidence: 99%

Global trends in mangrove forest fragmentation

Bryan-Brown

Connolly

Richards

et al. 2020

Sci Rep

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174

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fragmentation is a major driver of ecosystem degradation, reducing the capacity of habitats to provide many important ecosystem services. Mangrove ecosystem services, such as erosion prevention, shoreline protection and mitigation of climate change (through carbon sequestration), depend on the size and arrangement of forest patches, but we know little about broad-scale patterns of mangrove forest fragmentation. Here we conduct a multi-scale analysis using global estimates of mangrove density and regional drivers of mangrove deforestation to map relationships between habitat loss and fragmentation. Mangrove fragmentation was ubiquitous; however, there are geographic disparities between mangrove loss and fragmentation; some regions, like cambodia and the southern caribbean, had relatively little loss, but their forests have been extensively fragmented. in Southeast Asia, a global hotspot of mangrove loss, the conversion of forests to aquaculture and rice plantations were the biggest drivers of loss (>50%) and fragmentation. Surprisingly, conversion of forests to oil palm plantations, responsible for >15% of all deforestation in Southeast Asia, was only weakly correlated with mangrove fragmentation. Thus, the management of different deforestation drivers may increase or decrease fragmentation. Our findings suggest that large scale monitoring of mangrove forests should also consider fragmentation. this work highlights that regional priorities for conservation based on forest loss rates can overlook fragmentation and associated loss of ecosystem functionality.Mangroves are intertidal wetlands found along coastlines in much of the tropical, subtropical and warm-temperate world. These forests provide valuable ecosystem services including preventing erosion 1 , providing habitat for fisheries species 2 , protecting coastal communities from extreme weather events 3,4 and storing large reserves of blue carbon, thus mitigating global climate change 5 . The services provided by mangroves are threatened by anthropogenic processes including deforestation 6 and sea-level rise 7,8 . Historically, mangroves were subject to high rates of deforestation of up to 3.6% per annum 9 . However, since the turn of the millennium global mangrove deforestation rates have slowed, with annual loss rates of 0.2-0.7% 10,11 . Lower rates of loss are due to near total historical loss of forest patches in some regions, but also improved conservation practices 11 and improvements in large scale monitoring techniques that provide more accurate estimates of cover and loss than were available historically 10,12 . The majority of contemporary mangrove loss occurs in Southeast Asia, where ~50% of the remaining global mangrove forest area is located, with nations such as Indonesia, Malaysia and Myanmar continuing to show losses of 0.26, 0.41 and 0.70% per year, respectively 10 .Recently, researchers have highlighted that simply reporting mangrove total loss rates is insufficient for prioritising conservation actions 11 , if there is insufficient knowledge...

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Section: Discussionmentioning

confidence: 99%

Global trends in mangrove forest fragmentation

Bryan-Brown

Connolly

Richards

et al. 2020

Sci Rep

Self Cite

174

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“…Intertidal mangrove forests are acknowledged as currently being one of the most important ecosystems worldwide due to the crucial services they provide to coastal populations, including food, fishery support, and blue carbon sequestration [1,2]. However, these forests are being lost worldwide due to anthropogenic threats, including overexploitation and conversion to aquaculture and agriculture, particularly in Southeast Asia and West Africa, despite multiple efforts at rehabilitation following international conservation strategies [3,4].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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This study proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data to estimate the mangrove above-ground biomass (AGB), including small and shrub mangrove patches in the Red River Delta biosphere reserve across the northern coast of Vietnam. We used the novel extreme gradient boosting decision tree (XGBR) technique together with genetic algorithm (GA) optimization for feature selection to construct and verify a mangrove AGB model using data from a field survey of 105 sampling plots conducted in November and December of 2018 and incorporated the dual polarimetric (HH and HV) data of the ALOS-2 PALSAR-2 L-band and the Sentinel-2 multispectral data combined with Sentinel-1 (C-band VV and VH) data. We employed the root-mean-square error (RMSE) and coefficient of determination (R 2 ) to evaluate the performance of the proposed model. The capability of the XGBR-GA model was assessed via a comparison with other machine-learning (ML) techniques, i.e., the CatBoost regression (CBR), gradient boosted regression tree (GBRT), support vector regression (SVR), and random forest regression (RFR) models. The XGBR-GA model yielded a promising result (R 2 = 0.683, RMSE = 25.08 Mg·ha −1 ) and outperformed the four other ML models. The XGBR-GA model retrieved a mangrove AGB ranging from 17 Mg·ha −1 to 142 Mg·ha −1 (with an average of 72.47 Mg·ha −1 ). Therefore, multisource optical and synthetic aperture radar (SAR) combined with the XGBR-GA model can be used to estimate the mangrove AGB in North Vietnam. The effectiveness of the proposed method needs to be further tested and compared to other mangrove ecosystems in the tropics.

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“…They perform a wide range of crucial functions, such as mitigating the effects of tropical typhoons and tsunami, reducing coastal erosion, and storing huge amounts of blue carbon [1,2]. Despite their functions and benefits, mangrove forests have been reduced and degraded worldwide, more seriously in South East Asia, where the decimation rate reached its highest level in the last 50 years [3,4]. The driving factors of mangrove deforestation and degradation are conversion to shrimp aquaculture, agriculture (particularly rice and oil palm in West Africa and Southeast Asia), urban development, poor governance, and overexploitation [3,5].…”

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confidence: 99%

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

Pham

et al. 2020

Remote Sensing

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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 (R 2 )) 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 R 2 = 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.Mangrove forests are among the most important components of natural ecosystems. They perform a wide range of crucial functions, such as mitigating the effects of tropical typhoons and tsunami, reducing coastal erosion, and storing huge amounts of blue carbon [1,2]. Despite their functions and benefits, mangrove forests have been reduced and degraded worldwide, more seriously in South East Asia, where the decimation rate reached its highest level in the last 50 years [3,4]. The driving factors of mangrove deforestation and degradation are conversion to shrimp aquaculture, agriculture (particularly rice and oil palm in West Africa and Southeast Asia), urban development, poor governance, and overexploitation [3,5]. Unfortunately, the loss of mangrove carbon on large spatial scales is little understood. Without this knowledge, we cannot mitigate the global loss of mangrove habitats [6].Land-cover change is thought to alter the above-ground biomass (AGB) in the tropical areas [7-9]. By mapping the spatial distribution of mangrove AGB and the carbon stocks associated with external factors, we could detect the changes in mangrove ecosystems, better understand the drivers of these changes, and reduce the uncertainty in estimating the loss of mangrove ecosystem services. A precise estimation of mangrove AGB is required for sustainably preserving and protecting mangrove ecosystems from loss and degradation under climate change and accelerated global warming...

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The State of the World's Mangrove Forests: Past, Present, and Future

Cited by 427 publications

References 133 publications

Global trends in mangrove forest fragmentation

Global trends in mangrove forest fragmentation

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

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

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