Understanding Dynamics of Mangrove Forest on Protected Areas of Hainan Island, China: 30 Years of Evidence from Remote Sensing

Liao, Jingjuan; Zhen, Jianing; Zhang, Li; Metternicht, Graciela

doi:10.3390/su11195356

Cited by 28 publications

(24 citation statements)

References 33 publications

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“…urban developments and oil palm plantations) and transiency (e.g. mangrove forestry) 37 . Future studies should seek to identify the impacts of fragmentation on mangrove ecosystems and understand how mangrove regrowth and restoration can reconnect fragmented forests.…”

Section: Discussionmentioning

confidence: 99%

Global trends in mangrove forest fragmentation

Bryan-Brown

Connolly

Richards

et al. 2020

Sci Rep

205

118

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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

205

118

View full text Add to dashboard Cite

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“…Remote sensing (RS) techniques and geographical information system (GIS) modeling approaches are widely used to monitor LULC changes and in studies on prediction modeling [13,[24][25][26][27][28][29][30][31], landscape risk (LR) [32,33] including mangrove cover mapping and distribution, change analysis [34][35][36][37][38][39][40][41][42][43][44][45][46], simulation [47,48], long-term changes in mangrove species composition and distribution [49] and conservation strategies [50]. MF cover quantification, considering multispectral passive sensors such as Landsat [21,[51][52][53][54], Sentinel-2 [55], IKONOS [56], Worldview-2 [57], Worldview-3 [58] with moderate to high resolution and active Sentinel-1 sensor synthetic aperture radar (SAR) [59], have been widely used by numerous researchers across the globe.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To understand the prediction of forest cover changes and degradation of SRF in Bangladesh, a PSRF conceptual framework was constructed in this study, which was adapted from Liao et al [48] with modification, exploring multiple drivers of changes, related pressures and the consequences on several states in individual forest compartments of SRF ( Figure 2). In this framework, pressures that act upon on SRF changes are due to natural and anthropogenic activities such as illegal logging, top dying disease, oil spillage, pollutants from upstream industries and natural phenomena (e.g., erosion, natural disasters, etc.…”

Section: Understanding Srf Changes Through Psrf Conceptual Frameworkmentioning

confidence: 99%

“…Moreover, implementing forest policies, continuous monitoring and forest inventory and SDSSs, as well as self-regeneration and damage recovery, helps responses to succeed and provides a sustainable future to achieve specific targets of UN 2030 SDGs. Conceptual framework of pressure-state-response-future for SRF (modified based on local SRF factors in [48]).…”

Section: Understanding Srf Changes Through Psrf Conceptual Frameworkmentioning

confidence: 99%

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Applying Multi-Temporal Landsat Satellite Data and Markov-Cellular Automata to Predict Forest Cover Change and Forest Degradation of Sundarban Reserve Forest, Bangladesh

Hasan

Nath

Sarker

et al. 2020

Forests

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Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF.

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“…While many studies exist which aim to monitor long-term change in mangrove ecosystems (e.g., [8][9][10][11][12], the focus has remained on single-image classification techniques, typically at yearly resolution or less. In recent years land cover monitoring has seen a move away from such approaches and towards methods which make use of all available observations [13].…”

Section: Introductionmentioning

confidence: 99%

Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region

et al. 2019

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Mangrove forests play a global role in providing ecosystem goods and services in addition to acting as carbon sinks, and are particularly vulnerable to climate change effects such as rising sea levels and increased salinity. For this reason, accurate long-term monitoring of mangrove ecosystems is vital. However, these ecosystems are extremely dynamic and data frequency is often reduced by cloud cover. The Continuous Change Detection and Classification (CCDC) method has the potential to overcome this by utilising every available observation on a per-pixel basis to build stable season-trend models of the underlying phenology. These models can then be used for land cover classification and to determine greening and browning trends. To demonstrate the utility of this approach, CCDC was applied to a 30-year time series of Landsat data covering an area of mangrove forest known as the Sundarbans. Spanning the delta formed by the confluence of the Ganges, Brahmaputra and Meghna river systems, the Sundarbans is the largest contiguous mangrove forest in the world. CCDC achieved an overall classification accuracy of 94.5% with a 99% confidence of being between 94.2% and 94.8%. Results showed that while mangrove extent in the Sundarbans has remained stable, around 25% of the area experienced an overall negative trend, probably due to the effect of die-back on Heritiera fomes. In addition, dates and magnitudes of change derived from CCDC were used to investigate damage and recovery from a major cyclone; 11% of the Sundarbans was found to have been affected by Cyclone Sidr in 2007, 47.6% of which had not recovered by mid-2018. The results indicate that while the Sundarbans forest is resilient to cyclone events, the long-term degrading effects of climate change could reduce this resilience to critical levels. The proposed methodology, while computationally expensive, also offers means by which the full Landsat archive can be analyzed and interpreted and should be considered for global application to mangrove monitoring.

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Understanding Dynamics of Mangrove Forest on Protected Areas of Hainan Island, China: 30 Years of Evidence from Remote Sensing

Cited by 28 publications

References 33 publications

Global trends in mangrove forest fragmentation

Global trends in mangrove forest fragmentation

Applying Multi-Temporal Landsat Satellite Data and Markov-Cellular Automata to Predict Forest Cover Change and Forest Degradation of Sundarban Reserve Forest, Bangladesh

Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region

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