Mangrove Forest Changes Research Articles

Application of Remote Sensing in Analyzing Mangrove Forest Changes and Drivers in Mui Ca Mau National Park, Vietnam

Mangroves are vital in coastal ecology and for sustaining and securing coastal communities. However, several factors such as human activities and climate change have changed mangroves' quantity and quality. This research analyses land-use/land cover (LULC) changes focusing on mangroves in Mui Ca Mau National Park (MCMNP) from 1995-2022. Collected Landsat imagery in 1995, 2003, and 2022 were applied to perform this research. Overall accuracy and Kappa of land cover classification were 88.5% and 0.86 in 2003, and 87.6% and 0.85 in 2022, respectively. The classified results showed that the mangrove area had increased significantly from 4,316.1 hectares in 1995 to 8,741.3 and 10,764.1 hectares in 2003 and 2022, respectively. In addition, the drivers of mangrove change were identified and analyzed. This study showed the important role of policy in mangrove conservation and the sustainable use of natural resources. The study provided useful information for policy-making in terms of forest conservation and management.

The Change Detection of Mangrove Forests Using Deep Learning with Medium-Resolution Satellite Imagery: A Case Study of Wunbaik Mangrove Forest in Myanmar

This paper presents the development of a U-Net model using four basic optical bands and SRTM data to analyze changes in mangrove forests from 1990 to 2024, with an emphasis on the impact of restoration programs. The model, which employed supervised learning for binary classification by fusing multi-temporal Landsat 8 and Sentinel-2 imagery, achieved a superior accuracy of 99.73% for the 2020 image classification. It was applied to predict the long-term mangrove maps in Wunbaik Mangrove Forest (WMF) and to detect the changes at five-year intervals. The change detection results revealed significant changes in the mangrove forests, with 29.3% deforestation, 5.75% reforestation, and −224.52 ha/yr of annual rate of changes over 34 years. The large areas of mangrove forests have increased since 2010, primarily due to naturally recovered and artificially planted mangroves. Approximately 30% of the increased mangroves from 2015 to 2024 were attributed to mangrove plantations implemented by the government. This study contributes to developing a deep learning model with multi-temporal and multi-source imagery for long-term mangrove monitoring by providing accurate performance and valuable information for effective conservation strategies and restoration programs.

Overestimation of Mangroves Deterioration From Sea Level Rise in Tropical Deltas

AbstractMangrove forests are critical coastal ecosystems that provide great socio‐ecological services, which are also highly vulnerable to climate change, particularly to sea level rise (SLR). Here we assess changes in mangrove forests in four distinct river/tide/wave‐dominant large deltas along the Indo‐Pacific coast based on 1,336 remote sensing images by machine learning techniques. We find that mangroves are migrating seaward at a rate of 18% ± 12% m/yr, which can offset landward mangroves loss, 67% of which caused by land use conversion. The fact that mangroves are expanding seaward with accretion rates exceeding SLR suggests that climate change has not yet triggered substantial loss in deltaic mangrove forests. Assuming that present environmental conditions do not change and that sediment and organic deposition in the deltaic topsets match SLR rates, we project that 90% of deltaic mangrove forests may start to retreat after 132–194 years. Early inundation of mangroves will occur in wave‐dominated delta.

Impact of Conservation in the Futian Mangrove National Nature Reserve on Water Quality in the Last Twenty Years

Mangroves play a crucial role in improving the water quality of mangrove wetlands. However, current research faces challenges, such as the difficulty in quantifying the impact of mangroves on water quality and the unclear pathways of influence. This study utilized remote sensing imagery to investigate the long-term changes in mangrove forests in the Futian Mangrove National Nature Reserve and constructed a water quality index based on water quality data. Finally, structural equation modeling was employed to explore the pathways of influence and quantify the impact effects of mangroves, climate, and water quality. The study findings revealed several key points: (1) The mangrove forests in the Futian Mangrove National Nature Reserve exhibited a trend of expansion towards the ocean during this period. (2) The seasonal and annual characteristics of water quality in Shenzhen Bay indicated a significant improvement in water quality from 2000 to 2020. (3) Mangroves have significant direct and indirect impacts on water quality, which are more pronounced than the effects of climate factors. These findings not only offer insights for the environmental management and conservation of Shenzhen Bay but also provide support for future comprehensive studies on the response relationships between the morphology, species, and physiological characteristics of mangroves and water quality.

Spatial-Temporal Patterns of Mangrove Forest Change in Zanzibar: A Case Study of Magharibi ‘B’ and Kati Districts in Unguja, Tanzania

Spatial-Temporal Patterns of Mangrove Forest Change in Zanzibar: A Case Study of Magharibi ‘B’ and Kati Districts in Unguja, Tanzania

Deep Learning Techniques for Enhanced Mangrove Land use and Land change from Remote Sensing Imagery: A Blue Carbon Perspective

Deep learning techniques are revolutionizing the analysis of remote sensing imagery for mangrove land use and land change studies. These methods improve the accuracy and efficiency of identifying changes in mangrove forests, providing valuable insights for environmental monitoring and conservation efforts. This study presents an in-depth analysis of land cover changes on Hainan Island, China, over 30 years from 1993 to 2023, using Landsat 5 imagery. Through the classification of land cover into six distinct categories—barren/agriculture, urban/infrastructure, high vegetation, low vegetation, water, and clouds—we examined the temporal shifts and trends in the island's landscape. An accuracy assessment using high-resolution Landsat Satellite imagery provided validation for the classification method, achieving an accuracy of over 70%. The results revealed a notable urban expansion and recuperation of high vegetation areas, indicating a strong interaction between human activities and natural ecosystems. The study underscores the dynamic nature of land cover influenced by both anthropogenic and natural factors and highlights the importance of remote sensing in environmental monitoring. Future work will involve the adoption of newer satellite technologies and machine learning techniques to enhance the precision of land cover classification, integrate additional ecological data, and employ predictive modeling for sustainable land management strategies. The continuation of these efforts is crucial for informing policy decisions to ensure the preservation of Hainan's rich biodiversity and natural resources.

The area and distribution changes of mangrove forests on the coast of Gresik Regency, East Java, 2012-2023

Abstract Environmental dynamics in coastal areas have resulted in changes in the area and distribution of mangrove forests in Gresik Regency. Mangroves have ecological and economic functions for living things. This study aims to analyse changes in the area and distribution of mangrove forests in Gresik Regency in 2012 and 2023. The data source is IKONOS imagery from Google Earth in 2012 and 2023, the coastal location of Gresik Regency, East Java Province. Interpretation and digitisation were done to determine the area and distribution of mangrove forests, and maps of the mangrove forests were produced in 2012 and 2023. Then, the two maps were overlayed to determine their area and distribution changes. Based on image interpretation and analysis, in 2012, the area with the largest mangrove forest was Ujung Pangkah District (802.75 hectares) or 63.21% of the total mangrove forest area of Gresik Regency. The largest mangrove forest in 2023 is still in Ujung Pangkah District, with an area of 1261.50 hectares or 64.26%. For ten years (from 2012 to 2023), Gresik Regency’s mangrove forests increased by 590.41 hectares, the most significant increase in Ujung Pangkah District (increased by 458; 76 hectares).

Multidecadal mangrove forest change in Macajalar Bay, Northern Mindanao, Philippines (1950-2020) using remote sensing and geographic information systems.

The mangrove forest in Macajalar Bay is regarded as an important coastal ecosystem since it provides numerous ecosystem services. Despite their importance, the clearing of mangroves has been rampant and has reached critical rates. Addressing this problem and further advancing its conservation require accurate mangrove mapping. However, current spatial information related to mangroves is sparse and insufficient to understand the historical change dynamics. In this study, the synergy of 1950 vegetation maps and Landsat images was explored to provide multidecadal monitoring of mangrove forest change dynamics in Macajalar Bay, Philippines. Vegetation maps containing the 1950 mangrove extent and Landsat images were used as input data to monitor the rates of loss over 70years. In 2020, the mangrove forest cover was estimated to be 201.73ha, equivalent to only 61.99% of the 325.43ha that was estimated in 1950. Between 1950 and 2020, net mangrove loss in Macajalar Bay totaled 324.29ha. The highest clearing rates occurred between 1950 and 1990 when it recorded a total of 258.51ha, averaging 6.46ha/year. The original mangrove forest that existed in 1950 only represents 8.56% of the 2020 extent, suggesting that much of the old-growth mangrove had been cleared before 2000 and the existing mangrove forest is mainly composed of secondary mangrove forest stands. Across Macajalar Bay, intensified clearing that happened between 1950 and 1990 has been driven by large-scale aquaculture developments. Mangrove gains on the other hand were evident and have increased the total extent by 79.84ha since 2000 as a result of several afforestation programs. However, approximately half of these gains that were observed since 2010 exhibited low canopy cover. As of writing, approximately 85% of the 2020 mangrove forest stands fall outside the 1950 original mangrove extent. Examining the viability of the original mangrove forest for mangrove reforestation together with promoting site-species matching, and biophysical assessment are necessary undertakings to advance current mangrove conservation initiatives in Macajalar Bay.

Spatial analysis of land cover changes in mangrove and peatland forests in North Sumatra, Indonesia

Forest management in Indonesia can be observed through land cover change as a macro indicator for forest loss or gains. In order to have a clear understanding of the trend of the land cover change in peatland and mangroves, we conduct the land cover change analysis using the last decadal or 10-year period. The land typologies used as analysis units are peat and mangrove ecosystem. The research area is located in North Sumatra Province, occupying a peat area of 50,089.9 ha and mangrove area of 86,907 ha. The spatial analysis includes land cover maps overlay and classification using GIS. The 2012-2022 analysis results indicate a trend of an increase in peat reforestation by 819.6 ha and deforestation by 5,043.5 ha, while the trend of mangrove reforestation increased by 12,801.5 ha and deforestation by 4,669.5 ha. Land cover changes in the peatland are predominantly derived due to conversion of plantation to secondary swamp forest and conversion of secondary swamp forest to bare land. In the mangrove ecosystem land cover changes are mainly derived due to the regrowth of shrubs to secondary mangrove forests and due to conversion or degradation of secondary mangrove forests to shrubs.

Improving the accuracy of mangrove forest mapping using sentinel image and developed classification algorithm based on machine learning

ABSTRACT The national mangrove rehabilitation required mangrove spatial information with very high accuracy. This study successfully developed the classification algorithm measurable for mapping mangrove forest change based on machine learning. We also modified the recursive feature elimination (RFE) method to obtain the most optimal feature importance (FI). The results showed that the developed classification algorithm was built based on the five most important features including digital elevation model (DEM), near-infrared (NIR), normalized difference moisture index (NDMI), normalized difference water index (NDWI), and distances from brackish water river (DBWR). The developed algorithm increased overall and kappa accuracy by 0.48% and 0.01, respectively.

MANGROVE FOREST MANAGEMENT STRATEGY IN BEDUL, BANYUWANGI: COLLABORATION BETWEEN COMMUNITY AND ALAS PURWO NATIONAL PARK FOR SUSTAINABLE ECOTOURISM DEVELOPMENT

This study aimed to: (1) identify the condition of the Bedul Banyuwangi mangrove forest area for sustainable ecotourism; (2) evaluate the fitness of the Bedul Banyuwangi mangrove forest for sustainable ecotourism; (3) evaluate collaboration between the community and the management of Alas Purwo National Park in conserving Bedul mangrove forest areas; and (4) design a management strategy for the Bedul mangrove forest for developing sustainable ecotourism. This research is a descriptive analytic study with a survey technique constructed using a remote sensing approach and a Geographic Information System (GIS). Landsat and Quickbird satellite imagery are used to identify temporal changes in mangrove forests and temperature over the last decade. Quickbird remote sensing data are used to evaluate the current land use associated with community activities in mangrove areas and government policies related to the presence of mangrove forests. The results showed that: 1) the condition of the Bedul Banyuwangi mangrove area from 1995-2022 was in a stable state. ; 2) the fitness of the Bedul Banyuwangi mangrove forest for sustainable ecotourism is included in the S2 category or following with the Bedul mangrove ecotourism fitness index; 3) the collaborative management of the Bedul mangrove ecotourism between the community (fishing community, local government) and the management of Alas Purwo National Park is continuing good to maintain the condition of the mangroves and provide economic benefits to the community in a sustainable manner; 4) the management strategy implemented is to maintain the quality and quantity of Bedul mangroves and supporting facilities in a sustainable manner. This research can be used as a reference for future research into sustainable ecotourism, as well as for the government to create policies that support sustainable ecotourism.

Monitoring mangrove forest change and its impacts on the environment

With a focus on the Sundarbans, this study uses remote sensing methods to thoroughly evaluate the dynamics of mangrove forest change and the ensuing environmental effects from 2004 to 2022. Through a thorough time-series analysis, the research reveals the temporal fluctuations in forest cover by utilizing Vegetation Indices and Land Use and Land Cover analysis. The deep significance of these shifts in forest cover is illuminated by the association with specific environmental conditions.The study's conclusions show a concerning trend in the Sundarbans Forest Cover, with a steady reduction occurring at an average annual rate of 2.66%. The study period's sustained decline in mangrove cover highlights the continuous and growing danger to this vital ecosystem. Concurrently, the study notes a concerning increase in urban areas, mostly linked to fishing and sawmills. This indicates that human activity is a major factor in the destruction of the Sundarbans, highlighting the pressing need for focused conservation efforts.The study uses forecasts and simulations to anticipate trends in changes in forest cover up to 2030, extending its investigation beyond the observed timeframe. These forecasts demonstrate geographical differences in the distribution of these changes in addition to confirming the continuance of the downward trend. The need for proactive conservation initiatives is highlighted by the simulations' ability to provide vital foresight in addressing the long-term implications of mangrove destruction.The relationship between changes in forest cover and the ecosystem as a whole is illustrated by correlation analysis with a few chosen environmental indicators, such as precipitation, soil moisture, land surface temperature (LST), and CO2 emissions. This association provides important information for comprehending the wider ecological ramifications of these changes by highlighting the extensive effects of mangrove destruction on important environmental variables.Lagged correlation analysis, which explores the temporal dynamics of the relationship between forest changes and environmental data, is a noteworthy feature of the study. This analysis shows that the effects of deforestation eventually overwhelm the ecosystem, even in the face of initial resistance. The lagged correlation highlights the necessity of taking time lags into account in order to fully comprehend the range of environmental repercussions, as well as the delayed but significant effects of mangrove deforestation on the surrounding ecology.Both mangrove forests and the larger planetary environment will suffer from the predicted trends this study presents. A cycle of diminishing vegetation vigour is set off by the continuous loss of forest cover, which exacerbates environmental harm. This study's methodology is useful for evaluating and tracking ecosystem changes because it can be applied to many kinds of forests. The researchers do concede that on-site observations could yield better results, noting that direct, ground-level evaluations should be conducted in addition to remote sensing data for increased comprehensiveness and accuracy. The results highlight the critical need for conservation initiatives and sustainable land-use policies in order to lessen the growing risks to mangrove ecosystems and their priceless roles in maintaining biodiversity around the world and regulating climate change.

Evaluating Ecosystem Service Value Changes in Mangrove Forests in Guangxi, China, from 2016 to 2020

Mangrove forests play a vital role in maintaining ecological balance in coastal regions. Accurately assessing changes in the ecosystem service value (ESV) of these mangrove forests requires more precise distribution data and an appropriate set of evaluation methods. In this study, we accurately mapped the spatial distribution data and patterns of mangrove forests in Guangxi province in 2016 and 2020, using 10 m spatial resolution Sentinel-2 imagery, and conducted a comprehensive evaluation of ESV provided by mangrove forests. The results showed that (1) from 2016 to 2020, mangrove forests in Guangxi demonstrated a positive development trend and were undergoing a process of recovery. The area of mangrove forests in Guangxi increased from 6245.15 ha in 2016 to 6750.01 ha in 2020, with a net increase of 504.81 ha, which was mainly concentrated in Lianzhou Bay, Tieshan Harbour, and Dandou Bay; (2) the ESV of mangrove forests was USD 363.78 million in 2016 and USD 390.74 million in 2020; (3) the value of fishery, soil conservation, wave absorption, and pollution purification comprises the largest proportions of the ESV of mangrove forests. This study provides valuable insights and information to enhance our understanding of the relationship between the spatial pattern of mangrove forests and their ecosystem service value.

Machine Learning-Based examination of recent mangrove forest changes in the western Irrawaddy River Delta, Southeast Asia

Mangrove forests serve as a significant carbon sink and provide effective shoreline protection against devastating winds. However, these forests are facing an unprecedented decline in tropical and subtropical large deltas due to human activities and climate change. This study focuses on quantifying the dynamic changes of mangrove forests in the Western Irrawaddy River Delta (WIDR), which is part of Myanmar’s largest delta in Southeast Asia, Remote sensing images from 1988 to 2022 were utilized alongside a machine learning approach to analyze these changes. The findings demonstrate a significant decline of mangrove forests within the WIRD, with a reduction in area by 45.35% over the past 35 years. Additionally, the patches of mangrove forests have become increasingly fragmented. Losses predominantly occurred in the inland regions, while gains were observed along the seaward edges, suggesting progradation towards the sea, which compensated for a net loss of 2,812.32 ha during the study period. The intensification of human activities, specifically deforestation and aquaculture pond utilization, appears to be the leading cause of catastrophic internal degradation within the WIRD’s mangrove forests. Contrary to the influence of local sea level rise or variations in suspended sediment discharge into the WIRD, this study suggests that the persistent retreat of mangrove forest fringes is controlled by large waves generated by the southwest monsoon. Estuarine barriers situated in the WIRD act as buffers, dissipating wave energy and facilitating seaward growth. of mangrove forests. Our study reveals a significant pattern in the WIRD, where landward mangrove forest loss coincided with seaward mangrove forest gain. This work underscores the importance of addressing landward mangrove forest loss and recognizing the emergence of seaward gains in the WIRD, providing valuable insights for fostering successful and sustainable mangrove management and protection initiatives.

Estimating Structural Damage to Mangrove Forests Using Airborne Lidar Imagery: Case Study of Damage Induced by the 2017 Hurricane Irma to Mangroves in the Florida Everglades, USA.

In September 2017, Hurricane Irma made landfall in South Florida, causing a great deal of damage to mangrove forests along the southwest coast. A combination of hurricane strength winds and high storm surge across the area resulted in canopy defoliation, broken branches, and downed trees. Evaluating changes in mangrove forest structure is significant, as a loss or change in mangrove forest structure can lead to loss in the ecosystems services that they provide. In this study, we used lidar remote sensing technology and field data to assess damage to the South Florida mangrove forests from Hurricane Irma. Lidar data provided an opportunity to investigate changes in mangrove forests using 3D high-resolution data to assess hurricane-induced changes at different tree structure levels. Using lidar data in conjunction with field observations, we were able to model aboveground necromass (AGN; standing dead trees) on a regional scale across the Shark River and Harney River within Everglades National Park. AGN estimates were higher in the mouth and downstream section of Shark River and higher in the downstream section of the Harney River, with higher impact observed in Shark River. Mean AGN estimates were 46 Mg/ha in Shark River and 38 Mg/ha in Harney River and an average loss of 29% in biomass, showing a significant damage when compared to other areas impacted by Hurricane Irma and previous disturbances in our study region.

Discovering spatio-temporal dependencies in mangrove forest change: evidence from northwestern Asian communities

Abstract The Iranian southern intertidal ecosystems are colonized by mangrove forests (mostly Avicennia marina) and presented a notable mangrove area expansion over the past decades. To discover its spatio-temporal dependencies, five mangrove forests were studied along a coastline of more than 1,200 km. Cloud-free Landsat images were processed using a temporal median filter to produce one representative Landsat image for each year from 1988 to 2020. The images were categorized into mangrove and non-mangrove classes using an object-based algorithm and visual post-classification. A consistent mangrove area expansion was observed across all regions, which regressed linearly against time with an adjusted R2 range of 0.914–0.955 (p < 0.01). The expansions were also found to be highly significantly similar (0.182 < p < 0.822) using the t-test applied on the slope of region-specific regression models. Three spatial metrics of infilling, edge-expansion, and outlying were developed to investigate the spatial pattern of expansions. Results showed the dominance of the edge-expansion pattern across all regions (53–78% of the expansions), which was attributed to the better establishment of propagules near and under the canopy of parent trees. The outlying pattern (11–22%) was related to mangrove colonization along the inland creeks.

Spatiotemporal dynamics and potential restoration of mangroves in Circum-Xinying-Bay region, Hainan Province, China

Mangrove forests play an important role in absorbing carbon, filtering pollution, and protecting coastal residents from storms. However, mangrove forests are most susceptible to human interference and suffer more than one-third of losses globally. Therefore, quantifying their spatial-temporal changes and identifying their potential distributions are necessary for protection, restoration, and management. We first mapped and analysed the spatial-temporal changes in mangrove forests from 1993 to 2021 in the Xinying-Bay region (XYB), Hainan Province, China. Then, we predicted suitable habitats for mangrove forests by using the maximum entropy (MaxEnt) model based on mangrove distribution data for 2021 and related environmental data for the Circum-Xinying-Bay region (CXYB). The results show remarkable changes in mangrove forests in the XYB from 1993 to 2021, with the loss rate reaching 56 ha/y from 1998 to 2002. The MaxEnt model performs well, with the curve (AUC) extending beyond 0.9. Water quality and topography determined the distribution of mangrove forests, with the total contribution exceeding 65%. The six most important influential factors were salinity, DEM, precipitation in the driest quarter, pH, isothermality, and winter sea surface temperature. The area of mangroves highly suitable in the CXYB is approximately 3880 hm2 and is mainly located in the town of Xinzhou, the northeast area of the town of Mutang and the southwest area of the town of E'man. This suggests that areas prioritized for protection should include the northern and western areas of Xinzhou, northeastern areas of Mutang, and northern and western areas of E'man. These findings provide further understanding of mangrove distribution and can guide mangrove restoration and management.

Multi-spatiotemporal analysis of changes in mangrove forests in Palawan, Philippines: predicting future trends using a support vector machine algorithm and the Markov chain model.

Multi-temporal remote sensing imagery can be used to explore how mangrove assemblages are changing over time and facilitate critical interventions for ecological sustainability and effective management. This study aims to explore the spatial dynamics of mangrove extents in Palawan, Philippines, specifically in Puerto Princesa City, Taytay and Aborlan, and facilitate future predictions for Palawan using the Markov Chain model. The multi-date Landsat imageries during the period 1988-2020 were used for this research. The support vector machine algorithm was sufficiently effective for mangrove feature extraction to generate satisfactory accuracy results (>70% kappa coefficient values; 91% average overall accuracies). In Palawan, a 5.2% (2693 ha) decrease was recorded during 1988-1998 and an 8.6% increase in 2013-2020 to 4371 ha. In Puerto Princesa City, a 95.9% (2758 ha) increase was observed during 1988-1998 and 2.0% (136 ha) decrease during 2013-2020. The mangroves in Taytay and Aborlan both gained an additional 2138 ha (55.3%) and 228 ha (16.8%) during 1988-1998 but also decreased from 2013 to 2020 by 3.4% (247 ha) and 0.2% (3 ha), respectively. However, projected results suggest that the mangrove areas in Palawan will likely increase in 2030 (to 64,946 ha) and 2050 (to 66,972 ha). This study demonstrated the capability of the Markov chain model in the context of ecological sustainability involving policy intervention. However, as this research did not capture the environmental factors that may have influenced the changes in mangrove patterns, it is suggested adding cellular automata in future Markovian mangrove modelling.

Remote sensing monitoring of mangrove forest changes from 1990 to 2020 in Guangdong-Hong Kong-Macao Greater Bay Area

Remote sensing monitoring of mangrove forest changes from 1990 to 2020 in Guangdong-Hong Kong-Macao Greater Bay Area

Status of mangroves land use on the Brazilian Amazon coast from RapidEye imagery and GEOBIA approach.

Given the ecological and socioeconomic importance of the mangroves of the Brazilian Amazon coast, RapidEye satellite images were analyzed to recognize mangrove forest and salt flat changes to different land use through human activities. Results show that mangroves are still very well preserved, with less than 1% of the total converted to other uses, primarily urban areas and roads. These human activities have been the principal causes of use in the mangrove forest, driven by local anthropogenic pressures resulting from human settlements in the transition zone between the mainland and tidal flats. In contrast, aquaculture, the principal driver of the loss of mangroves in other regions of South America and in Asian countries, plays only a secondary role in habitat conversion on the Amazon coast. However, these human activities demand more attention and policies need to be supported by Brazilian legislation.