Mangrove Changes Research Articles

Mangroves Invaded by Spartina alterniflora Loisel: A Remote Sensing-Based Comparison for Two Protected Areas in China

Mangroves are one of the world’s most productive and ecologically important ecosystems, and they are threatened by the widespread invasion of Spartina alterniflora Loisel in China. As few studies have examined the spatial pattern differences of S. alterniflora invasion and the nearby mangroves in different latitudes, we chose the Zhangjiang Estuary and the Dandou Sea, two representative mangrove–salt marsh ecotones in the north and south of the Tropic of Cancer, as the study areas for comparison. The object-based image analysis and visual interpretation methods were combined to construct fine-scale mangrove and S. alterniflora maps using high-resolution satellite imagery from 2005 to 2019. We applied spatial analysis, centroid migration, and landscape indexes to analyze the spatio–temporal distribution changes of mangroves and S. alterniflora in these two ecotones over time. We used the landscape expansion index to investigate the S. alterniflora invasion process and expansion patterns. The annual change rates of mangrove and S. alterniflora areas in the Zhangjiang Estuary showed a continuous growth trend. However, the mangrove areas in the Dandou Sea showed a fluctuating trend of increasing, decreasing, and then increasing again, while S. alterniflora areas kept rising from 2005 to 2019. Spartina alterniflora showed larger annual change rates compared with mangroves, indicating rapid S. alterniflora invasion in the intertidal zones. The opposite centroid migration directions of mangroves and S. alterniflora and the decreasing distances between the mangrove and S. alterniflora centroids indirectly revealed the fierce competition between mangroves and S. alterniflora for habitat resources. Both regions saw a decrease in mangrove patch integrality and connectivity. The integrality of mangrove patches in the Zhangjiang Estuary was always higher than those in the Dandou Sea. We observed the growth stage (2011–2014) and outbreak stage (2014–2019) of S. alterniflora expansion in the Zhangjiang Estuary and the outbreak stage (2005–2009) and plateau stage (2009–2019) of S. alterniflora expansion in the Dandou Sea. The expansion pattern of S. alterniflora varies in time and place. Since the expansion of S. alterniflora in the outbreak stage is rapid, with a large annual change rate, early warning of S. alterniflora invasion is quite important for the efficient and economical removal of the invasive plant. Continuous and accurate monitoring of S. alterniflora is highly necessary and beneficial for the scientific management and sustainable development of coastal wetlands.

Long-term changes of mangrove distribution and its response to anthropogenic impacts in the Vietnamese Southern Coastal Region

Human activities related to land use and land cover (LULC) conversion have been the primary factor driving changes to mangrove distribution over recent decades. In order to quantify the anthropogenic influences associated with LULC changes on mangroves in the Vietnamese Southern Coast (VSC), we investigated the variations and trends in mangrove distribution between 1988 and 2023. We used a time-series of Landsat spectral indices from Google Earth Engine and applied hot spot analysis and machine learning algorithms to analyse mangrove variations and LULC classification, respectively. Our findings revealed that over the past 36 years, approximately half of the mangrove area has been lost due to LULC conversions. The most significant losses in mangrove cover occurred during the 1998–2011 period, with a decline of 46.79% in total area (an average of 3.6% per annum). The rate of mangrove deforestation more than halved to 17.49% (1.5% per annum) in the period between from 2011 to 2023. We attribute the reduction in mangrove loss to conservation efforts and natural regeneration processes. The emerging hot spot analysis indicated that the most significant restoration of new mangrove areas occurred between 1988 and 1998, totalling 1795 ha (1.4%), while the highest rate of mangrove deforestation was observed between 1998 and 2011, amounting to 2249 ha (2.0%). The primary causes of these variations in mangrove distribution were the conversion of mangrove areas to shrimp farming (38.91%), followed by other agricultural land use (5.82%) and the expansion of impervious surfaces (3.34%). In contrast, a result of enhanced conservation efforts and natural regeneration was associated with a 17.91% of mangrove area gain in the 2011–2023 period. Despite the regeneration potential of mangroves, our study highlighted the ongoing need to manage and protect mangrove forests to facilitate their expansion in the VSC. The analytical approach adopted in this study is applicable to other coastal areas when assessing changes in mangroves and land use practices.

Unveiling complementarities between mangrove restoration and global sustainable development goals

Indonesia, renowned as the most mangrove-rich nation, has committed to extensive mangrove restoration policies, but the effects of these policies have yet to be systematically evaluated. Our study conducts a comprehensive network analysis to investigate the synergies between mangrove restoration policy and global Sustainable Development Goals (SDGs) achievements by exploring their interactions. This investigation follows the ‘product space’ method in economics and creates the ‘Mangrove-SDG space’ to assess each metric pair's co-occurrence and comparative advantages with validated stability. Our analysis unveils a tripartite structure, encompassing socio-economic and environmental clusters, each significantly contributing to global sustainability and a distinctive mangrove cluster tied to land attributes. At the Goal level, mangrove loss showcases robust synergies with SDGs 12 (Responsible Consumption and Production) and 13 (Climate Change), and mangrove metrics such as tropical storm frequency and mangrove change, indicating strong interdependences between mangrove forests and SDGs. The result indicates that improved performance of climate change and responsible consumption can greatly enhance mangrove forests' performance in alleviating mangrove loss and reducing tropical storms. Moreover, our analysis underscores the central roles played by ‘bridge’ Goals. Indicator-level space details how they warrant prioritization because of their cascade synergistic enhancements across widely interconnected indicators, triggering systematic positive improvements. Turning to Indonesia, we advocate a strategic shift from solely expanding mangrove extent to focusing on four critical policy priorities: effective nitrogen management, enhancing Ramsar site efficiency, optimizing logistic performance, and addressing urban population conditions. These priorities are pivotal to seeking complementarities between Indonesia's international sustainability commitment and fostering mangrove restoration success.

Extraction of 10 m Resolution Global Mangrove in 2022

With the intensification of global climate change, there is an increasing emphasis on protecting natural resources. Mangrove forests, critical to tropical and subtropical intertidal ecosystems, have garnered considerable attention in recent years for their strong carbon sink capacity, rich species diversity, and abundant natural resources. This study utilizes the 2020 global mangrove vector data as a baseline to construct a reasonable buffer zone by calculating the increase in mangrove crown width. The Google Earth Engine (GEE) platform and its Sentinel-2 data from 2022 are employed to acquire synthetic images across all regions using the mosaic algorithm. Then, mangrove forests are extracted using the Otsu algorithm, and a map depicting the global spatial distribution of mangrove forests in 2022 is obtained. The average overall accuracy of the extracted mangrove forests in this study reaches 92.4%, and it is determined that the global mangrove forest area expanded by 4920.6 km2 between 2020 and 2022, This study provides crucial data support for the global monitoring of mangrove changes and holds significant importance for protecting and restoring mangrove ecosystems.

A novel use of mangrove root collar height to infer soil surface elevation change following forest degradation and clearance

Current methods for monitoring changes in mangrove soil surface elevation over time are limited in their spatial extent, costly and often involve difficult survey design. We developed a simple, extensive, rapid and inexpensive method to monitor soil surface elevation changes in mangroves following degradation: measuring the distance between the soil surface and the root collar (defined here as the height of emergence of a stem that is representative of the cross-sectional area of the main tree stem) of both live trees and dead stumps (called Δd). Δd was measured for two species, Bruguiera gymnorrhiza and Ceriops tagal, within the study area, which was categorised into intact, degraded and deforested mangroves in Tsimipaika Bay, Madagascar. A total of 227 subplots, containing 3066 individual tree stumps and live trees were surveyed within our study site, which spans approximately 4000 ha of mangroves. We used a linear model to test relationships between Δd and forest status, species, and live trees or remnant stumps. When measured on B. gymnorrhiza stumps, Δd (cm, mean ± standard error) increased from intact (18.4 ± 2.6) to degraded (24.7 ± 1.5) and finally to deforested mangroves (31.6 ± 0.7), indicating increasing soil elevation loss. A similar pattern did not emerge for C. tagal due to anthropogenic exploitation of their stumps, selectively removing large individuals for charcoal production, thereby confounding estimates of Δd. In addition, we used Δd measurements to construct a spatial analysis of carbon (C) loss following mangrove degradation in the bay. Our analysis predicted a mean C loss of 25.6 Mg C ha−1 (standard error 3.64 × 10−4) representing a lower mean loss value with reduced variability compared to previous Malagasy studies based on limited replicates within deforested areas. Our study demonstrated the use of this simple, yet scientifically rigorous approach to monitor soil elevation loss over time or following severe degradation in mangrove forests. These data facilitate estimates of C emissions from soils and vulnerability to sea level rise when mangroves are degraded. The simplicity and cost-effectiveness of our approach also provide an opportunity to actively involve local communities and practitioners in mangrove management and promote citizen science.

Spatiotemporal distribution of mangrove along the Egyptian Red Sea coast and analysis of hydrological impact on growth patterns

AbstractThis study presents an in-depth spatiotemporal analysis of mangrove ecosystems along Egypt’s Red Sea coast, utilizing satellite imagery and GIS to examine changes from 2003 to 2022. We evaluate the effects of hydrological factors, specifically rainfall and runoff -presented by Stream Power Index-, on mangrove growth patterns. Results indicate a significant increase in mangrove areas, with a notable annual growth rate, despite a reduction in a specific region. This research highlights the integral role of catchment area runoff (R2 = 0.735, R = 0.857, P-value = 0.003 < 0.05, CV = 70.26%), rather than direct rainfall, in mangrove expansion, contributing to the understanding of mangrove resilience and informing sustainable coastal management strategies. The study bridges a significant research gap by mapping decadal mangrove changes, offering insights into the dynamics affecting these crucial ecosystems.

SDG- and GMAG-oriented analysis of multi scenarios spatiotemporal changes and evaluation of the effectiveness and potential of mangrove forests

ABSTRACT It is crucial to mangrove management by understanding its conservation efforts. This study proposed a spatiotemporal analysis model to evaluate mangrove changes in the Guangxi Beibu Gulf (GBG) and the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), focusing on past, present, and future dynamics. The analysis covered mangrove loss, stability, and gain from 1990 to 2020 and provided projections for 2020 to 2035 across multiple scenarios. Then further assessed the efficacy of past mangrove conservation with the Sustainable Development Goals (SDGs) and the prospective conservation potential with the Global Mangrove Alliance Goal (GMAG) indicators. Findings indicate that by 2020, the GBG and GBA had 81.65 km² and 40.92 km² of mangroves, respectively, with increases of 31.51% and 164.09% since 1990. Projected expansions are anticipated under all three scenarios during 2020- 2035, with the highest growth projected under the trend continuation scenario, followed by ecological restoration and protection, and economic development. While mangrove protection in these regions met SDG targets 6.6.1 and 14.5.1 from 2015 to 2020, the 80% conservation milestone set by GMAG for 2030 was not achieved, necessitating the establishment of nature reserves. The study offers novel insights for sustainable governance of mangrove forests at local, regional, and global levels.

Dynamic Estimation of Mangrove Carbon Storage in Hainan Island Based on the InVEST-PLUS Model

Mangrove ecosystems are pivotal to the global carbon budget. However, there is still a dearth of research addressing the impact of regional mangrove land use and land cover change (LUCC) on carbon sequestration and its associated spatial distribution patterns. To investigate the impact of different development scenarios on the carbon storage capacity of mangrove ecosystems, we focused on Hainan Island. We used LUCC data from 2010 to 2020 from mangrove-inhabited regions. The Markov-PLUS model was applied to predict the spatiotemporal dynamics of mangrove coverage under the natural increase scenario (NIS) and the mangrove protection scenario (MPS) over the next 40 years. Carbon storage was estimated using the InVEST model based on field-measured carbon density data. The outcomes show the following: (1) The Markov-PLUS model, with an overall accuracy of 0.88 and a Kappa coefficient of 0.82, is suitable for predicting mangrove distribution patterns on Hainan Island. (2) Environmental factors were the main drivers of historical mangrove changes on Hainan Island, explaining 54% of the variance, with elevation, temperature, and precipitation each contributing over 13%. (3) From 2025 to 2065, the mangrove area on Hainan Island is projected to increase by approximately 12,505.68 ha, mainly through conversions from forest land (12.73% under NIS and 12.37% under MPS) and agricultural land (39.72% under NIS and 34.53% under MPS). (4) The carbon storage increment within Hainan Island’s mangroves is projected at 2.71 TgC over the whole island, with notable increases expected in the eastern, northern, and northwestern regions, and modest gains in other areas. In this study, we comprehensively investigated the spatiotemporal dynamics and future trends of carbon storage in the mangroves of Hainan Island, offering invaluable guidance for the long-term management of mangrove ecosystems and the realization of carbon neutrality goals by 2060.

Tracking the Effects of Mangrove Changes and Spartina alterniflora Invasion on Soil Carbon Storage: A Case Study of the Beibu Gulf of Guangxi, China

In order to clarify the long-term changes in mangroves in the Beibu Gulf of Guangxi and the carbon storage changes after the invasion of Spartina alterniflora (S. alterniflora) in the Dandou Sea area, the Continuous Change Detection and Classification (CCDC) algorithm combined with feature indices was first used to track the changes. Subsequently, the random forest algorithm was applied to classify each change segment, and then sampling was conducted based on the distribution of S. alterniflora in different invasion years. The results showed that the Kappa coefficient of the classification result of the latest change segment was 0.78. The rapid expansion of S. alterniflora, aquaculture pond construction, and land reclamation activities have led to changes in mangroves, causing a decrease in the area of the mangrove region. A total of 814.57 hectares of mangroves has been converted into other land-cover types, with most pixels undergoing one to two changes, and many of these changes were expected to continue until 2022. An analysis of the distribution characteristics and influencing factors of soil organic carbon (SOC) and soil organic carbon storage (SOCS) at different invasion stages revealed that SOC and SOCS were mainly influenced by soil bulk density, soil moisture content, and electrical conductivity. It was found that S. alterniflora had higher SOC content compared to the mudflats. With the increase in invasion years, S. alterniflora continuously increased the SOC and SOCS content in coastal wetlands.

Mangrove tree height growth monitoring from multi-temporal UAV-LiDAR

Quantifying the mangrove height growth rate is important for understanding mangrove growth as well as its coupling with the other ecosystems and the climate. However, research on this topic has been limited due to the lack of multi-temporal data on mangrove tree height (TH) and methods for tree-level analysis. This study investigates the use of multi-year UAV-LiDAR data for mangrove TH growth characterization. We evaluated the UAV-LiDAR data by comparing it with field measured THs, observing a deviation ranging between −9 and +11 cm. Notably, the deviation was minimal at only 1 cm when comparing datasets collected using the same LiDAR sensor, indicating the validity of adopting multi-year UAV-LiDAR data for mangrove TH growth analysis. Methodologically, we assessed a tree-based approach and a grid-based approach. The tree-based approach is considered more feasible as it mirrors field measurements, allowing for direct comparisons with field surveys. In addition, statistically significant differences were revealed in TH growth rates among mangrove species and clumpiness groups (p < 0.001), while the invasion of Spartina alterniflora did not significantly affect the growth rates. This paper presents the inaugural effort in mangrove TH growth quantification using multi-temporal UAV-LiDAR data, showcasing the potential of this quickly emerging remote sensing technique in individual mangrove change analysis. We proposed a recommended framework for UAV-LiDAR-based TH growth rate measurement. The limitations and uncertainties embedded in the data sources, methodology, and results interpretation were also discussed.

Extraction the Spatial Distribution of Mangroves in the Same Month Based on Images Reconstructed with the FSDAF Model

Mangroves have extremely high economic and ecological value. Through remote sensing, the spatial distribution of and spatiotemporal changes in mangroves can be accurately obtained, providing data support for the sustainable development of coastal wetlands. However, due to the cloudy and rainy conditions in the growing areas of mangroves, there are relatively few high-quality image data available, resulting in a time difference between regional mosaic images, with a maximum difference of several months, which has a certain impact on accuracy when extracting the spatial distribution of mangroves in some regions. At present, most regional mangrove research has ignored the impact of the time difference between mosaic images, which not only leads to inaccurate monitoring results of mangroves’ spatial distribution and dynamic changes but also limits the frequency of monitoring of regional mangrove dynamic changes to an annual scale, making it difficult to achieve more refined time scales. Based on this, this study takes the coastal mangrove distribution area in China as the research area, uses Landsat 8 and MODIS images as basic data, reconstructs the January 2021 images of the research area based on the FSDAF model, and uses a random forest algorithm to extract the spatial distribution of mangrove forests and analyze the landscape pattern. The results showed that the fused image based on the FSDAF model was highly similar to the validation image, with an R value of 0.85, showing a significant positive correlation, indicating that the fused image could replace the original image for mangrove extraction in the same month. The overall accuracy of the spatial distribution extraction of mangroves based on the fused image was 89.97%. The high sample separation and spectral curve changes highly similar to the validation image indicate that the fused image can more accurately obtain the spatial distribution of mangroves. Compared to the original image, the fused image based on the FSDAF model is closer to the validation image, and the fused image can reflect the changes in mangroves in time series, thus achieving accurate acquisition of dynamic change information in a short time span. It provides data and methodological support for future monitoring of dynamic changes in large-scale mangroves. The total area of mangroves in China in January 2021 based on the fused image was 27,122.4 ha, of which Guangdong had the largest mangrove area, with 12,098.34 ha, while Macao had the smallest mangrove area of only 16.74 ha. At the same time, the mangroves in Guangdong and Guangxi had a high degree of fragmentation and were severely disturbed, requiring strengthened protection efforts, while the mangroves in Hong Kong, Zhejiang, and Macao had regular shapes, benefiting from local active artificial restoration.

Using Medium-Resolution Remote Sensing Satellite Images to Evaluate Recent Changes and Future Development Trends of Mangrove Forests on Hainan Island, China

Mangroves are endemic forest communities in tropical and subtropical coastal zones. China’s Hainan Island is very rich in mangrove plant species. In recent years, due to climate change and human disturbance, the living environment of many mangrove distribution areas has been seriously threatened. This study used land satellite series remote sensing images from 1990 to 2020 to monitor the coverage and area changes of mangroves on Hainan Island. The spatial distribution pattern and change trend of mangroves were explored using the standard deviation ellipse method, and the CA–Markov model was used to predict the possible changes of mangroves in the study area in 2025 and 2030. The development trend of mangroves in the future and the driving factors affecting the evolution of mangroves were also analyzed. Over the past 30 years, the area of mangroves has witnessed fluctuations. It decreased from 4578 hectares in 1990 to its lowest at 3870 hectares in 2005 and then rebounded to 4474 hectares by 2020, indicating an initial decline followed by a gradual increase. Although conservation and restoration efforts have yielded success, specific areas have witnessed a decline in mangrove coverage. From 1990 to 2020, mangrove areas in Huiwen decreased from 1055 hectares to 904 hectares, areas in Guannan decreased from 227 hectares to 167 hectares, areas in Xinyinggang decreased from 328 hectares to 298 hectares, areas in Yangpugang decreased from 747 hectares to 682 hectares, areas in Huachangwan decreased from 355 hectares to 327 hectares, and areas in Puqian decreased from 170 hectares to 141 hectares. In particular, the growth in the Eastern and Mayao port areas is especially significant. Additionally, data analysis has revealed the spatial distribution characteristics of mangroves in different regions, such as the mangrove area in Dongzhaigang, which remained relatively stable from 1990 to 2020, while in other areas like Huiwen and Guannan, the mangrove area decreased during these 20 years. By calculating the standard deviation ellipse, we observed that the overall change of mangroves on Hainan Island from 1990 to 2020 was relatively slow, mainly distributed along the northern coastal area of Hainan Island. Furthermore, the standard deviation ellipse and SDE center point of each mangrove growth area have visualized the growth trends of the mangroves. The Markov chain simulation results show that future changes in mangroves will mainly be concentrated in the marginal areas of the mangroves. These areas may be affected by rising sea levels, climate change, soil salinity, and human activities. In the future, mangrove areas are expected to display a dynamic equilibrium, experiencing periods of expansion and reduction, ultimately moving towards a more consistent state. To protect and restore mangroves, it is necessary to strengthen the monitoring and management of their ecological environment and socio-economic factors and improve their stability and diversity.

Mapping the Growth of Mangrove Forests in the United Arab Emirates from 2010 to 2020

Mangroves have a long rich history in the United Arab Emirates (UAE). Over time they have served as an important cultural, economic and ecological resource. To fully conserve these important ecosystems, it is essential to have accurate maps of their current and historical extent. Remote sensing is an excellent tool for the creation of this type of data. This paper tests the use of the Google Earth Engine Mangrove Mapping Methodology (GEEMMM) (Yancho et al., 2020) to monitor mangrove change in the UAE from 2010 to 2020. The maps developed are consistent with other research and show mangroves in the UAE have expanded to a total area of 169 km2, which is an increase of 34 km2 over the ten years included in this study. It further proposes that Ras Al Khaimah could utilize this approach to continue monitoring mangrove change in order to improve conservation and better understand how environmental conditions impact mangrove ecosystem health.

Monitoring temporal changes in coastal mangroves to understand the impacts of climate change: Red Sea, Egypt

This paper focuses on the current state of mangroves in Egypt, analyses how climate change is affecting mangrove forests and suggests a conservation strategy to safeguard this valuable maritime resource. Mangroves in Egypt are primarily found in regions around the Red Sea, making them popular for ecotourism across the globe. A crucial breeding and feeding ground for many commercial fishery species, mangroves are a maritime habitat that is both economically and environmentally significant and a variety of living things call them home. Mangroves are one of the most threatened tropical and subtropical ecoregions in the world. These threats come from both natural and human sources, including oil spills, human waste runoff, herbicide use, and coastal development, all of which have the potential to harm the environment. Mangrove habitats are immediately impacted by salinity changes and rising sea levels brought on by climate change. This is unfortunate as mangroves are one of the most important global carbon sinks, and their loss due to climate change may accelerate sea level rise and ocean acidification, which will cause the extinction of numerous commercial fish species. A conservation strategy should be implemented to safeguard the mangroves around the Egyptian Red Sea by using mitigation techniques, creating marine protected zones near the mangroves, and consulting the owners of the mangrove stalks. Analysis of multi-temporal satellite imagery is one of the most significant methods to assist decision-makers and environmental planners in obtaining high-precision information about environmental and climatic changes. Analysis of satellite imagery is a valuable way to detect, monitor, assess and map the environmental, human, and natural activities that are threatening the growth of the mangrove forest ecosystem. Change detection analyses using satellite imagery were conducted to evaluate the effects of rapid coastal sustainable development, including human urban and tourism activities; the decline and destruction of large areas of the mangrove forest for animal feeding along camel trade roads; and the threat of natural impacts such as flash flooding, coastal and soil erosion or human impacts such as established desalination plants and other human activities along the coastal zone of the Egyptian Red Sea.

Mangrove monitoring revealed by MDPrePost-Net using archived Landsat imageries

Acting as a global vital entity in the coastal ecosystem, mangroves are currently facing major threats to destruction due to anthropogenic activities. Restoration and rehabilitation measures of mangroves are being carried out, including in Indonesia. To support conservation actions in many islands in Indonesia, spatio-temporal information on mangroves is required. Remote sensing with advanced techniques such as machine learning and deep learning has proven the capability to spatially observe mangroves. This study aims to monitor mangrove change in three study areas, i.e., South Sumatra, North Kalimantan, and Southeast Sulawesi, using a fully convolutional network (FCN)-based MDPrePost-Net. This method was developed originally to assess the mangrove degradation due to a major event (i.e., Hurricane irma 2017 in southwest Florida), whereas this study adopts it for an extended observation period (1989–2022 for South Sumatra, 1991–2021 for North Kalimantan, and 1990–2021 for Southeast Sulawesi) using medium-resolution Landsat imageries. We observed that the mangroves remain stable within the national parks designated by the government. Outside the national parks, mangrove conversion massively occurred even though the areas are assigned as protection forests. The classification results showed satisfactory accuracy values of more than 84%. The maps produced have an advantage in the spatial change analysis compared to the global datasets such as Global Mangrove Watch version 3.0. Our method has a limitation when cloudless images are not available. The integration with Synthetic Aperture Radar (SAR) images and a rigorous cloud removal method for the optical images may improve the results of mangrove monitoring.

Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping

Detailed information about mangroves is crucial for ecological and environmental protection and sustainable development. It is difficult to capture small patches of mangroves from satellite images with relatively low to medium resolution. In this study, high-resolution (0.8–2 m) images from Chinese GaoFen (GF) and ZiYuan (ZY) series satellites were used to map the distribution of mangroves in coastal areas of Guangdong Province, China. A deep-learning network, U2-Net, with attention gates was applied to extract multi-scale information of mangroves from satellite images. The results showed that the attention U2-Net model performed well on mangrove classification. The overall accuracy, precision, and F1-score values were 96.5%, 92.0%, and 91.5%, respectively, which were higher than those obtained from other machine-learning methods such as Random Forest or U-Net. Based on the high-resolution mangrove maps generated from long satellite image time series, we also investigated the spatiotemporal evolution of the mangrove forest in Shuidong Bay. The results can provide crucial information for government administrators, scientists, and other stakeholders to monitor the dynamic changes in mangroves.

A regional map of mangrove extent for Myanmar, Thailand, and Cambodia shows losses of 44% by 1996

Southeast Asia is home to some of the planet’s most carbon-dense and biodiverse mangrove ecosystems. There is still much uncertainty with regards to the timing and magnitude of changes in mangrove cover over the past 50 years. While there are several regional to global maps of mangrove extent in Southeast Asia over the past two decades, data prior to the mid-1990s is limited due to the scarcity of Earth Observation (EO) data of sufficient quality and the historical limitations to publicly available EO. Due to this literature gap and research demand in Southeast Asia, we conducted a classification of mangrove extent using Landsat 1-2 MSS Tier 2 data from 1972 to 1977 for three Southeast Asian countries: Myanmar, Thailand, and Cambodia. Mangrove extent land cover maps were generated using a Random Forest machine learning algorithm that effectively mapped a total of 15,420.51 km2. Accuracy assessments indicated that the classification for the mangrove and non-mangrove class had a producer’s accuracy of 80% and 98% user’s accuracy of 90% and 96%, and an overall accuracy of 95%. We found a decline of 6,830 km2 between the 1970s and 2020, showing that 44% of the mangrove area in these countries has been lost in the past 48 years. Most of this loss occurred between the 1970s and 1996; rates of deforestation declined dramatically after 1996. This study also elaborated on the nature of mangrove change within the context of the social and political ecology of each case study country. We urge the remote sensing community to empathetically consider the local need of those who depend on mangrove resources when discussing mangrove loss drivers.

Natural Protected Areas effect on the cover change rate of mangrove forests in the Yucatan Peninsula, Mexico

Mangrove forests provide important ecosystem services that support the maintenance of coastal socio-ecological systems. Strategies such as the establishment of Natural Protected Areas (NPAs) have been encouraged worldwide to promote their conservation. However, mangroves are still being lost as a consequence of anthropogenic disturbance and environmental variation. In here, we determine the change rate in mangrove cover, between 2005–2015, in eight coastal NPAs and the corresponding four km adjacent transition area in the Yucatan Peninsula (YP), and assess the relationship with location (inside or outside the protected area), their time since establishment and distance to roads. In general, we observed higher positive rates of change in mangrove cover inside the protected areas than in the adjacent transition zones (p < 0.05). Change was related to both the distance to roads and the time since establishment of the NPAs (p < 0.05). Higher (positive and negative) mangrove rates of change were observed in areas closer to roads, and more stable areas were detected as the distance to the roads increased. The positive rate of change also increased with the age of the protected area. These patterns were more evident inside the NPAs. The continuous monitoring of mangrove change dynamics in protected areas in this important region and their drivers, is necessary to establish effective management strategies considering the spatial and temporal variability of environmental and anthropogenic disturbances.

Geospatial Assessment of Long-Term Changes (1937–2019) in Mangrove Vegetation and Shoreline Dynamics of Godavari Estuary, East Coast of India

Godavari mangroves are the second largest mangrove ecoregion of India located on its eastern coast. Mangroves perform major ecological functions including shoreline stabilization and coastal protection. They have undergone serious alterations, largely induced by human activities resulting in increased vulnerability to coastal flooding. A reliable database on mangroves distribution and temporal changes is essential for their effective management and conservation. Present study analyzes the long-term changes in mangroves, as well as the geomorphological changes along the coast of Godavari Estuary over a span of 82 years (1937–2019), using topographical maps, remotely sensed multi-temporal satellite imageries for five time periods. Results indicated that the rate of land uses conversions of agricultural fields, degraded areas, mudflats and mangrove vegetation into aquaculture significantly increased during the late 1990s. About 40 percent of it was constructed on mangrove areas between 1980 and 2019. The area under aquaculture was estimated to be 6,388.62 ha in 2000, almost doubling-up to 11,924.67 ha in 2019. Mangrove cover in this region increased by 15.95 percent between 2000 and 2019 (including 11.9 percent in Coringa Wildlife Sanctuary) due to increased protection, consequent regeneration and accretion. Shift in Hope Island spit was observed in north and northwest directions, gradually growing nearly 3.84 km. High-resolution declassified satellite data were found very helpful for long-term mangrove mapping and analyzing geomorphological changes; enabling a better understanding of these regions to help decision makers/planners to find better solutions to existing coastal issues. The paper concludes with some recommendations to restore and conserve the existing mangrove cover in the study area.

Collaborative multiple change detection methods for monitoring the spatio-temporal dynamics of mangroves in Beibu Gulf, China

ABSTRACT Mangrove ecosystems are one of the most diverse and productive marine ecosystems around the world, although losses of global mangrove area have been occurring over the past decades. Therefore, tracking spatio-temporal changes and assessing the current state are essential for mangroves conservation. To solve the issues of inaccurate detection results of single algorithms and those limited to historical change detection, this study proposes the detect–monitor–predict (DMP) framework of mangroves for detecting time-series historical changes, monitoring abrupt near-real-time events, and predicting future trends in Beibu Gulf, China, through the synergetic use of multiple detection change algorithms. This study further developed a method for extracting mangroves using multi-source inter-annual time-series spectral indices images, and evaluated the performance of twenty-one spectral indices for capturing expansion events of mangroves. Finally, this study reveals the spatio-temporal dynamics of mangroves in Beibu Gulf from 1986 to 2021. In this study, we found that our method could extract mangrove growth regions from 1986 to 2021, and achieved 0.887 overall accuracy, which proved that this method is able to rapidly extract large-scale mangroves without field-based samples. We confirmed that the normalized difference vegetation index and tasseled cap angle outperform other spectral indexes in capturing mangrove expansion changes, while enhanced vegetation index and soil-adjusted vegetation index capture the change events with a time delay. This study revealed that mangrove changes displayed historical changes in the hierarchical gradient from land to sea with an average annual expansion of 239.822 ha in the Beibu Gulf during 1986–2021, detected slight improvements and deteriorations of some contemporary mangroves, and predicted 72.778% of mangroves with good growth conditions in the future.