Search Results (144)

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. Full article

In the Bamen Bay area of the Qinglan Harbor Mangrove Provincial Nature Reserve in Wenchang, Hainan Province, China, mangrove aboveground biomass (AGB) was estimated using high-resolution UAV ortho-imagery and UAV LiDAR data. The spatial distribution characteristics of AGB were studied using global Moran’s I index and hotspot analysis. Optimal geographic detectors and regression models were employed to analyze the relationship between AGB and key environmental factors. The results indicate that (1) the average AGB in the study area was 141.22 Mg/ha, with significant spatial variation. High AGB values were concentrated in the southwestern and northeastern regions, while low values were mainly found in the central and southeastern regions. (2) Plant species, water pH, soil total potassium, salinity, dissolved oxygen, elevation, soil organic matter, soil total phosphorus, and soil total nitrogen were identified as major factors influencing the spatial distribution of AGB. The interaction results indicate either bifactor enhancement or nonlinear enhancement, showing a significantly higher impact compared with single factors. (3) Comprehensive regression model results reveal that soil total nitrogen was the primary factor affecting AGB, followed by soil total potassium, with water pH having the least impact. Factors positively correlated with AGB promoted biomass growth, while elevation negatively affected AGB, inhibiting biomass accumulation. The findings provide critical insights that can guide targeted conservation efforts and management strategies aimed at enhancing mangrove ecosystem health and resilience, particularly by focusing on key areas identified for potential improvement and by addressing the complex interactions among environmental factors. Full article

Mangrove forests are essential for coastal protection and carbon sequestration, yet accurately mapping their distribution remains challenging due to spectral similarities with other vegetation. This study introduces a novel unsupervised learning method, the Elite Individual Adaptive Genetic Algorithm-Semantic Inference (EIAGA-S), designed for the high-precision semantic segmentation of mangrove forests using remote sensing images without the need for ground truth samples. EIAGA-S integrates an adaptive Genetic Algorithm with an elite individual’s evolution strategy, optimizing the segmentation process. A new Mangrove Enhanced Vegetation Index (MEVI) was developed to better distinguish mangroves from other vegetation types within the spectral feature space. EIAGA-S constructs segmentation rules through iterative rule stacking and enhances boundary information using connected component analysis. The method was evaluated using a multi-source remote sensing dataset covering the Hainan Dongzhai Port Mangrove Nature Reserve in China. The experimental results demonstrate that EIAGA-S achieves a superior overall mIoU (mean intersection over union) of 0.92 and an F1 score of 0.923, outperforming traditional models such as K-means and SVM (Support Vector Machine). A detailed boundary analysis confirms EIAGA-S’s ability to extract fine-grained mangrove patches. The segmentation includes five categories: mangrove canopy, other terrestrial vegetation, buildings and streets, bare land, and water bodies. The proposed EIAGA-S model offers a precise and data-efficient solution for mangrove semantic mapping while eliminating the dependency on extensive field sampling and labeled data. Additionally, the MEVI index facilitates large-scale mangrove monitoring. In future work, EIAGA-S can be integrated with long-term remote sensing data to analyze mangrove forest dynamics under climate change conditions. This innovative approach has potential applications in rapid forest change detection, environmental protection, and beyond. Full article

Mangrove ecosystems provide numerous ecological services and serve as vital habitats for a wide range of flora and fauna. Thus, accurate mapping and monitoring of relevant land covers in mangrove ecosystems are crucial for effective conservation and management efforts. In this study, we proposed a novel approach for mangrove ecosystem mapping using a Hybrid Selective Kernel-based Convolutional Neural Network (HSK-CNN) framework and multi-temporal Sentinel-2 imagery. A time series of the Normalized Difference Vegetation Index (NDVI) products derived from Sentinel-2 imagery was produced to capture the temporal behavior of land cover types in the dynamic ecosystem of the study area. The proposed algorithm integrated Selective Kernel-based feature extraction techniques to facilitate the effective learning and classification of multiple land cover types within the dynamic mangrove ecosystems. The model demonstrated a high Overall Accuracy (OA) of 94% in classifying eight land cover classes, including mangrove, tidal zone, water, mudflat, urban, and vegetation. The HSK-CNN demonstrated superior performance compared to other algorithms, including random forest (OA = 85%), XGBoost (OA = 87%), Three-Dimensional (3D)-DenseNet (OA = 90%), Two-Dimensional (2D)-CNN (OA = 91%), Multi-Layer Perceptron (MLP)-Mixer (OA = 92%), and Swin Transformer (OA = 93%). Additionally, it was observed that the structure of the network, such as the types of convolutional layers and patch sizes, affected the classification accuracy using the proposed model and, thus, the optimum scenarios and values of these parameters should be determined to obtain the highest possible classification accuracy. Overall, it was observed that the produced map could offer valuable insights into the distribution of different land cover types in the mangrove ecosystem, facilitating informed decision-making for conservation and sustainable management efforts. Full article

Spaceborne spectroscopic imaging offers the potential to improve our understanding of biodiversity and ecosystem services, particularly for challenging and rich environments like mangroves. Understanding the signals present in large volumes of high-dimensional spectroscopic observations of vegetation communities requires the characterization of seasonal phenology and response to environmental conditions. This analysis leverages both spectroscopic and phenological information to characterize vegetation communities in the Sundarban riverine mangrove forest of the Ganges–Brahmaputra delta. Parallel analyses of surface reflectance spectra from NASA’s EMIT imaging spectrometer and MODIS vegetation abundance time series (2000–2022) reveal the spectroscopic and phenological diversity of the Sundarban mangrove communities. A comparison of spectral and temporal feature spaces rendered with low-order principal components and 3D embeddings from Uniform Manifold Approximation and Projection (UMAP) reveals similar structures with multiple spectral and temporal endmembers and multiple internal amplitude continua for both EMIT reflectance and MODIS Enhanced Vegetation Index (EVI) phenology. The spectral and temporal feature spaces of the Sundarban represent independent observations sharing a common structure that is driven by the physical processes controlling tree canopy spectral properties and their temporal evolution. Spectral and phenological endmembers reside at the peripheries of the mangrove forest with multiple outward gradients in amplitude of reflectance and phenology within the forest. Longitudinal gradients of both phenology and reflectance amplitude coincide with LiDAR-derived gradients in tree canopy height and sub-canopy ground elevation, suggesting the influence of surface hydrology and sediment deposition. RGB composite maps of both linear (PC) and nonlinear (UMAP) 3D feature spaces reveal a strong contrast between the phenological and spectroscopic diversity of the eastern Sundarban and the less diverse western Sundarban. Full article

Springtails, vital for ecosystem assessment, are often overshadowed by taxonomy-focused research, which mostly neglects their ecology and distribution, particularly in the Neotropical Region. The objective of this study was to identify how environmental factors, especially vegetation types, affect the availability of food resources for epiedaphic Collembola and influence their diversity patterns in three vegetation types (riparian forest, mangrove, and restinga) in the Canárias Island, in Delta do Parnaíba Environmental Protection Area, Brazil (APADP). We collected samples along 200 m transects in each vegetation type during the dry and rainy seasons. After, specimens were sorted, counted and identified. Alpha (species richness, Shannon, Simpson, and Pielou indices) and beta diversity (Whittaker index) were analyzed, along with environmental factors’ influence through Redundancy Analysis (RDA). We sampled a total of 5346 specimens, belonging to three orders, eight families, 23 genera, 31 morphospecies, and one nominal species. Species abundance was positively influenced by soil moisture, plant richness, and leaf litter. The riparian forest sheltered a higher species richness and diversity, and its biotic and abiotic factors likely enhanced the food resource availability, including vegetal organic matter, fungi, and bacteria. These results provide the first taxonomic and ecological data on the Collembola fauna in the APADP. Full article

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. Full article

Accurate and timely acquisition of the spatial distribution of mangrove species is essential for conserving ecological diversity. Hyperspectral imaging sensors are recognized as effective tools for monitoring mangroves. However, the spatial complexity of mangrove forests and the spectral redundancy of hyperspectral images pose challenges to fine classification. Moreover, finely classifying mangrove species using only spectral information is difficult due to spectral similarities among species. To address these issues, this study proposes an object-oriented multi-feature combination method for fine classification. Specifically, hyperspectral images were segmented using multi-scale segmentation techniques to obtain different species of objects. Then, a variety of features were extracted, including spectral, vegetation indices, fractional order differential, texture, and geometric features, and a genetic algorithm was used for feature selection. Additionally, ten feature combination schemes were designed to compare the effects on mangrove species classification. In terms of classification algorithms, the classification capabilities of four machine learning classifiers were evaluated, including K-nearest neighbor (KNN), support vector machines (SVM), random forests (RF), and artificial neural networks (ANN) methods. The results indicate that SVM based on texture features achieved the highest classification accuracy among single-feature variables, with an overall accuracy of 97.04%. Among feature combination variables, ANN based on raw spectra, first-order differential spectra, texture features, vegetation indices, and geometric features achieved the highest classification accuracy, with an overall accuracy of 98.03%. Texture features and fractional order differentiation are identified as important variables, while vegetation index and geometric features can further improve classification accuracy. Object-based classification, compared to pixel-based classification, can avoid the salt-and-pepper phenomenon and significantly enhance the accuracy and efficiency of mangrove species classification. Overall, the multi-feature combination method and object-based classification strategy proposed in this study provide strong technical support for the fine classification of mangrove species and are expected to play an important role in mangrove restoration and management. Full article

In the Mexican Caribbean, environmental changes, hydrometeorological events, and anthropogenic activities promote dynamism in the coastal vegetation cover associated with the dune; however, their pace and magnitude remain uncertain. Using Landsat 7 imagery, spatial and temporal changes in coastal dune vegetation were estimated for the 2011–2020 period in the Sian Ka’an Biosphere Reserve. The SAVI index revealed cover changes at different magnitudes and paces at the biannual, seasonal, and monthly timeframes. Climatic seasons had a significant influence on vegetation cover, with increases in cover during northerlies (SAVI: p = 0.000), while the topographic profile of the dune was relevant for structure. Distance-based multiple regressions and redundancy analysis showed that temperature had a significant effect (p < 0.05) on SAVI patterns, whereas precipitation showed little influence (p > 0.05). The Mann–Kendall tendency test indicated high dynamism in vegetation loss and recovery with no defined patterns, mostly associated with anthropogenic disturbance. High-density vegetation such as mangroves, palm trees, and shrubs was the most drastically affected, although a reduction in bare soil was also recorded. This study demonstrated that hydrometeorological events and climate variability in the long term have little influence on vegetation dynamism. Lastly, it was observed that anthropogenic activities promoted vegetation loss and transitions; however, the latter were also linked to recoveries in areas with pristine environments, relevant for tourism. Full article

The study of Land Use and Landscape Patterns (LULPs) changes and their ecological quality effects in Haikou city under the background of the Hainan Free Trade Port Plan (HFTPP) helps to promote coordinated development between cities and the environment. To date, most research on ecological quality has focused on areas with extremely fragile ecology and/or is related to LULP analysis. There are few studies in the literature focusing on the impact of high-intensity human activities caused by relevant policies on urban LULPs. The purpose of this research was to design a framework that monitors urban ecological security by considering the effect of the developing free trade port. The proposed framework was constructed by integrating multi-temporal Sentinel-2 remote sensing images, night light remote sensing data, digital elevation model (DEM) data, and spectral index features such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), bare soil index (BSI), and normalized intertidal mangrove index (NIMI), as well as analytical approaches such as the land use transfer matrix, land use dynamic degree, land use degree and transfer matrix, land use gravity center measurement, and landscape pattern index. The framework takes advantage of the Google Earth Engine (GEE) cloud platform and was applied to a highly developed Haikou city, the capital of Hainan province. Maps of brightness (SBI), greenness (GVI), and humidity (WET) were created annually from 2016 to 2021, enabling detailed ecological environment quality evaluation and analysis. The advantages of this study are (1) reliable land cover results obtained automatically and quickly; (2) the strong objectivity of the quantitative research on landscape patterns and land use; and (3) deep integration with free trade port policies. Through the research on the ecological quality problems caused by the change in LULP in the study area, the research results show that, from 2016 to 2021, the spatial distribution of land use and landscape pattern in Haikou city had been constantly changing; the area of construction land has decreased, with most of it having been converted into forest land and farmland; the gravity center of the building land has moved to the northwest; the degree of landscape fragmentation has decreased and the heterogeneity of landscape distribution has increased; the free trade port policies have promoted Haikou’s economic development and ecological civilization construction; and finally, Haikou’s ecological environmental quality has improved significantly. Full article

Cynometra iripa Kostel. is a Fabaceae species of mangrove used in traditional Ayurvedic medicine for treating inflammatory conditions. The present study aims to establish monographic botanical and chemical quality criteria for C. iripa leaf and bark as herbal substances and to evaluate their in vitro antioxidant potential. Macroscopic and microscopic qualitative and quantitative analyses, chemical LC-UV/DAD-ESI/MS profiling, and the quantification of key chemical classes were performed. Antioxidant activity was evaluated by DPPH and FRAP assays. Macroscopically, the leaf is asymmetrical with an emarginated apex and cuneate base. Microscopically, it shows features such as two-layered adaxial palisade parenchyma, vascular bundles surrounded by 3–6 layers of sclerenchyma, prismatic calcium oxalate crystals (5.89 ± 1.32 μm) along the fibers, paracytic stomata only on the abaxial epidermis (stomatal index–20.15), and non-glandular trichomes only on petiolules. The microscopic features of the bark include a broad cortex with large lignified sclereids, prismatic calcium oxalate crystals (8.24 ± 1.57 μm), and secondary phloem with distinct 2–5 seriated medullary rays without crystals. Chemical profile analysis revealed that phenolic derivatives, mainly condensed tannins and flavonoids, are the main classes identified. A total of 22 marker compounds were tentatively identified in both plant parts. The major compounds identified in the leaf were quercetin-3-O-glucoside and taxifolin pentoside and in the bark were B-type dimeric proanthocyanidins and taxifolin 3-O-rhamnoside. The total phenolics content was higher in the leaf (1521 ± 4.71 mg GAE/g dry weight), while the total flavonoids and condensed tannins content were higher in the bark (82 ± 0.58 mg CE/g and 1021 ± 5.51 mg CCE/g dry weight, respectively). A total of 70% of the hydroethanolic extracts of leaf and bark showed higher antioxidant activity than the ascorbic acid and concentration-dependent scavenging activity in the DPPH assay (IC₅₀ 23.95 ± 0.93 and 23.63 ± 1.37 µg/mL, respectively). A positive and statistically significant (p < 0.05) correlation between the phenol content and antioxidant activity was found. The results obtained will provide important clues for the quality control criteria of C. iripa leaf and bark, as well as for the knowledge of their pharmacological potential as possible anti-inflammatory agents with antioxidant activity. Full article

There is limited information regarding the influence of mangrove growth on the morphological evolution of intertidal mudflats. In this study, Tan Phu Dong district, Tien Giang Province, Vietnam, was selected for investigating how mangrove growth influenced the morphological evolution of an intertidal mudflat. The authors analyzed a series of satellite images (from 1995 and 2022), calculated the enhanced vegetation index (EVI), and documented field visits and observations in pursuit of the objective of the study. The findings revealed that fine-grained sediment accumulated as unconsolidated substratum (US) in the first step of the morphological evolution of the intertidal mudflat, with sediment accumulation of 910 ha in 1995. The US provided favorable conditions for mangroves to grow, while mangrove growth helped compact the US into a compact substratum (CS) in addition to promoting continuous sediment accumulation, increased the vegetation cover of the island, and elevated the substrate density of the remaining areas. As a result, the US and CS decreased steadily between 1995 and 2020, from 910 ha in 1995 to 401 ha in 2020 and from 433 ha in 2005 to 111 ha in 2020, respectively. Meanwhile, the low-vegetation area (LVA), medium-vegetation area (MVA), and high vegetation area (HVA) gradually increased between 1995 and 2015, from 0 ha in 1995 to 104 ha in 2015, from 0 ha in 1995 to 96 ha in 2015, and from 0 ha in 1995 to 114 ha in 2015, respectively. However, the LVA decreased slightly between 2015 and 2020 due to significant sand accumulation, which significantly killed the mangrove trees. In contrast, the MVA and HVA steadily increased between 2015 and 2020, from 96 ha in 2015 to 116 ha in 2020 and from 114 ha in 2015 to 221 ha in 2020, respectively. In 2022, there was a steady increase in HVA (298 ha in 2022), although the date of the 2022 satellite retrieval was 28 January 2022. This study recommends that the technical design of the existing coastal protection works should be revised or adapted to take account of sediment accumulation as the first step in the morphological evolution of the examined intertidal mudflat, rather than mangrove growth. Full article

Tropical cyclones, one of the most extreme and destructive meteorological incidents, cause extensive damage to lives and livelihoods worldwide. This study utilized remotely sensed data along with multi-criteria decision-making, geospatial techniques, and major cyclonic events Aila, Amphan, and Yaas to identify the changes in the vulnerability of cyclone-induced floods in the 19 community development blocks of Indian Sundarbans in the years 2009–2010, 2020–2021, and 2021–2022 (the post-cyclonic timespan). The Sundarbans are a distinctive bioclimatic region located in a characteristic geographical setting along the West Bengal and Bangladesh coasts. In this area, several cyclonic storms had an impact between 2009 and 2022. Using the variables NDVI, MNDWI, NDMI, NDBI, BSI, and NDTI, Landsat 8 Operational Land Imager, Thermal Infrared Sensor, Resourcesat LISS-III, and AWiFS data were primarily utilized to map the cyclonic flood-effective zones in the research area. The findings indicated that the coastline, which was most impacted by tropical storms, has significant physical susceptibility to floods, as determined by the AHP-weighted overlay analysis. Significant positive relationships (p < 0.05, n = 19 administrative units) were observed between mangrove damage, NDFI, and physical flood susceptibility indicators. Mangrove damage increased with an increase in the flood index, and vice versa. To mitigate the consequences and impacts of the vulnerability of cyclonic events, subsequent flood occurrences, and mangrove damage in the Sundarbans, a ground-level implementation of disaster management plans proposed by the associated state government, integrated measures of cyclone forecasting, mangrove plantation, coastal conservation, flood preparedness, mitigation, and management by the Sundarban Development Board are appreciably recommended. Full article

Marine ecotourism is one of the pivotal sectors that supports the sustainability of marine ecosystems and elevates the socioeconomic status of the country. Karangsong is located on the northern side of the Indramayu districts, covering approximately 25 hectares of mangrove. The significant number of tourists visiting sites of mangrove ecotourism in Indonesia contributed to the tourist intentions associated with the diversity of mangrove and management strategies. How the community-based tourism (CBT) index aligns with biophysical assessment is still unclear. This study aimed to investigate the interconnectedness between the community-based tourism (CBT) index and biophysical assessment to measure the ratio number of tourists using the carrying capacity and sustainability index in Karangsong. A potential new suitability index for mangrove tourism was created. The various factors such as the mangrove ecosystem, substrate, and suitability that impacted the development of mangrove tourism were quantitatively analyzed. Our study identified that the majority of mangrove ecotourism sites in Karangsong Indramayu Regency consist of rich diverse mangrove species. The biophysical characteristics of the mangrove ecosystem were assessed with a suitability index of 83.7%. Our results indicated that the operations of mangrove tourism in the Karangsong region are well-managed and maintained. The region has a carrying capacity of 803 people/day, which refers to the maximum number of individuals for this area. This finding provides a more detailed understanding of the contribution of the new sustainability index of mangrove tourism and community-based tourism (CBT) approach assessing the potential development and challenges in the management of mangrove forests ecotourism. Full article

Mangrove plant seedling cultivation is crucial for the protection, management, and restoration of the mangrove ecosystem. In this study, we focused on Kandelia obovata Sheue & al., a typical mangrove, and evaluated nursery cultivation with different combinations of three salinity levels (S1: 0 ppt, S2: 10 ppt, and S3: 20 ppt), three genealogies (EZD, JX, and YZ), and five growth media (M1: 100% loess, M2: 100% sandy, M3: 50% loess + 50% sandy, M4: 40% loess + 40% sandy + 20% peat, and M5: 40% loess + 40% sandy + 20% coir), by measuring the growth parameters such as mortality rate, seedling height, seedling diameter, and biomass partition. These growth indexes were significantly affected by salinity and medium, and genealogies also had significant effects on mortality rate and biomass accumulation. S2 or S3 both had lower mortality and higher growth indexes than S1. M1 was the medium that increased seedling height, diameter, and biomass the most and had the lowest death rate. EZD and JX were also at higher levels than YZ in these indicators, but the difference between them was not obvious. S3, M1, and EZD consistently performed well in fuzzy evaluation and quality assessment (Dickson quality index: 1.179, 1.478, and 1.089, respectively). Furthermore, combinations involving these treatments also produced highly favorable results. This indicates that the quality of seedlings produced under these conditions was high. These results furnish both a theoretical and practical foundation for advancing nursery cultivation techniques and germplasm breeding of K. obovata in mangroves. Full article