Analysis of changes in mangrove cover during 2005-2018 using LANDSAT Satellite Imagery in Pangandaran District

This study provides the capability of mapping, monitoring distribution and changes of regional mangroves using remote sensing data. The study area located in Mekong delta, Vietnam. Landsat ETM+ and digital elevation model (DEM) were used to enhance the discrimination between mangrove and non-mangrove forests and MAD technique was used to assess the changes in the mangrove cover in study area. Results showed that information from satellite remote sensing can play a useful role in determining the changes in land area and mangrove cover in Mekong Delta, especially mangrove forests has been significantly reduced in Tra Vinh province during 2000-2005.

Land cover changes significantly affect mangrove forests, driven by both anthropogenic activities and natural processes. The Banlaem mangrove in Nakhon Si Thammarat, Thailand, supports numerous mangrove plantation projects but lacks comprehensive assessments and monitoring related to land cover changes. This study aimed to (1) investigate land cover changes in the Banlaem mangrove from 1995 to 2023, and (2) generate a predictive model for future land cover changes. For land cover assessment, satellite imagery from multiple sources, including Sentinel-2 (Level 2A) and Landsat (Collection 2 Level 2), was utilized to examine and classify changes in mangrove cover within the Banlaem mangrove forest from 1995 to 2023, using supervised classification with the maximum likelihood algorithm. Various regression models were analysed to develop a predictive model based on area size and time. The mangrove area in the Banlaem mangrove forest steadily grew throughout the study period, with the total area increasing from 56.16 ha in 1995 to 527.55 ha in 2023. This study represents the first analysis of changes in the Banlaem mangrove cover. Throughout the tested models, they reveal an unclear pattern of mangrove expansion, yet they indicate a high rate of expansion in the Banlaem mangrove forest. In addition, these results are expected to encourage greater community involvement in the monitoring and management of the Banlaem mangrove. We recommend establishing a community monitoring network to engage local residents in tracking changes in mangrove cover, supported by training and resources.

The mangrove forest in Thailand provides food and many living organisms. Mangrove forests also provide important nursery habitat for many species of fishes. Therefore, knowledge on mangroves distribution and change is importance for effective management. Remote sensing and geographic information system has been widely used to characterize and monitor and studies on mangroves change. The main of research is monitoring mangrove forest land cover changes in Samut Songkhram Province from 2008 to 2018. This study used multi-temporal Landsat TM data and ancillary GIS data to quantify mangrove cover changes in the whole of Samut Songkhram. This research used supervised classification technique and NDVI index. Land-use was classed 6 groups which were mangrove forest area, urban area, agricultural area, bare land area, fishery farm area and water area. The result found that overall accuracy by calculating Error matrix during 53 to 83%. The mangrove forest area is increasing between 2008 to 2011 and 2015–2017. In the other hand, mangrove forest areas are decreased between 2011–2015. Information of mangrove forest area change of this study is very useful to local government and urban planners for the betterment of future plans of sustainable development of mangrove forest.

Mangrove forests have experienced a rapid decline. However, the rate of loss has decreased in recent years due to enhanced conservation and nature regeneration. The dynamics of mangrove forests in Panama have not been monitored since the year 2000, despite a significant loss during the 1980s. The objectives of our study were to quantify changes in mangrove cover and identify the dominant drivers of change in Parita Bay, Panama. Temporal changes in mangrove cover and the Normalized Difference Vegetation Index (NDVI) were determined using the supervised classification method on Landsat satellite images from 1987 to 2019. We identified a 4.7% increase in the mangrove area of Parita Bay during the 32 years; the mangrove forests were also considered healthy as reflected by high NDVI values. However, the conversion of mangroves to other land cover types resulted in a 1.26% decline in mangrove cover from 1987 to 1998. Moreover, the area of aquaculture and saltpans almost doubled during this period. During the following two decades, the conversion of other land cover classes (water, other vegetation, and bare soil) increased the mangrove area by 6%, and the annual rate of increase was greater during the second decade (0.43% year−1). From 2009 to 2019, mangroves declined at an annual rate of 0.11% in protected areas and increased at an annual rate of 0.50% in unprotected areas. Despite the regeneration potential of mangrove forests, our study highlights the need to continually manage and protect mangrove forests in order to facilitate their expansion in Parita Bay.

This study investigates the relationship between mangrove cover changes, shoreline dynamics, and urban development from 2014 to 2022 in a designated coastal area. Utilizing satellite imagery and GIS analysis, the research maps and quantifies shifts in mangrove distribution and shoreline erosion and accretion patterns. Additionally, changes in Land Surface Temperature (LST) are analyzed to understand their correlation with mangrove health and urbanization trends. Ground-truthing is conducted through strategically chosen sample points, some located within mangrove ecosystems and others along the urbanized shoreline, to assess environmental impacts accurately. The research reveals a notable increase in mangrove cover, rising from 4.9 km² to 12.9 km², highlighting the effectiveness of conservation efforts. In contrast, urban land expanded dramatically from 140 km² to 396 km², presenting significant environmental challenges. Analysis of LST showed a maximum decrease from 54°C in 2014 to 45°C in 2022, suggesting potential cooling effects linked to increased mangrove cover. Correlation analyses indicated a weak positive relationship between LST and the normalized difference built-up index (NDBI) and a negligible relationship with the normalized difference vegetation index (NDVI). These findings underscore the need for sustainable urban planning that prioritizes ecological integrity while accommodating rapid urban development. Further research is essential to explore these dynamics and inform effective environmental policies.

Abstract. Kaskoyo H, Hartati F, Bakri S, Febryano IG, Dewi BS, Nurcahyani N. 2023. Satellite based analysis of mangrove cover and density change in mangroves of Tulang Bawang District, Lampung Province, Indonesia. Biodiversitas 24: 3019-3028. Being a threatened ecosystem of the world, mangroves require regular monitoring to identify areas that need improvement in conservation measures. Considering this, the present study aimed to assess the mangrove cover and density change in mangrove forest of Tulang Bawang District, Lampung Province, Indonesia. Data was collected by remote sensing techniques using satellite imagery viz., Landsat 5 in 2000 and 2010 and Landsat 8 in 2020. It was then analyzed using remote sensing method, namely Object-Based Image Analysis techniques using the unguided classification method to obtain mangrove cover data; while mangrove density information was obtained using the Normalized Difference Vegetation Index algorithm. The results revealed the decrease in mangrove cover and density between 2000 and 2020. The mangrove cover decreased from 7529 ha in 2000 to 5551 ha in 2020. Furthermore, density of mangrove forest has also witnessed change from dense density class in 2000 to medium density class in 2020. From this result, it is evident that mangroves of East Rawajitu Sub-district are degrading and require focused efforts to prevent the degradation. Communities, entrepreneurs, and governments have different motives for utilizing mangrove forests that result in damage, so reasonable efforts are needed to manage mangrove forests sustainably.

Assessing the changes of mangrove ecosystem services value in the Pars Special Economic Energy Zone

One of the ecosystems in the coastal areas that have a very important role is mangrove forest. Segara Anakan, Cilacap is an area that has the largest mangrove potential in Java. However, the condition of the mangrove both qualitative and quantitative are decreasing. Utilization of remote sensing and GIS technology for the provision of information on the distribution and presence of mangrove forest is growing. The aim of this study is to create a model of distribution changes mangrove forests in 1987-2016 and forecast the distribution of mangrove forests in 2031 in Segara Anakan, Cilacap. Satellite imagery that used such imagery SPOT 1 in 1987, Landsat 7 ETM + in 2003 and Landsat 8 OLI 2016. The method used is a Markov-Chain, Cellular Automata with driving factors such as distance from settlements, population density, distance from the road, distance from the river, the distance from the center of activity, sedimentation and salinity. The results showed a decrease in the distribution of mangroves in the western part of Segara Anakan precisely in the north of the lagoon where the majority of mangrove forest land converted to the rice field. The predicted distribution of mangrove forests in 2031 is the addition of mangrove forests in the lagoon Segara Anakan, precisely the western part due to conversion to the rice field and dry land agriculture.

Global changes are causing broad-scale shifts in vegetation communities worldwide, including coastal habitats where the borders between mangroves and salt marsh are in flux. Coastal habitats provide numerous ecosystem services of high economic value, but the consequences of variation in mangrove cover are poorly known. We experimentally manipulated mangrove cover in large plots to test a set of linked hypotheses regarding the effects of changes in mangrove cover. We found that changes in mangrove cover had strong effects on microclimate, plant community, sediment accretion, soil organic content, and bird abundance within 2yr. At higher mangrove cover, wind speed declined and light interception by vegetation increased. Air and soil temperatures had hump-shaped relationships with mangrove cover. The cover of salt marsh plants decreased at higher mangrove cover. Wrack cover, the distance that wrack was distributed from the water's edge, and sediment accretion decreased at higher mangrove cover. Soil organic content increased with mangrove cover. Wading bird abundance decreased at higher mangrove cover. Many of these relationships were non-linear, with the greatest effects when mangrove cover varied from zero to intermediate values, and lesser effects when mangrove cover varied from intermediate to high values. Temporal and spatial variation in measured variables often peaked at intermediate mangrove cover, with ecological consequences that are largely unexplored. Because different processes varied in different ways with mangrove cover, the "optimum" cover of mangroves from a societal point of view will depend on which ecosystem services are most desired.

Medan City is the third-largest city in Indonesia and serves as the capital of North Sumatra Province. The city has several sub-districts with mangrove forests, with the largest area located in Medan Belawan Sub-district, followed by Medan Labuhan and Medan Marelan Sub-districts. The mangrove forests are situated in the downstream part of the Deli Watershed or the northern part of Medan City. Over the past fourteen years, there has been a significant change in the mangrove land cover, particularly in Medan Labuhan Sub-district. To address this issue, an integrated remote sensing, Geographic Information Systems (GIS), and Analytical Process Hierarchy (AHP) approach was used to map and analyze changes in mangrove cover and determine priority factors to prevent further changes. The study found that the mangrove forest area in Medan Labuhan Sub-district has decreased by 111.25 ha over the years, and the economy was identified as the top priority criterion for preventing changes in mangrove cover. The study recommends efforts from all parties to minimize changes in mangrove forest cover to maintain the function of the mangrove ecosystem, and this approach can help decision-makers and stakeholders make informed decisions about mangrove ecosystem management and conservation in Medan City.

Transitions in Ancient Inland Freshwater Resource Management in Sri Lanka Affect Biota and Human Populations in and around Coastal Lagoons

Nigeria has the third largest mangrove forest in the world, the largest in Africa with approximately 80% of its mangrove vegetation located within the Niger Delta region of the Country. Unfortunately, rapid urbanization has resulted in widespread mangrove loss which could lead to increased surface land temperatures (LST) culminating in the threat to the integrity of this ecosystem. Aim: The study assessed the impacts of mangrove cover changes on climate change in the Niger Delta Region of Nigeria with the aim of articulating sustainable mangrove management practices. Place and Duration of Study: Mangrove covers from 1987 to 2022 in the study area which includes nine states of Akwa Ibom, Bayelsa, Cross River, Delta, Edo, Imo, Ondo, Rivers, and Abia, and encompasses significant mangrove forests. Methodology: The methodology adopted a remote sensing-based research design utilizing satellite imagery to analyze temporal changes in mangrove cover and evaluated their association with climate variables such as CO2 emissions and LST of the study area. Each satellite image geo-referenced in ArcGIS 10.8 & LULC changes calculated using geometry module of ArcGIS 10.8. LST was derived from the geometrically corrected Landsat 5 and Landsat 8. Results: The data obtained revealed mangrove reduction from 12,991 km2 in 1987 to 9,089km2 in 2022 resulting in the increased LST from 26.01°C to 28.07°C respectively within the pace of thirty-five (35) years. These results illustrate a clear link between mangrove cover change and variation in the LST, highlighting the critical role mangroves play in regulating climate change. Conclusion: There are significant losses in mangrove cover have been closely associated with increased LST, thus reflecting the vital role these ecosystems play in carbon sequestration which underscores the importance of preserving these vital ecosystems to mitigate local and global climate impacts.

Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran

Abstract. Quantitative information on mangrove cover extents is essential in producing relevant resource management plans and conservation strategies. In the Philippines, mangrove rehabilitation was made a priority in relation to disaster risk response and mitigation following the calamities in the coastal communities during typhoon Haiyan/Yolanda; hence, baseline information on the extent of remaining mangrove cover was essential for effective site interventions. Although mangrove cover maps for the country already exists, analysis of mangrove cover changes were limited to the application of fixed annual deforestation rates due to the challenge of acquiring consistent temporal cloud-free optical satellite data over large landscapes. This study presents an initial analysis of SAR and optical imagery combined with field-based observations for detecting mangrove cover extent and changes through a straightforward graphical approach. The analysis is part of a larger study evaluating the synergistic use of time-series L-band SAR and optical data for mapping and monitoring of mangroves. Image segmentation was implemented on the 25-meter ALOS/PALSAR image mosaics, in which the generated objects were subjected to statistical analysis using the software R. In combination with selected Landsat bands, the class statistics from the image bands were used to generate decision trees and thresholds for the hierarchical image classification. The results were compared with global mangrove cover dataset and validated using collected ground truth data. This study developed an integrated replicable approach for analyzing future radar and optical datasets, essential in national level mangrove cover change monitoring and assessment for long-term conservation targets and strategies.

Quantitative information on mangrove cover extents is essential in producing relevant resource management plans and conservation strategies. In the Philippines, mangrove rehabilitation was made a priority in relation to disaster risk response and mitigation following the calamities in the coastal communities during typhoon Haiyan/Yolanda; hence, baseline information on the extent of remaining mangrove cover was essential for effective site interventions. Although mangrove cover maps for the country already exists, analysis of mangrove cover changes were limited to the application of fixed annual deforestation rates due to the challenge of acquiring consistent temporal cloud-free optical satellite data over large landscapes. This study presents an initial analysis of SAR and optical imagery combined with field-based observations for detecting mangrove cover extent and changes through a straightforward graphical approach. The analysis is part of a larger study evaluating the synergistic use of time-series L-band SAR and optical data for mapping and monitoring of mangroves. Image segmentation was implemented on the 25-meter ALOS/PALSAR image mosaics, in which the generated objects were subjected to statistical analysis using the software R. In combination with selected Landsat bands, the class statistics from the image bands were used to generate decision trees and thresholds for the hierarchical image classification. The results were compared with global mangrove cover dataset and validated using collected ground truth data. This study developed an integrated replicable approach for analyzing future radar and optical datasets, essential in national level mangrove cover change monitoring and assessment for long-term conservation targets and strategies.