Monitoring of mangrove dynamic change in Beibu Gulf of Guangxi based on reconstructed time series images

In the mangrove growth area, the availability of high-quality optical images is limited throughout the year due to cloud cover, precipitation, and sensor revisiting cycles. In the worst-case scenario, severe conditions may lead to the unavailability of, causing variations in monitoring times for mangroves across different years. This significantly impacts the accuracy of long-term sequence monitoring of mangrove dynamics. To monitor long-term dynamic changes in mangrove spatial distribution, area, and ecology we reconstructed comprehensive time series images from 2000 to 2020 based on Landsat, Sentinel-2, and moderate-resolution imaging spectroradiometer (MODIS) images. We employed neighborhood-similar pixel interpolator (NSPI) strip filling, Fmask and temporal NSPI cloud-removal and filling, and FSDAF model to monitor the long-term dynamic changes in mangrove spatial distribution, area, and ecology. All three methods effectively reconstructed the images, with the FSDAF model exhibiting the greatest accuracy. The reconstructed images suggested that the mangroves demonstrated an overall growth trend from 2000 to 2020, with an increase from 3796.74 ha to 7676.89 ha, an increase of approximately 3880.15 ha over 20 years. Despite this growth, the number of patches gradually increased, the degree of fragmentation consistently worsened, and the landscape shape gradually became irregular. The study area demonstrated pronounced overall heterogeneity, with a gradually increase in the degree of dispersion, indicating evident overall instability. Additionally, the centroid of the mangroves moved towards the ocean, which complicated their growth environment and posed a serious threat to their growth and recovery. Anthropogenic disturbance is the main factor driving changes in mangrove areas. Driving factors that affected the change in mangrove areas were ranked as follows: GDP > highway mileage > population density > precipitation > humidity > wind speed > sunshine > temperature. The results of this study provide comprehensive data for the protection and restoration of mangroves.

Rainfall Variability and Tidal Inundation Influences on Mangrove Greenness in Karimunjawa National Park, Indonesia

Mangroves, which are vulnerable to natural threats and human activities on small islands in the tropics, play an essential role as carbon sinks, helping to mitigate climate change. In this study, we discussed the effect of natural factors on mangrove sustainability by analyzing the impact of rainfall, land surface temperature (LST), and tidal inundation on the greenness of mangroves in Karimunjawa National Park (KNP), Indonesia. We used Sentinel-2 image data to obtain the normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) during the dry season to determine the effect of inundation on mangrove greenness and soil moisture. The tidal inundation area was calculated using topographic data from the KNP and tidal observations from the area adjacent to it. Unmanned autonomous vehicles and topographic data were used to estimate mangrove canopy height. We also calculated mangrove greenness phenology and compared it to rainfall from satellite data from 2019–2021. Results show that the intertidal area is dominated by taller mangroves and has higher NDVI and NDMI values than non-intertidal areas. We also observed that mangroves in intertidal areas are mostly evergreen, and optimum greenness in KNP occurs from February to October, with maximum greenness in July. Cross-correlation analysis suggests that high rainfall affects NDVI, with peak greenness occurring three months after high rainfall. The LST and NDVI cross-correlation showed no time lag. This suggests that LST was not the main factor controlling mangrove greenness, suggesting tides and rainfall influence mangrove greenness. The mangroves are also vulnerable to climate variability and change, which limits rainfall. However, sea-level rise due to climate change might positively impact mangrove greenness.

Successive Cyclones Attacked the World’s Largest Mangrove Forest Located in the Bay of Bengal under Pandemic

Despite the global focus on the COVID-19 pandemic, the promise of impact to tropical coastlines and stochasticity of destruction caused by tropical cyclones remains unaltered, forcing human societies to adapt to new unadaptable scenarios. Super Cyclone Amphan’s landfall—the third cyclone of the season within the world’s largest mangrove forest—brought a new uncertainty to this undeveloped region of South Asia. How do vulnerable people deal with multiple disasters that limit necessary humanitarian response while still maintaining the natural environmental integrity of a system harboring critical wildlife populations and protecting people from further disaster? We explored this reality for the Sundarbans region using a remote sensing technique and found that the western part of Sundarbans mangroves was severely damaged by Amphan, suggesting that rapid remote sensing techniques can help direct resources, and recognize the eventuality that response will be a best effort for now. If 2020 is a window, multiple disaster management scenarios may become more common in the future. Yet, society’s obligation for maintaining environmental integrity remains unchanged.

Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia

Wetlands are one of the most biologically productive ecosystems. Wetland ecosystem services, ranging from provision of food security to climate change mitigation, are enormous, far outweighing those of dryland ecosystems per hectare. However, land use change and water regulation infrastructure have reduced connectivity in many river systems and with floodplain and estuarine wetlands. Mangrove forests are critical communities for carbon uptake and storage, pollution control and detoxification, and regulation of natural hazards. Although the clearing of mangroves in Australia is strictly regulated, Great Barrier Reef catchments have suffered landscape modifications and hydrological alterations that can kill mangroves. We used remote sensing datasets to investigate land cover change and both intra- and inter-annual seasonality in mangrove forests in a large estuarine region of Central Queensland, Australia, which encompasses a national park and Ramsar Wetland, and is adjacent to the Great Barrier Reef World Heritage site. We built a time series using spectral, auxiliary, and phenology variables with Landsat surface reflectance products, accessed in Google Earth Engine. Two land cover classes were generated (mangrove versus non-mangrove) in a Random Forest classification. Mangroves decreased by 1480 hectares (−2.31%) from 2009 to 2019. The overall classification accuracies and Kappa coefficient for 2008–2010 and 2018–2020 land cover maps were 95% and 95%, respectively. Using an NDVI-based time series we examined intra- and inter-annual seasonality with linear and harmonic regression models, and second with TIMESAT metrics of mangrove forests in three sections of our study region. Our findings suggest a relationship between mangrove growth phenology along with precipitation anomalies and severe tropical cyclone occurrence over the time series. The detection of responses to extreme events is important to improve understanding of the connections between climate, extreme weather events, and biodiversity in estuarine and mangrove ecosystems.