Understanding the natural expansion of white mangrove (Laguncularia racemosa) in an ephemeral inlet based on geomorphological analysis and remote sensing data.

The interactions between local tides and river discharges are crucial in the processes related to the recruitment of mangrove propagules in estuarine systems. This investigation aimed to determine the causes of the recent natural recruitment and expansion of Laguncularia racemosa in mudflats within an ephemeral inlet in Mexico. We conducted a fluvial and coastal geomorphology assessment with spaceborne and UAV-based images. We deployed and recorded continuous data loggers in the estuarine system to assess water level and salinity. Depending on the available data, we used a combination of cloud-computing Google Earth Engine, UAV-Digital Surface Models, LiDAR, Google Earth images, and biophysical variables to monitor mangrove forests from 2005 to 2022. When the inlet is open, the estuarine system presents a full tidal range (∼1-1.5 m) with a strong salinity gradient (0-35 mS/cm), in contrast to the strong freshwater influence and minimal water level variability (<10 cm) that prevails for three months when the inlet is closed. Once the mouth of the river closes, there is considerable sediment accumulation, creating mudflat areas adjacent to the mangrove forests where Laguncularia racemosa propagules begin to establish under minimal water level variability and oligohaline conditions. After 16 years, the new forest expanded by 12.3 ha, presenting a very high density (10000 stems/ha), a considerable basal area (54-63 m2/ha), and a maximum canopy height of 15.8 m, which largely surpasses that of other semiarid Laguncularia racemosa forests within permanent open-inlet systems or even in ephemeral inlets with different hydrological conditions. Our study will help to understand the causes of natural Laguncularia racemosa recruitment in extremely dynamic systems.

Monitoring detailed mangrove hurricane damage and early recovery using multisource remote sensing data.

Due to their location in tropical latitudes, mangrove forests are susceptible to the impact of hurricanes and can be vastly damaged by their high-speed winds. Given the logistic difficulties regarding field surveys in mangroves, remote sensing approaches have been considered a reliable alternative. We quantified trends in damage and early signs of canopy recovery in a fringe Rhizophora mangle area of Marismas Nacionales, Mexico, following the landfall of Hurricane Willa in October 2018. We monitored (2016-2021) broad canopy defoliation using 21 vegetation indices (VI) from the Google Earth Engine tool (GEE). We also mapped a detailed canopy fragmentation and developed digital surface models (DSM) during five study periods (2018-2021) with a consumer-grade unmanned aerial vehicle (UAV) over an area of 100 ha. Based on optical data from the GEE time series, results indicated an abrupt decline in the overall mangrove canopy. The VARI index was the most reliable VI for the mangrove canopy classification from a standard RGB sensor. The impact of the hurricane caused an overall canopy defoliation of 79%. The series of UAV orthomosaics indicate a gradual recovery in the mangrove canopy, while the linear model predicts at least 8.5 years to reach pre-impact mangrove cover conditions. However, the sequence of DSM estimates that the vertical canopy configuration will require a longer time to achieve its original structure.

Extrapolating canopy phenology information using Sentinel-2 data and the Google Earth Engine platform to identify the optimal dates for remotely sensed image acquisition of semiarid mangroves.

Continuum monitoring of mangrove ecosystems is required to maintain and improve upon national mangrove conservation strategies. In particular, mangrove canopy assessments using remote sensing methods can be undertaken rapidly and, if freely available, optimize costs. Although such spaceborne data have been used for such purposes, their application to map mangroves at the species level has been limited by the capacity to provide continuous data. The objective of this study was to assess mangrove seasonal patterns using seven multispectral vegetation indices based on a Sentinel-2 (S2) time series (July 2018 to October 2019) to assess phenological trajectories of various semiarid mangrove classes in the Google Earth Engine platform using Fourier analysis for an area located in Western Mexico. The results indicate that the months from November through December and from May through July were critical in mangrove species discrimination using the EVI2, NDVI, and VARI series. The Random Forest classification accuracy for the S2 image was calculated at 79% during the optimal acquisition period (June 25, 2019), whereas only 55% accuracy was calculated for the non-optimal image acquired date (March 2, 2019). Although mangroves are considered evergreen forests, the phenological pattern of various mangrove canopies, based on these indices, were shown to be very similar to the surrounding land-based semiarid deciduous forest. Consequently, it is believed that the rainfall pattern is likely to be the key environmental factor driving mangrove phenology in this semiarid coastal system and thus the degree of success in mangrove remote sensing classification endeavors. Identifying the optimal dates when canopy spectral conditions are ideal in achieving mangrove species discrimination could be of utmost importance when purchasing more expensive very-high spatial resolution satellite images or collecting spatial data from UAVs.

Spatiotemporal shoreline dynamics of Marismas Nacionales, Pacific coast of Mexico, based on a remote sensing and GIS mapping approach.

Within the last few decades, tropical coastal systems such as beaches, dunes, and mangrove forests have experienced high annual rates of loss worldwide due to natural and anthropogenic impacts. Historical remote sensing data have been used to map and monitor these fragile systems, as well as to track specific events through time. The purpose of this study was to examine coastal trends along Marismas Nacionales in Mexico, which is the largest wetland complex of the western coast of the Pacific Ocean. The opening of the Cuautla Canal in 1976 and the construction of several hydroelectric power dams have severely impacted this wetland system. Shoreline variability was estimated based on representative remote sensing images over half a century (1970 to 2019). Results indicate that, after 49 years, 805 ha of beach deposits have been lost in the Cuautla Canal and at the beach ridge region that should otherwise be an accretional coastal zone. Conversely, the southern section of the study site shows 406 ha of constant accretion during the same period due to the presence of the unobstructed San Pedro River. Our study highlights the adverse effects of engineering projects, such as inlets and hydroelectric dams throughout tropical coastal systems that have strongly depended on freshwater input from upstream rivers.