RESUMO
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.
Assuntos
Tempestades Ciclônicas , Rhizophoraceae , México , Tecnologia de Sensoriamento Remoto/métodos , Áreas AlagadasRESUMO
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.
Assuntos
Ecossistema , MéxicoRESUMO
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.
Assuntos
Monitoramento Ambiental/métodos , Sistemas de Informação Geográfica , Engenharia , México , Oceano Pacífico , Tecnologia de Sensoriamento Remoto , Rios , Áreas AlagadasRESUMO
The present work had the goal of assessing anthropization of mangroves in Mexico during the 2005-2015 period. We modified the Relative Integrated Anthropization Index (INRA in its Spanish acronym) method developed by Martínez-Dueñaz (Intrópica: 37-46, 2010) for its use in Mexico. The maps of the Mexican Mangrove Monitoring System of the National Commission for the Knowledge and Use of Biodiversity (SMMM CONABIO in its Spanish acronym) for the years 2005, 2010, and 2015 were classified into 10 land use categories and 21 subcategories. The relative anthropization values were obtained for subcategories and classes in the input maps through consultation with mangrove specialists and the results were integrated to calculate the INRA values in 500 m2 units. Our results showed an increase in the surface of most anthropic categories and a sustained increment of the mean INRA values at the national level.