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Tropical sandy beaches provide essential ecosystem services and support many local economies. In recent times, however, there has been a massive infrastructure expansion in popular tourist destinations worldwide. To investigate the shoreline variability at a popular tourist destination in Mexico, we used the novel semi-automatic CoastSat program (1980 to 2020) and the climate dataset ERA5 (wave energy and direction). We also measured the beach cross-shore distance and the foredune height with topographic surveys. The results indicate that the section of real estate seafront infrastructure in the study site presents a considerable shoreline erosion due to the fragmentation between the foredune ridge and the beach berm, based on the in situ transects. Moreover, foredune corridors with cross-shore distances of up to 70 to 90 m and dune heights of 8 m, can be seen in the short unobstructed passages between buildings. In the south section we found the coastline in a much more stable condition because this area has not had coastal infrastructures, as of yet. For the most part, the remote sensing analysis indicates constant erosion since 1990 in the real estate section (mainly seafront hotels) and an overall accretion pattern at the unobstructed beach-dune locations. This study demonstrates the catastrophic consequences of beach fragmentation due to unplanned real estate developments, by combining in situ surveys and a freely available big-data approach (CoastSat).

Given the scale and rate of mangrove loss globally, it is increasingly important to map and monitor mangrove forest health in a timely fashion. This study aims to identify the conditions of mangroves in a coastal lagoon south of the city of Mazatlán, Mexico, using proximal hyperspectral remote sensing techniques. The dominant mangrove species in this area includes the red (Rhizophora mangle), the black (Avicennia germinans) and the white (Laguncularia racemosa) mangrove. Moreover, large patches of poor condition black and red mangrove and healthy dwarf black mangrove are commonly found. Mangrove leaves were collected from this forest representing all of the aforementioned species and conditions. The leaves were then transported to a laboratory for spectral measurements using an ASD FieldSpec® 3 JR spectroradiometer (Analytical Spectral Devices, Inc., USA). R2 plot, principal components analysis and stepwise discriminant analyses were then used to select wavebands deemed most appropriate for further mangrove classification. Specifically, the wavebands at 520, 560, 650, 710, 760, 2100 and 2230 nm were selected, which correspond to chlorophyll absorption, red edge, starch, cellulose, nitrogen and protein regions of the spectrum. The classification and validation indicate that these wavebands are capable of identifying mangrove species and mangrove conditions common to this degraded forest with an overall accuracy and Khat coefficient higher than 90% and 0.9, respectively. Although lower in accuracy, the classifications of the stressed (poor condition and dwarf) mangroves were found to be satisfactory with accuracies higher than 80%. The results of this study indicate that it could be possible to apply laboratory hyperspectral data for classifying mangroves, not only at the species level, but also according to their health conditions.