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As the interface between land and water, coastlines are highly dynamic and intricately tied to the sediment budget. These regions have a high functional diversity and require enlightened management to preserve their value for the future. In this study we assess changes to the São Paulo State (SE Brazil) coastline over the last 36 years. The study innovatively employs big data remote sensing techniques and multivariate statistical models to evaluate and generate erosion/accretion rates (1985-2021) relative to beach orientation and slope. Shoreline change rates have been obtained for sandy beaches at 485 one-kilometer spaced transects. Our findings capture the complexity and heterogeneity of the analyzed coastline, at a regional and local scale. No association was found between shoreline changes and beach face orientation. Nonetheless, a dependency relationship was found between dissipative beaches with moderate to high accretion. Beaches facing south, with relative stability, were prone to sediment accumulation. Locations with slow accretion, like sandy spits and tombolo-protected beaches, were associated with dissipative beaches with moderate to high accretion. The southeast-oriented beaches are more prone to erosion due to storm waves from the south. Results provide a broad, fast, and relatively low-cost methodology that can be used in any sandy beach context, bringing essential information for coastal management and decision-making related to the use and occupation of the coastal zones

Mass movement mapping is essential for susceptibility, vulnerability and risk assessments. Various mapping approaches based on Earth observation (EO) data have been used to identify different types of hazards. Object-based image analysis (OBIA) has been employed for EO-based landslide mapping worldwide. The development and application of efficient methods for recognition and mapping are essential to create standards for landslide inventory mapping, notably in Brazil where landslides are a frequent natural hazard. This study aims to detect landslide features and differentiate them into shallow landslides and debris flows using a semi-automated OBIA approach. RapidEye satellite images (5 m) were analysed and the Normalized Difference Vegetation Index (NDVI) was calculated. A Digital Elevation Model (DEM) (12.5 m) and its derived products were integrated into the analysis to support the OBIA landslide mapping. The results show that the method is suitable for the recognition of this type of hazard and are potentially of use for local stakeholders and decision-makers in disaster management.

This study aims to evaluate land use and cover change (LUCC) between 2000 and 2017 in the area of Volta Grande do Xingu, Par��, associating these changes to the main pression inducers of landscape transformation. For this propose, two Landsat satellite images were treated and classified (forest, agropasture, river and rocks classes). Using geoprocessing techniques, we observed and quantified changes induced by the damming in the Xingu River, which flooded areas upstream and exposed rocky areas downstream of the Belo Monte Hydroelectric Plant. In addition to that, forest areas were replaced by exposed soil, related to agriculture and pasture activities.

Aim There is little consensus on which environmental variables are best at predicting multiple dimensions of diversity. We ask whether there are common environmental correlates of diversity, despite ecological differences, across nine clades of plants and animals distributed along a single rainforest domain. For that, we compare the environmental correlates of species richness, phylogenetic diversity, and phylogenetic endemism. Location Brazilian Atlantic Forest. Taxon:Five clades of plants (Bromelioideae, Miconieae, Bertolonia, Cambessedesieae, and the Fridericia and allies) and four clades of animals (butterlies in the tribe Ithomiini, frogs in the genera Boana and Proceratophrys, and birds in the subfamily Thraupinae). Methods Using curated occurrence localities and phylogenetic data, we generated maps of (a) species richness, (b) phylogenetic diversity, (c) residuals of phylogenetic diversity regressed on species richness, and (d) phylogenetic endemism for all groups. We also compiled a set of 30 environmental descriptors, including records of current temperature and precipitation, climatic stability over time, and topography. Through a machine learning framework, we explored the environmental correlates of each of these diversity measures for each group. Results The environmental variables used here were strong predictors of diversity for all studied groups. However, models for phylogenetic endemism had lower predictive power. Although patterns of diversity are different among groups, correlates of diversity are consistent across taxa. For both species richness and phylogenetic diversity, current precipitation and precipitation stability over time were consistently ranked among the variables that strongly correlate with diversity patterns. The correlates of phylogenetic endemism were less homogeneous across groups. The results suggest that including climate stability over time is important when predicting diversity measures that reflect historical components. Main conclusions Investigating environmental correlates of diversity for multiple clades and diversity measures in a single geographic area allows for a better understanding of common patterns across taxa. This study shows that environmental conditions, particularly precipitation, are good predictors of the patterns of species richness and phylogenetic diversity‐but not phylogenetic endemism‐across different Atlantic Forest groups.