This paper presents the validation of the End Point Rate (EPR) tool for QGIS (EPR4Q), a tool built-in QGIS graphical modeler for calculating the shoreline change with the end point rate method. The EPR4Q tries to fill the gaps in user-friendly and free open-source tools for shoreline analysis in a geographic information system environment since the most used software—Digital Shoreline Analysis System (DSAS)—although being a free extension, it is created for commercial software. Additionally, the best free, open-source option to calculate EPR is called Analyzing Moving Boundaries Using R (AMBUR); since it is a robust and powerful tool, the complexity can restrict the accessibility and simple usage. The validation methodology consists of applying the EPR4Q, DSAS, and AMBUR with different types of shorelines found in nature, extracted from the US Geological Survey Open-File. The obtained results of each tool were compared with Pearson’s correlation coefficient. The validation results indicate that the EPR4Q tool acquired high correlation values with DSAS and AMBUR, reaching a coefficient of 0.98 to 1.00 on linear, extensive, and non-extensive shorelines, proving that the EPR4Q tool is ready to be freely used by the academic, scientific, engineering, and coastal managers communities worldwide.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of free and open-source models to estimate the sea-level impact can contribute to improve coastal management. This study aims to develop and validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE)—a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the sea-level rise impact module of TerrSet—Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses the Bruun rule formula implemented in GEE and can determine the coastline retreat of a profile by creatting a simple vector line from topo-bathymetric data. The model shows a very high correlation (0.97) with a classical Bruun rule study performed in the Aveiro coast (NW Portugal). Therefore, the achieved results disclose that the GEE platform is suitable to perform these analysis. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific community.

NASA’s TESS mission yields light curves for tens of millions of stars spread across the entire sky, a data set that will be a challenge to fully exploit without help from citizen scientists. To address this, we launched a new citizen science project, called “Planet Patrol”, designed to analyze TESS data on exoplanet and eclipsing binary candidates. The project will also serve to benchmark different data reduction pipelines and help analyze unusual light curves that might defeat automated algorithms. The first stage of the project ran on the Zooniverse platform between 2020 September and November and involved more than 5500 registered volunteers. The Planet Patrol citizen scientists produced nearly 400,000 classifications of difference images used for photocenter analysis of about 1000 planet candidates from TESS. The results were incorporated into the photocenter module of the Discovery And Vetting of Exoplanets (DAVE) pipeline to improve its reliability. Specifically, the classifications indicated that all per-transit difference images are appropriate for photocenter analysis for about 40% of the planet candidates, and the corresponding measurements are sound. In contrast, the volunteers found that all per-transit difference images are dominated by astrophysical contamination and/or systematic effects for about 10% of the planet candidates. This indicated that the corresponding photocenter measurements are unreliable. Finally, the fraction of images appropriate for photocenter analysis varies between 0 and 1 for half the candidates. Removing the images classified as poor from DAVE’s analysis of most of these candidates helped reduce the corresponding photocenter uncertainty by up to ∼30%. We plan to implement the output from another module of DAVE, designed for lightcurve vetting, into a second stage of the Planet Patrol project.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal man-agement. This study aims to develop and to validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule formula implemented in GEE and is capable to determine the coastline retreat of a profile through the creation of a simple vector line from topo-bathymetric data. The model shows a very high cor-relation (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE platform seems to be an important tool for coastal management. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific commu-nity.