The great majority of the world population resides nowadays in urban areas. Understanding their physical and social structure, and especially their urban land use pattern dynamics throughout time, becomes crucial for successful, effective management of such areas. This study is committed to simulate and predict urban land use change in a pilot city belonging to the São Paulo Metropolitan Region, southeast of Brazil, by means of a cellular automata model associated with the Markov chain. This model is driven by data derived from orbital and airborne remotely sensed images and is parameterized by the Bayesian weights of evidence method. Several layers related to infrastructure and biophysical aspects of the pilot city, São Caetano do Sul, were used as evidence in the simulation process. Alternative non-stationary scenarios were generated for the short-run, and the results obtained from past simulations were statistically validated using a multiresolution “goodness-of-fit” metric relying on fuzzy logic. The best simulations reached fuzzy similarity indices around 0.25–0.58 for small neighborhood windows when an exponential decay approach was employed for the analysis, and approximately 0.65–0.95 when a constant decay and larger windows were considered. The adopted Bayesian inference method proved to be a good parameterization approach for simulating processes of urban land use change involving multiple classes and transitions.

Over the past few decades, socio-natural disasters have become an increasingly prominent theme in the daily lives of individuals in both developed and developing countries. This study aims to provide a general overview of nearly 60 000 records of water-related disasters (WRDs — i.e. floods, storms, landslides, and droughts) that occurred in Brazil from 1991 to 2024. The impacts are presented according to the country’s geographic regions. The number of registered WRDs has significantly increased over the analyzed period, driven by improved recording tools, particularly after the launch of the S2ID platform in 2013. It is important to highlight that although all data used are publicly available, there are still some limitations in data collection, particularly regarding the lack of a multi-hazard approach for managing complex extreme events. Official records are often unable to capture concurrent events, which means only one type of disaster is recorded during such incidents. The findings of this work enable the construction of a comprehensive overview of both the occurrences and recurrences of these disasters throughout the country and its geographic regions, while also supporting effective planning in risk management and disaster reduction.

Digital terrain models (DTMs) are digital elevation models (DEMs) that represent the bare ground surface. They are created by multiple sources, including satellite remote sensing, aerial photography, and ground-based surveys, and are often combined with other data sources to create highly detailed models. As the demand for accurate and detailed information about the Earth's surface continues to grow, DTMs have become an increasingly important tool for researchers in different fields. This study aims to create a DTM with a spatial resolution of 0.50 m for São Caetano do Sul, São Paulo, Brazil, integrated with a topobathymetric map of three water courses running along the borders of the study area. For the conventional DTM generation, a WV-2 stereo pair was used. A total of 55 ground control points (GCPs) were collected using the GNSS-RTK method, being 60% used for model building and 40% employed for validation. The topobathymetric survey was accomplished using a GNSS-RTK device placed along the analyzed open streams. For validation purposes, we used bias and MAE metrics. Overall, the methodology presented in this article provides a useful approach for generating high-resolution DTMs that can be used in a range of applications, especially in urban hydrodynamic studies.