Exploratory Spatiotemporal Analysis and Preliminary Conditional Scenarios of Land Cover and Surface Temperature for Sustainable Coastal Planning: A Case Study of Arraial do Cabo (Rio de Janeiro, Brazil)

Evaluating spatiotemporal dynamics is essential to measure and monitor environmental sustainability in coastal zones under rapid urban expansion. Future scenarios of the earth’s surface can be estimated from identified changes in remote sensing. The objectives of this study were to evaluate changes in the land surface temperature (LST), estimated with Landsat 8 images of the Brazilian coastal municipality of Arraial do Cabo on two summer dates—2014 and 2017—and to project a scenario for 2030. Data processing was performed in the open GIS QGIS version 3.2.0, while the comparison and the future projection were performed with Land Change Modeler tools in the commercial GIS IDRISI Selva version 17.0. Spatial analysis revealed a unilateral expansion of the ‘heat’ class and a simultaneous contraction of the ‘coolness’ class, revealing a severe loss of local microclimatic sustainability. The prediction of the Markov matrix for 2030 indicated a 98.9% probability of transition from the medium to the heat class and a 96.9% probability of transition from the coolness to the heat class, highlighting the critical vulnerability of coastal environmental indicators. It also showed more urban areas replacing green areas, salt ponds and exposed soil. These trends warn of negative environmental impacts on the native ecosystem, the local state park, and Araruama Lagoon, which may influence LST. Effective environmental conservation policies are essential to guarantee ecological sustainability, mitigating the loss of vegetated areas and regulating local surface temperatures for long-term climate resilience. Projections related to different environmental issues should include other factors to be applied to environmental planning. This study is presented as a preliminary exploratory analysis. The 2030 projection represents a conditional scenario demonstrating the potential impacts if the extreme short-term transitions observed between 2014 and 2017 were to continue. These baseline findings highlight the sensitivity of the coastal environment and underscore the need for future robust time-series modeling.