Afonso Oliveira

Afonso Oliveira, João P. Matos-Carvalho, Filipe Moutinho et al.

The increasing frequency and severity of wildfires requires advanced methods for effective surveillance and management. Traditional ground-based observation techniques often struggle to adapt to rapidly changing fire behavior and environmental conditions. This paper examines the application of multispectral aerial and satellite imagery in wildfire management, emphasizing the identification and analysis of key factors influencing wildfire behavior, such as combustible vegetation and water features. Through a comprehensive review of current literature and the presentation of two practical case studies, we assess various multispectral indices and evaluate their effectiveness in extracting critical environmental attributes essential for wildfire prevention and management. Our case studies highlight several indices as particularly effective for segmentation and extraction: NVDI for vegetation, MNDWI for water features, and MSR for artificial structures. These indices significantly enhance wildfire data processing, thereby supporting improved monitoring and response strategies.

Afonso Oliveira, Nuno Fachada, João P. Matos-Carvalho

Raster Forge is a Python library and graphical user interface for raster data manipulation and analysis. The tool is focused on remote sensing applications, particularly in wildfire management. It allows users to import, visualize, and process raster layers for tasks such as image compositing or topographical analysis. For wildfire management, it generates fuel maps using predefined models. Its impact extends from disaster management to hydrological modeling, agriculture, and environmental monitoring. Raster Forge can be a valuable asset for geoscientists and researchers who rely on raster data analysis, enhancing geospatial data processing and visualization across various disciplines.

Afonso Oliveira, Nuno Fachada, João P. Matos-Carvalho

The integration of data science into Geographic Information Systems (GIS) has facilitated the evolution of these tools into complete spatial analysis platforms. The adoption of machine learning and big data techniques has equipped these platforms with the capacity to handle larger amounts of increasingly complex data, transcending the limitations of more traditional approaches. This work traces the historical and technical evolution of data science and GIS as fields of study, highlighting the critical points of convergence between domains, and underlining the many sectors that rely on this integration. A GIS application is presented as a case study in the disaster management sector where we utilize aerial data from Tróia, Portugal, to emphasize the process of insight extraction from raw data. We conclude by outlining prospects for future research in integration of these fields in general, and the developed application in particular.