The use of remote sensing technologies for mapping forest fires has experienced significant growth in recent decades, driven by advancements in remote sensors, processing platforms, and artificial intelligence algorithms. This study presents a review of 192 scientific articles published between 1990 and 2024, selected using PRISMA criteria from the Scopus database. Trends in the use of active and passive sensors, spectral indices, software, and processing platforms as well as machine learning and deep learning approaches are analyzed. Bibliometric analysis reveals a concentration of publications in Northern Hemisphere countries such as the United States, Spain, and China as well as in Brazil in the Southern Hemisphere, with sustained growth since 2015. Additionally, the publishers, journals, and authors with the highest scientific output are identified. The normalized burn ratio (NBR) and the normalized difference vegetation index (NDVI) were the most frequently used indices in fire mapping, while random forest (RF) and convolutional neural networks (CNN) were prominent among the applied algorithms. Finally, the main technological and methodological limitations as well as emerging opportunities to enhance fire detection, monitoring, and prediction in various regions are discussed. This review provides a foundation for future research in remote sensing applied to fire management.

Unplanned urban expansion poses significant challenges to sustainable territorial development in intermediate cities. This study analyzes the dynamics of urban expansion and land use change in the city of Cajamarca (Peru) during the period 1986-2040, integrating Geographic Information Systems (GIS) techniques, Google Earth Engine (GEE) and CA-Markov prediction models. Landsat satellite images from 1986, 2004 and 2022, classified by Random Forest (RF), were used to generate thematic maps and evaluate their accuracy. Subsequently, a spatial simulation model was implemented to project urban expansion to 2040. The results indicate an increase in the urban area from 789.68 hectares to 5,768.19 hectares, while forests and crops also changed. The driving factors for this expansion include rural-urban migration, the availability of services, and real estate development. Projections highlight growth toward the east, southeast, and south of the city. This approach provides strategic inputs for sustainable urban planning and effective land management in transforming Andean cities.

Los cuerpos de agua son ecosistemas importantes que albergan una gran biodiversidad y son el sustento de muchas poblaciones. El objetivo de este estudio fue analizar el espacio temporal de cuerpos de agua y su predicción al 2031, mediante técnicas de teledetección en Amazonas. Se utilizaron imágenes satelitales Landsat de los años 1988, 1998, 2007 y 2019; así como, el Índice de Agua de Diferencia Normalizada (NDWI), para la creación de reportes y representaciones cartográficas del Cambios de Cobertura y Uso de Suelo (CCUS). El procesamiento de datos se realizó en un entorno de Sistemas de Información Geográfica (SIG) y modelo MOLUSCE, para predecir los CCUS para 2031. Los resultados reportaron precisiones cartográficas entre 81 y 91 % y a nivel de cuerpos de agua, el lago Pomacochas, laguna Burlan y de los Cóndores se reducirán a 425,10; 45,24 y 121.54 ha, respectivamente. En los últimos años, la temperatura superficial ha aumentado, principalmente en espacios urbanos y carentes de vegetación. Se concluye que para el año 2031, los cuerpos de agua se reducirán debido a las actividades humanas y efecto del cambio climático, impactando negativamente a la población y los ecosistemas.