Cuadernos de Investigacion Geografica

Forest Fires: Risk, Consequences, and Technological Advances in their Analysis and Management

2025-12-16

Fernando Pérez-Cabello, Raquel Montorio, Marcos Francos

Educate to Prevent: Best Practices of University Service in Wildfire Prevention within the Framework of the ‘Facing the Fire’ Project (2020-2025)

2025-12-16

Beatriz Cobo Sánchez, Agustín Merino, Beatriz Omil, David García Romero, José Reyes Ruiz Gallardo, Daniel Moya Navarro, Mar Lorenzo Moledo, Antonio Girona-García, Minerva García-Carmona, José Valentín Roces-Díaz, Cristina Santín, Javier Madrigal Olmo, Eva Luna Lara Lópe, Sara De Paula, Cristina Carrillo, Mercedes Guijarro, Álvaro Fajardo Cantos, Alberto Ledo Moure, Juan Carlos Rodríguez, Mónica Boucard Aguado, Alberto Monzón Atance, Alba Cantalejo Coserría, Eduardo Tolosana Esteban, Roi Méndez Fernández, David Badía-Villas, Alejandro Cotillas Cruz, María Melero, Juan Ramón Molina Martínez, Alexandro B. Leverkus, Lucía Torres Muros, José Sánchez, David Aguilera, Victoria Arcenegui Baldó, Jorge Mataix-Solera, Elena Marcos, Flor Álvarez Taboada, Otilia Reyes Ferreira, Roberto Mourente Blanco, Sheila Fernández Riveiro, Óscar Cruz De La Fuente, María Jesús Taboada Iglesias, Aurora Puentes Graña, Josefa Facal Martínez, Xavier Úbeda, Virginia Carracedo Martín, Andrea Pérez, María Fernández Sánchez, David Pereiro Sánchez, Ana Teijeiro Cruz, Víctor Méndez Vales, Carmen Archana Cuenca Honrubia, Cristian Tévar Alarcón, Francisco Monteagudo Talavera, Verónica Pérez Pastor, Pablo Souza-Alonso

In recent decades, problems associated with forest fires have intensified, particularly in the Mediterranean Basin, posing a serious threat to ecosystems and human populations. Although their frequency has decreased, wildfires have become more intense and severe, often exceeding suppression capacity, as evidenced by the emergence of sixth-generation fires on the Iberian Peninsula. Multiple factors contribute to this situation, including socio-economic dynamics (depopulation, land-use change), inadequate forest management, insufficient public policies, and climate change—further exacerbated by misinformation and media sensationalism. Within this socio-environmental context, and given the need for high-quality information, it is essential to strengthen the capacity of future specialists in prevention and restoration through a variety of strategies: on the one hand, improving forest management in order to create stands that hinder the spread of wildfires and enhance post-fire regeneration; and on the other hand, addressing ignition sources by raising public awareness of their underlying causes. The Plantando Cara al Fuego (PCF) initiative aims to improve environmental awareness and foster citizen participation in combating forest fires by involving the public in projects that apply academic knowledge to community needs. To this end, PCF develops educational innovation projects based on the Service-Learning (S-L) pedagogical approach, which integrates university education with forest-fire prevention and restoration efforts. The aim of this study is to compile experiences carried out between 2020 and 2025 in different Spanish regions, in which university students from various degree programmes have implemented S-L projects to address real community challenges related to forest fires through the design and development of activities in collaboration with public and private entities. A total of 35 projects is analyzed and classified into five categories (prevention, restoration, training, multidisciplinary and dissemination). These projects are examined alongside indicators of their educational and social impact, including the number of participating students, collaborating companies and institutions, outputs produced, bachelor’s and master’s theses completed, and the actual population reached. The results show that Service-Learning strengthens students’ professional skills and environmental commitment, thereby contributing to sustainable land management.

Spectral-Temporal Analysis Of Wetland Fires In The Pantanal-Brazil Supported By UAV Multispectral Imagery

2025-12-03

Gustavo Manzon Nunes, Cátia Nunes da Cunha, Nubia da Silva

The Wetlands of the Pantanal in Brazil are highly sensitive to environmental changes, and events such as wildfires pose a significant threat to biodiversity. In 2020, approximately 80% of its area was affected by high-intensity fires. Therefore, this study aimed to analyze, both spectrally and temporally over a three-year period (2019, 2020, and 2021), the behavior of four macrohabitats located within two study areas of the Private Reserve of Natural Heritage SESC Pantanal (RPPN SESC Pantanal), situated in the state of Mato Grosso, Brazil. For the analysis conducted over the three-year period in the study areas, the Micasense Altum multispectral camera was employed, along with processing methods involving spectral and temporal analysis. The results revealed a drastic decrease in reflectance within the red-edge and near-infrared (NIR) spectral bands in 2020, following the fire event, in both mapped areas. A subsequent recovery was observed in 2021, although reflectance levels remained below those recorded in 2019 (pre-fire conditions). The Acurizal and Tabocal macrohabitats exhibited the highest reflectance amplitudes and the greatest variability over the years, particularly in longer wavelengths (NIR). The Campina macrohabitat showed the lowest reflectance values, due to its vegetation being composed predominantly of shrub and herbaceous species. The Dry Forest (Mata Seca) displayed the highest spectral stability and demonstrated a continuous downward trend in average reflectance, indicating a loss of species diversity following the fire event. The findings contribute to the enhancement of conservation measures in wetland ecosystems, the management of protected areas, and the effectiveness of public policies, highlighting the potential of high-resolution multispectral data for spectral monitoring as a tool for detecting environmental changes.

Analysis Of The Positioning Accuracy Of Geotagged Photos Taken With Mobile Devices In Various Terrain Conditions

2025-12-01

Anna Szafarczyk, Beata Baziak, Marek Bodziony

Currently, most mobile devices can capture geotagged photos—i.e., images to which the location of capture is assigned. Despite extensive literature on the use of geotagging, there is a limited number of studies addressing the accuracy of the recorded locations. Therefore, this research was undertaken to assess the positional accuracy of geotagged photos, defined, among other metrics, by the mean unit error of the assigned coordinates. This article presents the results of test measurements conducted using various mobile devices to determine situational coordinates within the applicable coordinate system. The study discusses the satellite systems currently in use, as well as the measurement technologies that influence geolocation accuracy in smartphones and cameras equipped with a geotagging feature. Test measurements involved comparing the coordinates embedded in geotagged photos with those obtained using a high-precision GNSS receiver. Depending on the device and technology used, the mean unit location errors ranged from 4.0 metres to nearly 50 metres. These findings highlight the low precision of such devices in determining exact positions. To explore ways of improving accuracy, additional tests were carried out using various features and applications available on different devices, assessing their impact on location determination based on geotagged photos. Notably, the use of the GPS Test application for position stabilisation reduced mean unit errors by nearly 45%. The results of this study led to the development of recommendations aimed at enabling the determination of a mobile device’s X and Y coordinates with an accuracy of several metres. This level of precision may be sufficient for many practical applications and presents a cost-effective alternative to expensive GPS receivers, which require specialised geodetic knowledge for professional use.

Effects of Pre-Fire Land Use on Recovery of Soil Chemical Properties After a Prescribed Fire: A Short-, Medium- and Long-term Case Study in the NE Iberian Peninsula

2025-09-01

Marcos Francos, Sebastian Alfaro, Luis Outeiro, Xavier Úbeda

Rural land abandonment means that some rural areas fall into disuse or are taken over by forest stands. In this context, prescribed burning is a widely used forest management tool, but few studies have analyzed the influence of pre-burn land use on post-burn soil recovery. This study seeks to determine the impact of prescribed burning on soil chemical properties and to examine any differences in these parameters based on prior land use. The study is conducted in two plots – one, a forest plot (TV2); the other an abandoned agricultural terrace (TV3) – located in Tivissa (southern Catalonia), both dominated by Pinus halepensis Mill . and Quercus ilex L ., and situated on Lithic Calcixerept soils. The plots, located 3 km apart, share a similar topography, exposure, and vegetation structure. Low-intensity prescribed burns were conducted in 2001, and soil samples (0–5 cm depth) were collected in five campaigns: just before the fire (BPF), immediately after the fire (APF), and at 1-, 3-, and 13-years post-burn (1YAPF, 3YAPF, and 13YAPF, respectively). In each sampling period, 30 samples were collected from an experimental plot of 72 m 2 (4 x 18 m). The soil properties analyzed included total carbon (TC), total nitrogen (TN), pH, electrical conductivity (EC), and extractable calcium (Ca), magnesium (Mg), potassium (K), and available phosphorus (P) concentrations. In TV2 soil, TC and TN increase at short and medium term, soil pH increases after fire and decreases gradually, EC decreases at short and medium term, and increases at long-term, and extractable major cations increase until medium term and are reduced at long-term. In TV3 soil TC decreases and TN increases gradually over time, pH increases at short and decreases at long-term, EC decreases from short to long-term with a slight increase at medium-term, and extractable major cations (except P which decrease over time) increase until long-term. Changes caused by different pre-fire land use and consequently differences caused by different vegetation cover minor over time. Despite differences in soil properties in the short and medium term due to land use prior to prescribed fire, after 13 years, soil conditions had largely stabilized, and there was no evidence of horizontal or vertical continuity in plant fuel. These findings suggest that prescribed burning does not result in long-term soil degradation and, thus, remains a viable tool for sustainable forest management under different land uses.

Estimating Forest Variables from Lidar and Optical Sensors Using Artificial Intelligence

2025-08-17

Mihai Tanase, Juan Pablo Martini, Pablo Miranda, Daniel Garcia Garcia, Victoria Wilke, Jaime Diez, Sergio Natal, Daniel San Martin

Accurate and continuous characterization of forest fuel properties is essential for assessing wildfire risk and predicting fire behavior. This study addresses the limitations posed by the scarce temporal availability of lidar data by integrating temporally consistent optical imagery (Landsat) with occasional lidar acquisitions to estimate structural forest variables such as canopy cover fraction (FCC) and canopy height (H) in two regions of peninsular Spain: Madrid and the Basque Country. These variables are widely recognized as proxies for fuel properties characterization, reflecting both the amount and spatial continuity of forest fuels. Machine Learning (ML) models (Random Forest and Extreme Gradient Boosting) were compared with Deep Learning architectures, (DL) including transformer-based models with self-attention mechanisms (NeNeT). The results show that DL models significantly improve accuracy, with an average reduction in root mean square error (RMSE) of 30% compared to traditional methods. NeNeT, in particular, demonstrated strong performance in capturing complex spatial relationships, improving height estimates in dense forests of the Basque Country (RMSE was reduced from 7.0 to 4.0 m). In contrast, differences were smaller in the more open Mediterranean forests of Madrid, suggesting that less computationally demanding methods like XGB may be suitable in certain contexts. Despite their advantages, DL models present operational limitations, particularly due to high computational demands. For instance, producing historical maps for the peninsular Spain would require up to four years of processing with NeNeT versus seven months with XGB, assuming no parallelization. Moreover, DL models tend to learn spurious patterns related to image acquisition (e.g., number of observations or dates), which can introduce biases if not properly controlled. In conclusion, combining lidar and optical sensors with advanced artificial intelligence models enables highly accurate estimation of key variables for wildfire management. However, model choice should balance achieved precision with available computational resources, taking into account the ecosystem type and specific application needs.