Resumo em Inglês:

Abstract: This study aims to optimize the planning of forest road networks using the Analytic Hierarchy Process (AHP) integrated into a Geographic Information System (GIS) environment. The variables used in this study were: permanent preservation area (PPA), slope (S), slope orientation (SO), commercial volume of timber (CV), and land cover class (LC). The best scenario was achieved when the five variables were employed in the AHP analysis, with a coherence index (CI) of 0.04%, and a consistency ratio (CR) of 3.9%. Then, the suitability of the existing forest roads was verified using the least-cost path tool associated with the map provided by the best AHP scenario. The results indicate that only 1/3 of the roads are placed in areas classified as ‘good’ or ‘optimal’, suggesting that the current road network can be improved. Adopting the road network proposed by this study, the length and density of roads would decrease by 10%. Consequently, a reduction in transport costs and an increase in the planted area are expected. We propose that this cost-effective approach can be leveraged for the reappraisal and suitability assessment of forest road density in areas exhibiting substantial timber resources, including those managed by mature forest enterprises.

Resumo em Inglês:

Abstract: This study addresses the challenge of accurately classifying land use and land cover (LULC) changes in landscapes influenced by anthropogenic activities. By leveraging multi-temporal satellite imagery and a hierarchical multistep classification approach, we enhance the differentiation of LULC transitions, improving model accuracy and environmental monitoring. This study presents an enhanced LULC classification framework that uses multi-temporal satellite imagery and hierarchical, multistep analysis to improve the accuracy of class detection in landscapes. We compare three supervised, pixel-based classification approaches - Single-step, Sequential Binary, and Accuracy-based Binary Classification - across a case study in the Furnas Reservoir Watershed, Southeast Brazil. The Accuracy-based Binary Classification method achieved the highest overall accuracy (87.37%), outperforming the other approaches by prioritizing classes with higher classification accuracy. Seasonal composite imagery and feature-engineering, such as spectral indices and quality mosaics, improved classification precision, particularly in heterogeneous and seasonally variable landscapes. The findings underscore the importance of integrating temporal dynamics in LULC mapping to inform sustainable land management in regions undergoing rapid environmental change.

Resumo em Inglês:

Abstract: Spatial interpolation of categorical variables is a challenge in geosciences and other applied fields, especially when attempting to predict categories in unsampled locations. In this study, we evaluated the performance of generalized multiquadric radial basis functions (GM RBFs) in the interpolation of categorical variables, considering different parameters (a and b), local neighborhoods (k neighbors), and number of categories (2, 4, and 8). Simulated scenarios with 200 points in a two-dimensional grid were used to control the distribution of categories, allowing the comparison of 12 versions of the GM RBFs using metrics such as accuracy, mean of F1 score, and global variance (GV). The results showed that the best performance occurred for local neighborhoods (k = 10), with a parameter values ​​close to zero. For two or four categories, b = 0 presented the best results, while for eight categories, b = -1 was more efficient. Increases in the number of categories increased GV and reduced accuracy, demonstrating greater complexity in spatial prediction. The results reinforce the importance of adjusting RBF parameters and the number of neighbors according to the context, in addition to highlighting the impact of the number of categories and data imbalance on the interpolation efficiency.

Resumo em Inglês:

Abstract: Urban public space is a fundamental component of the built environment, yet it remains predominantly shaped by infrastructure for motorized transport, often to the detriment of pedestrian accessibility. Walking, however, contributes significantly to physical and mental well-being, social cohesion, and urban sustainability. In this context, walkability has emerged as a key parameter in planning strategies aimed at reducing traffic congestion, promoting health, and enhancing quality of life. This study presents and validates a novel methodological framework for assessing walkability through remote analysis of street-level imagery (SLI). The method is based on an adaptation of the Walkability Index (iCam, initially developed by the Institute for Transportation and Development Policy (ITDP)) to a virtual environment, utilizing publicly accessible Google Street View imagery. Notably, the index was expanded to include a dedicated Accessibility category, addressing the often-overlooked needs of individuals with disabilities or reduced mobility. The methodology was applied to a pilot area in Salvador, Brazil. The results classified the average walkability condition as “sufficient”, with a composite score of 1.75, and confirmed through field validation (score: 1.86). Categories such as Mobility, Attraction, and Public Safety performed well, while Accessibility and Environmental Quality revealed areas that require targeted intervention. Despite limitations related to image temporality, spatial coverage gaps, licensing constraints associated with proprietary imagery, and subjectivity in interpretation, the proposed framework demonstrates high potential for cost-effective, scalable, and transferable urban diagnostics. It enables broader spatial coverage and supports periodic monitoring of pedestrian infrastructure. The findings provide actionable insights for urban planners, policymakers, and researchers committed to building more inclusive, walkable, and sustainable cities.

Resumo em Inglês:

Abstract: This study aims to compute two gravimetric quasi-geoid models for the Paraná state, Brazil, called PR_QG_A and PR_QG_B. The first referenced to the International Height Reference System and the second to the Brazilian Vertical Local Datum in Imbituba. A dataset of 67,618 terrestrial and oceanic gravimetric data has been used. The Remove-Compute-Restore technique was employed in conjunction with the scalar-free solution of the Geodesy Boundary Value Problem, taking into account the zero and first-order terms of Molodenskii’s series. Before computing the models, an optimal configuration in terms of transition degree/order, modification degree of Stokes’ kernel, and cap size was identified. Iterative multiple pointwise solutions were calculated for GNSS/leveling co-location stations. After validating the quasi-geoid models against GNSS/leveling data, both presented a standard deviation of 0.199 m in terms of height anomaly. For additional validation purposes, the results were compared with those obtained from the SAM_QGEOID2023 model. The differences in terms of standard deviation were approximately 2 mm. However, the values can differ by up to 0.707 meters in certain regions. The computed models will be able to provide normal height values more practically and economically, representing an alternative to leveling, within the indicated accuracy limit.

Resumo em Inglês:

Abstract: Pathological manifestations in civil structures represent one of the main problems that compromise the useful life of constructions, among the most common are cracks or fissures. In this research, the feasibility of using point clouds in the determination and measurement of cracks was investigated. The experiment was carried out in a controlled environment comparing the values obtained by point clouds using photogrammetry with the reference values obtained by laser interferometer (our reference). The results indicated that point clouds generated from a scanner that uses structured light for modeling exhibited high consistency with reference values, with differences of less than a millimeter. Conversely, portable scanning equipment with lower resolution demonstrated limited performance, unable to accurately define crack dimensions below one centimeter. These findings highlight that the applicability of point clouds for crack monitoring is strongly dependent on the scanning technology employed. The research confirms the potential of structured-light 3D scanning as a reliable and cost-effective approach for structural health monitoring and early damage assessment.

Resumo em Inglês:

Abstract: Predicting ionospheric irregularities poses a significant challenge for applications that rely on real-time positioning accuracy using the GNSS. This work presents a comparative analysis between the Prophet and XGBoost models in the prediction of the ROTI index, using time series collected by the ITAI RBMC station, located in Foz do Iguaçu/PR, during the year 2024 (peak of solar cycle 25). This study contributes to the literature by providing a direct comparison between an additive statistical model and a gradient boosting algorithm for ROTI prediction in the Equatorial Ionization Anomaly region during solar maximum conditions. Both models were subjected to the same database and predictor variables. Training and validation were conducted through cross-validation specific to time series, utilizing the chronological division of the data and evaluating performance based on the RMSE, MAE, and R² metrics. The results obtained demonstrated that XGBoost outperformed Prophet in all analyzed metrics, indicating a greater capacity for adjustment and generalization. Statistical analysis of the residuals, including normality testing and non-parametric hypothesis tests, confirmed the robustness and consistency of the XGBoost predictions. However, we noted that the operational test window covered only 72 hours (1-3 October 2024), which constrained the generalizability of these results.

Resumo em Inglês:

Abstract: This paper presents the application of Design Thinking to the participatory development of geoinformation systems in Brazil and South Africa. The study explores how empathy-based, iterative design methods can elicit not only functional requirements but also reveal semantic and terminological variations among stakeholders with diverse backgrounds. Through structured workshops, participants collaboratively defined user needs, mapped spatial concepts, and co-designed system features for two distinct scenarios: a national land management platform in Brazil and a geospatial dashboard for Sustainable Development Goals (SDGs) in South Africa. As part of the process, a reusable script for participatory sessions and a comparative vocabulary of 91 geospatial terms were developed. Results show how shared core concepts coexist with region-specific terminologies, underscoring the need for standardisation to enhance interoperability. The study demonstrates that Design Thinking can facilitate semantic alignment and functional adequacy in multilingual and multi-institutional environments, offering replicable tools for inclusive, user-centred geospatial system design.