Eyes from Above: SAR-Based Remote Sensing for Flood and Landslide Risk – The Case That Shocked South Brazil in 2024

Eyes from Above: SAR-Based Remote Sensing for Flood and Landslide Risk – The Case That Shocked South Brazil in 2024 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Eyes from Above: SAR-Based Remote Sensing for Flood and Landslide Risk – The Case That Shocked South Brazil in 2024 Thyago Anthony Soares Lima, Marcelo Reis, Ramon Alves de Santana, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7521122/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The hydrometeorological disaster that struck Rio Grande do Sul, Brazil, in 2024 resulted in floods and landslides of unprecedented magnitude, surpassing historical events recorded in 1941 and 2023. Among the most severely affected regions, the Baixo-Taquari-Antas Valley tood out due to its high population density, critical infrastructure concentration, and extensive socio-economic damage. This study developed an operational, multi-sensor workflow to rapidly delineate impacts in nine municipalities by fusing Sentinel-1 SAR and Sentinel-2 optical data within a DEM-constrained framework. Pre-/post-event median composites (pre: Dec-2020–Mar-2024; post: Apr–11 May 2024) were differenced to map backscatter changes, applying fixed thresholds (Δσ⁰ ≤ −5 dB for flood; Δσ⁰ ≥ +3 dB for landslides). Flood extent was further refined with NDWI from Sentinel-2, while landslide candidates were restricted to slopes > 15°. Object-based image analysis with SVM on optical scenes produced building footprints that were intersected with hazard layers to quantify exposure. Field surveys at 30 control points confirmed mapping accuracies of 92% (floods) and 85% (landslides). Results show ~ 174 km² of inundation and ~ 84.8 km² of landslides across the nine cities, with ~ 34,603 buildings affected by flooding and ~ 1,114 by slope failures. The integrated SAR–optical approach proved robust under cloud cover and heterogeneous urban fabrics, delivering actionable situational awareness for emergency response and early recovery. The results provided critical support for emergency response and post-disaster mitigation planning, demonstrating the operational value of integrated SAR–optical remote sensing for rapid disaster risk assessment in highly vulnerable regions. Sentinel-1 SAR NDWI Flood mapping Landslide detection Multi-hazard assessment Rio Grande do Sul Brazil Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 INTRODUCTION Climate change, which can be defined as long-term shifts in temperature and weather patterns, provoke effects on the environment, health, and economy (IPCC, 2021 ; Romanello et al., 2021 ). These impacts include rising temperatures, more frequent and intense extreme weather events such as heat waves and droughts, sea level rise, and changes in precipitation patterns (IPCC, 2021 ; Seneviratne et al., 2021 ). In the specific case of the latter, there is an intrinsic association with the frequency and intensity of floods and droughts, which cause crop damage, soil erosion, and disruptions to ecosystems and human health. Moreover, in the aftermath of flooding events, there is an increased risk of landslides and the development of both physical and mental health issues (Alderman & Tong. 2012) Over the past decades, extreme hydrometeorological events and the associated risks have intensified globally both in frequency and severity, potentially driven by climate change and rapid urbanization in hazard-prone areas (IPCC, 2021 ; UNDRR, 2022). Particularly, floods and land mass movements are ranked among the most dangerous and devastating natural disasters worldwide, both in terms of casualties and economic losses (Haque et al., 2016 ; Koks et al., 2015 ). In Brazil, historical records reveal a significant increase in the occurrence of such phenomena, with notable disasters since the early 2010s, especially in the southern region (Inter-American Commission on Human Rights, 2025 ), culminating with the 2024 tragedy that devastated the southernmost Brazil’s state of Rio Grande do Sul (Marengo et al 2025 , Mantovani et al 2025 ). The tragedy itself represented a hydrological and geotechnical of catastrophic proportions, particularly in the Baixo-Taquari-Antas Valley, which suffered the highest precipitation levels, experienced peak river discharges, and endured the greatest concentration of material and human losses (ONS, 2024). The region’s geomorphological features—characterized by extensive alluvial plains and steep slopes—combined with dense urban occupation contributed significantly to its susceptibility to flooding and consequent landslides (Guzzetti et al., 2006 ; Ribeiro & Werlang, 2010 ; Alves et al., 2014 ; Fernandes & Almeida, 2023 ). The consequences associated with these tragic events often lead to significant material damage, displacement of populations, and disruption of essential services such as water supply, sanitation, and healthcare (Du et al 2010 ). Moreover, it causes a major economic burden due to infrastructure repairs and loss of livelihoods. In these cases, prevention and rapid emergency response can make a substantial difference, especially in regions with limited resources (Oxera & ICC, 2024; Allstate & U.S. Chamber, 2024). Therefore, identifying and monitoring areas at risk of flooding and landslides is pivotal, not only for environmental management but for further safeguarding public health and socio-economic stability. Accurate and timely identification of high-risk zones is crucial for disaster risk reduction and resilience planning (Agbehadji, 2023). Among the available and most cost-effective techniques, remote sensing technologies such as satellites and radars provide a powerful tool for the systematic detection and monitoring of disaster-prone areas, providing accurate data for decision-making even under adverse conditions. In this context, Synthetic Aperture Radar (SAR) remote sensing has emerged as an effective tool for mapping disaster-affected areas, primarily due to its ability to acquire data regardless of weather and lighting conditions (Torres et al., 2012 ; Mason et al., 2010 ). Recent studies have demonstrated the successful use of Sentinel-1 data for urban flood detection through backscatter change analysis (Chini et al., 2017 ; Twele et al., 2016 ), as well as the identification of shallow landslide areas using optical imagery and semi-automated modeling approaches (Mondini et al., 2011 ). The present study was conducted to organize the guidelines of an integrated, multisensorial methodology for detailed mapping of areas affected by floods and landslides. The method was validated through in situ assessment in one of the regions that presented the greatest extent of damage during the 2024 disaster in Southern Brazil: The Baixo-Taquari-Antas Valley. This approach included a multitemporal analysis of imagery of two satellites, calibrated spectral thresholding, digital elevation modeling, and object-based image classification techniques. This study aims to advance remote sensing techniques through the integration of optical data for the spatiotemporal characterization of hydrometeorological extremes. By doing so, we seek to support improvements in urban planning, disaster prevention, and emergency response strategies, ultimately enhancing the safety and well-being of affected communities. REMOTE SENSING FUNDAMENTALS FOR DISASTER ASSESSMENT The detection of flood-affected areas through SAR backscatter change analysis has become a widely adopted approach in recent years, especially in regions where optical data acquisition is limited by persistent cloud cover (Martinis et al., 2018 ; Chini et al., 2017 ). Threshold-based methods applied to multitemporal Sentinel-1 datasets have shown high effectiveness in identifying inundated zones, leveraging the characteristic reduction in backscatter values over water surfaces (Twele et al., 2016 ; Pulvirenti et al., 2011 ). Automated processing chains, such as those developed by Martinis et al. ( 2018 ) and Chini et al. ( 2017 ), have facilitated rapid flood mapping, even in urban environments where radar signal behavior is more complex (Mason et al., 2010 ). Recently, studies by Plank et al. ( 2017 ) and Sharma et al. (2025) have advanced the use of Sentinel-1 time series for continuous flood dynamics monitoring, highlighting the importance of appropriate pre-event reference image selection and backscatter normalization techniques to reduce false detections. The Normalized Difference Water Index (NDWI) has also been widely used for flood detection due to its sensitivity to surface water presence and its ease of application to multispectral satellite imagery, particularly from Sentinel-2 (GAO, 1996 ; MCFEETERS, 1996 ). By comparing pre- and post-event images, NDWI enables the identification of newly inundated areas through threshold-based classification, where positive index variations generally indicate increased surface water coverage (Du et al., 2016 ). Sentinel-2’s spectral resolution in the green (B3) and near-infrared (B8) bands has proven particularly effective for NDWI calculations in flood mapping applications (Drusch et al., 2012 ). Studies by Islam et al. (2023) and Sivanpillai et al. ( 2021 ) have demonstrated the utility of NDWI time series for capturing flood dynamics across diverse geographic settings. Despite its advantages, NDWI-based flood mapping is sensitive to vegetation cover and requires careful temporal window selection to minimize misclassification. In the field of landslide detection and monitoring, the application of multitemporal SAR interferometry techniques, such as Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS), has enabled the mapping of surface deformation patterns associated with slope movements, even in areas with dense vegetation and adverse atmospheric conditions (Ferretti et al., 2001 ; Berardino et al., 2003 ). For instance, Motagh et al. ( 2013 ) demonstrated the capability of InSAR in characterizing active landslides in mountainous regions of Central Asia. More recently, Vassileva et al. ( 2023 ) applied a multisensory time-series analysis to monitor the reactivation of an old landslide in Iran, highlighting the potential of these techniques in high-risk scenarios. Additionally, integrated approaches combining interferometric SAR data with Digital Elevation Models (DEMs) and optical imagery have further enhanced the ability to discriminate between different types of mass movements, as discussed by Raspini et al. ( 2021 ). Landslide detection through SAR backscatter change analysis has also emerged as an effective approach for rapid event mapping, particularly in post-rainfall scenarios and in areas with dense vegetation cover (Mondini et al., 2019 ; Burrows et al., 2019 ). This technique relies on the premise that landslides induce significant alterations in surface roughness and soil moisture content, resulting in detectable changes in backscatter values between pre- and post-event images (Mondini, 2017 ). Studies such as Mondini et al. ( 2019 ) successfully applied empirical backscatter variation thresholds to Sentinel-1 data to identify recently affected landslide areas in Italy. Similarly, Burrows et al. ( 2019 ) demonstrated the applicability of this method across distinct topographic settings in Nepal and Taiwan, emphasizing the importance of appropriate reference image selection and topographic control. Additionally, approaches based on spatial autocorrelation analysis of backscatter, as proposed by Mondini ( 2017 ), have contributed to reducing false positives in areas with non-landslide-related surface changes. Simultaneously, the advancement of cloud-based geospatial platforms, such as Google Earth Engine (Gorelick et al., 2017 ), has enabled the integration of diverse orbital and topographic datasets with machine learning algorithms, including Support Vector Machines (SVM), expanding the analytical capacity for disaster mapping (Maxwell et al., 2018 ; Huang et al., 2014 ). Despite these international advancements, Brazil still lacks integrated studies that combine SAR data, spectral indices, and machine-learning techniques for the simultaneous detection of floods and landslides, especially those validated with field data. Quantifying the number of affected buildings following flood and landslide events is essential for damage assessment and risk analysis. Recent advancements in object-based image analysis (OBIA), combined with machine learning classifiers such as SVM, have enabled the semi-automated extraction of building footprints from high-resolution optical imagery, including Sentinel-2 data (Blaschke et al., 2014 ; Huang et al., 2014 ). Texture, shape, and spectral features are typically used as input variables for classification models, enhancing the discrimination between built-up areas and natural land covers (Maxwell et al., 2018 ). Ok et al. ( 2013 ) and Li et al. ( 2019 ) demonstrated the effectiveness of these approaches for large-scale building extraction, even in heterogeneous urban environments. The resulting vectorized building footprints can then be spatially intersected with hazard extent layers (e.g., flood or landslide maps) to estimate the number of affected structures. Integrating SAR backscatter analysis, optical indices like NDWI, topographic data (DEM), and building footprint extraction has proven to significantly improve the accuracy and reliability of disaster impact assessments (Giustarini et al., 2015; Martinis et al., 2018 ). The synergistic use of multitemporal Sentinel-1 and Sentinel-2 datasets allows for the combined detection of both hydrological and geomorphological hazards, addressing the limitations inherent in single-sensor approaches (Plank et al., 2017 ; Sharma et al., 2025). Data fusion techniques, especially when implemented in cloud-based geospatial platforms such as Google Earth Engine, enable efficient processing of large datasets and the generation of consolidated impact maps (Gorelick et al., 2017 ). This integrated framework facilitates not only the delineation of hazard extents but also the spatial quantification of exposed elements, thereby supporting more comprehensive disaster risk assessments (Martinis et al., 2018 ; Huang et al., 2014 ). METHODS Area Characterization and Flooding Event The most meridional state of Brazil, Rio Grande do Sul, with an area of ​​281,730 km² and continentally bordering Uruguay, Argentina, and Santa Catarina State, as well as the Atlantic Ocean to the east (IBGE, 1986). The Rio Grande do Sul State presents extensive geographic diversity, marked by plateaus, plains, and mountain areas, with noteworthy mentions of the Southern Plateau and the Southeastern Mountain Range (Caldas, 1938 , Müller Filho 1970 , IBGE 1986). The overall climate is subtropical, with lower temperatures in winter and mild summers, and the main biomes include the Atlantic Rainforest, the Pampas, and a portion of the Brazilian Coastal-Marine System (Alvares et al 2013 , Roesh et al 2009). The Taquari Valley is situated in the central region of Rio Grande do Sul, circa 117 km from the state capital, Porto Alegre. Comprising 36 municipalities within its 4,821.1 km² area, the region holds significant cultural, economic, and touristic importance, characterized by German and Italian heritage and a diversified economy based on agroindustry (swine, poultry, dairy), as well manufacturing of footwear, textile, and metal ( Siebeneichler et al 2019 , Caldas, 1938 , Müller Filho 1970 , IBGE 1986). Its geographic features are marked specially by floodplains and urbanized areas, rendering it susceptible to natural hazards (Tognoli et al 2021 ). The main focus of this study is the Baixo-Taquari–Antas River Valley, encompassing nine municipalities (Table 1 ; Fig. 1 ) that were visited and analyzed in this research. These municipalities were the most severely affected in the region, with their local governments declaring a state of emergency. Brazil’s National Civil Defense officially designated this portion of the valley—especially the nine municipalities examined here—as a high-priority area due to the extent of the observed damage. In addition, the agency recommended that the region serve as a reference for the development and implementation of standardized disaster-prevention and mitigation measures. Table 1 – Geographical and macroeconomic data from the nine municipalities considered by this study. Population and gross domestic product (GDP) were respectively obtained for 2022 and 2021 census conducted by the Brazilian Institute of Geography and Statistics (IBGE). Municipality Pop. GDP *1.000 BRL Area (km²) Centroid Lajeado 93,646 5,596,168.71 90.8 52°00′25″W 29°26′35″S Estrela 32,183 2,171,440.71 185.03 51°55′12″W 29°30′32″S Encantado 22,962 1,168,354.66 140.006 51°55′19″W 29°12′47″S Arroio do Meio 21,958 1,536,556.29 157.09 51°57′54″W 29°21′43″S Cruzeiro do Sul 11,600 533,002.44 155.48 52°01′59″W 29°32′24″S Roca Sales 10,418 576,081.09 208.11 51°49′44″W 29°15′25″S Muçum 4,601 301,850.72 111.25 51°48′58″W 29°08′24″S Marques de Souza 3,969 124,134.27 125.71 52°08′56″W 29°17′20″S Putinga 3,747 133,441.73 216.16 52°08′35″W 29°02′20″S Total 205,084 12,141,031.00 1,390 Data Collection Geospatial data used in this study were obtained from official government databases, including the National Spatial Data Infrastructure (INDE), the Brazilian Institute of Geography and Statistics (IBGE), and the Brazilian Geological Survey (SGB). Satellite imagery was acquired from the Sentinel-1 and Sentinel-2 missions via the Google Earth Engine (GEE) platform. Sentinel-1 provides Synthetic Aperture Radar (SAR) data, which are independent of weather and lighting conditions and thus suitable for disaster monitoring (Torres et al., 2012 ). Sentinel-2 offers high-resolution multispectral optical imagery, commonly applied for spectral indices and land-cover mapping (Drusch et al., 2012 ). To ensure the quality and relevance of the analysis, all images were filtered by acquisition date and cloud cover (< 20%) and grouped into pre-event and post-event periods corresponding to the 2024 disaster window. Flooding and Landslide Analysis An integrated workflow was developed to identify areas affected by flooding and landslides, combining multispectral and radar remote sensing, spectral indices, topographic constraints, object-based image classification, and field validation. Sentinel-1 (IW mode, VV polarization, 10 m resolution) and Sentinel-2 (L2A level, 10–20 m resolution) images were processed in the GEE environment for two distinct timeframes: a pre-event period (01 December 2020–31 March 2024) and a post-event period (01 April – 11 May 2024). Flooded areas were mapped using the Normalized Difference Water Index (NDWI), computed from Sentinel-2 green (B3) and near-infrared (B8) bands (Eq. 1): \(\:NDWI=\:\frac{(Green-NIR\:)}{(Green+NIR)}\) Eq. 1 Positive values are generally associated with the presence of surface water. Originally proposed by McFeeters ( 1996 ) and refined by Gao ( 1996 ), the NDWI remains widely used in flood studies. In this research, a threshold of + 0.1 was applied to differentiate flooded from non-flooded areas, based on median composites of pre- and post-event imagery. Recent studies confirm the robustness of NDWI for Sentinel-2 applications, including enhanced spatial resolution mapping (Du et al., 2016 ), time-series integration with SAR data (Martinis et al., 2018 ), rapid flood detection (Sivanpillai et al., 2021 ), and flash-flood monitoring under extreme rainfall (Islam et al., 2023). It is important to distinguish NDWI from the Normalized Difference Vegetation Index (NDVI), is derived from red and near-infrared bands (NDVI=(NIR − Red)/(NIR + Red)), is widely applied to measure vegetation vigour (Rouse et al., 1974 ). Conversely, NDWI was specifically designed to highlight water features by contrasting green reflectance with near-infrared absorption (McFeeters, 1996 ; Gao, 1996 ). Given the objectives of this study, NDWI was preferred, as the aim was to delineate flood extent rather than vegetation cover. NDWI was preferred, as the aim was to delineate flood extent rather than vegetation cover. SAR backscatter change analysis was applied to Sentinel-1 imagery to map both flooding and landslides. Median composites were generated for the pre- and post-event periods, and the difference between them was analysed. For flood mapping, a threshold of − 5 dB was adopted, as significant reductions in backscatter are characteristic of open water surfaces (Mason et al., 2010 ; Twele et al., 2016 ). For landslide mapping, a threshold of + 3 dB was applied, reflecting increases in surface roughness and soil moisture linked to slope failures (Zhou et al., 2019; Mondini et al., 2019 ). To refine the classification, slope data derived from the SRTM-GL1 DEM were integrated. Areas with slope 15° were retained as potential landslide zones (Guzzetti et al., 2006 ; Plank et al., 2017 ). Recent works have demonstrated the reliability of such integrated SAR time-series analyses for flood and mass-movement monitoring (Martinis et al., 2018 ; Sharma et al, 2025). The final flood extent map was generated by integrating the outputs of Sentinel-1 backscatter thresholding and Sentinel-2 NDWI classification. Both approaches are complementary: SAR is insensitive to cloud cover and captures changes in surface roughness, while NDWI highlights spectral variations in water presence. By fusing both datasets, false positives were minimized, and flood detection accuracy was significantly improved, particularly in urban areas where radar backscatter behaviour is complex and optical reflectance may be influenced by vegetation. The integration process consisted of overlaying the binary flood layers from each sensor and retaining pixels flagged as inundated by at least one source, followed by slope masking (< 15°). This fusion ensured a consolidated and reliable flood mapping product. The potentially affected buildings were delineated using an Object-Based Image Analysis (OBIA) approach combined with supervised classification via Support Vector Machines (SVM). Sentinel-2 imagery was pre-processed and segmented in QGIS using the Orfeo Toolbox plugin, applying metrics such as shape compactness, spectral homogeneity, and texture. The SVM classifier was trained with 150 manually labelled samples, including buildings, vegetation, and bare soil. The classifier’s performance was validated using five-fold cross-validation, yielding an overall accuracy of 94.3% and a Kappa coefficient of 0.91. Classified building objects were vectorized into footprints and exported as shapefiles. In the final step, the building footprint vectors were intersected with flood and landslide hazard rasters in ArcGIS Pro. Each building polygon overlapping at least 20% with a hazard map was classified as affected. Results were aggregated by municipality, enabling the generation of impact statistics by event type and geographic unit (Fig. 2 ). In Situ Validation The methodology was validated through fieldwork conducted between May and June 2024. Thirty sample sites were visited, including 20 in flooded areas, 7 in landslide zones, and 3 in unaffected control areas. Field teams verified locations using GPS devices, photographic documentation, and interviews with local residents. The comparison between observed conditions and automated outputs yielded an accuracy of 92% for flooding and 85% for landslides, with a low incidence of false positives and false negatives. These results confirm the robustness and reliability of the adopted method for emergency planning and disaster response. Spatial analytical analyses for flooding and landslide were performed using QGIS software (version 3.36.3) to quantify the properties in each municipality within the study area. The process included the identification and mapping of the buildings affected by the flooding and landslide events in detail. Furthermore, applying geoprocessing tools allowed to characterize impacted areas by each type of disaster and extrapolate the total number of properties affected, providing a comprehensive overview of the extent of the damage caused. RESULTS The results demonstrated that the developed algorithm, implemented on the Google Earth Engine (GEE) platform, successfully integrated datasets from the National Spatial Data Infrastructure (INDE), the Brazilian Institute of Geography and Statistics (IBGE), and the Brazilian Geological Survey (SGB). This integration enabled precise delimitation of municipal boundaries and hydrographic features, as well as the analysis of RGB satellite imagery to identify affected properties and critical infrastructure. In this study, we adopted the concept of "footprint", which basically consists of the spatial delimitation of areas occupied by buildings, allowing a detailed analysis of the damage caused by the catastrophic event (Fig. 3 ). By leveraging Sentinel-1 synthetic aperture radar (SAR) imagery—particularly VV polarization, which is sensitive to surface changes such as flooding and landslides—the algorithm effectively detected alterations in the landscape. Moreover, advanced image processing techniques were applied to analyze differences in radar backscatter between pre- and post-event scenes. Through segmentation and classification of the SAR images, the algorithm identified spatial patterns indicative of flood-affected areas and landslide-prone zones, thereby contributing to a comprehensive assessment of the disaster’s impacts (Fig. 3 ). A similar approach was applied to identify flooded areas, using pre- and post-disaster remote sensing data. Specifically, we employed the Normalized Difference Water Index (NDWI), which enhances the presence of surface water by contrasting the reflectance of near-infrared (NIR) and green bands. NDWI was calculated using high-resolution Sentinel-2 imagery, enabling the detection of changes in surface water extent with high spatial detail. The algorithm processed both pre-event and post-event Sentinel-2 images to generate NDWI maps for each period. By comparing these maps, the algorithm effectively highlighted areas where significant increases in water presence were identified, functioning as indicative of flooding. This differential analysis allowed for precise spatial delimitation of newly inundated zones (Fig. 5 ). This integrated approach, combining different data sources and processing techniques, has proven essential for the accurate and rapid identification of areas affected by floods and mass movements (Fig. 6 ). The results of the accounting of affected areas, both by flooding and mass movement, as well as the accounting of properties through the cross-referencing of the generated data, will be detailed further. Damage by municipality Overall, results highlighted the differential impact of floods and landslides based on the number of buildings affected by floods and landslides across nine municipalities in Rio Grande do Sul, southern Brazil, following the 2024 flood events. Floods caused widespread damage across all cities, affecting over 34,000 buildings, or more than 25% of the buildings in five of the nine municipalities. In contrast, landslide-related damage was more localized and on a considerably lower scale, affecting between 2.5 and 3.5% of the structures. Nonetheless, it is noteworthy that these numbers, despite apparently low, summed a total 1114 affected buildings, with noteworthy mentions of Encantado, Muçum and Roca Sales, where respectively 380, 131 and 137 structures suffered damages by landslides (Table 2 ). Table 2 – Identified number of buildings affected and percentage per municipality in the nine cities selected to evaluate floods and landslides after 2024 floodings in Rio Grande do Sul, southern Brazil. Municipality Estimated Buildings (n) Total (n) affected by flood (n) % affected by landslide (n) % Lajeado 36,952 13,947 37.74 81 0.22 Estrela 22,095 5,864 26.54 17 0.08 Muçum 3,905 1,020 26.12 131 3.35 Encantado 13,317 3,755 28.20 380 2.85 Roca Sales 9,014 1,671 18.54 137 1.52 Marques de Souza 4,535 1,107 24.41 128 2.82 Cruzeiro do Sul 10,788 2,580 23.92 81 0.75 Putinga 4,476 1,206 26.94 81 1.81 Arroio do Meio 15,643 3,453 22.07 78 0.50 Total 120,725 34,603 - 1,114 - Flooding and landslide detected Variations in the impact of flooding and landslides among municipalities can be explained by several factors, such as slope density and steepness, the presence of flat areas, vegetation cover, and the types of land use and occupation. Nevertheless, a simple comparison between total area of each of the evaluated towns and the overall surface of flooding and landslide areas provide a glimpse in the magnitude of the damages. In the Baixo-Taquari-Antas River Basin, which encompasses 23 municipalities over an area of 2,720 km², satellite data indicate that the 2024 flooding event affected a total area of 323 km² (≈ 11.9% of the basin), while landslides affected 185 km² (≈ 6.8%). It is important to note, however, that the built-up area across these 23 municipalities accounted for only 4 km² (≈ 0.14% of the total basin area) at the time. Within the basin, the nine selected municipalities accounted for over half of the area affected by the 2024 flooding event, summing over 174 km 2 of flooded areas, and nearly 85 km 2 of areas where landslides were detected (Table 3 ). Among the municipalities selected for this study, Cruzeiro do Sul, a city with less than 12,000 inhabitants, presented almost 20% of its territory with flooded areas, followed by Lajeado (≈ 17%), and Estrela (≈ 15%). On the other hand, landslides affect more severely Marques de Souza (≈ 11%) and Muçum (≈ 10%). The municipalities with less covered by the flooding event were Arroio do Meio (≈ 10%), Encantado (≈ 9%) e Marques de Souza (≈ 8%). Table 3 – Comparison between total, flooding and landslide areas and percentage per municipality in the nine cities selected to evaluate floods and landslides impacts after 2024 extreme weather events of 2024 in Rio Grande do Sul, southern Brazil. Municipality Area (Km 2 ) Total Flooded Area % Landslide Area % Lajeado 90.8 15.15 16.69 0.79 0.87 Estrela 185.03 28.87 15.60 0.35 0.19 Muçum 111.25 13.86 12.46 11.71 10.53 Encantado 140.01 12.68 9.06 13.97 9.98 Roca Sales 208.11 22.53 10.83 15.93 7.65 Marques de Souza 125.71 10.33 8.22 14.24 11.33 Cruzeiro do Sul 155.48 29.33 18.86 0.31 0.20 Putinga 216.16 26.16 12.10 18.98 8.78 Arroio do Meio 157.09 15.15 9.64 8.48 5.40 Total 174.06 - 84.76 - Figure 7 presents the spatial distribution of flooded and waterlogged areas in Cruzeiro do Sul and Lajeado, the two municipalities with the highest number of affected dwellings (18,86% and 16,69% of the municipal housing stock, respectively). While the full analysis encompassed nine municipalities of the Taquari Basin, these two cases are shown here in detail to illustrate the most critical impacts. The maps highlight the concentration of inundated areas along river valleys and low-lying floodplains, supporting the quantitative estimates presented in Table 3 and exemplifying the heterogeneity of impacts among municipalities. Figure 8 shows the spatial extent of landslides in Marques de Souza and Muçum, the two municipalities with the highest proportion of affected dwellings in the Taquari Basin (11.33% and 10.53% of the municipal housing stock, respectively). Although the complete assessment covered all nine municipalities, these two cases are highlighted here because they exemplify the most severe slope instabilities. The mapped scars, derived from Sentinel-1 backscatter variations and refined by topographic constraints, cluster predominantly along steep hillslopes adjacent to the Taquari River and its tributaries. This spatial concentration underscores the susceptibility of these municipalities to compound hazards triggered by extreme rainfall. Figure 8- Spatial extent of mapped landslides in Marques de Souza and Muçum (Taquari Basin, RS) during the 2024 event. Red polygons show landslide scars derived from Sentinel-1 SAR (VV) backscatter change (Δσ⁰ ≥ +3 dB) and constrained to slopes > 15° (SRTM-GL1 DEM); turquoise lines represent hydrography; gray shading is DEM hillshade. Field Validation The accuracy of the flood and landslide maps was assessed through a systematic field survey conducted in nine municipalities of the Taquari Basin. A total of 30 control points were selected across urban and rural settings, covering representative locations of inundated floodplains, waterlogged streets, and slope failures. Field observations included georeferenced photographs, water marks on buildings, road disruptions, and fresh landslide scars. These ground-based records were spatially matched with satellite-derived layers, allowing a direct comparison between remote sensing outputs and in situ evidence. Overall, the validation confirmed the robustness of the methodology, with mapping accuracies reaching 92% for flood delineation and 85% for landslide detection. Figure 9 - Field photographs documenting flood and landslide impacts in the Baixo -Taquari–Antas Valley (May–June 2024). (A) Lajeado/Cruzeiro do Sul—bank erosion and foundation undermining of a riverside dwelling. (B) Lajeado—riverside promenade with high-water marks and displaced pavement. (C) Roca Sales—severe structural damage and debris accumulation after the flood surge. (D) Lajeado/Cruzeiro do Sul—partial collapse and debris field around a residence. (E) Marques de Souza—shallow landslide scar on a steep slope observed during field inspection. (F) Marques de Souza—mud-covered street with debris-flow deposits along the valley floor. DISCUSSION The spatial extent of the 2024 hydrometeorological disaster in the Baixo-Taquari-Antas Valley highlights its unprecedented severity. Remote sensing mapping indicated that the floods affected a total of 323 km² of the all Baixo-Valley — equivalent to 11.9% of its total area — with 174 km² concentrated in the nine municipalities which were the focus of the present study. Landslides were also significant, representing an area of approximately 185 km² across the entire basin, with 84.76 km² of this disaster effectively mapped in the nine selected municipalities. To contextualize the magnitude of the episode, the inundated surface exceeded the entire urban footprint of other major cities of Brazil, such as Recife (≈ 220 km²; IBGE, 2022 ) and is comparable to the metropolitan area of Fortaleza (≈ 314 km²; IBGE, 2022 ). Moreover, in global scales, the extend of this disaster far surpassing the urbanized areas of Lisbon (≈ 100 km²; PORDATA, 2023 ) and Barcelona (≈ 101 km²; Idescat, 2023 ), and was almost twice the size of Tel Aviv (≈ 170 km²; Israel Central Bureau of Statistics, 2023 ), larger than Montevideo (≈ 201 km²; INE-Uruguay, 2023 ), and greater than Kigali (≈ 280 km²; National Institute of Statistics of Rwanda, 2022). It is also similar in size to Singapore (≈ 281 km²; Singapore Department of Statistics, 2023 ) and Wellington (≈ 290 km²; Stats NZ, 2023 ), approaching the dimension of Philadelphia (≈ 347 km²; U.S. Census Bureau, 2023 ). These comparisons demonstrate that the event went far beyond the scale of local or riverine floods, configuring a regional-scale catastrophe with international projection, capable of simultaneously disrupting densely populated urban centers and extensive rural areas. The geomorphological and hydrological interpretation of the data helps to explain this extent. The Baixo-Taquari-Antas Valley presents physical characteristics that significantly increased susceptibility: extensive alluvial plains, which functioned as natural water accumulation zones, and steep slopes, which favored the triggering of gravitational mass movements. The combination of factors such as these may provide insights into why under intense rainfall, the impact was amplified by the conditions of relief and geological structure. The association between flooding and slope instabilities serves as indicative that this may not be an isolated phenomenon, but rather a systemic process in which hydrological and geotechnical mechanisms interacted, producing large-scale damage. The integrated analysis of the phenomena showed that flood and landslide processes occurred synergistically. While the floods spread diffusely across the basin, the landslides were concentrated in more susceptible sectors, reinforcing the multi-hazard nature of the disaster. This behavior confirms that the event was not merely a case of river overflow, but a broader territorial collapse, in which different hydrological and geotechnical processes interacted and intensified the damage. The results provided by this study indicate that the 2024 disaster was not only historically unprecedented in Rio Grande do Sul, but also ranks among the most extensive flood episodes recorded in South America in recent decades. These values far exceed the average of previous events documented in the region and place the 2024 flood as the most extensive and destructive ever recorded in southern Brazil (Marengo et al., 2025 ; Mantovani et al., 2025 ). The spatial magnitude of the event not only characterizes it as a riverine flood episode, but rather as a multi-hazard territorial collapse, in which diffuse flooding and mass movements occurred synchronously. Under a global perspective, flood episodes covering more than 300 km² are considered rare outside large transnational basins. Similar events have been observed in recent catastrophes in Southeast Asia, such as the Pakistan floods of 2022, which devastated thousands of square kilometers and displaced millions of people (Sajjad, 2022). Although smaller in scale, the Baixo-Taquari-Antas Valley case approaches these global catastrophe patterns due to the high population density affected and the simultaneity of floods and landslides. In terms of human and territorial severity, the data presented here demonstrate that the Brazilian event cannot be classified as local or episodic, but as part of the global trend of hydrometeorological extremes intensified by climate change (IPCC, 2021 ; UNDRR, 2022). On a continental scale, South America has a recurrent history of hydrological disasters, notably floods in the Paraguayan and Argentine Chaco and the Amazon floods in Brazil and Peru (UNDRR, 2022). Nevertheless, unlike those regions, which are marked by floodplains of lower population density, the Baixo-Taquari-Antas Valley combines high socioeconomic vulnerability with strong urbanization in risk areas, which exponentially amplified the impact. A comparison between the 2024 South Brazil events and the 2011 floods in the mountainous region of Rio de Janeiro, which resulted in thousands of deaths (Dourado et al., 2012 ), is pertinent: both episodes reveal the convergence of extreme rainfall with urban occupation in susceptible areas. Although Rio de Janeiro’s disaster was characterized mostly by landslides, the Baixo-Taquari-Antas Valley’s event presented a hybrid character, where massive flooding was accompanied by hundreds of slope instabilities. The 2024 event transcended other recent other episodes with floods and landslides in the country, such as Petrópolis in February 2022 — where a record rainfall volume (≈ 258 mm in only 3 hours) triggered the city’s greatest tragedy which accounted with 231 deaths (Alcântara et al., 2023 ) — and the extratropical cyclone that affected southern Brazil in June 2023, causing major damage and losses, including fatalities in Rio Grande do Sul (Magalhães et al., 2025 ). These recent events illustrate the growing frequency and intensity of hydrometeorological extremes in Brazil and reaffirm that the Baixo-Taquari-Antas Valley disaster represents a new and exceptional stage of complexity and impact. The number of affected buildings, estimated through the intersection of urban footprints with the flood and instability maps, confirms the extreme degree of exposure of the local population. At the regional scale, the results showed that impacts were not limited to a single municipality, but simultaneously affected twenty-three cities, including the nine selected for the present study in diverse levels. This spatial range confirms that the disaster should not be interpreted as an isolated event, but as a systemic basin-scale crisis, affecting both densely populated urban areas and agriculturally important zones. The intersection of flooded and unstable areas produced a cascade effect: communities became isolated, transport routes were interrupted, and emergency response capacity was severely compromised. Finally, at the local scale, the accuracy achieved by the method (92% for floods and 85% for landslides) confirmed by field surveys that the flood extent spread diffusely, inundating entire neighborhoods and isolating communities. The building footprint analysis showed that thousands of residential and commercial properties were directly affected, reinforcing the idea that vulnerability is not restricted to informal housing, but also includes critical infrastructure and strategic productive activities for the region. The disaster therefore exposed not only the physical fragility of the territory but also the social and economic vulnerability of its populations, which became hostages to the absence of preventive planning. In summary, the multiscale analysis presented in this work demonstrated that the 2024 Baixo-Taquari-Antas Valley event represented a turning point in the history of Brazilian hydrometeorological disasters: locally devastating, regionally systemic, nationally unprecedented in extent, and internationally comparable to the most severe recent episodes. The methodology employed proved effective not only for detecting and quantifying the impacts but also for contextualizing the event within a global panorama of climate intensification. The challenge, therefore, is to transform these data into concrete public policies capable of reducing population exposure, reorganizing the territory, and preparing communities to face a reality of increasingly frequent extremes. CONCLUSION The 2024 hydrometeorological disaster in the Baixo-Taquari-Antas Valley marked a watershed moment in understanding flood and landslide dynamics in South America, with multi-sensor satellite data (Sentinel-1 SAR, Sentinel-2 optical, and DEM-derived slope constraints) and in situ validation enabling precise mapping of affected areas at accuracies of 92% for floods and 85% for landslides. The event reached an unprecedented magnitude, with 323 km² inundated (11.9% of the basin) and 185 km² of slope instabilities, revealing its systemic, multi-hazard nature and surpassing the urban areas of major Brazilian and international cities. Locally devastating, regionally systemic, nationally unprecedented, and comparable to recent catastrophes in the last decades, the Baixo-Taquari-Antas Valley floods combined diffuse inundation with widespread slope failures, exposing the vulnerability of entire territories. This study highlighted the operational and strategic importance of integrating satellite-based monitoring into disaster risk governance, demonstrating how the synergy between orbital data and ground validation can deliver timely and reliable assessments of hazard extent, exposure, and impact. As a dark moment in Brazilian disaster history, the event emphasizes the urgency to connect scientific tools to public policymaking—increasing seriousness of land-use regulation and infrastructure resilience to community safety—to minimize exposure and create adaptive response to increasing weather extreme events. Declarations Funding This research was funded by financial support received from Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq Brazil under process (88887.984816/2024-00 CAPES PDS) CRediT authorship contribution statement Thyago Anthony Soares Lima: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization, creation and implementation of the code. Marcelo Reis: Writing – review & editing, Writing – original draft, Visualization, Formal analysis Ramon Santana: Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization, creation and implementation of the code. Adson Gomes: Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization, Silvio Simões: Supervision, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization, Writing – review & editing, Writing – original draft. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability The data supporting the findings of this study will be made available by the corresponding author upon reasonable request. 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17:41:27","extension":"html","order_by":57,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":203922,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7521122/v1/64215aad907caeab07f103ba.html"},{"id":94546609,"identity":"82a455cf-a194-4b22-a9ce-e844e9111d2a","added_by":"auto","created_at":"2025-10-28 17:39:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":386561,"visible":true,"origin":"","legend":"\u003cp\u003eBrazil’s map with Rio Grande do Sul State highlighted in red (bottom left). Taquari River Basin circled with red line (top left) and the nine municipalities affected by the 2024 flood event considered by this study (right).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7521122/v1/02d679c81a27809749ba0b91.png"},{"id":94546762,"identity":"f8ae79d3-6a43-46ba-8583-44ceba245a14","added_by":"auto","created_at":"2025-10-28 17:40:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":500826,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the methodology for flooding and landslide analysis applied in this study.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7521122/v1/f8e5897e19c09eebdae55d79.png"},{"id":94547021,"identity":"a8e6f95c-907e-42f9-a2d4-4cfc767aa268","added_by":"auto","created_at":"2025-10-28 17:41:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":865219,"visible":true,"origin":"","legend":"\u003cp\u003eApplication of algorithm and satellite imagery composition: (a) municipal boundaries; (b) drainage networks; (c) identifying water masses; (d) final footprints with buildings.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7521122/v1/bfc367e07dccd974917b0e22.png"},{"id":94547072,"identity":"be556f04-7b78-4bc6-8ca8-68ea0f2dd6a1","added_by":"auto","created_at":"2025-10-28 17:42:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":367842,"visible":true,"origin":"","legend":"\u003cp\u003eSentinel-1 imagery comparison of pre- (above) and post- (below) flooding events of 2024 from the municipality of Lajeado, Rio Grande do Sul State, Brazil.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7521122/v1/5fca43c14ab55bf382bcec18.png"},{"id":94546860,"identity":"44d3e0bc-cd48-4f44-adda-e6691fb2ec90","added_by":"auto","created_at":"2025-10-28 17:41:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":335949,"visible":true,"origin":"","legend":"\u003cp\u003eSentinel-2 imagery comparison of pre- (above) and post- (below) flooding events of 2024 from the municipality of Lajeado, Rio Grande do Sul State, Brazil.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7521122/v1/3ef237cdfc0fad232090b3a8.png"},{"id":94546993,"identity":"9a114c99-de49-48cf-90a3-e637e1a0d008","added_by":"auto","created_at":"2025-10-28 17:41:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":377720,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated result of identification of flooded (blue) and landslide (red) areas after flooding events of 2024 from the municipality of Lajeado, Rio Grande do Sul State, Brazil using Sentinel-1 synthetic aperture radar (SAR) imagery and Sentinel-2 Normalized Water Index (NDWI).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7521122/v1/bf2e1f807ffb84ce77ccf745.png"},{"id":94547077,"identity":"df947f02-664b-4cb0-b21e-f77d509a5fbe","added_by":"auto","created_at":"2025-10-28 17:42:03","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":517618,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of flooded and waterlogged areas in Cruzeiro do Sul and Lajeado (Taquari Basin, RS) mapped from Sentinel-1 SAR (VV) and Sentinel-2 NDWI during the 2024 event. Blue polygons indicate inundation/waterlogging; beige polygons are building footprints; turquoise lines represent hydrography; gray shading is DEM hillshade.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7521122/v1/365f8555642cefab2a02d514.jpeg"},{"id":94547024,"identity":"bc557ca0-126d-4194-914a-d33d9ff0db5b","added_by":"auto","created_at":"2025-10-28 17:41:48","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":537330,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial extent of mapped landslides in Marques de Souza and Muçum (Taquari Basin, RS) during the 2024 event. Red polygons show landslide scars derived from Sentinel-1 SAR (VV) backscatter change (Δσ⁰ ≥ +3 dB) and constrained to slopes \u0026gt;15° (SRTM-GL1 DEM); turquoise lines represent hydrography; gray shading is DEM hillshade.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7521122/v1/8869c008b3ac4414e65a4deb.jpeg"},{"id":94546707,"identity":"da3335fa-1bfa-4d8a-b506-07f1a92340fd","added_by":"auto","created_at":"2025-10-28 17:40:23","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":354429,"visible":true,"origin":"","legend":"\u003cp\u003eField photographs documenting flood and landslide impacts in the Baixo -Taquari–Antas Valley (May–June 2024). (A) Lajeado/Cruzeiro do Sul—bank erosion and foundation undermining of a riverside dwelling. (B) Lajeado—riverside promenade with high-water marks and displaced pavement. (C) Roca Sales—severe structural damage and debris accumulation after the flood surge. (D) Lajeado/Cruzeiro do Sul—partial collapse and debris field around a residence. (E) Marques de Souza—shallow landslide scar on a steep slope observed during field inspection. (F) Marques de Souza—mud-covered street with debris-flow deposits along the valley floor.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7521122/v1/8e6e9f0a1c43c39c5c126eed.png"},{"id":100370131,"identity":"a54f6969-c660-406f-b041-0b6e2cc8ae35","added_by":"auto","created_at":"2026-01-16 08:00:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5120849,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7521122/v1/0d90d111-5cbf-4ee9-8851-45377acd1d1d.pdf"}],"financialInterests":"","formattedTitle":"Eyes from Above: SAR-Based Remote Sensing for Flood and Landslide Risk – The Case That Shocked South Brazil in 2024","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eClimate change, which can be defined as long-term shifts in temperature and weather patterns, provoke effects on the environment, health, and economy (IPCC, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Romanello et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These impacts include rising temperatures, more frequent and intense extreme weather events such as heat waves and droughts, sea level rise, and changes in precipitation patterns (IPCC, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Seneviratne et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the specific case of the latter, there is an intrinsic association with the frequency and intensity of floods and droughts, which cause crop damage, soil erosion, and disruptions to ecosystems and human health. Moreover, in the aftermath of flooding events, there is an increased risk of landslides and the development of both physical and mental health issues (Alderman \u0026amp; Tong. 2012)\u003c/p\u003e\u003cp\u003eOver the past decades, extreme hydrometeorological events and the associated risks have intensified globally both in frequency and severity, potentially driven by climate change and rapid urbanization in hazard-prone areas (IPCC, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; UNDRR, 2022). Particularly, floods and land mass movements are ranked among the most dangerous and devastating natural disasters worldwide, both in terms of casualties and economic losses (Haque et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Koks et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Brazil, historical records reveal a significant increase in the occurrence of such phenomena, with notable disasters since the early 2010s, especially in the southern region (Inter-American Commission on Human Rights, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), culminating with the 2024 tragedy that devastated the southernmost Brazil\u0026rsquo;s state of Rio Grande do Sul (Marengo et al \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Mantovani et al \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The tragedy itself represented a hydrological and geotechnical of catastrophic proportions, particularly in the Baixo-Taquari-Antas Valley, which suffered the highest precipitation levels, experienced peak river discharges, and endured the greatest concentration of material and human losses (ONS, 2024). The region\u0026rsquo;s geomorphological features\u0026mdash;characterized by extensive alluvial plains and steep slopes\u0026mdash;combined with dense urban occupation contributed significantly to its susceptibility to flooding and consequent landslides (Guzzetti et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ribeiro \u0026amp; Werlang, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Alves et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fernandes \u0026amp; Almeida, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe consequences associated with these tragic events often lead to significant material damage, displacement of populations, and disruption of essential services such as water supply, sanitation, and healthcare (Du et al \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Moreover, it causes a major economic burden due to infrastructure repairs and loss of livelihoods. In these cases, prevention and rapid emergency response can make a substantial difference, especially in regions with limited resources (Oxera \u0026amp; ICC, 2024; Allstate \u0026amp; U.S. Chamber, 2024). Therefore, identifying and monitoring areas at risk of flooding and landslides is pivotal, not only for environmental management but for further safeguarding public health and socio-economic stability. Accurate and timely identification of high-risk zones is crucial for disaster risk reduction and resilience planning (Agbehadji, 2023). Among the available and most cost-effective techniques, remote sensing technologies such as satellites and radars provide a powerful tool for the systematic detection and monitoring of disaster-prone areas, providing accurate data for decision-making even under adverse conditions.\u003c/p\u003e\u003cp\u003eIn this context, Synthetic Aperture Radar (SAR) remote sensing has emerged as an effective tool for mapping disaster-affected areas, primarily due to its ability to acquire data regardless of weather and lighting conditions (Torres et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mason et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Recent studies have demonstrated the successful use of Sentinel-1 data for urban flood detection through backscatter change analysis (Chini et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Twele et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), as well as the identification of shallow landslide areas using optical imagery and semi-automated modeling approaches (Mondini et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe present study was conducted to organize the guidelines of an integrated, multisensorial methodology for detailed mapping of areas affected by floods and landslides. The method was validated through in situ assessment in one of the regions that presented the greatest extent of damage during the 2024 disaster in Southern Brazil: The Baixo-Taquari-Antas Valley. This approach included a multitemporal analysis of imagery of two satellites, calibrated spectral thresholding, digital elevation modeling, and object-based image classification techniques. This study aims to advance remote sensing techniques through the integration of optical data for the spatiotemporal characterization of hydrometeorological extremes. By doing so, we seek to support improvements in urban planning, disaster prevention, and emergency response strategies, ultimately enhancing the safety and well-being of affected communities.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eREMOTE SENSING FUNDAMENTALS FOR DISASTER ASSESSMENT\u003c/h3\u003e\n\u003cp\u003eThe detection of flood-affected areas through SAR backscatter change analysis has become a widely adopted approach in recent years, especially in regions where optical data acquisition is limited by persistent cloud cover (Martinis et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chini et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Threshold-based methods applied to multitemporal Sentinel-1 datasets have shown high effectiveness in identifying inundated zones, leveraging the characteristic reduction in backscatter values over water surfaces (Twele et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pulvirenti et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Automated processing chains, such as those developed by Martinis et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Chini et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), have facilitated rapid flood mapping, even in urban environments where radar signal behavior is more complex (Mason et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Recently, studies by Plank et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Sharma et al. (2025) have advanced the use of Sentinel-1 time series for continuous flood dynamics monitoring, highlighting the importance of appropriate pre-event reference image selection and backscatter normalization techniques to reduce false detections.\u003c/p\u003e\u003cp\u003eThe Normalized Difference Water Index (NDWI) has also been widely used for flood detection due to its sensitivity to surface water presence and its ease of application to multispectral satellite imagery, particularly from Sentinel-2 (GAO, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; MCFEETERS, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). By comparing pre- and post-event images, NDWI enables the identification of newly inundated areas through threshold-based classification, where positive index variations generally indicate increased surface water coverage (Du et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Sentinel-2\u0026rsquo;s spectral resolution in the green (B3) and near-infrared (B8) bands has proven particularly effective for NDWI calculations in flood mapping applications (Drusch et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Studies by Islam et al. (2023) and Sivanpillai et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have demonstrated the utility of NDWI time series for capturing flood dynamics across diverse geographic settings. Despite its advantages, NDWI-based flood mapping is sensitive to vegetation cover and requires careful temporal window selection to minimize misclassification.\u003c/p\u003e\u003cp\u003eIn the field of landslide detection and monitoring, the application of multitemporal SAR interferometry techniques, such as Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS), has enabled the mapping of surface deformation patterns associated with slope movements, even in areas with dense vegetation and adverse atmospheric conditions (Ferretti et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Berardino et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). For instance, Motagh et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) demonstrated the capability of InSAR in characterizing active landslides in mountainous regions of Central Asia. More recently, Vassileva et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) applied a multisensory time-series analysis to monitor the reactivation of an old landslide in Iran, highlighting the potential of these techniques in high-risk scenarios. Additionally, integrated approaches combining interferometric SAR data with Digital Elevation Models (DEMs) and optical imagery have further enhanced the ability to discriminate between different types of mass movements, as discussed by Raspini et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLandslide detection through SAR backscatter change analysis has also emerged as an effective approach for rapid event mapping, particularly in post-rainfall scenarios and in areas with dense vegetation cover (Mondini et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Burrows et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This technique relies on the premise that landslides induce significant alterations in surface roughness and soil moisture content, resulting in detectable changes in backscatter values between pre- and post-event images (Mondini, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Studies such as Mondini et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) successfully applied empirical backscatter variation thresholds to Sentinel-1 data to identify recently affected landslide areas in Italy. Similarly, Burrows et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrated the applicability of this method across distinct topographic settings in Nepal and Taiwan, emphasizing the importance of appropriate reference image selection and topographic control. Additionally, approaches based on spatial autocorrelation analysis of backscatter, as proposed by Mondini (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), have contributed to reducing false positives in areas with non-landslide-related surface changes.\u003c/p\u003e\u003cp\u003eSimultaneously, the advancement of cloud-based geospatial platforms, such as Google Earth Engine (Gorelick et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), has enabled the integration of diverse orbital and topographic datasets with machine learning algorithms, including Support Vector Machines (SVM), expanding the analytical capacity for disaster mapping (Maxwell et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Despite these international advancements, Brazil still lacks integrated studies that combine SAR data, spectral indices, and machine-learning techniques for the simultaneous detection of floods and landslides, especially those validated with field data.\u003c/p\u003e\u003cp\u003eQuantifying the number of affected buildings following flood and landslide events is essential for damage assessment and risk analysis. Recent advancements in object-based image analysis (OBIA), combined with machine learning classifiers such as SVM, have enabled the semi-automated extraction of building footprints from high-resolution optical imagery, including Sentinel-2 data (Blaschke et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Texture, shape, and spectral features are typically used as input variables for classification models, enhancing the discrimination between built-up areas and natural land covers (Maxwell et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Ok et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Li et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrated the effectiveness of these approaches for large-scale building extraction, even in heterogeneous urban environments. The resulting vectorized building footprints can then be spatially intersected with hazard extent layers (e.g., flood or landslide maps) to estimate the number of affected structures.\u003c/p\u003e\u003cp\u003eIntegrating SAR backscatter analysis, optical indices like NDWI, topographic data (DEM), and building footprint extraction has proven to significantly improve the accuracy and reliability of disaster impact assessments (Giustarini et al., 2015; Martinis et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The synergistic use of multitemporal Sentinel-1 and Sentinel-2 datasets allows for the combined detection of both hydrological and geomorphological hazards, addressing the limitations inherent in single-sensor approaches (Plank et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sharma et al., 2025). Data fusion techniques, especially when implemented in cloud-based geospatial platforms such as Google Earth Engine, enable efficient processing of large datasets and the generation of consolidated impact maps (Gorelick et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This integrated framework facilitates not only the delineation of hazard extents but also the spatial quantification of exposed elements, thereby supporting more comprehensive disaster risk assessments (Martinis et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003eArea Characterization and Flooding Event\u003c/h2\u003e\u003cp\u003eThe most meridional state of Brazil, Rio Grande do Sul, with an area of ​​281,730 km\u0026sup2; and continentally bordering Uruguay, Argentina, and Santa Catarina State, as well as the Atlantic Ocean to the east (IBGE, 1986). The Rio Grande do Sul State presents extensive geographic diversity, marked by plateaus, plains, and mountain areas, with noteworthy mentions of the Southern Plateau and the Southeastern Mountain Range (Caldas, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1938\u003c/span\u003e, M\u0026uuml;ller Filho \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1970\u003c/span\u003e, IBGE 1986). The overall climate is subtropical, with lower temperatures in winter and mild summers, and the main biomes include the Atlantic Rainforest, the Pampas, and a portion of the Brazilian Coastal-Marine System (Alvares et al \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Roesh et al 2009).\u003c/p\u003e\u003cp\u003eThe Taquari Valley is situated in the central region of Rio Grande do Sul, circa 117 km from the state capital, Porto Alegre. Comprising 36 municipalities within its 4,821.1 km\u0026sup2; area, the region holds significant cultural, economic, and touristic importance, characterized by German and Italian heritage and a diversified economy based on agroindustry (swine, poultry, dairy), as well manufacturing of footwear, textile, and metal \u003cb\u003e(\u003c/b\u003eSiebeneichler et al \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Caldas, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1938\u003c/span\u003e, M\u0026uuml;ller Filho \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1970\u003c/span\u003e, IBGE 1986). Its geographic features are marked specially by floodplains and urbanized areas, rendering it susceptible to natural hazards (Tognoli et al \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe main focus of this study is the Baixo-Taquari\u0026ndash;Antas River Valley, encompassing nine municipalities (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) that were visited and analyzed in this research. These municipalities were the most severely affected in the region, with their local governments declaring a state of emergency. Brazil\u0026rsquo;s National Civil Defense officially designated this portion of the valley\u0026mdash;especially the nine municipalities examined here\u0026mdash;as a high-priority area due to the extent of the observed damage. In addition, the agency recommended that the region serve as a reference for the development and implementation of standardized disaster-prevention and mitigation measures.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026ndash; Geographical and macroeconomic data from the nine municipalities considered by this study. Population and gross domestic product (GDP) were respectively obtained for 2022 and 2021 census conducted by the Brazilian Institute of Geography and Statistics (IBGE).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMunicipality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePop.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGDP *1.000 BRL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArea (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCentroid\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLajeado\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e93,646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,596,168.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52\u0026deg;00\u0026prime;25\u0026Prime;W 29\u0026deg;26\u0026prime;35\u0026Prime;S\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstrela\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32,183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,171,440.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e185.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51\u0026deg;55\u0026prime;12\u0026Prime;W 29\u0026deg;30\u0026prime;32\u0026Prime;S\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEncantado\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22,962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,168,354.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51\u0026deg;55\u0026prime;19\u0026Prime;W 29\u0026deg;12\u0026prime;47\u0026Prime;S\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArroio do Meio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21,958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,536,556.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e157.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51\u0026deg;57\u0026prime;54\u0026Prime;W 29\u0026deg;21\u0026prime;43\u0026Prime;S\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCruzeiro do Sul\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e533,002.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e155.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52\u0026deg;01\u0026prime;59\u0026Prime;W 29\u0026deg;32\u0026prime;24\u0026Prime;S\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoca Sales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10,418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e576,081.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e208.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51\u0026deg;49\u0026prime;44\u0026Prime;W 29\u0026deg;15\u0026prime;25\u0026Prime;S\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMu\u0026ccedil;um\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e301,850.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e111.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51\u0026deg;48\u0026prime;58\u0026Prime;W 29\u0026deg;08\u0026prime;24\u0026Prime;S\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarques de Souza\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e124,134.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52\u0026deg;08\u0026prime;56\u0026Prime;W 29\u0026deg;17\u0026prime;20\u0026Prime;S\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePutinga\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e133,441.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e216.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52\u0026deg;08\u0026prime;35\u0026Prime;W 29\u0026deg;02\u0026prime;20\u0026Prime;S\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e205,084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12,141,031.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eGeospatial data used in this study were obtained from official government databases, including the National Spatial Data Infrastructure (INDE), the Brazilian Institute of Geography and Statistics (IBGE), and the Brazilian Geological Survey (SGB). Satellite imagery was acquired from the Sentinel-1 and Sentinel-2 missions via the Google Earth Engine (GEE) platform. Sentinel-1 provides Synthetic Aperture Radar (SAR) data, which are independent of weather and lighting conditions and thus suitable for disaster monitoring (Torres et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Sentinel-2 offers high-resolution multispectral optical imagery, commonly applied for spectral indices and land-cover mapping (Drusch et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). To ensure the quality and relevance of the analysis, all images were filtered by acquisition date and cloud cover (\u0026lt;\u0026thinsp;20%) and grouped into pre-event and post-event periods corresponding to the 2024 disaster window.\u003c/p\u003e\n\u003ch3\u003eFlooding and Landslide Analysis\u003c/h3\u003e\n\u003cp\u003eAn integrated workflow was developed to identify areas affected by flooding and landslides, combining multispectral and radar remote sensing, spectral indices, topographic constraints, object-based image classification, and field validation. Sentinel-1 (IW mode, VV polarization, 10 m resolution) and Sentinel-2 (L2A level, 10\u0026ndash;20 m resolution) images were processed in the GEE environment for two distinct timeframes: a pre-event period (01 December 2020\u0026ndash;31 March 2024) and a post-event period (01 April \u0026ndash; 11 May 2024).\u003c/p\u003e\u003cp\u003eFlooded areas were mapped using the Normalized Difference Water Index (NDWI), computed from Sentinel-2 green (B3) and near-infrared (B8) bands (Eq.\u0026nbsp;1):\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NDWI=\\:\\frac{(Green-NIR\\:)}{(Green+NIR)}\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;1\u003c/p\u003e\u003cp\u003ePositive values are generally associated with the presence of surface water. Originally proposed by McFeeters (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and refined by Gao (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), the NDWI remains widely used in flood studies. In this research, a threshold of +\u0026thinsp;0.1 was applied to differentiate flooded from non-flooded areas, based on median composites of pre- and post-event imagery. Recent studies confirm the robustness of NDWI for Sentinel-2 applications, including enhanced spatial resolution mapping (Du et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), time-series integration with SAR data (Martinis et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), rapid flood detection (Sivanpillai et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and flash-flood monitoring under extreme rainfall (Islam et al., 2023). It is important to distinguish NDWI from the Normalized Difference Vegetation Index (NDVI), is derived from red and near-infrared bands (NDVI=(NIR\u0026thinsp;\u0026minus;\u0026thinsp;Red)/(NIR\u0026thinsp;+\u0026thinsp;Red)), is widely applied to measure vegetation vigour (Rouse et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). Conversely, NDWI was specifically designed to highlight water features by contrasting green reflectance with near-infrared absorption (McFeeters, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Gao, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Given the objectives of this study, NDWI was preferred, as the aim was to delineate flood extent rather than vegetation cover. NDWI was preferred, as the aim was to delineate flood extent rather than vegetation cover.\u003c/p\u003e\u003cp\u003eSAR backscatter change analysis was applied to Sentinel-1 imagery to map both flooding and landslides. Median composites were generated for the pre- and post-event periods, and the difference between them was analysed. For flood mapping, a threshold of \u0026minus;\u0026thinsp;5 dB was adopted, as significant reductions in backscatter are characteristic of open water surfaces (Mason et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Twele et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For landslide mapping, a threshold of +\u0026thinsp;3 dB was applied, reflecting increases in surface roughness and soil moisture linked to slope failures (Zhou et al., 2019; Mondini et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo refine the classification, slope data derived from the SRTM-GL1 DEM were integrated. Areas with slope\u0026thinsp;\u0026lt;\u0026thinsp;15\u0026deg; were prioritized in flood mapping, while only areas with slope\u0026thinsp;\u0026gt;\u0026thinsp;15\u0026deg; were retained as potential landslide zones (Guzzetti et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Plank et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Recent works have demonstrated the reliability of such integrated SAR time-series analyses for flood and mass-movement monitoring (Martinis et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sharma et al, 2025).\u003c/p\u003e\u003cp\u003eThe final flood extent map was generated by integrating the outputs of Sentinel-1 backscatter thresholding and Sentinel-2 NDWI classification. Both approaches are complementary: SAR is insensitive to cloud cover and captures changes in surface roughness, while NDWI highlights spectral variations in water presence. By fusing both datasets, false positives were minimized, and flood detection accuracy was significantly improved, particularly in urban areas where radar backscatter behaviour is complex and optical reflectance may be influenced by vegetation. The integration process consisted of overlaying the binary flood layers from each sensor and retaining pixels flagged as inundated by at least one source, followed by slope masking (\u0026lt;\u0026thinsp;15\u0026deg;). This fusion ensured a consolidated and reliable flood mapping product.\u003c/p\u003e\u003cp\u003eThe potentially affected buildings were delineated using an Object-Based Image Analysis (OBIA) approach combined with supervised classification via Support Vector Machines (SVM). Sentinel-2 imagery was pre-processed and segmented in QGIS using the Orfeo Toolbox plugin, applying metrics such as shape compactness, spectral homogeneity, and texture. The SVM classifier was trained with 150 manually labelled samples, including buildings, vegetation, and bare soil. The classifier\u0026rsquo;s performance was validated using five-fold cross-validation, yielding an overall accuracy of 94.3% and a Kappa coefficient of 0.91. Classified building objects were vectorized into footprints and exported as shapefiles.\u003c/p\u003e\u003cp\u003eIn the final step, the building footprint vectors were intersected with flood and landslide hazard rasters in ArcGIS Pro. Each building polygon overlapping at least 20% with a hazard map was classified as affected. Results were aggregated by municipality, enabling the generation of impact statistics by event type and geographic unit (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eIn Situ Validation\u003c/h3\u003e\n\u003cp\u003eThe methodology was validated through fieldwork conducted between May and June 2024. Thirty sample sites were visited, including 20 in flooded areas, 7 in landslide zones, and 3 in unaffected control areas. Field teams verified locations using GPS devices, photographic documentation, and interviews with local residents. The comparison between observed conditions and automated outputs yielded an accuracy of 92% for flooding and 85% for landslides, with a low incidence of false positives and false negatives. These results confirm the robustness and reliability of the adopted method for emergency planning and disaster response.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSpatial analytical analyses for flooding and landslide were performed using QGIS software (version 3.36.3) to quantify the properties in each municipality within the study area. The process included the identification and mapping of the buildings affected by the flooding and landslide events in detail. Furthermore, applying geoprocessing tools allowed to characterize impacted areas by each type of disaster and extrapolate the total number of properties affected, providing a comprehensive overview of the extent of the damage caused.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe results demonstrated that the developed algorithm, implemented on the Google Earth Engine (GEE) platform, successfully integrated datasets from the National Spatial Data Infrastructure (INDE), the Brazilian Institute of Geography and Statistics (IBGE), and the Brazilian Geological Survey (SGB). This integration enabled precise delimitation of municipal boundaries and hydrographic features, as well as the analysis of RGB satellite imagery to identify affected properties and critical infrastructure.\u003c/p\u003e\u003cp\u003eIn this study, we adopted the concept of \"footprint\", which basically consists of the spatial delimitation of areas occupied by buildings, allowing a detailed analysis of the damage caused by the catastrophic event (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBy leveraging Sentinel-1 synthetic aperture radar (SAR) imagery\u0026mdash;particularly VV polarization, which is sensitive to surface changes such as flooding and landslides\u0026mdash;the algorithm effectively detected alterations in the landscape. Moreover, advanced image processing techniques were applied to analyze differences in radar backscatter between pre- and post-event scenes. Through segmentation and classification of the SAR images, the algorithm identified spatial patterns indicative of flood-affected areas and landslide-prone zones, thereby contributing to a comprehensive assessment of the disaster\u0026rsquo;s impacts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA similar approach was applied to identify flooded areas, using pre- and post-disaster remote sensing data. Specifically, we employed the Normalized Difference Water Index (NDWI), which enhances the presence of surface water by contrasting the reflectance of near-infrared (NIR) and green bands. NDWI was calculated using high-resolution Sentinel-2 imagery, enabling the detection of changes in surface water extent with high spatial detail. The algorithm processed both pre-event and post-event Sentinel-2 images to generate NDWI maps for each period. By comparing these maps, the algorithm effectively highlighted areas where significant increases in water presence were identified, functioning as indicative of flooding. This differential analysis allowed for precise spatial delimitation of newly inundated zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis integrated approach, combining different data sources and processing techniques, has proven essential for the accurate and rapid identification of areas affected by floods and mass movements (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The results of the accounting of affected areas, both by flooding and mass movement, as well as the accounting of properties through the cross-referencing of the generated data, will be detailed further.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eDamage by municipality\u003c/h3\u003e\n\u003cp\u003eOverall, results highlighted the differential impact of floods and landslides based on the number of buildings affected by floods and landslides across nine municipalities in Rio Grande do Sul, southern Brazil, following the 2024 flood events. Floods caused widespread damage across all cities, affecting over 34,000 buildings, or more than 25% of the buildings in five of the nine municipalities. In contrast, landslide-related damage was more localized and on a considerably lower scale, affecting between 2.5 and 3.5% of the structures. Nonetheless, it is noteworthy that these numbers, despite apparently low, summed a total 1114 affected buildings, with noteworthy mentions of Encantado, Mu\u0026ccedil;um and Roca Sales, where respectively 380, 131 and 137 structures suffered damages by landslides (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026ndash; Identified number of buildings affected and percentage per municipality in the nine cities selected to evaluate floods and landslides after 2024 floodings in Rio Grande do Sul, southern Brazil.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMunicipality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eEstimated Buildings (n)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eaffected by flood (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eaffected by\u003c/p\u003e\u003cp\u003elandslide (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLajeado\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36,952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13,947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstrela\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22,095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMu\u0026ccedil;um\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEncantado\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13,317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoca Sales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarques de Souza\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCruzeiro do Sul\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10,788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePutinga\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArroio do Meio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15,643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e120,725\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e34,603\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1,114\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eFlooding and landslide detected\u003c/h3\u003e\n\u003cp\u003eVariations in the impact of flooding and landslides among municipalities can be explained by several factors, such as slope density and steepness, the presence of flat areas, vegetation cover, and the types of land use and occupation. Nevertheless, a simple comparison between total area of each of the evaluated towns and the overall surface of flooding and landslide areas provide a glimpse in the magnitude of the damages.\u003c/p\u003e\u003cp\u003eIn the Baixo-Taquari-Antas River Basin, which encompasses 23 municipalities over an area of 2,720 km\u0026sup2;, satellite data indicate that the 2024 flooding event affected a total area of 323 km\u0026sup2; (\u0026asymp;\u0026thinsp;11.9% of the basin), while landslides affected 185 km\u0026sup2; (\u0026asymp;\u0026thinsp;6.8%). It is important to note, however, that the built-up area across these 23 municipalities accounted for only 4 km\u0026sup2; (\u0026asymp;\u0026thinsp;0.14% of the total basin area) at the time.\u003c/p\u003e\u003cp\u003eWithin the basin, the nine selected municipalities accounted for over half of the area affected by the 2024 flooding event, summing over 174 km\u003csup\u003e2\u003c/sup\u003e of flooded areas, and nearly 85 km\u003csup\u003e2\u003c/sup\u003e of areas where landslides were detected (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among the municipalities selected for this study, Cruzeiro do Sul, a city with less than 12,000 inhabitants, presented almost 20% of its territory with flooded areas, followed by Lajeado (\u0026asymp;\u0026thinsp;17%), and Estrela (\u0026asymp;\u0026thinsp;15%). On the other hand, landslides affect more severely Marques de Souza (\u0026asymp;\u0026thinsp;11%) and Mu\u0026ccedil;um (\u0026asymp;\u0026thinsp;10%). The municipalities with less covered by the flooding event were Arroio do Meio (\u0026asymp;\u0026thinsp;10%), Encantado (\u0026asymp;\u0026thinsp;9%) e Marques de Souza (\u0026asymp;\u0026thinsp;8%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026ndash; Comparison between total, flooding and landslide areas and percentage per municipality in the nine cities selected to evaluate floods and landslides impacts after 2024 extreme weather events of 2024 in Rio Grande do Sul, southern Brazil.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMunicipality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eArea (Km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFlooded\u003c/p\u003e\u003cp\u003eArea\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLandslide Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLajeado\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstrela\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e185.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMu\u0026ccedil;um\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e111.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEncantado\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e140.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoca Sales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e208.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarques de Souza\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e125.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCruzeiro do Sul\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e155.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePutinga\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e216.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArroio do Meio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e157.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e174.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e84.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the spatial distribution of flooded and waterlogged areas in Cruzeiro do Sul and Lajeado, the two municipalities with the highest number of affected dwellings (18,86% and 16,69% of the municipal housing stock, respectively). While the full analysis encompassed nine municipalities of the Taquari Basin, these two cases are shown here in detail to illustrate the most critical impacts. The maps highlight the concentration of inundated areas along river valleys and low-lying floodplains, supporting the quantitative estimates presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and exemplifying the heterogeneity of impacts among municipalities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure 8 shows the spatial extent of landslides in Marques de Souza and Mu\u0026ccedil;um, the two municipalities with the highest proportion of affected dwellings in the Taquari Basin (11.33% and 10.53% of the municipal housing stock, respectively). Although the complete assessment covered all nine municipalities, these two cases are highlighted here because they exemplify the most severe slope instabilities. The mapped scars, derived from Sentinel-1 backscatter variations and refined by topographic constraints, cluster predominantly along steep hillslopes adjacent to the Taquari River and its tributaries. This spatial concentration underscores the susceptibility of these municipalities to compound hazards triggered by extreme rainfall.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 8-\u003c/b\u003e Spatial extent of mapped landslides in Marques de Souza and Mu\u0026ccedil;um (Taquari Basin, RS) during the 2024 event. Red polygons show landslide scars derived from Sentinel-1 SAR (VV) backscatter change (Δσ⁰ \u0026ge; +3 dB) and constrained to slopes\u0026thinsp;\u0026gt;\u0026thinsp;15\u0026deg; (SRTM-GL1 DEM); turquoise lines represent hydrography; gray shading is DEM hillshade.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eField Validation\u003c/h2\u003e\u003cp\u003eThe accuracy of the flood and landslide maps was assessed through a systematic field survey conducted in nine municipalities of the Taquari Basin. A total of 30 control points were selected across urban and rural settings, covering representative locations of inundated floodplains, waterlogged streets, and slope failures. Field observations included georeferenced photographs, water marks on buildings, road disruptions, and fresh landslide scars. These ground-based records were spatially matched with satellite-derived layers, allowing a direct comparison between remote sensing outputs and in situ evidence.\u003c/p\u003e\u003cp\u003eOverall, the validation confirmed the robustness of the methodology, with mapping accuracies reaching 92% for flood delineation and 85% for landslide detection.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e-\u003c/b\u003e Field photographs documenting flood and landslide impacts in the Baixo -Taquari\u0026ndash;Antas Valley (May\u0026ndash;June 2024). (A) Lajeado/Cruzeiro do Sul\u0026mdash;bank erosion and foundation undermining of a riverside dwelling. (B) Lajeado\u0026mdash;riverside promenade with high-water marks and displaced pavement. (C) Roca Sales\u0026mdash;severe structural damage and debris accumulation after the flood surge. (D) Lajeado/Cruzeiro do Sul\u0026mdash;partial collapse and debris field around a residence. (E) Marques de Souza\u0026mdash;shallow landslide scar on a steep slope observed during field inspection. (F) Marques de Souza\u0026mdash;mud-covered street with debris-flow deposits along the valley floor.\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe spatial extent of the 2024 hydrometeorological disaster in the Baixo-Taquari-Antas Valley highlights its unprecedented severity. Remote sensing mapping indicated that the floods affected a total of 323 km\u0026sup2; of the all Baixo-Valley \u0026mdash; equivalent to 11.9% of its total area \u0026mdash; with 174 km\u0026sup2; concentrated in the nine municipalities which were the focus of the present study. Landslides were also significant, representing an area of approximately 185 km\u0026sup2; across the entire basin, with 84.76 km\u0026sup2; of this disaster effectively mapped in the nine selected municipalities.\u003c/p\u003e\u003cp\u003eTo contextualize the magnitude of the episode, the inundated surface exceeded the entire urban footprint of other major cities of Brazil, such as Recife (\u0026asymp;\u0026thinsp;220 km\u0026sup2;; IBGE, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and is comparable to the metropolitan area of Fortaleza (\u0026asymp;\u0026thinsp;314 km\u0026sup2;; IBGE, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, in global scales, the extend of this disaster far surpassing the urbanized areas of Lisbon (\u0026asymp;\u0026thinsp;100 km\u0026sup2;; PORDATA, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Barcelona (\u0026asymp;\u0026thinsp;101 km\u0026sup2;; Idescat, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and was almost twice the size of Tel Aviv (\u0026asymp;\u0026thinsp;170 km\u0026sup2;; Israel Central Bureau of Statistics, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), larger than Montevideo (\u0026asymp;\u0026thinsp;201 km\u0026sup2;; INE-Uruguay, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and greater than Kigali (\u0026asymp;\u0026thinsp;280 km\u0026sup2;; National Institute of Statistics of Rwanda, 2022). It is also similar in size to Singapore (\u0026asymp;\u0026thinsp;281 km\u0026sup2;; Singapore Department of Statistics, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Wellington (\u0026asymp;\u0026thinsp;290 km\u0026sup2;; Stats NZ, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), approaching the dimension of Philadelphia (\u0026asymp;\u0026thinsp;347 km\u0026sup2;; U.S. Census Bureau, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These comparisons demonstrate that the event went far beyond the scale of local or riverine floods, configuring a regional-scale catastrophe with international projection, capable of simultaneously disrupting densely populated urban centers and extensive rural areas.\u003c/p\u003e\u003cp\u003eThe geomorphological and hydrological interpretation of the data helps to explain this extent. The Baixo-Taquari-Antas Valley presents physical characteristics that significantly increased susceptibility: extensive alluvial plains, which functioned as natural water accumulation zones, and steep slopes, which favored the triggering of gravitational mass movements. The combination of factors such as these may provide insights into why under intense rainfall, the impact was amplified by the conditions of relief and geological structure. The association between flooding and slope instabilities serves as indicative that this may not be an isolated phenomenon, but rather a systemic process in which hydrological and geotechnical mechanisms interacted, producing large-scale damage.\u003c/p\u003e\u003cp\u003eThe integrated analysis of the phenomena showed that flood and landslide processes occurred synergistically. While the floods spread diffusely across the basin, the landslides were concentrated in more susceptible sectors, reinforcing the multi-hazard nature of the disaster. This behavior confirms that the event was not merely a case of river overflow, but a broader territorial collapse, in which different hydrological and geotechnical processes interacted and intensified the damage.\u003c/p\u003e\u003cp\u003eThe results provided by this study indicate that the 2024 disaster was not only historically unprecedented in Rio Grande do Sul, but also ranks among the most extensive flood episodes recorded in South America in recent decades. These values far exceed the average of previous events documented in the region and place the 2024 flood as the most extensive and destructive ever recorded in southern Brazil (Marengo et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mantovani et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The spatial magnitude of the event not only characterizes it as a riverine flood episode, but rather as a multi-hazard territorial collapse, in which diffuse flooding and mass movements occurred synchronously.\u003c/p\u003e\u003cp\u003eUnder a global perspective, flood episodes covering more than 300 km\u0026sup2; are considered rare outside large transnational basins. Similar events have been observed in recent catastrophes in Southeast Asia, such as the Pakistan floods of 2022, which devastated thousands of square kilometers and displaced millions of people (Sajjad, 2022). Although smaller in scale, the Baixo-Taquari-Antas Valley case approaches these global catastrophe patterns due to the high population density affected and the simultaneity of floods and landslides. In terms of human and territorial severity, the data presented here demonstrate that the Brazilian event cannot be classified as local or episodic, but as part of the global trend of hydrometeorological extremes intensified by climate change (IPCC, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; UNDRR, 2022).\u003c/p\u003e\u003cp\u003eOn a continental scale, South America has a recurrent history of hydrological disasters, notably floods in the Paraguayan and Argentine Chaco and the Amazon floods in Brazil and Peru (UNDRR, 2022). Nevertheless, unlike those regions, which are marked by floodplains of lower population density, the Baixo-Taquari-Antas Valley combines high socioeconomic vulnerability with strong urbanization in risk areas, which exponentially amplified the impact. A comparison between the 2024 South Brazil events and the 2011 floods in the mountainous region of Rio de Janeiro, which resulted in thousands of deaths (Dourado et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), is pertinent: both episodes reveal the convergence of extreme rainfall with urban occupation in susceptible areas. Although Rio de Janeiro\u0026rsquo;s disaster was characterized mostly by landslides, the Baixo-Taquari-Antas Valley\u0026rsquo;s event presented a hybrid character, where massive flooding was accompanied by hundreds of slope instabilities.\u003c/p\u003e\u003cp\u003eThe 2024 event transcended other recent other episodes with floods and landslides in the country, such as Petr\u0026oacute;polis in February 2022 \u0026mdash; where a record rainfall volume (\u0026asymp;\u0026thinsp;258 mm in only 3 hours) triggered the city\u0026rsquo;s greatest tragedy which accounted with 231 deaths (Alc\u0026acirc;ntara et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) \u0026mdash; and the extratropical cyclone that affected southern Brazil in June 2023, causing major damage and losses, including fatalities in Rio Grande do Sul (Magalh\u0026atilde;es et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These recent events illustrate the growing frequency and intensity of hydrometeorological extremes in Brazil and reaffirm that the Baixo-Taquari-Antas Valley disaster represents a new and exceptional stage of complexity and impact. The number of affected buildings, estimated through the intersection of urban footprints with the flood and instability maps, confirms the extreme degree of exposure of the local population.\u003c/p\u003e\u003cp\u003eAt the regional scale, the results showed that impacts were not limited to a single municipality, but simultaneously affected twenty-three cities, including the nine selected for the present study in diverse levels. This spatial range confirms that the disaster should not be interpreted as an isolated event, but as a systemic basin-scale crisis, affecting both densely populated urban areas and agriculturally important zones. The intersection of flooded and unstable areas produced a cascade effect: communities became isolated, transport routes were interrupted, and emergency response capacity was severely compromised.\u003c/p\u003e\u003cp\u003eFinally, at the local scale, the accuracy achieved by the method (92% for floods and 85% for landslides) confirmed by field surveys that the flood extent spread diffusely, inundating entire neighborhoods and isolating communities. The building footprint analysis showed that thousands of residential and commercial properties were directly affected, reinforcing the idea that vulnerability is not restricted to informal housing, but also includes critical infrastructure and strategic productive activities for the region. The disaster therefore exposed not only the physical fragility of the territory but also the social and economic vulnerability of its populations, which became hostages to the absence of preventive planning.\u003c/p\u003e\u003cp\u003eIn summary, the multiscale analysis presented in this work demonstrated that the 2024 Baixo-Taquari-Antas Valley event represented a turning point in the history of Brazilian hydrometeorological disasters: locally devastating, regionally systemic, nationally unprecedented in extent, and internationally comparable to the most severe recent episodes. The methodology employed proved effective not only for detecting and quantifying the impacts but also for contextualizing the event within a global panorama of climate intensification. The challenge, therefore, is to transform these data into concrete public policies capable of reducing population exposure, reorganizing the territory, and preparing communities to face a reality of increasingly frequent extremes.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe 2024 hydrometeorological disaster in the Baixo-Taquari-Antas Valley marked a watershed moment in understanding flood and landslide dynamics in South America, with multi-sensor satellite data (Sentinel-1 SAR, Sentinel-2 optical, and DEM-derived slope constraints) and in situ validation enabling precise mapping of affected areas at accuracies of 92% for floods and 85% for landslides. The event reached an unprecedented magnitude, with 323 km\u0026sup2; inundated (11.9% of the basin) and 185 km\u0026sup2; of slope instabilities, revealing its systemic, multi-hazard nature and surpassing the urban areas of major Brazilian and international cities. Locally devastating, regionally systemic, nationally unprecedented, and comparable to recent catastrophes in the last decades, the Baixo-Taquari-Antas Valley floods combined diffuse inundation with widespread slope failures, exposing the vulnerability of entire territories. This study highlighted the operational and strategic importance of integrating satellite-based monitoring into disaster risk governance, demonstrating how the synergy between orbital data and ground validation can deliver timely and reliable assessments of hazard extent, exposure, and impact. As a dark moment in Brazilian disaster history, the event emphasizes the urgency to connect scientific tools to public policymaking\u0026mdash;increasing seriousness of land-use regulation and infrastructure resilience to community safety\u0026mdash;to minimize exposure and create adaptive response to increasing weather extreme events.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by financial support received from Conselho Nacional de\u0026nbsp;Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico - CNPq Brazil under process (88887.984816/2024-00 CAPES PDS)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThyago Anthony Soares Lima:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization, creation and implementation of the code. \u003cstrong\u003eMarcelo Reis:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Visualization, Formal analysis \u003cstrong\u003eRamon Santana:\u003c/strong\u003e Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization, creation and implementation of the code. \u003cstrong\u003eAdson Gomes:\u003c/strong\u003e Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization, \u003cstrong\u003eSilvio Sim\u0026otilde;es:\u0026nbsp;\u003c/strong\u003eSupervision, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization, Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study will be made available by the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to national civil protection, civil defense of the state of Rio Grande do Sul, and civil defense of the municipality of Maceio for providing great help to retrieve local information and datas.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgbehadji, I. 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Computers \u0026amp; Geosciences, 123, 103\u0026ndash;110. https://doi.org/10.1016/j.cageo.2018.11.002\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sentinel-1 SAR, NDWI, Flood mapping, Landslide detection, Multi-hazard assessment, Rio Grande do Sul, Brazil","lastPublishedDoi":"10.21203/rs.3.rs-7521122/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7521122/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe hydrometeorological disaster that struck Rio Grande do Sul, Brazil, in 2024 resulted in floods and landslides of unprecedented magnitude, surpassing historical events recorded in 1941 and 2023. Among the most severely affected regions, the Baixo-Taquari-Antas Valley tood out due to its high population density, critical infrastructure concentration, and extensive socio-economic damage. This study developed an operational, multi-sensor workflow to rapidly delineate impacts in nine municipalities by fusing Sentinel-1 SAR and Sentinel-2 optical data within a DEM-constrained framework. Pre-/post-event median composites (pre: Dec-2020\u0026ndash;Mar-2024; post: Apr\u0026ndash;11 May 2024) were differenced to map backscatter changes, applying fixed thresholds (Δσ⁰ \u0026le; \u0026minus;5 dB for flood; Δσ⁰ \u0026ge; +3 dB for landslides). Flood extent was further refined with NDWI from Sentinel-2, while landslide candidates were restricted to slopes\u0026thinsp;\u0026gt;\u0026thinsp;15\u0026deg;. Object-based image analysis with SVM on optical scenes produced building footprints that were intersected with hazard layers to quantify exposure. Field surveys at 30 control points confirmed mapping accuracies of 92% (floods) and 85% (landslides). Results show\u0026thinsp;~\u0026thinsp;174 km\u0026sup2; of inundation and ~\u0026thinsp;84.8 km\u0026sup2; of landslides across the nine cities, with ~\u0026thinsp;34,603 buildings affected by flooding and ~\u0026thinsp;1,114 by slope failures. The integrated SAR\u0026ndash;optical approach proved robust under cloud cover and heterogeneous urban fabrics, delivering actionable situational awareness for emergency response and early recovery. The results provided critical support for emergency response and post-disaster mitigation planning, demonstrating the operational value of integrated SAR\u0026ndash;optical remote sensing for rapid disaster risk assessment in highly vulnerable regions.\u003c/p\u003e","manuscriptTitle":"Eyes from Above: SAR-Based Remote Sensing for Flood and Landslide Risk – The Case That Shocked South Brazil in 2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 02:12:28","doi":"10.21203/rs.3.rs-7521122/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5627607e-69c4-4a34-8b68-0a9c62f13a09","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-14T09:21:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-28 02:12:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7521122","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7521122","identity":"rs-7521122","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}