Mapping an urban flood area in the Amazon: a SAR potential application for disaster management

Alcantara, Tais Carolina de Oliveira; Gonçalves, Carolina da Silva; Polidori, Laurent; Andrade, Milena Marilia Nogueira de

doi:10.1590/s1982-21702025000100014

Abstract:

Floods are frequent disasters in the urban areas of the Amazon region. Accurate delineation of flood extents is essential for effective disaster prevention and response; however, despite technological advancements, significant challenges remain in processing Sentinel-1 SAR (Synthetic Aperture Radar) data to produce reliable inundation maps. In 2017, the municipality of Alenquer in the state of Pará declared a state of emergency due to severe flooding, which caused substantial harm to the local population. This article aimed to analyze the potential of SAR data Sentinel-1 images in mapping flood extent and structures exposed in the urban area of Alenquer. Geoprocessing and remote sensing techniques were applied from the Google Earth Engine to obtain the flood extent mask. The area and quantification of the affected buildings and roads were conducted in QGis. The results obtained for the vertical-vertical (VV) and vertical-horizontal (VH) polarizations produced flood extents of 1.81 and 2.21 km², respectively. Through VV polarization extension, we detected 21 buildings and seven affected roads, whereas through VH polarization, we detected 12 buildings and seven roads. The methodology proved to be efficient but the methodological reproduction in other area in other Amazonian and Brazilian cities must consider seeking support from field data whenever possible.

Keywords:
Alenquer; Hazard; Google Earth Engine; Sentinel-1

1. Introduction

Floods are the most widespread hydrological disaster worldwide, impacting water management, economic activities, hydro-morphological changes in ecosystem services, and human life. Owing to climate change, flood events have become more frequent and unpredictable in recent years, accounting for 44% of disasters between 2000 and 2019 (Pralle, 2019). The inundation is a phenomenon that occur naturally owing to climatic and regional processes and corresponds to the overflow of a large amount of water beyond its normal limits (Viana, 2016; United Nations, 2017).

In the Amazon River basins, the severity of hydrological events has increased alongside the rising frequency of extreme weather occurrences (Espinoza et al.,2022). The convergence of large rivers, such as the Negro and Tapajós, into the Amazon River contributes to the seasonal flood cycle, which is influenced by the characteristics of the rainfall regime of each river (Andrade et al., 2017; Filizola, 2002). Cities along the Amazon River have experienced extreme floods recorded in 1953, 1989, 1999, 2009, 2012-2015 (Marengo; Espinoza, 2016). In Pará state between 2013 and 2023 a total of 1,108 Emergency decree were published related do flooding (CNM, 2024). Particularly, the West Pará mesoregion is characterized by a high number of hydrological events directly related to flood damage (CEPED, 2013; Souza et al., 2025). Among the most affected municipalities figures Alenquer (Souza et al., 2025). Alenquer registered five decrees of emergency (Emergency Situation Decree) in Integrated Disaster Information System (S2iD) between 2014 and 2021. However, the current data are available mainly at municipal level, and local data considering the urban areas affected by floods are still insufficient.

In Amazonian urban areas affected by floods, the extent of damage is intensified by various factors, including the spatial overlap between hazards and exposed elements, population vulnerability, low Human Development Indices (HDI), high population density, and impact on the city’s services and infrastructure (Andrade; Szlafsztein, 2019). According to Adger (2006), exposure is defined as the natural intensity of environmental or socio-political stress experienced by a system; thus, understanding the magnitude, frequency, duration, and scope of a flood is crucial for identifying a particular exposed population.

To understand the extent of flooding and exposed areas, Synthetic Aperture Radar (SAR) data has increasingly been used in disaster management (Priya; Divya, 2019). Among the advantages of SAR data compared to optical data are its low costs, reliable acquisition schedule, ability to cover remote areas, and capacity to capture images even during rainfall (Refice et al., 2018; Yang et al., 2021). In the Amazon region, the use of SAR fused with optical images has been tested largely tested for regional floodplains understanding (Aldsdorf et al., 2007; Fassoni-Andrade et al., 2020). However few studies focus on a more detailed urban local scale (Andrade; Brabo, 2019). Additionally, SAR data images have been used for real-time monitoring and assessment of flood disasters (Zhang et al., 2021; Vanama; Rao, 2019; Vassileva, 2015). There is increasing exploratory research using Sentinel-1 and Google Earth Engine to map floods in urban environments for disaster management purposes (Islam; Meng, 2022). Identifying the extent of flooding in Amazonian urban areas contributes to the analysis of the hazard map and is extremely relevant, considering the development of cities on the banks of rivers and their predominant location on flood plains, namely, places that naturally experience seasonal flooding. Within this context, in this study, we aimed to analyze the potential of Sentinel-1 SAR data images to map the flood extent and exposure of urban infrastructure in an urban area of Alenquer in the Amazon region.

2. Material And Methods

2.1. Study Area

The study area corresponds to the urban center of the municipality of Alenquer (Figure 1). Located in the Santarém micro-region, it is an urban area with an estimated population of 57,390 people spread over 23,645.452 km² according to Brazilian Institute of Geography and Statistics (IBGE) data (2010). Its economy is predominantly based on commerce and services, along with seasonal activities, such as extractive activities, fishing, livestock, and agriculture (Rodrigues, 2018; Pará, 2010).

The altimetry in the urban area of Alenquer is predominantly low, ranging from 11 to 26 m, with the lowest elevations (between 0 and 10 m) located on the edge of the city, which is a region frequently affected by seasonal flooding. The relief of the municipality is characterized as the Amazon Plain, varying from flat to gently undulating. The relief is characterized by low plateaus and river plains, which experience flooding during river floods (Silva Júnior, 2010; Rodrigues, 2018).

According to the automatic mapping conducted by the European Space Agency (ESA) in 2021(https://developers.google.com/earthengine/datasets/catalog/ESA_WorldCover_v200?hl=pt-br), a mixed occupation pattern was observed in land-use and land-cover area: arboreal vegetation, pasture, agricultural areas, anthropic areas, bare soil, water and wetlands. Vegetation is a significant class in the border of the urban area, representing areas with dense vegetation cover, such as forests, parks, and green areas. Conversely, pasture areas represent sites destined for agriculture, livestock, or with low vegetation density, whereas built-up urban areas, such as streets, buildings, houses, and infrastructure, are represented by the anthropogenic area class (Foley et al., 2005; United Nations, 2015). Historical registers due water events such as flash floods, heavy rainfall, and floods reveal a tendency for the annual totals to be more intense mainly from 2017 onwards in Pará western including Alenquer (Souza et al., 2024). During floods essential services such as water and energy facilities remain in high-risk areas (Silva Junior; Szlafsztein, 2010). On May 8, 2017, the municipal government of Alenquer declared Emergency Situation due to severe flooding caused by the overflowing of the Surubiú River. This natural disaster affected 22 riverside communities and 7 urban neighborhoods, including the city center, and damaged approximately 6 kilometers of main streets (DOE, 2017).

Figure 1
From top to bottom: A- Location of the study area and overview of the Alenquer City in a Sentinel-2 image. B- Elevation (SRTM) and C- land use and landcover (ESA WorldCover 2021) maps. Views of the city during flooding events from left to right: 1. Downtown Alenquer flooded. Photo: Francisco Ferreira (2017); 2. Rising river flood already affects 600 families in the urban area of Alenquer. Photo: Frank de Oliveira / TV Tapajós (2015); 3. The city of Alenquer is suffering from the river flood. Photo: Ascom Alenquer City Hall (2017).

The historical series of average rainfall between 1991 and 2017 in the study area reveals a marked trend in the first six months of the year, with March and April showing the highest monthly averages, totaling 297.48 and 281.26 mm, respectively. In contrast, during 2017 (the year of the Emergency Situation decree), the average rainfall peaked in February, reaching 331 mm, whereas the minimum rainfall was observed in December, namely, 75 mm. The historical records indicate that the maximum rainfall values were recorded in March 2014 (604.9 mm) and April 1996 (597.2 mm), whereas the minimum values were observed between the months of July and December in 2017 year (Figure 2).

Considering the average fluviometric levels of the Amazon River observed over the 32-year historical series (1985-2017), the records indicate that the highest values were predominantly recorded in the first half of the year, especially in April and May, when the average values reached 744 and 802 cm, respectively. In 2017, April showed the highest average level, whereas the maximum level of the municipality was observed in March, reaching 1330 cm (Figure 3). Considering the historical series, the maximum level showed two notable peaks in October 2001 and May 2008, reaching 3954 cm and 4349 cm, respectively. Conversely, the minimum levels were recorded between May 1988 (100 cm) and April 2002 (61 cm).

Figure 2
Precipitation average, minimum, and maximum of water river levels (cm) for 2017 compared with those in the historical series (h.s.) for the city of Alenquer.

Figure 3
Average, minimum, and maximum of water river levels (cm) for 2017 in comparison with historical 32 years (1985-2017).

2.2. Methodological procedures

The materials used were two Sentinel-1 SAR data images with a spatial resolution of 10 m, descending orbit, level 1-GRD (Ground Range Detected), interferometric wide swath (IW), and VV and VH polarizations, acquired on January 1, 2017 (pre-flood event) and March 26, 2017 (post-flood event) and accessed from the GEE image catalog. These images were chosen because they were from the year of a major flood in 2017 in a normal month, and on a date closer to the maximum river level.

The Sentinel-1 images were processed at the Geodesastres Laboratory located at the Federal Rural University of Amazonia (UFRA) using the Google Earth Engine (GEE) cloud computing platform and the free QGIS software (version 3.28), in order to detect changes between the two dates. The images first went through a pre-processing stage, to improve their radiometric and geometric quality and to avoid bias in their comparison. The pre-processing steps were updating the orbital metadata, removing low-intensity noise and invalid data at the edges of the scenes, reducing thermal noise, radiometric calibration, converting the backscatter coefficient to decibels and orthorectification. Speckle filtering (Lee filter) was then applied to smooth out the speckle noise in order to improve the contrast between flooded and non-flooded areas.

Subsequently, changes were detected between the pre- and post-event image for the two polarizations. The backscatter response for VH polarization has higher returns in areas of volume scattering, whereas VV polarization has higher returns in areas of specular scattering (Islam; Meng, 2022; Inglada; Mercier, 2007). The change detection was conducted by calculating the ratio between the post- and pre-event images for each polarization, resulting in a new image called the difference image in which the high values (light pixels) indicate large changes, whereas low values (dark pixels) indicate smaller changes. The ratio between images was chosen instead of subtraction because the images are in decibels, which provides more accurate and well-distributed results (Podest et al., 2019). Later, in the images from each of the two dates, the extent of the flooding was determined by thresholding the radiometric values to distinguish between flooded and non-flooded areas. The thresholds were chosen by the trial-and-error method based on visual control in a few areas with a clear radiometric contrast between flooded and non-flooded areas.

This methodology has been used in previous studies to map the extent of flooding (Li et al., 2018; Liang; Liu, 2020; Vekaria et al., 2022; 2022; Hamidi et al., 2023). The connectivity of flood pixels was evaluated to eliminate those connected to eight or more neighbors to reduce the noise of the potential flood extent generated (Benoudtjit; Guida, 2019; Ali et al. 2018).

To refine the flood layer generated, the Joint Research Centre (JRC) Global Surface Water Dataset layer was used, with a resolution of 30 m, to mask areas covered by water for more than 10 months a year. This procedure ensures to avoid misclassifying permanent water bodies such as river channel water as flood. Additionally, areas with a slope of more than 5% were removed using the World Wildlife Fund (WWF) HydroSHEDS elevation model, based on Shuttle Radar Topography Mission (SRTM) data, with a final product of 30 m spatial resolution.

The flood extent area of the generated raster layer was then calculated by determining the area in of each pixel and summing up all the pixels to obtain the total flood area. Finally, the buildings determined from OpenStreetMaps collaborative mapping were inserted to visualize the affected urban area. For a more detail analysis of the properties affected by flooding, the generated flood extent masks were integrated into the QGIS software. Overlaying the flood extent layers with urban buildings enabled the identification of the affected buildings for both polarizations, resulting in a final map with the relevant information (Figure 4).

Figure 4
Flowchart of the process’s steps, first in Google Earth Engine, then in QGIS. The speckle reduction noise process for VV polarization displays A - Image with speckle noise, B - Image with speckle noise filtering, C- Flood extension with speckle noise, and D - Extension with speckle filtering; for the VH polarization in detail E - Image with speckle noise, F - Image with speckle noise filtering, G- Flood extension with speckle noise, H - Extension with speckle noise filtering.

3. Results and Discussion

The results present total flooding area in the Alenquer urban area, resulting in material damage and road interruption. The results for the flood mask indicated that in the SAR images, water bodies, such as lakes, rivers, wetlands, stand out owing to the high contrast between soil and water. Normally, water bodies appear with darker tones in radar images, contrasting significantly with other elements. The values obtained over flooded areas with VV polarization range between-15.0 and -22.3 dB in the pre-event image, and between -16.6 and -24 dB in the post-event image (Figure 5). The difference in water backscatter between the two periods is too high to be explained by a residual miscalibration, and the higher values observed in the pre-event image could be due to rain or wind over the water.

Figure 5
Pre-event (A) and post-event (B) images and their respective histograms in VV polarization.

The best thresholds established to identify the limit between flooded and non-flooded area in the difference image were 1.40 and 1.25 for VV and VH polarizations, respectively. The fact that the thresholds depend on subjective visual inspection limits the use of the values chosen to this particular experiment, making it impossible to apply the same thresholds to images from other sensors or other situations. However, uncertainty about these values would have little impact if they were applied to another experiment, as the histograms show that the thresholds are located within a radiometric range corresponding to few pixels, which would not significantly change the extent of the flooded areas.

The flooding extents were estimated at 1.82, and 2.21 km² for VV and VH polarizations, respectively. The analysis revealed that the extent obtained by VV polarization included 21 buildings and seven affected roads, whereas that obtained by VH polarization included 12 buildings and seven roads (Figure 6). The difference in flood extent obtained with VV and VH polarisation is due to the fact that the backscatter coefficient is much lower in VH, which can lead to ground or vegetation pixels being classified as water. This overestimates the flooded area, as shown by our results.

The affected roads identified based on the flood extent with VV polarization were predominantly located to the southwest, north, and occasionally northeast of the urban center. The affected roads identified based on the flood extent with VH polarization were located on the eastern and southwestern edges of the city. A greater flood extent was obtained with VH polarization than with VV polarization. This is ascribed to slightly higher radiometric quality of VV polarization compared to VH for flood inundation detection (Twele et al., 2016; Clement; Kilsby; Moore, 2018; Cao et al., 2019).

Figure 6
Final map showing the affected areas using VV (top) (in detail points 1-2-3-4) and VH (bottom) (in detail points 1-2-3) polarizations in the urban center of Alenquer in 2017.

We observed that the buildings impacted by the two flooding extensions were mainly located on the southwestern edges of the urban center with an altimetry of less than 5 m (Figure 7). Altitude plays a significant role in flood susceptibility, mainly owing to the accumulation of water in lower regions (Magalhães et al., 2011). Low altimetry values associated with a dense drainage network have the potential to influence flooding processes during periods of high rainfall, resulting in an increase in flooding events, as observed in the Alenquer region. In particular, regions characterized by low dissected plateaus and river terraces, which are geomorphological characteristics of Alenquer, are intrinsically susceptible to flooding (João et al., 2013). As highlighted by Assine et al. (2016) and Sarker and Rashid (2013), in addition to rainfall, geomorphological and geological factors also influence the magnitude, duration, and frequency of flooding. Similar processes have been observed in cities located in urban areas with an extensive hydrographic network and present within flood plains, as is the case of the city of Tarauacá in the state of Acre (Andrade; Brabo, 2022). Additionally, Silva Junior (2010) obtained similar results estimating a high susceptible area to flood are in 8.4% (63 ha.) of the urban center in Alenquer with an altimetry of up to 5 m, with records in the months from January to June (Silva Junior, 2010).

Figure 7
Aspect of geomorphological difference from lower topography in fluvial plains (vegetated area) and low plateaus occupied in the urban center of Alenquer. Photo: Milena Andrade.

Moreover, contributing to the qualitative validation of the data generated in this study, the risk sectorization report conducted by the Geological Brazilian Service (SGB/CPRM, 2019) classified one risk area as possessing a very high degree of flooding, affecting approximately 580 homes. The total buildings identified in this paper findings using SAR data images by both VV and VH polarization are included in this previous risk area mapped. These areas are located on the right bank of the Surubiú River on the edge of the city, along Avenida Getúlio Vargas. This sector corresponds to the river’s flood plain, densely occupied by houses and buildings, including historic heritage buildings. As remarked by Maistrou et al., (2023) it is urgent to take action due climate change and increase of precipitation and floods in historic settlements. However, the lack of ground-truth data for the 2017 event is a limitation to provide a direct quantitative assessment of the map’s accuracy. Additional limitations to SAR data images at the urban scale are vegetated narrow water bodies, signal interference, and the difficulty in distinguishing between water and water-like surfaces, which point to emerging exploratory studies in this field using Google Earth Engine (Islam and Meng, 2022; Shen et al., 2019).

The SAR data can provide useful flood information to disaster management actions in a context of increasing flood frequency in the Amazon region (Espinoza et al., 2022; Pralle, 2019). Specially in disaster risk geographical contexts that indicates a combination of other hazard registers such as drought (Souza et al., 2024). Previous research from Magalhães et. al. (2022) used multi-temporal SAR images from Sentinel-1 to analyze flooding in the central Amazon region with good results, which corroborates the potential of these satellite images. By applying linear regression models to VH and VV polarizations, they observed a good correlation between the areas measured in both polarizations, with no significant differences between the samples. This suggests that both polarizations are viable for delimiting flooded areas in the region. Analyses using Sentinel-1 SAR images for flood mapping in this region have proved to be effective, corroborating other studies conducted in spatially similar regions and from a detailed geomorphological perspective (Cortes; Szlafsztein; Luvizotto, 2020; Silva; Pestana, 2021). As final remarks, it is important to report some issues that can improve results, such as being aware of different scales between SAR products and OSM data, and attention in differentiating between shaded and water regions in radar images (Tavus et al., 2018; Vanama; Rao, 2019). It is suggested to test multi-sensor information to improve the detection accuracy of areas affected by flooding, despite the limitation of optical images during floods.

4. Final Considerations

In this study, we verified the relevance of Sentinel-1 SAR image processing to map urban flooding. The effectiveness demonstrated in identifying the extent of flooding in urban areas, combined with the ability to understand complex spatial-temporal dynamics, highlights the potential of these techniques for monitoring and managing natural disasters. Additionally, the practical implications of these results highlight the importance of ensuring the availability of radar data in the Amazon region, considering its dense cloud cover and high humidity. The ability of these data to contribute to flood monitoring and warning is crucial for mitigating damage and protecting affected populations. Therefore, we recommend that research in this field should continue, exploring different methodologies and data sources, such as alternative orbital sensors and aerial survey technologies enabling a better understanding of flooding processes and facilitating informed decision-making for natural disaster management in Amazon urban areas.

ACKNOWLEDGEMENT

The authors acknowledge the Graduate Program in Risk and Natural Disaster Management in the Amazon and the Geodesastres Research and Extension Group for their institutional support and encouragement of this research.

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Souza, E.B. et al. (2024) ‘Intensification of natural disasters in the State of Pará and the triggering mechanisms across the Eastern Amazon’, Atmosphere, 16(1). https://doi.org/10.3390/atmos16010007
» https://doi.org/https://doi.org/10.3390/atmos16010007
Sudam (2008) Atlas climatológico da Amazônia brasileira Belém: SUDAM/PHCA, 125 p.
Szlafsztein, C. & Silva Junior, O. (2013) ‘A importância dos conceitos de ameaça, vulnerabilidade e risco em planos diretores municipais: estudo de caso no município de Alenquer (Pará)’, Revista GeoAmazônia, 1(1), pp. 55-71.
Tavus, B. et al. (2018) ‘Considerations on the use of Sentinel-1 data in flood mapping in urban areas: Ankara (Turkey) 2018 floods’, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, pp. 575-581.
Twele, A. et al. (2016) ‘Sentinel-1-based flood mapping: A fully automated processing chain’, International Journal of Remote Sensing, 37(13), pp. 2990-3004.
United Nations (2015) World Urbanization Prospects: The 2014 Revision - Highlights New York: Department of Economic and Social Affairs, Population Division.
United Nations (2017) Natural Hazards Definitions United Nations.
Vanama, V. & Rao, Y. (2019) ‘Change detection-based flood mapping of 2015 flood event of Chennai city using Sentinel-1 SAR images’, in IGARSS 2019 - IEEE International Geoscience and Remote Sensing Symposium IEEE, pp. 9729-9732.
Vasquez, M.L. & Rosa-Costa, L. (orgs.) (2008) Geologia e Recursos Minerais do Estado do Pará: texto explicativo do mapa geológico e de recursos minerais do Estado do Pará Belém: CPRM.
Vassileva, M. et al. (2015) ‘Unsupervised flood extent detection from SAR imagery applying shadow filtering from SAR simulated image’, in IGARSS 2015 - IEEE International Geoscience and Remote Sensing Symposium Milan: IEEE, pp. 2707-2710.
Vekaria, D. et al. (2022) ‘A change detection approach to flood inundation mapping using multi-temporal Sentinel-1 SAR images, the Brahmaputra River, Assam (India): 2015-2020’, Journal of Earth System Science, 132(1), p. 3.
Viana, V. (2016) Gestão do risco de desastres no Brasil: leitura das estratégias locais de redução dos riscos de inundação em Nova Friburgo, RJ Tese (Doutorado em Meio Ambiente) - Universidade do Estado do Rio de Janeiro, Rio de Janeiro.
Yang, Q. et al. (2021) ‘A high-resolution flood inundation archive (2016-present) from Sentinel-1 SAR imagery over CONUS’, Bulletin of the American Meteorological Society, pp. 1-40.
Young, A. & Papini, J. (2020) ‘How can scenarios on flood disaster risk support urban response? A case study in Campinas Metropolitan Area (São Paulo, Brazil)’, Sustainable Cities and Society, 61, p. 102253.
Zhang, H. et al. (2021) ‘An urban flooding index for unsupervised inundated urban area detection using Sentinel-1 polarimetric SAR images’, Remote Sensing , 13(22), p. 4511.

DATA AVAILABILITY
The entire dataset supporting the results of this study has been made available in https://www.ppggrd.propesp.ufpa.br/index.php/br/impacto/acoes-dirigidas-ao-publico-externo and can be accessed at the technical product Alcântara & Andrade (2023) in "ROTEIRO METODOLÓGICO PARA MAPEAMENTO DE EXTENSÃO DE INUNDAÇÃO EM ALENQUER (PA) A PARTIR DE SENSORES ATIVOS".

Data availability

The entire dataset supporting the results of this study has been made available in https://www.ppggrd.propesp.ufpa.br/index.php/br/impacto/acoes-dirigidas-ao-publico-externo and can be accessed at the technical product Alcântara & Andrade (2023) in "ROTEIRO METODOLÓGICO PARA MAPEAMENTO DE EXTENSÃO DE INUNDAÇÃO EM ALENQUER (PA) A PARTIR DE SENSORES ATIVOS".

Publication Dates

Publication in this collection
28 Nov 2025
Date of issue
2025

History

Received
11 Feb 2025
Accepted
14 Oct 2025

This is an open-access article distributed under the terms of the Creative Commons Attribution License

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