Brazil GIS data
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aircraft imagery by T, acquired 2025-08-30 to 2025-08-30, 2.9 cm per pixel, published on OpenAerialMap.
aircraft imagery by T, acquired 2025-08-30 to 2025-08-30, 2.9 cm per pixel, published on OpenAerialMap.
aircraft imagery by T, acquired 2025-08-30 to 2025-08-31, 2.8 cm per pixel, published on OpenAerialMap.
uav imagery by Utaiguara da Nóibrega Borges, acquired 2025-09-16 to 2025-09-16, 11.7 cm per pixel, published on OpenAerialMap.
uav imagery by Utaiguara da Nóbrega Borges, acquired 2025-09-16 to 2025-09-16, 1.2 cm per pixel, published on OpenAerialMap.
uav imagery by Utaiguara da Nóbrega Borges, acquired 2025-09-16 to 2025-09-16, 5.8 cm per pixel, published on OpenAerialMap.
uav imagery by Utaiguara da Nóibrega Borges, acquired 2025-09-16 to 2025-09-16, 14.9 cm per pixel, published on OpenAerialMap.
uav imagery by Utaiguara da Nóbrega Borges, acquired 2025-09-16 to 2025-09-16, 0.8 cm per pixel, published on OpenAerialMap.
uav imagery by Utaiguara da Nóbrega Borges, acquired 2025-09-16 to 2025-09-16, 3.2 cm per pixel, published on OpenAerialMap.
uav imagery by Utaiguara da Nóibrega Borges, acquired 2025-09-16 to 2025-09-16, 13.3 cm per pixel, published on OpenAerialMap.
uav imagery by Utaiguara da Nóibrega Borges, acquired 2025-09-16 to 2025-09-16, 10.8 cm per pixel, published on OpenAerialMap.
uav imagery by Utaiguara da Nóbrega Borges, acquired 2025-09-16 to 2025-09-16, 3.7 cm per pixel, published on OpenAerialMap.
uav imagery by Prefeitura, acquired 2025-09-17 to 2025-09-17, 8.9 cm per pixel, published on OpenAerialMap.
aircraft imagery by T, acquired 2025-09-19 to 2025-09-19, 1.2 cm per pixel, published on OpenAerialMap.
uav imagery by T, acquired 2025-10-18 to 2025-10-18, 2.2 cm per pixel, published on OpenAerialMap.
uav imagery by Mauro Alixandrini, acquired 2025-11-21 to 2025-11-21, 3.3 cm per pixel, published on OpenAerialMap.
uav imagery by Mauro Alixandrini, acquired 2025-11-22 to 2025-11-22, 6.8 cm per pixel, published on OpenAerialMap.
Dates of Images: Pre-Event: 04/20/2024 (pre-flood) Post-Event: 05/06/2024, 05/07/2024, 05/09/2024, 05/15/2024, 05/21/2024 Time Zone: GMT (local crossing time of ~1:00 pm) Summary: This product is the apparent visible wavelength (AVW) of each pixel in a scene, approximating the overall color of the surface. It is calculated by taking the harmonic mean of surface reflectances from 350 nm to 895 nm, and can provide a sense of what has changed in the landscape by comparing several days against each other, as the change in apparent color of a surface often occurs under drastic circumstances. Dates covered include one pre-flood day (04/20/2024) and 5 flood days ending on 05/21/2024 at ~1:00 local time. AVW for each day is delivered, as is a % change from the pre-flood day to each of the 5 flood days afterward, showing the magnitude of color change in the area. Depending on the direction of change, one can infer the type of surface change from AVW. Typically, this metric is calculated from Rrs used for ocean color analyses (see https://doi.org/10.1016/j.rse.2020.111900), but can be made from surface reflectances and used over land as well. Suggested Use: Given that AVW is the apparent color of pixel, it can be used to track changes in land and water surfaces. The AVW value indicates average color reflected from a surface, e.g., healthy vegetation will have AVW in the NIR, water in the blues and greens, and turbid water in the reds. During flood days, the area around the Jacui River gets significantly redder (from previous NIR values), which can indicate turbid water supressing the typical vegetated signal in that area. Also, the Lagoa dos Patos gets significantly redder (from the greens), which indicates different conditions in the water (i.e., injection of sediment, which has average colors in the red). Images are typically displayed using the "Spectral" color map, with bounds from 400 - 700 (see attachment). For the percent change images, negative changes signify shorter average wavelengths (can be thought of as blue-shifting) and positive % changes are elongated average wavelengths (i.e., red-shifts). In other words, these shifting wavelengths tell us that some environmental change has caused the average color of a pixel to change. Users should note that pixels on the edges of masked clouds may see an elevated magnitude of percent change relative to neighboring non-cloud-edge pixels due to the adjacency effect”. Satellite/Sensor/Resolution: Satellite: PACE, sensor: OCI, temporal resolution: daily, spatial resolution: gridded to 0.015 degrees from a Level 2 product in instrument swath (varying pixel size, 1.2 km at nadir). Credits: Credit: NASA PACE Team, POC: Skye Caplan, Link to data: https://search.earthdata.nasa.gov/search/granules?p=C3620139902-OB_CLOUD&pg[0][v]=f&pg[0][gsk]=-start_date&q=oci%20sfrefl Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_pace_avw/ImageServer/WMSServer
uav imagery by Vitor Gotardo, acquired 2025-12-24 to 2025-12-24, 2.7 cm per pixel, published on OpenAerialMap.
Dates of Images: Post-Event: May 8, 2024; May 15, 2024 Pre-Event: April 6, 2024; April 21, 2024 Summary: True Color: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. Color Infrared: The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted from the fires. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. Suggested Use: True Color: provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. Color Infrared: depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. Satellite/Sensor: Landsat 9 Operational Land Imager (OLI) Resolution: 30 meters Credits: NASA/MSFC, USGS Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_BRAZIL/ex2602_landsat/ImageServer/WMSServer
uav imagery by Iron Drones, acquired 2026-01-02 to 2026-01-03, 2.0 cm per pixel, published on OpenAerialMap.
uav imagery by Bruno , acquired 2026-01-30 to 2026-01-30, 3.0 cm per pixel, published on OpenAerialMap.
aircraft imagery by T, acquired 2025-11-02 to 2025-11-02, 3.0 cm per pixel, published on OpenAerialMap.
satellite imagery by Filipe Manso Valença, acquired 2025-08-20 to 2025-08-20, 4.9 cm per pixel, published on OpenAerialMap.
satellite imagery by Kerim, acquired 2026-02-06 to 2026-02-07, 20.0 cm per pixel, published on OpenAerialMap.
uav imagery by Esdras de Lima Andrade, acquired 2025-08-15 to 2025-08-15, 5.3 cm per pixel, published on OpenAerialMap.
uav imagery by Mauro Alixandrini, acquired 2025-11-21 to 2025-11-21, 3.7 cm per pixel, published on OpenAerialMap.
aircraft imagery by GEONORTH, acquired 2026-06-05 to 2026-06-05, 5.5 cm per pixel, published on OpenAerialMap.
Date of Images: During Event: 5/6/2024 Pre-Event: 4/21/2024 Summary: The Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at NASA's Jet Propulsion Laboratory and California Institute of Technology derived the surface water extent flood maps using the OPERA Dynamic Surface Water Extent (DSWx) from NASA Harmonized Landsat Sentinel-2 (HLS) products. The results posted here are preliminary and unvalidated results, primarily intended to aid the field response and people who wanted to have a rough first look at the inundation extent. ARIA/OPERA flood map derived from DSWx-HLS The ARIA/OPERA flood map is derived from two OPERA DSWx-HLS images taken on April 21, 2024 and May 06, 2024. These maps depict areas of new water detection that is interpreted as flood. The flood map was created by reclassifying the DSWx-HLS data into two new classes (1) water and (2) not water then taking the difference between the two images. The new water class includes DSWx-HLS classes for open water, partial surface water, and HLS snow/ice. We note the HLS snow/ice mask often misclassified sediment rich water as snow/ice. This reclassification was necessary to capture flood extent. The new not water class includes DSWx-HLS classes not water and HLS cloud/cloud shadow. OPERA DSWx-HLS OPERA DSWx-HLS data was used to identify surface water using the B01_WTR layer. Two images were examined 1) April 21, 2024 and 2) May 6, 2024. Each image consists of multiple MGRS tiles that were merged together for a composite image saved as a GeoTIFF file. OPERA DSWx-HLS data availability The post-processed products are available to download at https://aria-share.jpl.nasa.gov/202405-RioGrandeSul_Brazil-floods/. The OPERA DSWx-HLS products have been in production since April 2023, are freely distributed to the public via NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC), and can be downloaded through NASA's Earthdata search. For more information about the OPERA project and other products, visit https://www.jpl.nasa.gov/go/opera. For more information about the Dynamic Surface Water eXtent product suite, please refer to the DSWx Product page: https://www.jpl.nasa.gov/go/opera/products/dswx-product-suite For more information about the Caltech-JPL ARIA project, visit https://aria.jpl.nasa.gov Suggested Use: The OPERA DSWx-HLS Water product classifies the Harmonized Landsat Sentinel-2 (HLS) input imagery into "not water", "open surface water", and “partial surface water”. The "HLS cloud/cloud shadow" and "HLS snow/ice" layers are direct inputs from the HLS FMask. Areas with "open water" are dark blue and "partial surface water" are light blue in the OPERA WTR layer. Areas with clouds or cloud shadows are light gray. An area identified as cloud, cloud shadow, or adjacent to cloud/cloud shadow according to input HLS quality assurance (QA) data. Areas with no water detected are white. An area with valid data that is not water, snow/ice, cloud/cloud shadow, or ocean masked. This layer is meant to provide users with a quick view for water/no-water. Invalid data classes (cloud/cloud shadow along with adjacent to cloud/cloud shadow) are also provided to indicate areas in which the classification does not provide water/no-water classification. Note: Sediment rich water is sometimes misclassified as snow/ice by the HLS QA mask. For more information about how the OPERA DSWx-HLS Water product classifies data: https://d2pn8kiwq2w21t.cloudfront.net/documents/ProductSpec_DSWX_URS309746.pdf Satellite/Sensor: Harmonized Landsat Sentinel-2 (HLS) MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: 30 meters Credits: NASA JPL-Caltech ARIA Team, NASA, NASA Disasters Program Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_BRAZIL/SENTINEL2/ImageServer/WMSServer Data Download: https://aria-share.jpl.nasa.gov/202405-RioGrandeSul_Brazil-floods/DSWx-HLS/
geoBoundaries ADM0 boundaries for Brazil: 1 units, representing 2020. Source: Instituto Brasileiro de Geografia e Estatística (IBGE).
The Cooperative Open Online Landslide Repository (COOLR) contains citizen science data from Landslide Reporter Catalog (LRC), data from NASA’s Global Landslide Catalog (GLC), NASA manually or automatically digitized landslides, and other inventories contributed by the research community. These are sorted into two main layers, "coolr_reports" and "coolr_events". The layers are further differentiated into a point or polygon layer, named accordingly. Within these layers, the user can further query out the manual or automated landslide inventories, events of a certain date, event triggers, and more. Each feature has a “title” in the attribute table with the digitization method, location, and event date (written as YYYY-MM-DD). The citation for each event inventory is located within the attribute table field “citation”. See the methods section for further detail about manual vs automatic landslide detection. The citation for each event inventory is located within the attribute table field “citation”. If you utilize this inventory, please reference each citation of the event(s) used. For example, if you use the entire inventory layer, please reference all the event citations contained within the layer. If you use just the events past a certain date, or another subset of the layer, please query those citations out and reference those. In addition, a CSV containing all citations can be found on the download page: https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521. Methods We use 'landslide' as a general term to represent all event types. The catalog differentiates between landslide types when the information is available in the ls_type field. Manually digitized landslides (method = Manual): These are hand-digitized landslide points or polygons, created by NASA scientists or other institutions. They are created on a case-by-case basis, depending on time and resources, or if there is an event where a partner requests it. The ability to hand-digitize depends on the availability of high-resolution cloud-free imagery, ideally before and after the event. Depending on the event size, location, and the satellite overpass, imagery from multiple dates may be used. For points, the scientist tries to identify where the initiation zone of each landslide is using an elevation layer. For polygons, the initiation zone is not differentiated. Automatically digitized landslides (method = Automatic): The open-source Semi-Automatic Landslide Detection (SaLaD) system was created to rapidly map landslides. SALaD uses object-based image analysis and machine learning to detect landslides from optical imagery. To successfully detect landslides, cloud-free imagery from before and after an event is needed, in addition to training data from that region or a region nearby. The raw polygon output of this system can be useful for situational awareness, but it will require manual corrections to weed out false positives for other use cases. Once that is done, initiation points can be created using a DEM. For Further Reference Landslides.nasa.gov Amatya, P., Kirschbaum, D., Stanley, T., and Tanyas, H. (2021). Landslide mapping using object-based image analysis and open source tools. Engineering Geology. 282. doi: 10.1016/j.enggeo.2021.106000. Amatya, P., Kirschbaum, D., and Stanley, T. (2021). Rainfall-induced landslide inventories for Lower Mekong based on Planet imagery and a semi-automatic mapping method. Geoscience Data Journal. doi: 10.1002/gdj3.145. Juang CS, Stanley TA, and Kirschbaum DB (2019) Using citizen science to expand the global map of landslides: Introducing the Cooperative Open Online Landslide Repository (COOLR). PLOS ONE. 14(7): e0218657. doi: 1371/journal.pone.0218657. Kirschbaum, D.B., Stanley, T., & Zhou, Y. (2015). Spatial and temporal analysis of a global landslide catalog. Geomorphology, 249, 4-15. doi:10.1016/j.geomorph.2015.03.016 Kirschbaum, D.B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52, 561-575. doi:10.1007/s11069-009-9401-4
Dates of Images: Pre-Event: 04/20/2024 (pre-flood) Post-Event: 05/06/2024, 05/07/2024, 05/09/2024, 05/15/2024, 05/21/2024 Time Zone: GMT (local crossing time of ~1:00 pm) Summary: These are NDWI measurements from PACE OCI using surface reflectances from OCI's SFREFL suite of the 2024 flooding in Brazil. NDWI is a normalized band ratio using the green (555 nm) and NIR (860 nm) for water content detection (green - nir / green + nir). The images show how the flood developed from a pre-flood day (04/20/2024) to the proceeding flooded days (through 05/21/2024) at ~1:00 local time (the overpass of the instrument). The changing extent of the floodwaters, along with what looks to be the amount of suspended sediment, is especially clear against the background of vegetation. Users should take into account that these floodwaters were sediment-laden, and thus some of that signal may depress the water signal. However, the change in NDWI from the pre-flood day can be used to identify flood extent. Suggested Use: NDWI is a normalized band ratio measuring the presence of water. Higher positive values water surfaces, low positive values to ~0 indicates flooding or high humidity, and negative values mean non-aqueous or drought conditions. In these images, it is clear that the presence of water increases greatly around the Jacui River during flood days compared to the pre-flood day (04/20/2024), showing the transition of the area around the Jacui River from non-flooded conditions (low NDWI values) to flooded (higher NDWI). Clouds have been screened from these products. Satellite/Sensor/Resolution: Satellite: PACE, sensor: OCI Temporal resolution: daily Spatial resolution: gridded to 0.015 degrees from a Level 2 product in instrument swath (varying pixel size, 1.2 km at nadir). Credits: NASA PACE Team, POC: Skye Caplan, Link to data: https://search.earthdata.nasa.gov/search/granules?p=C3620139902-OB_CLOUD&pg[0][v]=f&pg[0][gsk]=-start_date&q=oci%20sfrefl Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_pace_ndwi/ImageServer/WMSServer
Dates of Images: Post-Event: 5/6/2018; 5/15/2024 Pre-Event: 4/8/2018; 4/21/2024 Date of Next Image: Unknown Summary: NDVI: The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. Suggested Use: NDVI: Dark green colors are areas with a lot of green leaf growth which indicates the presence of chlorophyll. Chlorophyll reflects more infrared light and less visible light. Areas with some green leaf growth are in light greens, and areas with little to no vegetation growth are even lighter greens. Satellite/Sensor: Landsat 9 Operational Land Imager (OLI) Resolution: 30 meters Credits: NASA/MSFC, USGS Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_landsat_ndvi/ImageServer/WMSServer
Dates of Images: 5/5/2024, 5/6/2025, 5/15/2025 Background: MODIS Global Flood Product, MCDWD, showing flooding observed on 5/5, 5/6, and 5/15/2024 in Rio Grande do Sol, Brazil. Product is based on optical imagery from the MODIS sensor, on both Terra and Aqua spacecraft. Observation times are approximately 10:30 am and 1:30 pm local time; flood extents at other times may not be observed. The 1-day composite product is delivered here; this uses observations from only the given date. For this event, 5/5/2024 was the first clear day after the weather system passed. By 5/15, substantial flood water was still visible. Caveats: Flooding is determined by comparing observed water extent to a reference water map, which may be inaccurate in fine details; some "flood" may be expected surface water, and some "surface water" may actually be flood, but both indicate areas where water was detected. The 1-day product can be particularly susceptible to false-positives from cloud shadows, which often can look like water in the source data. This product looks reasonable with few such false-positives, but users are advised to review the optical imagery (Terra and Aqua) in Worldview to confirm (https://go.nasa.gov/globalflood). Worldview also hosts the 2- and 3-day composite products, but not the 1-day delivered here. At 250 m resolution, this product will also not reliably capture flood extents smaller than 250 m. As this is optical imagery, flooding under clouds will not be detected. Suggested Use: Red in the displayed image indicates flood; blue indicates expected surface water. Satellite/Sensor/Resolution: Terra and Aqua satellites, MODIS sensor, daily 250m resolution data (~10:30 am local time observation for Terra, ~1:30 pm for Aqua) Credits: NRT MODIS/Aqua+Terra Global Flood Product MCDWD_L3_NRT distributed from NASA LANCE. Available online at: https://www.earthdata.nasa.gov/global-flood-product. doi:10.5067/MODIS/MCDWD_L3_NRT.061 Project website includes FAQs and contacts for support: https://www.earthdata.nasa.gov/global-flood-product Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_modis_mcdwd/ImageServer/WMSServer
aircraft imagery by Victor Gomes, acquired 2025-04-01 to 2025-04-30, 5.0 cm per pixel, published on OpenAerialMap.