Brazil environment data

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/

Date of Images: 5/6/2024 Date of Next Image: Unknown Summary: The floodwater depth raster produced for the 06th May 2024 for long-term floods in southern Brazil was generated using the Flood Water Depth Estimation Tool (Cohen et al, 2018, 2019; Peter et al, 2020). The Floodwater Depth Estimation Tool (FwDET) is a solution for producing timely floodwater depth data during flood activations that require emergency response and post-flood assessment. FwDET is based solely on a flood extent layer and a digital elevation model (DEM). The DEM used here was the NASA SRTM data. The flood extent layer was derived from the OPERA Dynamic Surface Water eXtent (DSWx) from the Harmonized Landsat Sentinel-2 (HLS) product. The DEM used was the NASA Shuttle Radar Topography Mission (SRTM) data. The computation involves identifying elevations at the boundaries of the flood extent layer and using those elevations to assign floodwater surface elevation to each cell within the flooded domain by identifying its nearest boundary cell. The modeled flood surface elevation is then subtracted from the original DEM to retrieve depth. 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 flood inundation depth. The flood depth map may contain errors due to inaccurate elevations in the NASA SRTM DEM or the water surface estimation. Data Sources: OPERA Dynamic Surface Water eXtent from Harmonized Landsat Sentinel-2 (DSWx-HLS) - based flood inundation extent raster generated by the ARIA/OPERA group at NASA JPL.  NASA SRTM (Shuttle Radar Topography Mission) Digital Elevation 30 m (openly available on Google Earth Engine) Suggested Use: The darkest shades of blue indicate where the product is estimating the largest floodwater depth, while lighter shades indicate shallower flooding Satellite/Sensor: Harmonized Landsat Sentinel-2 (HLS) MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites NASA SRTM (Shuttle Radar Topography Mission) Digital Elevation 30 m (openly available on Google Earth Engine) Resolution: 30 meters Credits: Dinuke Munasinghe - NASA JPL Water and Ecosystems Team, NASA JPL ARIA/OPERA Team Product POCs: Dinuke Munasinghe (dinuke.nanayakkara.munasinghe@jpl.nasa.gov) FwDET Algorithm Documentation: Cohen, S., Brakenridge, G.R., Kettner, A., Bates, B., Nelson, J., McDonald, R., Huang, Y, Munasinghe, D., and J. Zhang (2018). Estimating Floodwater Depths from Flood Inundation Maps and Topography. Journal of the American Water Resources Association, 54(4), 847-858. DOI: https://doi.org/10.1111/1752-1688.12609 Cohen, S., Raney, A., Munasinghe D., Loftis, D., Molthan, A., Bell, J., Rogers, L., Galantowicz, J., Brakenridge, G.R., Kettner, A., Huang, Y., and Y. Tsang (2019). The Floodwater Depth Estimation Tool (FwDET v2.0) for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19(9), 2053-2065. DOI: https://doi.org/10.5194/nhess-19-2053-2019 Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.E., 2007, The shuttle radar topography mission: Reviews of Geophysics, v. 45, no. 2, RG2004, at https://doi.org/10.1029/2005RG000183. Peter, B., Cohen, S., Lucey, R., Munasinghe, D., Raney, A., and G. Brakenridge (2020). "Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for Rapid and Large Scale Flood Analysis". IEEE Geoscience and Remote Sensing Letters. DOI: https://doi.org/10.1109/lgrs.2020.3031190 OPERA. 2023. OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 CalVal Database (Version 1). Ver. 1.0. PO.DAAC, CA, USA. Dataset accessed [2024-05-09] at https://doi.org/10.5067/OPDSW-PCVV1 Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_BRAZIL/2407_opera_fwdet_hls/ImageServer/WMSServer

Date of Image: Pre-Event: March 2024 Post-Event: 5/7/2024, 5/8/2024, 5/9/2024, 5/10/2025, 5/11/2024, 5/12/2024, 5/15/2024 Date of Next Image: Unknown Summary: These Black Marble High-Definition (BMHD) images were created by the NASA Black Marble Science team, with directed funding the NASA-Google Partnership program. The images map the impact of the major flooding in Rio Grande do Sul on electric grids in Porto Alegre. The baseline image (before the floods) was from May 2024, a cloud-free, moon-free composite, and the “after" image was from May 7, 2024. There is a layer to display where clouds are present in the "after" images. This comparison between the two images is meant as a visual assessment of outage impacts from the flooding to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NOTE: Black Marble HD images are downscaled from NASA’s Black Marble nighttime lights product (VNP46), and as such are a “modelled” or “best guess” estimate of how lights are distributed at a 30m resolution. These images should be used for visualization purposes, not for quantitative analysis. The image is in inferno color scale. Yellow represents presence of more light; dark blue less lights. Satellite/Sensor: The primary data source, NASA’s Black Marble nighttime lights product suite (VNP46), utilized to generate this product is derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP) along with high resolution base layers - Landsat derived normalized index products (NDVI and NDWI) and OpenStreetMap (OSM) derived road layer Resolution: Scaled resolution of 30 meters Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Esri REST Endpoint: See URL section on right side of page. WMS Endpoint: https://maps.disasters.nasa.gov/ags03/services/brasil_flood_202405/blackmarble_hd/MapServer/WMSServe

Buildings Existing Before the May 2024 Rio Grande do Sul Floods. This dataset contains buildings and destroyed buildings mapped as part of the HOTOSM Rio Grande do Sul Floods Activation mapping project.

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

Dates of Images: 5/6/2024 Summary: The NASA GSFC landslides team produced this landslide polygon map. The landslide map was produced automatically from Planet imagery using the prototype Global Landslide Detection for Rapid response (GLiDEr) system, currently under development as a joint effort between the NASA GSFC landslides team and Data Science group. Pre-event imagery = mosaic of images from 04/20/2024 and 04/21/2024 Post-event imagery = mosaic of images from 05/05/2024 and 05/06/2024 This map has undergone manual false positive removal, but we have not corrected erroneous landslide areas, added missed landslides and resolved amalgamation. Suggested Use: This map can be used to identify location and size of landslides. For landslide mapping extent, refer to the mapping_AOI layer. Credits: NASA GSFC landslides team, NASA GSFC Data Science group, Planet Labs Esri REST Endpoint: See URL on the right side of the page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/EX2602_planet_landslide_vector/MapServer/WMSServer

This harmonized set of soil parameter estimates for Brazil. The 1:5M scale Soil and Terrain Database for Latin America and the Caribbean (FAO et al. 1998), provided the basis for the current study. The data set has been prepared for the project on Assessment of soil organic carbon stocks and change at national scale (GEF-SOC), which has the Brazilian Amazon as one of its four case ... study areas. The land surface of Brazil has been characterized using 299 unique SOTER units, corresponding with 839 polygons. The major soils have been described using 584 profiles, selected by national soil experts as being representative for these units. The associated soil analytical data have been derived from soil survey reports. Gaps in the measured soil profile data have been filled using a step-wise procedure which includes three main stages: (1) collating additional measured soil analytical data where available; (2) filling gaps using expert knowledge and common sense; (3) filling the remaining gaps using a scheme of taxotransfer rules. Parameter estimates are presented by soil unit for fixed depth intervals of 0.2 m to 1 m depth for: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminum saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity of saturated paste (ECe), bulk density, content of sand, silt and clay, content of coarse fragments (less than 2mm), and available water capacity (-33 to -1500 kPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and analyses of global environmental change. The current parameter estimates should be seen as best estimates based on the current selection of soil profiles and data clustering procedure. Taxotransfer rules have been flagged to provide an indication of the possible confidence in the derived data. Results are presented as summary files and can be linked to the 1:5 M scale SOTER map in a GIS, through the unique SOTER-unit code. The subset for the Amazon region, the Brazilian GEF-SOC case study area, has been clipped out of the national set using GIS. It includes 193 unique SOTER units, corresponding with 571 mapped polygons. The secondary data set is considered appropriate for studies at the national scale and regional scale (greater than1:5M). Correlation of soil analytical data, however, should be done more rigorously when more detailed scientific work is considered.

The Free Brazilian Repository for Open Soil Data – febr, www.ufsm.br/febr – is a centralized repository targeted at storing open soil data and serving it in a standardized and harmonized format. The repository infrastructure was built using open source and/or free (of cost) software, and was primarily designed for the individual management of datasets. A dataset-driven structure helps datasets authors to be properly acknowledged. Moreover, it gives the flexibility to accommodate many types of data of any soil variable. This is accomplished by storing each dataset using a collection of spreadsheets accessible through an online application. Spreadsheets are familiar to any soil scientist, the reason why it is easier to enter, manipulate and visualize soil data in febr. They also facilitate the participation of soil survey experts in the recovery and quality assessment of legacy data. Soil scientists can help in the definition of standards and data management choices through a public discussion forum, febr-forum@googlegroups.com. A comprehensive documentation is available to guide febr maintainers and data contributors. A detailed catalog gives access to the 14 477 soil observations – 42% of them from south and southeastern Brazil – from 232 datasets contained in febr. Global and dataset-specific visualization and search tools and multiple download facilities are available. The latter includes standard file formats and connections with R and QGIS through the febr package. Various products can be derived from data in febr: specialized databases, pedotransfer functions, fertilizer recommendation guides, classification systems, and detailed soil maps. By sharing data through a centralized soil data storing and sharing facility, soil scientists from different fields have the opportunity to increase collaboration and the much needed soil knowledge.

NOTE: Due to the higher resolution of this data, it may be slow to load or require the user to zoom to a smaller area of interest. Date of Images: 4/20/2024, 5/6/2024, 5/7/2024 Date of Next Image: Unknown Summary: 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. Suggested Use: The True Color RGB 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. Satellite/Sensor: PlanetScope Resolution: 3 meters Esri REST Endpoint: See URL section on right side of page