This layer includes Landsat 8 and 9 imagery rendered on-the-fly as NBR Raw for use in analysis. This layer is time enabled and includes a number of band combinations and indices rendered on demand. The imagery includes eight multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.Geographic CoverageGlobal Land Surface.Polar regions are available in polar-projected Imagery Layers: Landsat Arctic Views and Landsat Antarctic Views.Temporal CoverageThis layer is updated daily with new imagery.Working in tandem, Landsat 8 and 9 revisit each point on Earth's land surface every 8 days.Most images collected from January 2015 to present are included.Approximately 5 images for each path/row from 2013 and 2014 are also included.Product LevelThe Landsat 8 and 9 imagery in this layer is comprised of Collection 2 Level-1 data.The imagery has Top of Atmosphere (TOA) correction applied.TOA is applied using the radiometric rescaling coefficients provided the USGS.The TOA reflectance values (ranging 0 – 1 by default) are scaled using a range of 0 – 10,000.Image Selection/FilteringA number of fields are available for filtering, including Acquisition Date, Estimated Cloud Cover, and Product ID.To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images.Visual RenderingDefault rendering is NBR Raw computed as (b5 - b7) / (b5 + b7) on Apparent Reflectance.Raster Functions enable on-the-fly rendering of band combinations and calculated indices from the source imagery.The DRA version of each layer enables visualization of the full dynamic range of the images.Other pre-defined Raster Functions can be selected via the renderer drop-down or custom functions can be created.This layer is part of a larger collection of Landsat Imagery Layers that you can use to perform a variety of mapping analysis tasks.Pre-defined functions: Natural Color with DRA, Agriculture with DRA, Geology with DRA, Color Infrared with DRA, Bathymetric with DRA, Short-wave Infrared with DRA, Normalized Difference Moisture Index Colorized, NDVI Raw, NDVI Colorized, NBR Raw15 meter Landsat Imagery Layers are also available: Panchromatic and Pansharpened.Multispectral BandsThe table below lists all available multispectral OLI bands. NBR Raw consumes bands 5 and 7.BandDescriptionWavelength (µm)Spatial Resolution (m)1Coastal aerosol0.43 - 0.45302Blue0.45 - 0.51303Green0.53 - 0.59304Red0.64 - 0.67305Near Infrared (NIR)0.85 - 0.88306SWIR 11.57 - 1.65307SWIR 22.11 - 2.29308Cirrus (in OLI this is band 9)1.36 - 1.38309QA Band (available with Collection 1)*NA30*More about the Quality Assessment BandTIRS BandsBandDescriptionWavelength (µm)Spatial Resolution (m)10TIRS110.60 - 11.19100 * (30)11TIRS211.50 - 12.51100 * (30)*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.Additional Usage NotesImage exports are limited to 4,000 columns x 4,000 rows per request.This dynamic imagery layer can be used in Web Maps and ArcGIS Pro as well as web and mobile applications using the ArcGIS REST APIs.WCS and WMS compatibility means this imagery layer can be consumed as WCS or WMS services.The Landsat Explorer App is another way to access and explore the imagery.This layer is part of a larger collection of Landsat Imagery Layers.Data SourceLandsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted by the Amazon Web Services as part of their Public Data Sets program.For information, see Landsat 8 and Landsat 9.

Lesson 1. An Introduction to working with multispectral satellite data in ArcGIS Pro In which we learn: • How to unpack tar and gz files from USGS EROS • The basic map interface in ArcGIS • How to add image files • What each individual band of Landsat spectral data looks like • The difference between: o Analysis-ready data: surface reflectance and surface temperature o Landsat Collection 1 Level 3 data: burned area and dynamic surface water o Sentinel2data o ISRO AWiFS and LISS-3 data Lesson 2. Basic image preprocessing In which we learn: • How to composite using the composite band tool • How to represent composite images • All about band combinations • How to composite using raster functions • How to subset data into a rectangle • How to clip to a polygon Lesson 3. Working with mosaic datasets In which we learn: o How to prepare an empty mosaic dataset o How to add images to a mosaic dataset o How to change symbology in a mosaic dataset o How to add a time attribute o How to add a time dimension to the mosaic dataset o How to view time series data in a mosaic dataset Lesson 4. Working with and creating derived datasets In which we learn: • How to visualize Landsat ARD surface temperature • How to calculate F° from K° using ARD surface temperature • How to generate and apply .lyrx files • How to calculate an NDVI raster using ISRO LISS-3 data • How to visualize burned areas using Landsat Level 3 data • How to visualize dynamic surface water extent using Landsat Level 3 data

RdNBR is a remotely sensed index of the pre- to post-fire change in vegetation greenness, in this case the growing seasons in the year prior to and the year after the year in which the fire occurred. The mean composite scene selection method utilizes all valid pixels in all Landsat scenes over a specified date range to calculate the fire severity index. The CBI is a standardized field measure of vegetation burn severity (Key and Benson 2006), which here is predicted from a remotely sensed fire severity index using regression equations developed between CBI field plot data and the remote index, RBR (Parks et al 2019). The dataset featured provides an estimation of fire severity of past fires, with fire severity defined here as fire-induced change to vegetation. The dataset is limited to fires included in CAL FIRE’s Historic Wildland Fire Perimeters database and therefore is subject to the same limitations in terms of missing or erroneous data. This web app was developed to satisfy the requirements of Senate Bill No. 1101: An act to amend Sections 10295 and 10340 of the Public Contract Code, and to add Section 4114.4 to the Public Resources Code, relating to fire prevention.Methods:To develop these datasets, a feature service for fire perimeters was created from the CAL FIRE Fire and Resource Assessment Program’s Historic Wildland Fire Perimeters database (firep23_1) for fires or fires that were a part of complexes >= 1,000 acres from 2015 to 2023. This feature service is viewable on the California Vegetation Burn Severity Viewer and used to discover the RdNBR and CBI vegetation burn severity datasets. The feature service is titled Burn Severity Fire Perimeters (firep23_1_2015_2023_Fires_Complex_1000ac). After this feature service was uploaded to Google Earth Engine (GEE) as an asset, the Parks et al. 2018 script was used to generate RdNBR values with offset (rdnbr_w_offset) data for each individual fire and the Parks et al. 2019 script was used to generate bias corrected Composite Burn Index values (cbi_bc) data for each individual fire using 30m resolution Landsat Collection 2 data. To specify the date range of Landsat satellite images to be queried to create the one-year pre-fire and one-year post-fire mean composite image scenes in both scripts, the variable 'startday' was set to 152 (June 1st) and the variable 'endday' was set to 258 (September 15th) for all fires, as specified in Parks et al. (2019). These variables were used to define the ranges of Landsat scenes that were queried to create the one-year-pre-fire and one-year-post-fire mean composite Landsat scenes. These values were used, as they were detailed as the leaf-on period for the State of California in Parks et al. 2019. Once the RdNBR raster data for each fire had been produced using Parks et al. 2018's GEE script and the CBI raster data for each fire had been produced using Parks et al. 2019's GEE script, a Python script (run in a Jupyter Notebook embedded in the ArcGIS Pro software) was used to clip each fire-specific, continuous feature class to the extent of its fire perimeter. Each CBI feature class was additionally clipped to the extent of Conifer Forest and Hardwood Forest classes (defined in FVEG15's WHR13 Lifeform class for fires from 2015 to 2021 and defined in FVEG22's WHR13 Lifeform class for fires from 2022 to 2023).Once each continuous feature class had been clipped, values were reclassified to create a discrete RdNBR and CBI feature classes. Classes for RdNBR were arbitrarily chosen and do not correspond to meaningful categories of burn severity. Higher RdNBR values do indicate greater loss of vegetation greenness and negative values indicate an increase in greenness, but there is not necessarily a direct or linear correlation between RdNBR values and impacts to vegetation or ecological effects. Remotely sensed fire severity indices are translated into CBI using regression equations developed between CBI field plot data and the remote indices. Very few CBI plots exist in California or elsewhere in the U.S. for vegetation types other than forest. We therefore chose to include only forest vegetation in our CBI dataset. Classes for RdNBR were as follows: Code | Lower Limit (RdNBR) | Upper Limit (RdNBR) 1 < -1,000 -1,000 2 -1,000 -800 3 -800 -600 4 -600 -400 5 -400 -200 6 -200 0 7 0 200 8 200 400 9 400 600 10 600 800 11 800 1,000 12 1,000 1,200 13 1,200 1,400 14 1,400 1,600 15 1,600 > 1,600 Classes for CBI were as follows: Code | Lower Limit (CBI) | Upper Limit (CBI) | Burn Severity 1 0.00 0.10 Unburned 2 0.10 1.25 Low Vegetation Burn Severity 3 1.25 2.25 Moderate Vegetation Burn Severity 4 2.25 3.00 High Vegetation Burn Severity The discrete raster feature classes were then converted to vector feature classes. Finally, all individual discrete vector feature classes for individual fires were merged into two vector datasets, RdNBR Burn Severity Data (BurnSeverityRdNBR1523_1) and CBI Burn Severity Data (BurnSeverityCBIForest1523_1). These feature services are viewable on this web app, the California Vegetation Burn Severity Viewer.

AbstractFires in Australia’s Forests 2016-21 (2024) is a continental spatial dataset of the extent and frequency of planned and unplanned fires occurring in forest in the five financial years between July 2016 and June 2021, assembled for the 2024 update of Indicator 3.1b for Australia's State of the Forests Report. It was developed from multiple fire area datasets contributed by state and territory government agencies, after consultation with Australia’s Forest Fire Management Group. The fire dataset is then combined with forest cover information sourced from the Forests of Australia (2023) dataset, and forest tenure information sourced from the Tenure of Australia's forests (2023) dataset.Planned fire: Fire started in accordance with a fire management plan or planned burning program, such as fuel-reduction burning or prescribed burning.Unplanned fire: Fire started naturally (such as by lightning), accidentally, or deliberately (such as by arson), but not in accordance with planned fire management prescriptions. Also called bushfire or wildfire.The dataset was compiled by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) for the National Forest Inventory (NFI), a collaborative partnership between the Australian and state and territory governments. The role of the NFI is to collate, integrate and communicate information on Australia's forests. The NFI applies a national classification to state and territory data to allow seamless integration of these datasets. Multiple independent sources of external data are used to fill data gaps and improve the quality of the final dataset.Forest areas burnt by fire are allocated by the month of the fire to a financial year (July–June inclusive). Where more than one fire event occurs on any one hectare during a financial year, only the first fire is recorded for that area in the financial year. Fires are also classified into two categories, planned and unplanned, based on the fire seasonality and advice from state and territory agencies.The Fires in Australia’s forests 2016–21 (2024) dataset is produced to fulfil requirements of Australia's National Forest Policy Statement and the Regional Forests Agreement Act 2002 (Cwth), and is used by the Australian Government for domestic and international reporting.CurrencyDate Modified: 30 June 2021Publication Date: 28 October 2024Modification Frequency: As neededData ExtentCoordinate Reference: WGS84 / Mercator Auxiliary SphereSpatial ExtentNorth: -8.2South: -44.4East: 157.2West: 109.2Source InformationData, Metadata, Maps and Interactive views are available from Fires in Australia's Forests 2016-21 (2024), Descriptive Metadata PDF.The data was obtained from Department of Agriculture, Fisheries and Forestry - Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES).Lineage StatementDocumentation on dataset lineage, and attribute data descriptions and lookup tables for the Fires in Australia’s forest 2016–21 (2024) dataset, are available from the Fires in Australia’s forests spatial data webpage.Note: The Digital Atlas of Australia downloaded and created a copy of the source data in February 2025 that was suitable to be hosted through ArcGIS Image Dedicated. A copy of the raster dataset was created with RGB fields as a colour map with Geoprocessing tools in ArcGIS Pro, and the raster dataset was re-projected from 1994 Australia Albers to WGS 1984 Web Mercator (Auxiliary Sphere). This Web Mapping Service is for display purposes only. It is in Web Mercator projection and not suitable for deriving area statistics. For any analyses use the original dataset published in Albers projection.Data DictionaryField NameData TypeDescriptionVALUENumericUnique identifier for each unique combination of attribute table field values.COUNTNumericNumber of cells that occur for a particular VALUE. For this dataset the cell size is 100 by 100 metres. The COUNT value is equivalent to the area in hectares.BURN OCCURRENCEString (Text)Groups forest by number of times burnt: 1, 2, 3, 4, 5, Forest not burnt.ContactAustralian Government Department of Agriculture, Fisheries and Forestry – Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES),Info.ABARES@aff.gov.au.

AbstractFires in Australia’s Forests 2016-21 (2024) is a continental spatial dataset of the extent and frequency of planned and unplanned fires occurring in forest in the five financial years between July 2016 and June 2021, assembled for the 2024 update of Indicator 3.1b for Australia's State of the Forests Report. It was developed from multiple fire area datasets contributed by state and territory government agencies, after consultation with Australia’s Forest Fire Management Group. The fire dataset is then combined with forest cover information sourced from the Forests of Australia (2023) dataset, and forest tenure information sourced from the Tenure of Australia's forests (2023) dataset.Planned fire: Fire started in accordance with a fire management plan or planned burning program, such as fuel-reduction burning or prescribed burning.Unplanned fire: Fire started naturally (such as by lightning), accidentally, or deliberately (such as by arson), but not in accordance with planned fire management prescriptions. Also called bushfire or wildfire.The dataset was compiled by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) for the National Forest Inventory (NFI), a collaborative partnership between the Australian and state and territory governments. The role of the NFI is to collate, integrate and communicate information on Australia's forests. The NFI applies a national classification to state and territory data to allow seamless integration of these datasets. Multiple independent sources of external data are used to fill data gaps and improve the quality of the final dataset.Forest areas burnt by fire are allocated by the month of the fire to a financial year (July–June inclusive). Where more than one fire event occurs on any one hectare during a financial year, only the first fire is recorded for that area in the financial year. Fires are also classified into two categories, planned and unplanned, based on the fire seasonality and advice from state and territory agencies.The Fires in Australia’s forests 2016–21 (2024) dataset is produced to fulfil requirements of Australia's National Forest Policy Statement and the Regional Forests Agreement Act 2002 (Cwth), and is used by the Australian Government for domestic and international reporting.CurrencyDate Modified: 30 June 2021Publication Date: 28 October 2024Modification Frequency: As neededData ExtentCoordinate Reference: WGS84 / Mercator Auxiliary SphereSpatial ExtentNorth: -8.2South: -44.4East: 157.2West: 109.2Source InformationData, Metadata, Maps and Interactive views are available from Fires in Australia's Forests 2016-21 (2024), Descriptive Metadata PDF.The data was obtained from Department of Agriculture, Fisheries and Forestry - Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES).Lineage StatementDocumentation on dataset lineage, and attribute data descriptions and lookup tables for the Fires in Australia’s forest 2016–21 (2024) dataset, are available from the Fires in Australia’s forests spatial data webpage.Note: The Digital Atlas of Australia downloaded and created a copy of the source data in February 2025 that was suitable to be hosted through ArcGIS Image Dedicated. A copy of the raster dataset was created with RGB fields as a colour map with Geoprocessing tools in ArcGIS Pro, and the raster dataset was re-projected from 1994 Australia Albers to WGS 1984 Web Mercator (Auxiliary Sphere). This Web Mapping Service is for display purposes only. It is in Web Mercator projection and not suitable for deriving area statistics. For any analyses use the original dataset published in Albers projection.Data DictionaryField NameData TypeDescriptionVALUENumericUnique identifier for each unique combination of attribute table field values.COUNTNumericNumber of cells that occur for a particular VALUE. For this dataset the cell size is 100 by 100 metres. The COUNT value is equivalent to the area in hectares.FOR_BURN_TString (Text)Groups forest burnt by burn type: Unplanned burns only, Planned burns only, Planned and Unplanned burns, Forest not burnt.ContactAustralian Government Department of Agriculture, Fisheries and Forestry – Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES),Info.ABARES@aff.gov.au.