2c:null 2d:["$","title",null,{"children":"Identifying Carr Wildfire with Landsat 8 | GeoSurge | Observable"}] 32:[] 30:{"id":"878506c1f2ced48f","slug":"identifying-carr-wildfire-with-landsat-8","title":"Identifying Carr Wildfire with Landsat 8","thumbnail":"3217b77ab4816af32c05ce78d665f1d1ebd0701c8af9f3b07ea2c84084ee0569","access_level":"public","create_time":"$D2019-04-16T20:03:55.399Z","update_time":"$D2021-04-23T02:02:55.524Z","view_time":null,"first_public_time":"$D2019-04-18T15:12:09.968Z","has_unpublished_edits":null,"fork":false,"stars":3,"starred":false,"owner":{"type":"team","id":"3ed5fa3863496294","login":"geosurge","name":"GeoSurge","avatar_url":"https://avatars-next.observableusercontent.com/d6653d49bb2d126d5e686eeadc70f93ee30c3146a0d7e7912c62c771c5b9c43b","tier":"starter_2024"},"version":273,"publish_version":273,"role":null,"authored":false,"authors":[{"type":"individual","id":"20518a8433e55d81","login":"victoriamak","name":"Victoria Mak","avatar_url":"https://avatars-next.observableusercontent.com/b610a13871cfe20220d3207f665f933ec95f2013dfe2e8ccc4f3f4cdacb93d21","tier":null}],"members":[{"type":"team","id":"3ed5fa3863496294","login":"geosurge","name":"GeoSurge","avatar_url":"https://avatars-next.observableusercontent.com/d6653d49bb2d126d5e686eeadc70f93ee30c3146a0d7e7912c62c771c5b9c43b","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":6,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Identifying Carr Wildfire with Landsat 8","cells":[{"id":0,"value":"md`# Identifying Carr Wildfire with Landsat 8`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"md`### Landsat 8\n

Sensors: OLI bands(1-9) and TIRS (bands 10-11)

Spatial Resolution: bands 1-7,9 = 30m.  Band 8 = 15m.  Bands 10-11 = 100m

Temporal Resolution: 16 Days

Scene size: 185 km × 180 km

Map Projection: UTM

\n`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":51,"value":"md`### Acquiring Data:\nFind a tile using https://search.remotepixel.ca.\nHere's what we use in this dataset: https://landsatonaws.com/L8/045/032/LC08_L1TP_045032_20180811_20180815_01_T1\n`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":249,"value":"html`

`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"geoblaze = require('geoblaze@0.3.2')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":45,"value":"md `### Band 5, NIR (Near Infrared), comprises wavelengths 0.851 - 0.879µm`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"urlBand5 = \"https://s3.amazonaws.com/geoblaze/LC08_L1TP_045032_20180811_20180815_01_T1_B5_100x100.TIF\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"band5Raster = geoblaze.load(urlBand5)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":241,"value":"band5Raster.noDataValue = 0;","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"band5Raster.toCanvas({width: 500, height: 500})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"md`### Band 7, SWIR (Short-Wave Infrared) 2, comprises wavelengths 2.107 - 2.294 µm`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"urlBand7 = \"https://s3.amazonaws.com/geoblaze/LC08_L1TP_045032_20180811_20180815_01_T1_B7_100x100.TIF\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"band7Raster = geoblaze.load(urlBand7)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":127,"value":"band7Raster.noDataValue = 0;","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"band7Raster.toCanvas({width:500, height:500})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":62,"value":"md`### Daytime Detection Algorithm:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":66,"value":"tex`R_{75} > 0.25 \\quad \\textrm{AND} \\quad p_7-p_5 > 0.3 \\quad \\textrm{AND} \\quad p_7 > 0.5`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":142,"value":"newValues = {\n // we know each raster is only one band, so used index [0]\n const band5Values = band5Raster.values[0];\n const band7Values = band7Raster.values[0];\n // we know height and width same between rasters\n const { height, width } = band5Raster;\n return [band5Values.map((band5Row, rowIndex) => {\n const band7Row = band7Values[rowIndex];\n // iterates through cells or columns in the current row\n return band5Row.map((band5Value, band5ColumnIndex) => {\n if (band5Value == band5Raster.noDataValue) {\n return 999;\n }\n \n const band7Value = band7Row[band5ColumnIndex];\n const R75 = band7Value / band5Value;\n if (R75 > 0.25 && (band7Value - band5Value) > 0.3 && band7Value > 0.5) {\n return 1;\n } else {\n return 0;\n }\n });\n })];\n};","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":160,"value":"parseGeoRaster = require(\"georaster@1.5.4\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":165,"value":"newGeoRaster = {\n const { height, pixelHeight, pixelWidth, width, xmin, xmax, ymin, ymax } = band5Raster;\n const metadata = {\n height,\n noDataValue: 999,\n pixelHeight,\n pixelWidth,\n width,\n xmin,\n xmax,\n ymin,\n ymax\n }; \n return parseGeoRaster(newValues, metadata)\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":173,"value":"resultCanvas = newGeoRaster.toCanvas({ height: 500, width: 500 })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":261,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"md`#### You can read more about the algorithm utilized here: Wilfrid Schroeder, Patricia Oliva, Louis Giglio et al. Active fire detection using Landsat-8/OLI data\nhttps://doi.org/10.1016/j.rse.2015.08.032.\n(http://www.sciencedirect.com/science/article/pii/S0034425715301206)`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W32","stdlib":"1"}} 31:"$30:body"