25:["$","title",null,{"children":"Coulter Family Ranch Cliff-Dunc parcel | Peter Donovan | Observable"}] 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29:{"id":"be67de6bf4a02705","slug":null,"title":"Coulter Family Ranch Cliff-Dunc parcel","thumbnail":"83754d2271fcfbe26b64c38dacdcc316fd4635c8315a4efd2f1ae296bed59d39","access_level":"public","create_time":"$D2023-06-15T17:29:52.981Z","update_time":"$D2023-12-10T18:49:24.456Z","view_time":null,"first_public_time":"$D2023-06-15T17:29:52.981Z","has_unpublished_edits":null,"fork":{"id":"1dfa0c47c4bc259c","slug":"time-series-explorer-coulter-family-ranch-home-parcel","title":"Time Series Explorer: Coulter Family Ranch home parcel","owner":{"id":"e2b593d20ef3388a","login":"managingwholes","name":"Peter Donovan"}},"stars":0,"starred":false,"owner":{"type":"team","id":"e2b593d20ef3388a","login":"managingwholes","name":"Peter Donovan","avatar_url":"https://avatars-next.observableusercontent.com/6ff849db058c39c81342ebf90aa84a9f6a536ac9ded98087e309e6a9ce577bf5","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":702,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"0550a7b94f6b94ca","login":"managingwholes","name":"Peter Donovan","avatar_url":"https://avatars-next.observableusercontent.com/6ff849db058c39c81342ebf90aa84a9f6a536ac9ded98087e309e6a9ce577bf5","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"0550a7b94f6b94ca","login":"managingwholes","name":"Peter Donovan","avatar_url":"https://avatars-next.observableusercontent.com/6ff849db058c39c81342ebf90aa84a9f6a536ac9ded98087e309e6a9ce577bf5","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Coulter Family Ranch Cliff-Dunc parcel","cells":[{"id":0,"value":"# Coulter Family Ranch Cliff-Dunc parcel\n

Exploring some time series from remote sensing: rangelands.app and Open ET data explorer.

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":294,"value":"## 1. Cover estimates from rangelands.app\nThese use Landsat multispectral sensors to classify range cover. Landsat pixels cover about a quarter acre. The percentage of each cover type is averaged over the parcel, for each year.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":292,"value":"{\n const xy = {x: \"year\", y: \"value\", z: \"col\", order: covers, reverse: true, offset: \"expand\"};\n return Plot.plot({\n y: {\n grid: true,\n label: \"↑ Cover (%)\",\n percent: true\n },\n x: {tickFormat: \"\"},\n marks: [\n Plot.areaY(cover, Plot.stackY({...xy, fill: \"col\", title: d => `${d.year}\\n${d.value}`})),\n Plot.lineY(cover, Plot.stackY2({...xy, stroke: \"white\", strokeWidth: 1})),\n \n ],\n color: {\n legend: true,\n range: [\"lime\", \"#916a14\", \"#ccc03d\", \"green\", \"#404a42\", \"blue\"],\n }\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":701,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"## 2. Annual time series\nCheck boxes below to compare up to **three** time series charts, one on top of each other. The cover types are from rangelands.app (RAP).
\n**precipRAP**: annual precipitation in inches from the gridMET dataset.
\n**EnsembleET**: evapotranspiration (evaporation + transpiration in inches) from the OpenET tool.
\nMouse over the chart line to get exact values.
\nNOTES:
\nGrasshoppers abundant 2019-2022.
\nGrazed each paddock twice in 2018 and 2019.
\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":14,"value":"viewof columns = Inputs.checkbox([\"bare\", \"litter\", \"annuals\", \"perennials\", \"shrub\", \"tree\", \"tempRAP\", \"precipRAP\",\"EnsembleET\",\"biomassRAP\", \"ADAs\"], {label: \"Annual time series\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":354,"value":"{\n // FIRST CHART\n return Plot.plot({\n height: 200,\n y: {\n grid: true,\n label: columns[0]\n },\n x: {\n nice: false,\n label: \"YEAR\",\n tickFormat: \"\"\n },\n marks: [\n Plot.frame(),\n Plot.areaY(data.filter(item => item.col === columns[0]), {x: \"year\", y: \"value\", fill: \"#cfc\", title: (d) =>\n `${d.year}:\\n${d.value}`}),\n Plot.lineY(data.filter(item => item.col === columns[0]), {x: \"year\", y: \"value\", title: (d) =>\n `${d.year}:\\n${d.value}`}),\n \n ]\n }\n )\n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":369,"value":"// SECOND CHART\n{\n return Plot.plot({\n height: 200,\n y: {\n grid: true,\n label: columns[1]\n },\n x: {\n nice: false,\n label: \"YEAR\",\n tickFormat: \"\"\n },\n marks: [\n Plot.frame(),\n Plot.areaY(data.filter(item => item.col === columns[1]), {x: \"year\", y: \"value\", fill: \"#fcc\", title: (d) =>\n `${d.year}:\\n${d.value}`}),\n Plot.lineY(data.filter(item => item.col === columns[1]), {x: \"year\", y: \"value\", title: (d) =>\n `${d.year}:\\n${d.value}`})\n ]\n })\n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":371,"value":"{\n // THIRD CHART \n return Plot.plot({\n height: 200,\n y: {\n grid: true,\n label: columns[2]\n },\n x: {\n nice: false,\n label: \"YEAR\",\n tickFormat: \"\"\n },\n marks: [\n Plot.frame(),\n Plot.areaY(data.filter(item => item.col === columns[2]), {x: \"year\", y: \"value\", fill: \"#ccf\", title: (d) =>\n `${d.year}:\\n${d.value}`}),\n Plot.lineY(data.filter(item => item.col === columns[2]), {x: \"year\", y: \"value\", title: (d) =>\n `${d.year}:\\n${d.value}`})\n ]\n })\n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":699,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":426,"value":"## 3. Monthly precip and evapotranspiration (ET)\nEvapotranspiration is the invisible half of the water cycle: the upward movement of water vapor into the atmosphere. It is estimated with remote sensing, for which thermal bands can detect its cooling effects. For vegetated surfaces, evapotranspiration is approximately proportional to plant growth.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":540,"value":"FileAttachment(\"wss-cycle-evapotranspiration-diagram.jpg\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":545,"value":"Precipitation from gridMET dataset in blue
\nEnsemble evapotranspiration (average of 5 evapotranspiration models) from OpenET in red
\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":464,"value":"Plot.plot({\n height: 400,\n color: {\n legend: true\n },\n y: {\n grid: true,\n label: \"↑ inches\"\n },\n marks: [\n Plot.line(monthly, {x: \"Month\", y: \"EnsembleET\", stroke: \"red\", title: (d) =>\n `${d.EnsembleET}`}),\n Plot.line(monthly, {x: \"Month\", y: \"precipRAP\", stroke: \"blue\", title: (d) =>\n `${d.precipGridMet}`}),\n \n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":681,"value":"## 4. Monthly NDVI \n(normalized difference veg index, proxy for photosynthetic activity)
\nThe NDVI curve resembles the evapotranspiration curve, but shows some differences.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":678,"value":"Plot.plot({\n height: 300,\n y: {\n grid: true,\n label: \"↑ NDVI ratio\"\n },\n marks: [\n Plot.line(monthly, {x: \"Month\", y: \"NDVI\", stroke: \"green\", title: (d) =>\n `${d.NDVI}`}),\n \n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":285,"value":"cover = data.filter(el => covers.includes(el.col))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":283,"value":"covers = [\"bare\",\"litter\",\"annuals\", \"perennials\",\"shrub\",\"tree\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":161,"value":"data = pivot(\n dataWide, \n dataWide.columns.slice(1), \n \"col\", \n \"value\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":492,"value":"dataWide = FileAttachment(\"cliff_dunc_annual@2.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":159,"value":"function pivot(data, columns, name, value, opts) {\n const keepCols = data.columns.filter(c => !columns.includes(c));\n const long = [];\n columns.forEach(col => {\n data.forEach(d => {\n const row = {};\n keepCols.forEach(c => {\n row[c] = d[c];\n });\n // TODO, add an option to ignore if fails a truth test to approximate `values_drop_na`\n row[name] = col;\n row[value] = d[col];\n long.push(row);\n });\n });\n return long;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":103,"value":"parseYear = d3.timeParse(\"%Y\");","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":415,"value":"import {Plot} from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":632,"value":"monthly = FileAttachment(\"cliff_dunc_monthly@2.csv\")\n .csv() // no `{typed: true}` in `csv()`\n .then((data) => {\n data.forEach((d) => {\n d.Month = parseMonth(d.Month);\n d.EnsembleET = Number(d.EnsembleET);\n d.precipRAP = Number(d.precipRAP);\n d.NDVI = Number(d.NDVI);\n\n });\n return data;\n })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":634,"value":"parseMonth = d3.timeParse(\"%m/%d/%Y\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W49","stdlib":"1"}} 24:null