27:null 28:["$","title",null,{"children":"planning a woodland sampling survey | joelondon | Observable"}] 2d:T403,md`# planning a woodland sampling survey whilst wandering around oxleas wood during lockdown, thinking about the time when it very nearly got concreted over in the wake of the "[roads for prosperity](https://en.wikipedia.org/wiki/Roads_for_Prosperity)" white paper, some questions came up: "can woodlands represent prosperity?" and "if so, how?" forest mensuration techniques put a value on woodlands by estimating the timber volume, so that's a simple materialistic comparison that could be used: wood v concrete. forest mensuration measures are also the same as the recommended tree attributes recorded on openstreetmap (scientific name, crown height, diameter at 1.3m - well, osm requests circumference but close enough), so a tree survey via osm could go some way towards scientifically recording the value of this ancient hilltop forest riding the thames chalk fault. instead of surveying all the trees, a possible way to learn about the life of the woodland would be to take a representative sample, so here we go... `2e:T75a,map = { const container = html`

`; yield container; const osm = L.tileLayer( "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", { attribution: "© OpenStreetMap contributors" } ); const sat = L.tileLayer( 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}', { attribution: 'Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community' } ); const jawg = L.tileLayer( 'https://{s}.tile.jawg.io/jawg-terrain/{z}/{x}/{y}{r}.png?access-token={accessToken}', { attribution: '© JawgMaps © OpenStreetMap contributors', minZoom: 0, maxZoom: 22, subdomains: 'abcd', accessToken: 'lvT0JBcPmzNqX4kyAkpKuf41Gsq9MmxXNY9lE6sdrRZsTHOeQtOWi3oPwHp6WGcT' } ); const LIDAR_Composite_DSM_1m = L.tileLayer.wms( '//environment.data.gov.uk/spatialdata/lidar-composite-digital-surface-model-dsm-1m/wms?', { layers: 'LIDAR_Composite_DSM_1m', zIndex: 2, detectRetina: true, minZoom: 10 } ); const sample = L.geoJSON(oxleasWoodGridFitIDsSample, { onEachFeature: (feature, layer) => layer.bindTooltip(JSON.stringify(feature.properties.id)) }); const map = L.map(container, { layers: [sat, sample] }); map.fitBounds(sample.getBounds(sample)); const baseMaps = { satellite: sat, osm: osm, terrain: jawg, lidar: LIDAR_Composite_DSM_1m }; const overlayMaps = { sample: sample }; L.control.layers(baseMaps, overlayMaps).addTo(map); }2f:[] 2b:{"id":"d9fff6011c995192","slug":null,"title":"planning a woodland sampling survey","thumbnail":"89ae57272f25d8706b5ae97db7042701542ffa1a31ff129952da354460751495","access_level":"unlisted","create_time":"$D2021-03-19T22:48:53.225Z","update_time":"$D2025-10-26T20:48:35.502Z","view_time":null,"first_public_time":null,"has_unpublished_edits":null,"fork":false,"stars":1,"starred":false,"owner":{"type":"team","id":"6edb1ee17def6298","login":"joelondon","name":"joelondon","avatar_url":"https://avatars-next.observableusercontent.com/1b5d1840f05e85f3b4385baf46d8db8872cd62175354f438fe517a84e9159486","tier":"starter_2024"},"version":775,"publish_version":775,"role":null,"authored":false,"authors":[{"type":"individual","id":"d8a94c1087e1b5bd","login":"joelondon","name":"joelondon","avatar_url":"https://avatars-next.observableusercontent.com/1b5d1840f05e85f3b4385baf46d8db8872cd62175354f438fe517a84e9159486","tier":null}],"members":[{"type":"individual","id":"280d7def8a1cee4f","login":"joe-liad","name":"Joe","avatar_url":"https://avatars-next.observableusercontent.com/3386e63b2849ee41fef7217c8f7ffd7d2a0179b87c5c0e9a841054cc2efdace1","tier":null,"role":"editor"},{"type":"individual","id":"d8a94c1087e1b5bd","login":"joelondon","name":"joelondon","avatar_url":"https://avatars-next.observableusercontent.com/1b5d1840f05e85f3b4385baf46d8db8872cd62175354f438fe517a84e9159486","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"title":"planning a woodland sampling survey","cells":[{"id":0,"value":"$2d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":684,"value":"md`## outline\n\n1. get the woodland's geometry from osm\n2. subdivide the woodland geometry into a 10m grid\n3. make a random sample of that grid\n4. survey the trees in that sample with osm, openstreetmap will be used to record the location, scientific name, height and dbh\n5. estimate the grand total, and analyse the attribute data\n6. somehow compare woodland to concrete\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"md`## get the woodland's geometry from osm`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"oxleasWoodOverpass = `[out:json][timeout:25];\n\n(\n way[\"name\"=\"Oxleas Wood\"];\n);\n\nout body;\n>;\nout skel qt;`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":91,"value":"viewof oxleasWoodGeojson = showGeoJSON(\n osmtogeojson(await overpass(oxleasWoodOverpass))\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":65,"value":"md`## subdivide the woodland geometry into a 10m grid\n\n10 metres seems a good size for a sample and can be staked out with GPS and a rope`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":310,"value":"viewof oxleasWoodGrid = {\n const bb = turf.bbox(oxleasWoodGeojson);\n const grid = turf.squareGrid(bb, 10, {\n units: 'metres',\n mask: oxleasWoodGeojson\n });\n return showGeoJSON(grid);\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":475,"value":"md`### filter out squares overlapping the boundary\n\nthis give us equally sized samples\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":497,"value":"// CLONE THE PREVIOUS VERSION\noxleasWoodGridFit = ({ ...oxleasWoodGrid })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":457,"value":"// MUTATE THE CLONE\n(oxleasWoodGridFit.features = oxleasWoodGrid.features.filter(feature =>\n turf.booleanWithin(feature, oxleasWoodGeojson.features[0])\n))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":472,"value":"showGeoJSON(oxleasWoodGridFit)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":485,"value":"md`### add an id to each cell for reference`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":510,"value":"// CLONE THE PREVIOUS VERSION\noxleasWoodGridFitIDs = ({ ...oxleasWoodGridFit })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":489,"value":"// MUTATE THE CLONE - ASSIGN AN ID TO EACH CELL\n(oxleasWoodGridFitIDs.features = oxleasWoodGridFit.features.map((el, i) => {\n el.properties.id = i;\n return el;\n}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":515,"value":"// id shows on feature click\nshowGeoJSON(oxleasWoodGridFitIDs)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":434,"value":"md`### quick analysis\n\n| | |\n| - | - |\n| area of \\`oxleasWoodGeojson\\` | ${turf\n .area(oxleasWoodGeojson)\n .toLocaleString()} | \n| area of \\`oxleasWoodGrid\\` | ${turf.area(oxleasWoodGrid).toLocaleString()} | \n| area of \\`oxleasWoodGridFit\\` | ${turf\n .area(oxleasWoodGridFit)\n .toLocaleString()} | \n| number of squares in \\`oxleasWoodGridFitIDs\\` | ${\n oxleasWoodGridFit.features.length\n} | \n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":340,"value":"md`## choose a sample size\na 5% subsample represents ${Math.round(\n oxleasWoodGridFitIDs.features.length * 0.05\n)} cells (a decent sample size but a lot of work)\n\na 1% subsample represents ${Math.round(\n oxleasWoodGridFitIDs.features.length * 0.01\n)} cells, which seems more achievable in the short-term\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":587,"value":"md`## make a random sample of that grid` ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":642,"value":"samplePercentage = 1","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":596,"value":"sample = {\n const sample = [];\n for (\n let i = 0;\n i <\n Math.round((oxleasWoodGridFitIDs.features.length * samplePercentage) / 100);\n i++\n ) {\n sample.push(getRandomIntInclusive(0, oxleasWoodGridFit.features.length));\n }\n return sample;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":616,"value":"// CLONE THE PREVIOUS VERSION\noxleasWoodGridFitIDsSample = ({ ...oxleasWoodGridFitIDs })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":619,"value":"// MUTATE THE CLONE - FILTER OUT CELLS THAT AREN'T IN THE SAMPLE LIST\n(oxleasWoodGridFitIDsSample.features = oxleasWoodGridFitIDs.features.filter(\n (el, i) => sample.includes(el.properties.id)\n))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":654,"value":"$2e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":629,"value":"createJsonDownloadButton(\n oxleasWoodGridFitIDsSample,\n `oxleas-wood-10m-grid-sample-${samplePercentage}-percent.geojson`,\n `download oxleas-wood-10m-grid-sample-${samplePercentage}-percent.geojson`\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":209,"value":"md`# imports`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"import { showGeoJSON } from '@gampleman/fetch-geojson'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":556,"value":"import { createJsonDownloadButton } from \"@trebor/download-json\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":593,"value":"// https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Math/random\nfunction getRandomIntInclusive(min, max) {\n min = Math.ceil(min);\n max = Math.floor(max);\n return Math.floor(Math.random() * (max - min + 1) + min); //The maximum is inclusive and the minimum is inclusive\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"overpass = query => fetch(`https://lz4.overpass-api.de/api/interpreter?data=${query}`).then((response) => response.json())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":84,"value":"osmtogeojson = require('osmtogeojson@3.0.0-beta.4/osmtogeojson.js')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":211,"value":"turf = require(\"@turf/turf\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":651,"value":"L = {\n const L = await require(\"leaflet@1/dist/leaflet.js\");\n if (!L._style) {\n const href = await require.resolve(\"leaflet@1/dist/leaflet.css\");\n document.head.appendChild(\n (L._style = html``)\n );\n }\n return L;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W2f","stdlib":"1","titling":"auto"}} 2c:"$2b:body"