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"cells": [
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"cell_type": "markdown",
"metadata": {},
"source": [
"# NDVI (Vegetationsindex)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Räumliche und zeitliche Ausdehnung\n",
"\n",
"Unterschungsbereich und Zeitraum festlegen."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"spatial_extent = {'west': 8.721771, 'south': 53.025027, 'east': 8.904419, 'north': 53.114965}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"temporal_extent = [\"2020-01-01\", \"2021-01-01\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Authentifizierung"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Um Satellitenbilder herunterzuladen wird ein Account bei [Dataspace Copernicus](https://dataspace.copernicus.eu) benötigt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import openeo\n",
"\n",
"connection = openeo.connect(\"https://openeo.dataspace.copernicus.eu/\").authenticate_oidc()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cube = connection.load_collection(\n",
" \"SENTINEL2_L2A\", # Sentinel 2 Satellit\n",
" spatial_extent=spatial_extent,\n",
" temporal_extent=temporal_extent,\n",
" bands=[\"B04\", \"B08\"], # Rot- und Infrarotkanal\n",
" max_cloud_cover=50\n",
")\n",
"\n",
"# Durchschnitt über alle Aufnahmezeitpunkte\n",
"cube = cube.mean_time()\n",
"\n",
"# Werte skalieren\n",
"cube = cube.apply(lambda v: v * 0.0001)\n",
"\n",
"# Rotes und Nah-Infrarot Band auslesen\n",
"red = cube.band(\"B04\")\n",
"nir = cube.band(\"B08\")\n",
"\n",
"# NDVI berechnen\n",
"ndvi = (nir - red) / (nir + red)\n",
"\n",
"# Data cube herunterladen\n",
"ndvi = ndvi.save_result(format=\"GTIFF\")\n",
"job = ndvi.create_job(title=\"NDVI Image\")\n",
"job.start_and_wait().download_result('results/ndvi.tif')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Anzeige"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import leafmap\n",
"\n",
"m = leafmap.Map()\n",
"\n",
"m.add_raster('results/ndvi.tif', colormap='RdYlGn', vmin=-0.2, vmax=0.8)\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Daten einlesen"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mit `rasterio` können wir die Messwerte in einem Array einlesen, um damit weitere Analyse vorzunehmen."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import rasterio as rio\n",
"\n",
"# Datei öffnen\n",
"src = rio.open('results/ndvi.tif')\n",
"\n",
"# Pixelwerte auslesen\n",
"data = src.read()\n",
"\n",
"data"
]
},
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"cell_type": "code",
"execution_count": null,
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"import plotly.express as px\n",
"\n",
"fig = px.imshow(\n",
" data[0],\n",
" color_continuous_scale='RdYlGn',\n",
" color_continuous_midpoint=0.1,\n",
" height=800\n",
")\n",
"\n",
"fig.show()"
]
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