8866 lines
264 KiB
Text
8866 lines
264 KiB
Text
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# NDVI (Vegetationsindex)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Räumliche und zeitliche Ausdehnung\n",
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"\n",
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"Unterschungsbereich und Zeitraum festlegen."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"spatial_extent = {'west': 8.721771, 'south': 53.025027, 'east': 8.904419, 'north': 53.114965}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"temporal_extent = [\"2020-01-01\", \"2021-01-01\"]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Authentifizierung"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Um Satellitenbilder herunterzuladen wird ein Account bei [Dataspace Copernicus](https://dataspace.copernicus.eu) benötigt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import openeo\n",
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"\n",
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"connection = openeo.connect(\"https://openeo.dataspace.copernicus.eu/\").authenticate_oidc()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"cube = connection.load_collection(\n",
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" \"SENTINEL2_L2A\", # Sentinel 2 Satellit\n",
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" spatial_extent=spatial_extent,\n",
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" temporal_extent=temporal_extent,\n",
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" bands=[\"B04\", \"B08\"], # Rot- und Infrarotkanal\n",
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" max_cloud_cover=50\n",
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")\n",
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"\n",
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"# Durchschnitt über alle Aufnahmezeitpunkte\n",
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"cube = cube.mean_time()\n",
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"\n",
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"# Werte skalieren\n",
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"cube = cube.apply(lambda v: v * 0.0001)\n",
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"\n",
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"# Rotes und Nah-Infrarot Band auslesen\n",
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"red = cube.band(\"B04\")\n",
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"nir = cube.band(\"B08\")\n",
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"\n",
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"# NDVI berechnen\n",
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"ndvi = (nir - red) / (nir + red)\n",
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"\n",
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"# Data cube herunterladen\n",
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"ndvi = ndvi.save_result(format=\"GTIFF\")\n",
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"job = ndvi.create_job(title=\"NDVI Image\")\n",
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"job.start_and_wait().download_result('results/ndvi.tif')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Anzeige"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import leafmap\n",
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"\n",
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"m = leafmap.Map()\n",
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"\n",
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"m.add_raster('results/ndvi.tif', colormap='RdYlGn', vmin=-0.2, vmax=0.8)\n",
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"\n",
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"m"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Daten einlesen"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Mit `rasterio` können wir die Messwerte in einem Array einlesen, um damit weitere Analyse vorzunehmen."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import rasterio as rio\n",
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"\n",
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"# Datei öffnen\n",
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"src = rio.open('results/ndvi.tif')\n",
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"\n",
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"# Pixelwerte auslesen\n",
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"data = src.read()\n",
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"\n",
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"data"
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]
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},
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"execution_count": null,
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"metadata": {},
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"source": [
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"import plotly.express as px\n",
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"\n",
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"fig = px.imshow(\n",
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" data[0],\n",
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" color_continuous_scale='RdYlGn',\n",
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" color_continuous_midpoint=0.1,\n",
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" height=800\n",
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")\n",
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"\n",
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"fig.show()"
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