Make this Notebook Trusted to load map: File -> Trust Notebook
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Import the matplotlib.pyplot module.\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Fetch a Landsat image.\n",
"img = ee.Image('LANDSAT/LC08/C02/T1_RT_TOA/LC08_001001_20251013')\n",
"\n",
"# Select Red and NIR bands, scale them, and sample 500 points.\n",
"sampFC = img.select(['B3','B4']).divide(10000).sample(scale=30, numPixels=500)\n",
"\n",
"# Arrange the sample as a list of lists.\n",
"sampDict = sampFC.reduceColumns(ee.Reducer.toList().repeat(2), ['B3', 'B4'])\n",
"sampList = ee.List(sampDict.get('list'))\n",
"\n",
"# Save server-side ee.List as a client-side Python list.\n",
"sampData = sampList.getInfo()\n",
"\n",
"# Display a scatter plot of Red-NIR sample pairs using matplotlib.\n",
"plt.scatter(sampData[0], sampData[1], alpha=0.2)\n",
"plt.xlabel('Red', fontsize=12)\n",
"plt.ylabel('NIR', fontsize=12)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "BQPPhgmbHzXP",
"outputId": "98f57ac0-57cb-4df8-bb37-857caeafd926"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'type': 'Image', 'bands': [{'id': 'elevation', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': -32768, 'max': 32767}, 'dimensions': [1296001, 417601], 'crs': 'EPSG:4326', 'crs_transform': [0.0002777777777777778, 0, -180.0001388888889, 0, -0.0002777777777777778, 60.00013888888889]}], 'version': 1641990767055141, 'id': 'USGS/SRTMGL1_003', 'properties': {'system:visualization_0_min': '0.0', 'type_name': 'Image', 'keywords': ['dem', 'elevation', 'geophysical', 'nasa', 'srtm', 'topography', 'usgs'], 'thumb': 'https://mw1.google.com/ges/dd/images/SRTM90_V4_thumb.png', 'description': '
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d0563ca5dfd4752aab3d7259e6f88af",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map(center=[21.79, 70.87], controls=(WidgetControl(options=['position', 'transparent_bg'], position='topright'…"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import geemap\n",
"geemap.ee_initialize()\n",
"\n",
"Map = geemap.Map(center=[21.79, 70.87], zoom=3)\n",
"image = ee.Image('USGS/SRTMGL1_003')\n",
"vis_params = {\n",
" 'min': 0,\n",
" 'max': 6000,\n",
" 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],\n",
"}\n",
"Map.addLayer(image, vis_params, 'SRTM')\n",
"Map"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"id": "hDlULpFDHzXR"
},
"outputs": [],
"source": [
"img=ee.Image('LANDSAT/LC08/C02/T1_RT_TOA/LC08_001001_20251013')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"id": "uN0uzde3HzXR",
"outputId": "20c263e6-96fa-4926-82dc-143fcd3532d6"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ee.Image({\n",
" \"functionInvocationValue\": {\n",
" \"functionName\": \"Image.load\",\n",
" \"arguments\": {\n",
" \"id\": {\n",
" \"constantValue\": \"LANDSAT/LC08/C02/T1_RT_TOA/LC08_001001_20251013\"\n",
" }\n",
" }\n",
" }\n",
"})\n"
]
}
],
"source": [
"print(img)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "g7dL59E1HzXS",
"outputId": "a6801d7f-b1ab-4dca-fbab-cd71376c539e"
},
"outputs": [
{
"data": {
"text/html": [
"![](\"https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/thumbnails/94d693cf6900c36d14632ed4d875bf45-8ee39219b2fdb84ef2b3d104496795b7:getPixels\"/)
"
],
"text/plain": [
""
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(url=img.getThumbURL({'bands':['B4','B3','B2'], 'min':100,'max':3500,\n",
" 'region':[[[-75.67086291359112,6.072916792136425],\n",
" [-75.25475573585675,6.072916792136425],\n",
" [-75.25475573585675,6.380084130088567],\n",
" [-75.67086291359112,6.380084130088567]]]}))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wOzKUk6UHzXS"
},
"outputs": [],
"source": [
"# Select Red and NIR bands, scale them, and sample 500 points.\n",
"samp_fc = img.select(['B3','B4']).divide(10000).sample(scale=30, numPixels=500)\n",
"\n",
"# Arrange the sample as a list of lists.\n",
"samp_dict = samp_fc.reduceColumns(ee.Reducer.toList().repeat(2), ['B3', 'B4'])\n",
"samp_list = ee.List(samp_dict.get('list'))\n",
"\n",
"# Save server-side ee.List as a client-side Python list.\n",
"samp_data = samp_list.getInfo()\n",
"\n",
"# Display a scatter plot of Red-NIR sample pairs using matplotlib.\n",
"plt.scatter(samp_data[0], samp_data[1], alpha=0.8)\n",
"plt.xlabel('Red', fontsize=12)\n",
"plt.ylabel('NIR', fontsize=12)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5GKSKixzHzXT",
"outputId": "af50c1be-7651-4f1f-df33-7e4531abbadb"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'emap' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Input \u001b[1;32mIn [34]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m Map \u001b[38;5;241m=\u001b[39m \u001b[43memap\u001b[49m\u001b[38;5;241m.\u001b[39mMap(center\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m40\u001b[39m,\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m100\u001b[39m], zoom\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m)\n\u001b[0;32m 2\u001b[0m Map\u001b[38;5;241m.\u001b[39madd_basemap(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mROADMAP\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;66;03m# Add Google Map\u001b[39;00m\n\u001b[0;32m 3\u001b[0m Map\n",
"\u001b[1;31mNameError\u001b[0m: name 'emap' is not defined"
]
}
],
"source": [
"Map = emap.Map(center=[40,-100], zoom=4)\n",
"Map.add_basemap('ROADMAP') # Add Google Map\n",
"Map\n",
"\n",
"\n",
"# Add Earth Engine dataset\n",
"# Get a download URL for an image.\n",
"image1 = ee.Image('srtm90_v4')\n",
"path = image1.getDownloadUrl({\n",
" 'scale': 30,\n",
" 'crs': 'EPSG:4326',\n",
" 'region': '[[-75.81280448742132,5.696650254732558], [-74.86248710460882,5.696650254732558], [-74.86248710460882,6.605986306802248], [-75.81280448742132,6.605986306802248]]'\n",
"})\n",
"\n",
"print(path)\n",
"vis_params = {'min': 0, 'max': 3000}\n",
"Map.addLayer(image1, vis_params)\n",
"\n",
"Map.addLayerControl()\n",
"Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DBn1ouM-HzXT"
},
"outputs": [],
"source": [
"#el mapa base...\n",
"Map = emap.Map(center=[40,-100], zoom=4)\n",
"Map.add_basemap('RELIEF')\n",
"Map"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bHWT_VQgHzXU"
},
"source": [
"Para importar y filtrar una imagen"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hCRcwhePHzXU",
"outputId": "dca55b08-1ef2-4ca8-dc1b-0fcaf8932cf1"
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Map' object has no attribute 'setCenter'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Input \u001b[1;32mIn [35]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;66;03m# Define visualization parameters in an object literal.\u001b[39;00m\n\u001b[0;32m 11\u001b[0m vizParams \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbands\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB4_median\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB3_median\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB2_median\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[0;32m 12\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m5000\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m15000\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgamma\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1.3\u001b[39m}\n\u001b[1;32m---> 14\u001b[0m \u001b[43mMap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msetCenter\u001b[49m(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m75.4\u001b[39m, \u001b[38;5;241m6.4\u001b[39m, \u001b[38;5;241m9\u001b[39m)\n\u001b[0;32m 15\u001b[0m Map\u001b[38;5;241m.\u001b[39maddLayer(median, vizParams, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMedian image\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 17\u001b[0m \u001b[38;5;66;03m#Para que aparezca el mapa\u001b[39;00m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'Map' object has no attribute 'setCenter'"
]
}
],
"source": [
"\n",
"\n",
"# Load a Landsat 8 collection.\n",
"collection = ee.ImageCollection('LANDSAT/LC08/C01/T1') \\\n",
" .filterBounds(ee.Geometry.Point(-75.4, 6.4)) \\\n",
" .filterDate('2019-01-01', '2019-12-31') \\\n",
" .sort('CLOUD_COVER')\n",
"\n",
"# Compute the median of each pixel for each band of the 5 least cloudy scenes.\n",
"median = collection.limit(5).reduce(ee.Reducer.median())\n",
"\n",
"# Define visualization parameters in an object literal.\n",
"vizParams = {'bands': ['B4_median', 'B3_median', 'B2_median'],\n",
" 'min': 5000, 'max': 15000, 'gamma': 1.3}\n",
"\n",
"Map.setCenter(-75.4, 6.4, 9)\n",
"Map.addLayer(median, vizParams, 'Median image')\n",
"\n",
"#Para que aparezca el mapa\n",
"Map.addLayerControl()\n",
"Map"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1-XU9YDIHzXU"
},
"source": [
"Para importar features desde las bases de datos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TRibWk_qHzXV"
},
"outputs": [],
"source": [
"Map = emap.Map(center=[40,-100], zoom=4)\n",
"Map.add_basemap('ROADMAP')\n",
"Map\n",
"\n",
"# Load a feature collection.\n",
"featureCollection = ee.FeatureCollection('TIGER/2016/States')\n",
"\n",
"# Filter\n",
"filteredFC = featureCollection.filter(ee.Filter.eq('NAME', 'California'))\n",
"\n",
"Map.addLayer(ee.Image().paint(filteredFC, 0, 2),\n",
" {'palette': 'red'}, 'California')\n",
"\n",
"Map.addLayerControl()\n",
"Map"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wLVgaqr9HzXV"
},
"source": [
"Para cargar un feature desde el Assets de GEE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "txl4ICwGHzXV"
},
"outputs": [],
"source": [
"Map = emap.Map(center=[40,-100], zoom=4)\n",
"Map.add_basemap('ROADMAP')\n",
"Map\n",
"\n",
"antioquia = ee.FeatureCollection('users/evaristizabalg/Antioquia')\n",
"\n",
"Map.center_object(antioquia,7)\n",
"Map.addLayer(antioquia, {}, 'Antioquia')\n",
"\n",
"Map.addLayerControl()\n",
"Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4PmdPKE_HzXb"
},
"outputs": [],
"source": [
"Map = emap.Map(center=[40,-100], zoom=4)\n",
"Map.add_basemap('ROADMAP')\n",
"Map\n",
"\n",
"AMVA = ee.FeatureCollection(\"G:\\My Drive\\INVESTIGACION\\Cartografia/AMVA\");\n",
"\n",
"Map.center_object(AMVA,7)\n",
"Map.addLayer(AMVA, {}, 'AMVA')\n",
"\n",
"Map.addLayerControl()\n",
"Map\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "g2FI3WW_HzXc",
"outputId": "af5aa270-21ce-460b-fc49-fb2aea21fb4d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"geemap package not installed. Installing ...\n"
]
}
],
"source": [
"# Installs geemap package\n",
"import subprocess\n",
"\n",
"try:\n",
" import geemap\n",
"except ImportError:\n",
" print('geemap package not installed. Installing ...')\n",
" subprocess.check_call([\"python\", '-m', 'pip', 'install', 'geemap'])\n",
"\n",
"# Checks whether this notebook is running on Google Colab\n",
"try:\n",
" import google.colab\n",
" import geemap.eefolium as emap\n",
"except:\n",
" import geemap as emap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xuBOSubKHzXc",
"outputId": "5a3f10e6-f341-4960-8274-723f72d24141"
},
"outputs": [],
"source": [
"Map = emap.Map(center=[40,-100], zoom=4)\n",
"Map.add_basemap('ROADMAP')\n",
"Map\n",
"\n",
"Tiempo1 = ee.ImageCollection ('COPERNICUS/S2') \\\n",
" .filterDate ('2017-01-01' ,'2018-01-01') \\\n",
" .sort('CLOUD_COVER')\n",
"Tiempo1b = Tiempo1.reduce(ee.Reducer.min())\n",
"\n",
"Tiempo2 = ee.ImageCollection ('COPERNICUS/S2') \\\n",
" .filterDate ('2018-01-01' ,'2019-01-01') \\\n",
" .sort('CLOUD_COVER');\n",
"Tiempo2b = Tiempo2.reduce(ee.Reducer.min())\n",
"\n",
"NDVI1 = Tiempo1b.normalizedDifference (['B8_min', 'B4_min'])\n",
"NDVI2 = Tiempo2b.normalizedDifference (['B8_min', 'B4_min'])\n",
"\n",
"# Compute the difference in NDVI.\n",
"ndviDifference = NDVI2.subtract(NDVI1)\n",
"\n",
"antioquia = ee.FeatureCollection('users/evaristizabalg/Antioquia')\n",
"\n",
"# Update the NDVI difference mask with the land mask.\n",
"maskedDifference = ndviDifference.clip(antioquia)\n",
"\n",
"# Display the masked result.\n",
"vizParams = {'min': -0.5, 'max': 0.5,\n",
" 'palette': ['FF0000', 'FFFFFF', '0000FF']}\n",
"Map.center_object(antioquia,7)\n",
"Map.addLayer(maskedDifference, vizParams, 'NDVI difference')\n",
"\n",
"Map.addLayerControl()\n",
"Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Gv0kbfe4HzXd",
"outputId": "7268201f-5b52-4f57-fa34-aecc8dad0b0b"
},
"outputs": [
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Map = emap.Map(center=[40,-100], zoom=4)\n",
"Map.add_basemap('ROADMAP') # Add Google Map\n",
"Map\n",
"\n",
"# This function gets NDVI from Landsat<8 imagery.\n",
"def getNDVI(image):\n",
" return image.normalizedDifference(['B4', 'B3'])\n",
"\n",
"image = ee.Image('LANDSAT/LC08/C01/T1_RT/LC08_009056_20190903')\n",
"\n",
"# Compute NDVI from the scenes.\n",
"ndvi = getNDVI(image)\n",
"\n",
"ndviParams = {'min':-0.2, 'max':0.2,'palette': ['#d73027', '#f46d43', '#fdae61',\n",
" '#fee08b', '#d9ef8b', '#a6d96a', '#66bd63', '#1a9850']}\n",
"Map.addLayer(ndvi, ndviParams, 'NDVI')\n",
"Map.centerObject(image, 10)\n",
"\n",
"# This function gets NDVI from Landsat 8 imagery.\n",
"def addNDVI(image):\n",
" return image.addBands(image.normalizedDifference(['B5', 'B4']))\n",
"\n",
"# filtrar\n",
"collection = ee.ImageCollection('LANDSAT/LC08/C01/T1') \\\n",
" .filterBounds(ee.Geometry.Point(-75.4, 6.4)) \\\n",
" .filterDate('2014-01-01', '2014-12-31')\n",
"\n",
"# aplicar a toda la colecction (Map)\n",
"ndviCollection = collection.map(addNDVI)\n",
"\n",
"first = ndviCollection.first()\n",
"print(first.getInfo())\n",
"\n",
"bandNames = first.bandNames()\n",
"print(bandNames.getInfo())\n",
"\n",
"Map.addLayerControl()\n",
"Map"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "75GH4I68HzXe"
},
"source": [
"## Sentinel 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LvRhv7wIHzXe",
"outputId": "c1a116a1-01f7-49be-d80d-f72eb818add5"
},
"outputs": [
{
"ename": "EEException",
"evalue": "Earth Engine client library not initialized. Run `ee.Initialize()`",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mEEException\u001b[0m Traceback (most recent call last)",
"Input \u001b[1;32mIn [9]\u001b[0m, in \u001b[0;36m| \u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m image \u001b[38;5;241m=\u001b[39m \u001b[43mee\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mImageCollection\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mCOPERNICUS/S2\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \\\n\u001b[0;32m 2\u001b[0m \u001b[38;5;241m.\u001b[39mfilterDate(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2017-01-01\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2017-01-02\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mmedian() \\\n\u001b[0;32m 3\u001b[0m \u001b[38;5;241m.\u001b[39mdivide(\u001b[38;5;241m10000\u001b[39m) \\\n\u001b[0;32m 4\u001b[0m \u001b[38;5;241m.\u001b[39mselect([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB1\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB2\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB3\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB4\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB5\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB6\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB7\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB8\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB8A\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB12\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
"File \u001b[1;32m~\\miniconda3\\envs\\gee\\lib\\site-packages\\ee\\computedobject.py:32\u001b[0m, in \u001b[0;36mComputedObjectMetaclass.__call__\u001b[1;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m args[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 31\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 32\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[1;32m~\\miniconda3\\envs\\gee\\lib\\site-packages\\ee\\imagecollection.py:38\u001b[0m, in \u001b[0;36mImageCollection.__init__\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, args):\n\u001b[0;32m 25\u001b[0m \u001b[38;5;124;03m\"\"\"ImageCollection constructor.\u001b[39;00m\n\u001b[0;32m 26\u001b[0m \n\u001b[0;32m 27\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[38;5;124;03m EEException: if passed something other than the above.\u001b[39;00m\n\u001b[0;32m 37\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 38\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minitialize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 40\u001b[0m \u001b[38;5;66;03m# Wrap single images in an array.\u001b[39;00m\n\u001b[0;32m 41\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(args, image\u001b[38;5;241m.\u001b[39mImage):\n",
"File \u001b[1;32m~\\miniconda3\\envs\\gee\\lib\\site-packages\\ee\\imagecollection.py:72\u001b[0m, in \u001b[0;36mImageCollection.initialize\u001b[1;34m(cls)\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[38;5;124;03m\"\"\"Imports API functions to this class.\"\"\"\u001b[39;00m\n\u001b[0;32m 71\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_initialized:\n\u001b[1;32m---> 72\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mImageCollection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minitialize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 73\u001b[0m apifunction\u001b[38;5;241m.\u001b[39mApiFunction\u001b[38;5;241m.\u001b[39mimportApi(\n\u001b[0;32m 74\u001b[0m \u001b[38;5;28mcls\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mImageCollection\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mImageCollection\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 75\u001b[0m apifunction\u001b[38;5;241m.\u001b[39mApiFunction\u001b[38;5;241m.\u001b[39mimportApi(\n\u001b[0;32m 76\u001b[0m \u001b[38;5;28mcls\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreduce\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mImageCollection\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
"File \u001b[1;32m~\\miniconda3\\envs\\gee\\lib\\site-packages\\ee\\collection.py:32\u001b[0m, in \u001b[0;36mCollection.initialize\u001b[1;34m(cls)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[38;5;124;03m\"\"\"Imports API functions to this class.\"\"\"\u001b[39;00m\n\u001b[0;32m 31\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_initialized:\n\u001b[1;32m---> 32\u001b[0m \u001b[43mapifunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mApiFunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimportApi\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mCollection\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mCollection\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 33\u001b[0m apifunction\u001b[38;5;241m.\u001b[39mApiFunction\u001b[38;5;241m.\u001b[39mimportApi(\n\u001b[0;32m 34\u001b[0m \u001b[38;5;28mcls\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAggregateFeatureCollection\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCollection\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124maggregate_\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 35\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_initialized \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
"File \u001b[1;32m~\\miniconda3\\envs\\gee\\lib\\site-packages\\ee\\apifunction.py:183\u001b[0m, in \u001b[0;36mApiFunction.importApi\u001b[1;34m(cls, target, prefix, type_name, opt_prepend)\u001b[0m\n\u001b[0;32m 170\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[0;32m 171\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mimportApi\u001b[39m(\u001b[38;5;28mcls\u001b[39m, target, prefix, type_name, opt_prepend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 172\u001b[0m \u001b[38;5;124;03m\"\"\"Adds all API functions that begin with a given prefix to a target class.\u001b[39;00m\n\u001b[0;32m 173\u001b[0m \n\u001b[0;32m 174\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 181\u001b[0m \u001b[38;5;124;03m added functions.\u001b[39;00m\n\u001b[0;32m 182\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 183\u001b[0m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minitialize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 184\u001b[0m prepend \u001b[38;5;241m=\u001b[39m opt_prepend \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 185\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, api_func \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_api\u001b[38;5;241m.\u001b[39mitems():\n",
"File \u001b[1;32m~\\miniconda3\\envs\\gee\\lib\\site-packages\\ee\\apifunction.py:154\u001b[0m, in \u001b[0;36mApiFunction.initialize\u001b[1;34m(cls)\u001b[0m\n\u001b[0;32m 152\u001b[0m \u001b[38;5;124;03m\"\"\"Initializes the list of signatures from the Earth Engine front-end.\"\"\"\u001b[39;00m\n\u001b[0;32m 153\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_api:\n\u001b[1;32m--> 154\u001b[0m signatures \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetAlgorithms\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 155\u001b[0m api \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m 156\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, sig \u001b[38;5;129;01min\u001b[39;00m signatures\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 157\u001b[0m \u001b[38;5;66;03m# Strip type parameters.\u001b[39;00m\n",
"File \u001b[1;32m~\\miniconda3\\envs\\gee\\lib\\site-packages\\ee\\data.py:1050\u001b[0m, in \u001b[0;36mgetAlgorithms\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1034\u001b[0m \u001b[38;5;124;03m\"\"\"Get the list of algorithms.\u001b[39;00m\n\u001b[0;32m 1035\u001b[0m \n\u001b[0;32m 1036\u001b[0m \u001b[38;5;124;03mReturns:\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1047\u001b[0m \u001b[38;5;124;03m is not specified.\u001b[39;00m\n\u001b[0;32m 1048\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1049\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1050\u001b[0m call \u001b[38;5;241m=\u001b[39m \u001b[43m_get_cloud_api_resource\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mprojects()\u001b[38;5;241m.\u001b[39malgorithms()\u001b[38;5;241m.\u001b[39mlist(\n\u001b[0;32m 1051\u001b[0m parent\u001b[38;5;241m=\u001b[39m_get_projects_path(), prettyPrint\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m 1052\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 1053\u001b[0m call \u001b[38;5;241m=\u001b[39m _get_cloud_api_resource()\u001b[38;5;241m.\u001b[39mprojects()\u001b[38;5;241m.\u001b[39malgorithms()\u001b[38;5;241m.\u001b[39mlist(\n\u001b[0;32m 1054\u001b[0m project\u001b[38;5;241m=\u001b[39m_get_projects_path(), prettyPrint\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
"File \u001b[1;32m~\\miniconda3\\envs\\gee\\lib\\site-packages\\ee\\data.py:284\u001b[0m, in \u001b[0;36m_get_cloud_api_resource\u001b[1;34m()\u001b[0m\n\u001b[0;32m 282\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_cloud_api_resource\u001b[39m():\n\u001b[0;32m 283\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _cloud_api_resource \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 284\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ee_exception\u001b[38;5;241m.\u001b[39mEEException(\n\u001b[0;32m 285\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEarth Engine client library not initialized. Run `ee.Initialize()`\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 286\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _cloud_api_resource\n",
"\u001b[1;31mEEException\u001b[0m: Earth Engine client library not initialized. Run `ee.Initialize()`"
]
}
],
"source": [
"image = ee.ImageCollection('COPERNICUS/S2') \\\n",
" .filterDate('2017-01-01', '2017-01-02').median() \\\n",
" .divide(10000) \\\n",
" .select(['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B12'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Z8Hm_lLIHzXe"
},
"outputs": [],
"source": [
"vis = {'bands': ['B4', 'B3', 'B2'], 'min': 0.05, 'max': 0.5}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bOCnsKG3HzXf"
},
"outputs": [],
"source": [
"# TEST: add feature to the Map\n",
"ee.mapclient.addToMap(image, vis,'S2')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-87kgiS7HzXf"
},
"source": [
"otros"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ipiRxsXqHzXr"
},
"outputs": [],
"source": [
"area = ee.Geometry.Polygon([[[-75.69327597674794,6.097637610738173],[-75.41861777362294,6.097637610738173],[-75.41861777362294,6.318123436693424],[-75.69327597674794,6.318123436693424],[-75.69327597674794,6.097637610738173]]])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IUEoC6xcHzXs"
},
"outputs": [],
"source": [
"# define the image\n",
"img = ee.Image(\"COPERNICUS/S2/20190827T152649_20190827T152643_T18NVM\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xpQY0irCHzXs"
},
"outputs": [],
"source": [
"# do any ee operation here\n",
"ndvi = ee.Image(img.normalizedDifference(['B8', 'B4']))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BHoXXtWuHzXt"
},
"outputs": [],
"source": [
"timedate = img.get('GENERATION_TIME').getInfo()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "k0dwJlMqHzXt"
},
"outputs": [],
"source": [
"# get the lat lon and add the ndvi\n",
"latlon = ee.Image.pixelLonLat().addBands(ndvi)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5kkNcH6pHzXt"
},
"outputs": [],
"source": [
"# apply reducer to list\n",
"latlon = latlon.reduceRegion(\n",
" reducer=ee.Reducer.toList(),\n",
" geometry=area,\n",
" maxPixels=1e8,\n",
" scale=20);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LbMwap7WHzXu"
},
"outputs": [],
"source": [
"# get data into three different arrays\n",
"data = np.array((ee.Array(latlon.get(\"nd\")).getInfo()))\n",
"lats = np.array((ee.Array(latlon.get(\"latitude\")).getInfo()))\n",
"lons = np.array((ee.Array(latlon.get(\"longitude\")).getInfo()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WT2je5KtHzXu"
},
"outputs": [],
"source": [
"# get the unique coordinates\n",
"uniqueLats = np.unique(lats)\n",
"uniqueLons = np.unique(lons)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Bn9o9TTQHzXv"
},
"outputs": [],
"source": [
"# get number of columns and rows from coordinates\n",
"ncols = len(uniqueLons)\n",
"nrows = len(uniqueLats)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fXEqgEvwHzXv"
},
"outputs": [],
"source": [
"# determine pixelsizes\n",
"ys = uniqueLats[1] - uniqueLats[0]\n",
"xs = uniqueLons[1] - uniqueLons[0]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lU7Be9M9HzXv"
},
"outputs": [],
"source": [
"# create an array with dimensions of image\n",
"arr = np.zeros([nrows, ncols], np.float32) #-9999"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CofvL32kHzXw"
},
"outputs": [],
"source": [
"# fill the array with values\n",
"counter =0\n",
"for y in range(0,len(arr),1):\n",
" for x in range(0,len(arr[0]),1):\n",
" if lats[counter] == uniqueLats[y] and lons[counter] == uniqueLons[x] and counter < len(lats)-1:\n",
" counter+=1\n",
" arr[len(uniqueLats)-1-y,x] = data[counter] # we start from lower left corner"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aH8CDnTQHzXw"
},
"outputs": [],
"source": [
"# in case you want to plot the image\n",
"import matplotlib.pyplot as plt\n",
"plt.imshow(arr)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8O4VrZnhHzXw"
},
"outputs": [],
"source": [
"# set the\n",
"#SetGeoTransform((originX, pixelWidth, 0, originY, 0, pixelHeight))\n",
"transform = (np.min(uniqueLons),xs,0,np.max(uniqueLats),0,-ys)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5x_RQmX5HzXx"
},
"outputs": [],
"source": [
"# set the coordinate system\n",
"target = osr.SpatialReference()\n",
"target.ImportFromEPSG(4326)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Qw5I7BtiHzXx"
},
"outputs": [],
"source": [
"# set driver\n",
"driver = gdal.GetDriverByName('GTiff')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uv80tbaRHzXy"
},
"outputs": [],
"source": [
"timestring = time.strftime(\"%Y%m%d_%H%M%S\")\n",
"outputDataset = driver.Create(\"G:\\My Drive\\ANALISIS GEOESPACIAL\\Talleres en Python/output.tif\", ncols,nrows, 1,gdal.GDT_Float32)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8zdjfx2BHzXy"
},
"outputs": [],
"source": [
"# add some metadata\n",
"outputDataset.SetMetadata( {'time': str(timedate), 'someotherInfo': 'lala'} )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hXnf2T3gHzXz"
},
"outputs": [],
"source": [
"outputDataset.SetGeoTransform(transform)\n",
"outputDataset.SetProjection(target.ExportToWkt())\n",
"outputDataset.GetRasterBand(1).WriteArray(arr)\n",
"outputDataset.GetRasterBand(1).SetNoDataValue(-9999)\n",
"outputDataset = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ihxJ_tPUHzXz"
},
"outputs": [],
"source": [
"# Define the roi\n",
"area = ee.Geometry.Polygon([[-75.69327597674794,6.197637610738173],\\\n",
" [-75.51861777362294,6.197637610738173],\\\n",
" [-75.51861777362294,6.318123436693424],\\\n",
" [-75.69327597674794,6.318123436693424],\\\n",
" [-75.69327597674794,6.197637610738173]])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "69WzlxciHzX0"
},
"outputs": [],
"source": [
"# define the image\n",
"collection = ee.ImageCollection(\"COPERNICUS/S2\").filterBounds(area)\\\n",
" .filterDate(\"2018-01-01\",\"2019-01-10\")\\\n",
" .filterMetadata(\"CLOUDY_PIXEL_PERCENTAGE\",\"less_than\",10)\\\n",
" .select(['B8', 'B4'])\n",
"\n",
"print(\" number of images: \",collection.size().getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3xvjODIMHzX0"
},
"outputs": [],
"source": [
"# perform any calculation on the image collection here\n",
"def anyFunction(img):\n",
" ndvi = ee.Image(img.normalizedDifference(['B8', 'B4'])).rename([\"ndvi\"])\n",
" return ndvi"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GVgXczT4HzX1"
},
"outputs": [],
"source": [
"# export the latitude, longitude and array\n",
"def LatLonImg(img):\n",
" img = img.addBands(ee.Image.pixelLonLat())\n",
"\n",
" img = img.reduceRegion(reducer=ee.Reducer.toList(),\\\n",
" geometry=area,\\\n",
" maxPixels=1e13,\\\n",
" scale=10);\n",
"\n",
" data = np.array((ee.Array(img.get(\"result\")).getInfo()))\n",
" lats = np.array((ee.Array(img.get(\"latitude\")).getInfo()))\n",
" lons = np.array((ee.Array(img.get(\"longitude\")).getInfo()))\n",
" return lats, lons, data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NhHPpz-uHzX1"
},
"outputs": [],
"source": [
"# covert the lat, lon and array into an image\n",
"def toImage(lats,lons,data):\n",
"\n",
" # get the unique coordinates\n",
" uniqueLats = np.unique(lats)\n",
" uniqueLons = np.unique(lons)\n",
"\n",
" # get number of columns and rows from coordinates\n",
" ncols = len(uniqueLons)\n",
" nrows = len(uniqueLats)\n",
"\n",
" # determine pixelsizes\n",
" ys = uniqueLats[1] - uniqueLats[0]\n",
" xs = uniqueLons[1] - uniqueLons[0]\n",
"\n",
" # create an array with dimensions of image\n",
" arr = np.zeros([nrows, ncols], np.float32) #-9999\n",
"\n",
" # fill the array with values\n",
" counter =0\n",
" for y in range(0,len(arr),1):\n",
" for x in range(0,len(arr[0]),1):\n",
" if lats[counter] == uniqueLats[y] and lons[counter] == uniqueLons[x] and counter < len(lats)-1:\n",
" counter+=1\n",
" arr[len(uniqueLats)-1-y,x] = data[counter] # we start from lower left corner\n",
" return arr"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Fi9g8S5LHzX1"
},
"outputs": [],
"source": [
"\n",
"# map over the image collection\n",
"myCollection = collection.map(anyFunction)\n",
"\n",
"# get the median\n",
"result = ee.Image(myCollection.median()).rename(['result'])\n",
"\n",
"# get the lon, lat and result as 1d array\n",
"lat, lon, data = LatLonImg(result)\n",
"\n",
"# 1d to 2d array\n",
"image = toImage(lat,lon,data)\n",
"\n",
"# in case you want to plot the image\n",
"import matplotlib.pyplot as plt\n",
"plt.imshow(image)\n",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "ml",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| | |
The Shuttle Radar Topography Mission (SRTM, see Farr\\net al. 2007)\\ndigital elevation data is an international research effort that\\nobtained digital elevation models on a near-global scale. This\\nSRTM V3 product (SRTM Plus) is provided by NASA JPL\\nat a resolution of 1 arc-second (approximately 30m).
This dataset has undergone a void-filling process using open-source data\\n(ASTER GDEM2, GMTED2010, and NED), as opposed to other versions that\\ncontain voids or have been void-filled with commercial sources.\\nFor more information on the different versions see the\\nSRTM Quick Guide.
Documentation:
Provider: NASA / USGS / JPL-Caltech
Bands
| Name | Description |
|---|---|
| elevation | Elevation |
Terms of Use
Unless otherwise noted, images and video on JPL public\\nweb sites (public sites ending with a jpl.nasa.gov address) may\\nbe used for any purpose without prior permission. For more information\\nand exceptions visit the JPL Image Use Policy site.
Suggested citation(s)
Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley,\\nS., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D.,\\nShaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank,\\nD., and Alsdorf, D.E., 2007, The shuttle radar topography mission:\\nReviews of Geophysics, v. 45, no. 2, RG2004, at\\nhttps://doi.org/10.1029/2005RG000183.
To authorize access needed by Earth Engine, open the following\n", " URL in a web browser and follow the instructions:
\n", " \n", "The authorization workflow will generate a code, which you\n", " should paste in the box below
\n", " " ], "text/plain": [ "