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giswqs/point_based_extraction_gee_102020_v3.ipynb

Last active October 23, 2020 02:42
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point_based_extraction_gee_102020_v3.ipynb
{
"cells": [
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "!pip install whitebox",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": "Requirement already satisfied: whitebox in /home/qiusheng/.conda/envs/geemap/lib/python3.8/site-packages (1.4.0)\nRequirement already satisfied: Click>=6.0 in /home/qiusheng/.local/lib/python3.8/site-packages (from whitebox) (7.1.2)\n\u001b[33mWARNING: You are using pip version 20.2.2; however, version 20.2.4 is available.\nYou should consider upgrading via the '/home/qiusheng/.conda/envs/geemap/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import os\nimport ee\nimport geemap\nimport whitebox",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "work_dir = os.getcwd()\nwork_dir",
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 3,
"data": {
"text/plain": "'/home/qiusheng/Documents/GEE'"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "wbt = whitebox.WhiteboxTools()",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "in_csv = os.path.join(work_dir, 'pts.csv')\nout_shp = os.path.join(work_dir, 'pts.shp')",
"execution_count": 5,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "wbt.csv_points_to_vector(in_csv, out_shp, xfield=2, yfield=3, epsg=4326)",
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": "./whitebox_tools --run=\"CsvPointsToVector\" --input='/home/qiusheng/Documents/GEE/pts.csv' --output='/home/qiusheng/Documents/GEE/pts.shp' --xfield=2 --yfield=3 --epsg='4326' -v\n\n********************************\n* Welcome to CsvPointsToVector *\n********************************\nReading data...\nProgress: 33%\nProgress: 44%\nProgress: 55%\nProgress: 66%\nProgress: 77%\nProgress: 88%\nProgress: 100%\nProgress: 111%\nProgress: 122%\nSaving data...\nOutput file written\nElapsed Time: 0.1s\n",
"name": "stdout"
},
{
"output_type": "execute_result",
"execution_count": 6,
"data": {
"text/plain": "0"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Map = geemap.Map()\nMap",
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "Map(center=[40, -100], controls=(WidgetControl(options=['position'], widget=HBox(children=(ToggleButton(value=…",
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d903560b6199403aaf24c5893733f646"
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# points = []\n# #with open('D:/pts.csv') as f:\n# with open('pts.csv') as f:\n# lines = f.readlines()\n# for line in lines[1:]:\n# lon = float(line.split(',')[2])\n# lat = float(line.split(',')[3])\n# points.append([lon, lat])",
"execution_count": 8,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# ee_list = ee.List(points)",
"execution_count": 9,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# ee_points = ee_list.map(lambda xy: ee.Feature(ee.Geometry.Point(xy)))",
"execution_count": 10,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# roi = ee.FeatureCollection(ee_points)",
"execution_count": 11,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "roi = geemap.shp_to_ee(out_shp)",
"execution_count": 12,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# roi.getInfo()",
"execution_count": 13,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Map.addLayer(roi, {}, 'points')\nMap.centerObject(roi, 15)",
"execution_count": 14,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "NEDelevation = ee.Image('USGS/NED')\nMap.addLayer(NEDelevation, {'min':0, 'max':4000},'NED')",
"execution_count": 15,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "slope = ee.Terrain.slope(NEDelevation)\nMap.addLayer(slope,{'min':0,'max':45},'Slope')",
"execution_count": 16,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "aspect = ee.Terrain.aspect(NEDelevation)\nMap.addLayer(aspect,{'min':0,'max':360},'Aspect')",
"execution_count": 17,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def maskS2clouds(image):\n qa = image.select('QA60')\n cloudBitMask = 1 << 10\n cirrusBitMask = 1 << 11\n mask = qa.bitwiseAnd(cloudBitMask).eq(0) \\\n .And(qa.bitwiseAnd(cirrusBitMask).eq(0))\n return image.updateMask(mask).divide(10000)",
"execution_count": 18,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "start_year = 2016\nend_year = 2020\nstart_month = 6\nend_month = 10\n\ndate_list = []\nfor year in range(start_year, end_year):\n for month in range(start_month, end_month):\n item = str(year) + '-' + str(month).zfill(2) + '-01'\n date_list.append(item)\nprint(date_list)",
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": "['2016-06-01', '2016-07-01', '2016-08-01', '2016-09-01', '2017-06-01', '2017-07-01', '2017-08-01', '2017-09-01', '2018-06-01', '2018-07-01', '2018-08-01', '2018-09-01', '2019-06-01', '2019-07-01', '2019-08-01', '2019-09-01']\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "dates = ee.List(date_list)",
"execution_count": 20,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "dates.getInfo()",
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 21,
"data": {
"text/plain": "['2016-06-01',\n '2016-07-01',\n '2016-08-01',\n '2016-09-01',\n '2017-06-01',\n '2017-07-01',\n '2017-08-01',\n '2017-09-01',\n '2018-06-01',\n '2018-07-01',\n '2018-08-01',\n '2018-09-01',\n '2019-06-01',\n '2019-07-01',\n '2019-08-01',\n '2019-09-01']"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def get_ndvi(start_date):\n start_date = ee.Date(start_date)\n end_date = start_date.advance(1, 'month')\n image = ee.ImageCollection('COPERNICUS/S2') \\\n .filterDate(start_date, end_date) \\\n .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 100)) \\\n .map(maskS2clouds) \\\n .mean() \n \n monthly_image = image.expression(\n \" (VNIR - RED) / sqrt((VNIR + RED))\",\n {\n 'RED': image.select(\"B4\"), \n 'VNIR': image.select(\"B8\"), \n } \n )\n \n return monthly_image.rename(ee.String(\"ndvi_\").cat(start_date.format('YYYY-MM-dd')))\n \n# return monthly_image.normalizedDifference(['B8', 'B4']).rename(start_date.format('YYYY-MM-dd'))",
"execution_count": 22,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "ndvi_images = ee.ImageCollection(dates.map(get_ndvi))\nndvi_bands = ndvi_images.toBands() # convert imageCollection to an image",
"execution_count": 23,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "ndvi_bands.bandNames().getInfo()",
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 24,
"data": {
"text/plain": "['0_ndvi_2016-06-01',\n '1_ndvi_2016-07-01',\n '2_ndvi_2016-08-01',\n '3_ndvi_2016-09-01',\n '4_ndvi_2017-06-01',\n '5_ndvi_2017-07-01',\n '6_ndvi_2017-08-01',\n '7_ndvi_2017-09-01',\n '8_ndvi_2018-06-01',\n '9_ndvi_2018-07-01',\n '10_ndvi_2018-08-01',\n '11_ndvi_2018-09-01',\n '12_ndvi_2019-06-01',\n '13_ndvi_2019-07-01',\n '14_ndvi_2019-08-01',\n '15_ndvi_2019-09-01']"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Map.addLayer(ndvi_bands, {}, 'ndvi')",
"execution_count": 25,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def get_gndvi(start_date):\n start_date = ee.Date(start_date)\n end_date = start_date.advance(1, 'month')\n image = ee.ImageCollection('COPERNICUS/S2') \\\n .filterDate(start_date, end_date) \\\n .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 100)) \\\n .map(maskS2clouds) \\\n .mean() \n \n \n monthly_image = image.expression(\n \" (VNIR - GREEN) / sqrt((VNIR + GREEN))\",\n {\n 'GREEN': image.select(\"B3\"), \n 'VNIR': image.select(\"B8\"), \n } \n )\n \n return monthly_image.rename(ee.String(\"gndvi_\").cat(start_date.format('YYYY-MM-dd')))\n \n# return monthly_image.normalizedDifference(['B8', 'B4']).rename(start_date.format('YYYY-MM-dd'))",
"execution_count": 26,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "gndvi_images = ee.ImageCollection(dates.map(get_gndvi))\ngndvi_bands = gndvi_images.toBands() # convert imageCollection to an image",
"execution_count": 27,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "gndvi_bands.bandNames().getInfo()",
"execution_count": 28,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 28,
"data": {
"text/plain": "['0_gndvi_2016-06-01',\n '1_gndvi_2016-07-01',\n '2_gndvi_2016-08-01',\n '3_gndvi_2016-09-01',\n '4_gndvi_2017-06-01',\n '5_gndvi_2017-07-01',\n '6_gndvi_2017-08-01',\n '7_gndvi_2017-09-01',\n '8_gndvi_2018-06-01',\n '9_gndvi_2018-07-01',\n '10_gndvi_2018-08-01',\n '11_gndvi_2018-09-01',\n '12_gndvi_2019-06-01',\n '13_gndvi_2019-07-01',\n '14_gndvi_2019-08-01',\n '15_gndvi_2019-09-01']"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Map.addLayer(gndvi_bands, {}, 'gndvi')",
"execution_count": 29,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Compute Optimized Soil Adjusted Vegetation Index (OSAVI)",
"execution_count": 30,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def get_osavi(start_date):\n start_date = ee.Date(start_date)\n end_date = start_date.advance(1, 'month')\n image = ee.ImageCollection('COPERNICUS/S2') \\\n .filterBounds(roi) \\\n .filterDate(start_date, end_date) \\\n .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 100)) \\\n .map(maskS2clouds) \\\n .mean() \\\n .clip(roi)\n \n monthly_image = image.expression(\n \" (1.0 + 0.16)*(VNIR - RED)/(VNIR + RED + 0.16)\",\n {\n 'RED': image.select(\"B4\"), \n 'VNIR': image.select(\"B8\"), \n } \n )\n \n return monthly_image.rename(ee.String(\"osavi_\").cat(start_date.format('YYYY-MM-dd')))\n \n# return monthly_image.normalizedDifference(['B8', 'B4']).rename(start_date.format('YYYY-MM-dd')) ",
"execution_count": 31,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "osavi_images = ee.ImageCollection(dates.map(get_osavi))\nosavi_bands = osavi_images.toBands() # convert imageCollection to an image",
"execution_count": 32,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "osavi_bands.bandNames().getInfo()",
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 33,
"data": {
"text/plain": "['0_osavi_2016-06-01',\n '1_osavi_2016-07-01',\n '2_osavi_2016-08-01',\n '3_osavi_2016-09-01',\n '4_osavi_2017-06-01',\n '5_osavi_2017-07-01',\n '6_osavi_2017-08-01',\n '7_osavi_2017-09-01',\n '8_osavi_2018-06-01',\n '9_osavi_2018-07-01',\n '10_osavi_2018-08-01',\n '11_osavi_2018-09-01',\n '12_osavi_2019-06-01',\n '13_osavi_2019-07-01',\n '14_osavi_2019-08-01',\n '15_osavi_2019-09-01']"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Map.addLayer(ndvi_bands, {}, 'osavi')",
"execution_count": 34,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Compute Renormalized Difference Vegetation Index (RDVI) ",
"execution_count": 35,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def get_rdvi(start_date):\n start_date = ee.Date(start_date)\n end_date = start_date.advance(1, 'month')\n image = ee.ImageCollection('COPERNICUS/S2') \\\n .filterBounds(roi) \\\n .filterDate(start_date, end_date) \\\n .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 100)) \\\n .map(maskS2clouds) \\\n .mean() \\\n .clip(roi)\n \n monthly_image = image.expression(\n \" (VNIR - RED)/sqrt(VNIR + RED)\",\n {\n 'RED': image.select(\"B4\"), \n 'VNIR': image.select(\"B8\"), \n } \n )\n \n return monthly_image.rename(ee.String(\"rdvi_\").cat(start_date.format('YYYY-MM-dd')))\n \n# return monthly_image.normalizedDifference(['B8', 'B4']).rename(start_date.format('YYYY-MM-dd')) ",
"execution_count": 36,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "rdvi_images = ee.ImageCollection(dates.map(get_rdvi))\nrdvi_bands = rdvi_images.toBands() # convert imageCollection to an image",
"execution_count": 37,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "rdvi_bands.bandNames().getInfo()",
"execution_count": 38,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 38,
"data": {
"text/plain": "['0_rdvi_2016-06-01',\n '1_rdvi_2016-07-01',\n '2_rdvi_2016-08-01',\n '3_rdvi_2016-09-01',\n '4_rdvi_2017-06-01',\n '5_rdvi_2017-07-01',\n '6_rdvi_2017-08-01',\n '7_rdvi_2017-09-01',\n '8_rdvi_2018-06-01',\n '9_rdvi_2018-07-01',\n '10_rdvi_2018-08-01',\n '11_rdvi_2018-09-01',\n '12_rdvi_2019-06-01',\n '13_rdvi_2019-07-01',\n '14_rdvi_2019-08-01',\n '15_rdvi_2019-09-01']"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Map.addLayer(ndvi_bands, {}, 'rdvi')",
"execution_count": 39,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Compute RGB432B\n# Compute SAVI \n# Compute SCL",
"execution_count": 40,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def get_tcari(start_date):\n start_date = ee.Date(start_date)\n end_date = start_date.advance(1, 'month')\n image = ee.ImageCollection('COPERNICUS/S2') \\\n .filterBounds(roi) \\\n .filterDate(start_date, end_date) \\\n .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 100)) \\\n .map(maskS2clouds) \\\n .mean() \\\n .clip(roi)\n \n monthly_image = image.expression(\n \" 3.0*(VNIR - RED) - 0.2*(VNIR - GREEN)*(VNIR / RED)\",\n {\n 'GREEN': image.select(\"B3\"),\n 'RED': image.select(\"B4\"), \n 'VNIR': image.select(\"B8\"), \n } \n )\n \n return monthly_image.rename(ee.String(\"tcari_\").cat(start_date.format('YYYY-MM-dd')))\n \n# return monthly_image.normalizedDifference(['B8', 'B4']).rename(start_date.format('YYYY-MM-dd')) ",
"execution_count": 41,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "tcari_images = ee.ImageCollection(dates.map(get_tcari))\ntcari_bands = tcari_images.toBands() # convert imageCollection to an image",
"execution_count": 42,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "tcari_bands.bandNames().getInfo()",
"execution_count": 43,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 43,
"data": {
"text/plain": "['0_tcari_2016-06-01',\n '1_tcari_2016-07-01',\n '2_tcari_2016-08-01',\n '3_tcari_2016-09-01',\n '4_tcari_2017-06-01',\n '5_tcari_2017-07-01',\n '6_tcari_2017-08-01',\n '7_tcari_2017-09-01',\n '8_tcari_2018-06-01',\n '9_tcari_2018-07-01',\n '10_tcari_2018-08-01',\n '11_tcari_2018-09-01',\n '12_tcari_2019-06-01',\n '13_tcari_2019-07-01',\n '14_tcari_2019-08-01',\n '15_tcari_2019-09-01']"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Map.addLayer(ndvi_bands, {}, 'tcari')",
"execution_count": 44,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Compute Triangular chlorophyll index (TCI)",
"execution_count": 45,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def get_tci(start_date):\n start_date = ee.Date(start_date)\n end_date = start_date.advance(1, 'month')\n image = ee.ImageCollection('COPERNICUS/S2') \\\n .filterBounds(roi) \\\n .filterDate(start_date, end_date) \\\n .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 100)) \\\n .map(maskS2clouds) \\\n .mean() \\\n .clip(roi)\n \n monthly_image = image.expression(\n \" 1.2*(VNIR - GREEN) - 1.5*(RED - GREEN)*sqrt(VNIR / RED)\",\n {\n 'GREEN': image.select(\"B3\"),\n 'RED': image.select(\"B4\"), \n 'VNIR': image.select(\"B8\"), \n } \n )\n \n return monthly_image.rename(ee.String(\"tci_\").cat(start_date.format('YYYY-MM-dd')))\n \n# return monthly_image.normalizedDifference(['B8', 'B4']).rename(start_date.format('YYYY-MM-dd')) ",
"execution_count": 46,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "tci_images = ee.ImageCollection(dates.map(get_tci))\ntci_bands = tci_images.toBands() # convert imageCollection to an image",
"execution_count": 47,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "tci_bands.bandNames().getInfo()",
"execution_count": 48,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 48,
"data": {
"text/plain": "['0_tci_2016-06-01',\n '1_tci_2016-07-01',\n '2_tci_2016-08-01',\n '3_tci_2016-09-01',\n '4_tci_2017-06-01',\n '5_tci_2017-07-01',\n '6_tci_2017-08-01',\n '7_tci_2017-09-01',\n '8_tci_2018-06-01',\n '9_tci_2018-07-01',\n '10_tci_2018-08-01',\n '11_tci_2018-09-01',\n '12_tci_2019-06-01',\n '13_tci_2019-07-01',\n '14_tci_2019-08-01',\n '15_tci_2019-09-01']"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Map.addLayer(ndvi_bands, {}, 'tci')",
"execution_count": 49,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Normalized Ratio between NIR band and a SWIR (NDWI)\n# NDWI is correlated with vegetaion water content ",
"execution_count": 50,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def get_ndwi(start_date):\n start_date = ee.Date(start_date)\n end_date = start_date.advance(1, 'month')\n image = ee.ImageCollection('COPERNICUS/S2') \\\n .filterBounds(roi) \\\n .filterDate(start_date, end_date) \\\n .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 100)) \\\n .map(maskS2clouds) \\\n .mean() \\\n .clip(roi)\n \n monthly_image = image.expression(\n \" (0.842*SWIR*2.190*SWIR)/(0.842*SWIR + 2.19*SWIR)\",\n {\n 'SWIR': image.select(\"B12\"),\n } \n )\n \n return monthly_image.rename(ee.String(\"ndwi_\").cat(start_date.format('YYYY-MM-dd')))\n \n# return monthly_image.normalizedDifference(['B8', 'B4']).rename(start_date.format('YYYY-MM-dd')) ",
"execution_count": 51,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "ndwi_images = ee.ImageCollection(dates.map(get_ndwi))\nndwi_bands = ndwi_images.toBands() # convert imageCollection to an image",
"execution_count": 52,
"outputs": []
},
{
"metadata": {
"scrolled": false,
"trusted": true
},
"cell_type": "code",
"source": "ndwi_bands.bandNames().getInfo()",
"execution_count": 53,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 53,
"data": {
"text/plain": "['0_ndwi_2016-06-01',\n '1_ndwi_2016-07-01',\n '2_ndwi_2016-08-01',\n '3_ndwi_2016-09-01',\n '4_ndwi_2017-06-01',\n '5_ndwi_2017-07-01',\n '6_ndwi_2017-08-01',\n '7_ndwi_2017-09-01',\n '8_ndwi_2018-06-01',\n '9_ndwi_2018-07-01',\n '10_ndwi_2018-08-01',\n '11_ndwi_2018-09-01',\n '12_ndwi_2019-06-01',\n '13_ndwi_2019-07-01',\n '14_ndwi_2019-08-01',\n '15_ndwi_2019-09-01']"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Map.addLayer(ndvi_bands, {}, 'ndwi')",
"execution_count": 54,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Normalized Ratio between SWIR band NIR (NDBI) ",
"execution_count": 55,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#ndbi_images = ee.ImageCollection(dates.map(get_ndbi))\n#ndbi_bands = ndbi_images.toBands() # convert imageCollection to an image",
"execution_count": 56,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#ndbi_bands.bandNames().getInfo()",
"execution_count": 57,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "image = NEDelevation.addBands(slope).addBands(aspect).addBands(ndvi_bands).addBands(gndvi_bands).addBands(osavi_bands).addBands(rdvi_bands).addBands(tcari_bands).addBands(tci_bands).addBands(ndwi_bands)\n#image = NEDelevation.addBands(slope).addBands(aspect).addBands(ndvi_bands).addBands(gndvi_bands)",
"execution_count": 58,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "values = image.sampleRegions(**{\n 'collection': roi,\n 'scale': 10\n})",
"execution_count": 59,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "out_csv = os.path.join(work_dir, 'values.csv')",
"execution_count": 60,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "geemap.ee_export_vector(values, out_csv)",
"execution_count": 61,
"outputs": [
{
"output_type": "stream",
"text": "Generating URL ...\nDownloading data from https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/tables/bcd745b6f43de0d33195dd0e0863f312-20d7bb5bcdfbd69e6d5b0e1989bbff5f:getFeatures\nPlease wait ...\nData downloaded to /home/qiusheng/Documents/GEE/values.csv\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# values.first().getInfo()",
"execution_count": 62,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# result_info = values.first().getInfo()",
"execution_count": 63,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# result_info",
"execution_count": 64,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# import pandas as pd\n# df = pd.DataFrame.from_dict(result_info)",
"execution_count": 65,
"outputs": []
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.8.2",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"hide_input": false,
"toc": {
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": true,
"base_numbering": 1,
"title_cell": "Table of Contents",
"title_sidebar": "Table of Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": true
},
"varInspector": {
"window_display": false,
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"library": "var_list.py",
"delete_cmd_prefix": "del ",
"delete_cmd_postfix": "",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"library": "var_list.r",
"delete_cmd_prefix": "rm(",
"delete_cmd_postfix": ") ",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
]
},
"gist": {
"id": "55ffe0fc32a86e4265bb2605d66cb1c6",
"data": {
"description": "point_based_extraction_gee_102020_v3.ipynb",
"public": true
}
},
"_draft": {
"nbviewer_url": "https://gist.github.com/55ffe0fc32a86e4265bb2605d66cb1c6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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