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| { | |
| "cells": [ | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import ee\nimport geemap", | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "scrolled": false, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "Map = geemap.Map()\nMap", | |
| "execution_count": 2, | |
| "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": "da0f2305a5614d2c89ba1699554f48ff" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Replace roi with your featureCollection" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "roi = ee.FeatureCollection('TIGER/2018/States') \\\n .filterBounds(ee.Geometry.Rectangle([-75.9311, 38.7880, -75.5467, 39.0956]))\nMap.centerObject(roi, 8)\nMap.addLayer(roi, {}, \"roi\")", | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Composite image bands" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "NEDelevation = ee.Image('USGS/NED').clip(roi)\nMap.addLayer(NEDelevation, {'min':0, 'max':4000},'NED')", | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "slope = ee.Terrain.slope(NEDelevation).clip(roi)\nMap.addLayer(slope,{'min':0,'max':45},'Slope')", | |
| "execution_count": 5, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "aspect = ee.Terrain.aspect(NEDelevation).clip(roi)\nMap.addLayer(aspect,{'min':0,'max':360},'Aspect')", | |
| "execution_count": 6, | |
| "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": 7, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Create monthly (or daily, yearly) date sequence " | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "# start_date = '2018-01-01'\n# end_date = '2018-06-30'\n# dates = geemap.date_sequence(start_date, end_date, 'month')", | |
| "execution_count": 8, | |
| "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": 9, | |
| "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": 10, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "dates.getInfo()", | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 11, | |
| "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": {}, | |
| "cell_type": "markdown", | |
| "source": "Generate NDVI image for a specific month (or year, day)" | |
| }, | |
| { | |
| "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 .filterBounds(roi) \\\n .filterDate(start_date, end_date) \\\n .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)) \\\n .map(maskS2clouds) \\\n .median() \\\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(start_date.format('YYYY-MM-dd'))\n \n# return monthly_image.normalizedDifference(['B8', 'B4']).rename(start_date.format('YYYY-MM-dd')) ", | |
| "execution_count": 12, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Use map() function to loop through the dates list" | |
| }, | |
| { | |
| "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": 13, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "ndvi_bands.bandNames().getInfo()", | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 14, | |
| "data": { | |
| "text/plain": "['0_2016-06-01',\n '1_2016-07-01',\n '2_2016-08-01',\n '3_2016-09-01',\n '4_2017-06-01',\n '5_2017-07-01',\n '6_2017-08-01',\n '7_2017-09-01',\n '8_2018-06-01',\n '9_2018-07-01',\n '10_2018-08-01',\n '11_2018-09-01',\n '12_2019-06-01',\n '13_2019-07-01',\n '14_2019-08-01',\n '15_2019-09-01']" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "Map.addLayer(ndvi_bands, {}, 'ndvi')", | |
| "execution_count": 15, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "image = NEDelevation.addBands(slope).addBands(aspect).addBands(ndvi_bands)", | |
| "execution_count": 16, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "# scale = 30 # spatial resolution to calculate statistics\n\n# This can take several minutes\n\n# statistics type can be: MEAN, MAXIMUM, MEDIAN, MINIMUM, STD, MIN_MAX, SUM\n\n# geemap.zonal_statistics(image, roi, 'mean.csv', statistics_type='MEAN', scale=30)", | |
| "execution_count": 17, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "scale = 1000\nmean = image.reduceRegions(collection=roi, reducer=ee.Reducer.mean(), scale=scale)\nmean = mean.map(lambda f: f.set({'stat_type': 'mean'}))\nstd = image.reduceRegions(collection=roi, reducer=ee.Reducer.stdDev(), scale=scale)\nstd = std.map(lambda f: f.set({'stat_type': 'std'}))\nminimum = image.reduceRegions(collection=roi, reducer=ee.Reducer.min(), scale=scale)\nminimum = minimum.map(lambda f: f.set({'stat_type': 'min'}))\nmaximum = image.reduceRegions(collection=roi, reducer=ee.Reducer.max(), scale=scale)\nmaximum = maximum.map(lambda f: f.set({'stat_type': 'max'}))", | |
| "execution_count": 18, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "result = mean.merge(std).merge(minimum).merge(maximum)\nresult_info = result.getInfo()", | |
| "execution_count": 19, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "# result_info", | |
| "execution_count": 20, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "result_info['columns']", | |
| "execution_count": 21, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 21, | |
| "data": { | |
| "text/plain": "{'0_2016-06-01': 'Float',\n '10_2018-08-01': 'Float',\n '11_2018-09-01': 'Float',\n '12_2019-06-01': 'Float',\n '13_2019-07-01': 'Float',\n '14_2019-08-01': 'Float',\n '15_2019-09-01': 'Float',\n '1_2016-07-01': 'Float',\n '2_2016-08-01': 'Float',\n '3_2016-09-01': 'Float',\n '4_2017-06-01': 'Float',\n '5_2017-07-01': 'Float',\n '6_2017-08-01': 'Float',\n '7_2017-09-01': 'Float',\n '8_2018-06-01': 'Float',\n '9_2018-07-01': 'Float',\n 'ALAND': 'Long',\n 'AWATER': 'Long',\n 'DIVISION': 'String',\n 'FUNCSTAT': 'String',\n 'GEOID': 'String',\n 'INTPTLAT': 'String',\n 'INTPTLON': 'String',\n 'LSAD': 'String',\n 'MTFCC': 'String',\n 'NAME': 'String',\n 'REGION': 'String',\n 'STATEFP': 'String',\n 'STATENS': 'String',\n 'STUSPS': 'String',\n 'aspect': 'Float',\n 'elevation': 'Float',\n 'slope': 'Float',\n 'stat_type': 'String',\n 'system:index': 'String'}" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import geopandas as gpd", | |
| "execution_count": 22, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df = gpd.GeoDataFrame.from_features(result_info['features'])", | |
| "execution_count": 23, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df", | |
| "execution_count": 24, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 24, | |
| "data": { | |
| "text/plain": " geometry 0_2016-06-01 \\\n0 GEOMETRYCOLLECTION (LINESTRING (-77.52722 39.1... 0.284564 \n1 POLYGON ((-75.78902 39.65843, -75.78898 39.658... 0.260565 \n2 GEOMETRYCOLLECTION (LINESTRING (-77.52722 39.1... 0.194916 \n3 POLYGON ((-75.78902 39.65843, -75.78898 39.658... 0.169765 \n4 GEOMETRYCOLLECTION (LINESTRING (-77.52722 39.1... -0.136002 \n5 POLYGON ((-75.78902 39.65843, -75.78898 39.658... -0.126619 \n6 GEOMETRYCOLLECTION (LINESTRING (-77.52722 39.1... 0.621621 \n7 POLYGON ((-75.78902 39.65843, -75.78898 39.658... 0.557600 \n\n 10_2018-08-01 11_2018-09-01 12_2019-06-01 13_2019-07-01 14_2019-08-01 \\\n0 0.283944 0.219309 0.316866 0.321156 0.286520 \n1 0.286552 0.184872 0.273753 0.290885 0.278545 \n2 0.202376 0.185845 0.200007 0.199094 0.190424 \n3 0.196017 0.181372 0.184979 0.195976 0.193160 \n4 -0.211282 -0.300452 -0.138128 -0.119868 -0.126583 \n5 -0.121586 -0.452180 -0.125007 -0.100369 -0.110864 \n6 0.653156 0.623258 0.682850 0.628371 0.599217 \n7 0.617467 0.623408 0.614060 0.590652 0.612473 \n\n 15_2019-09-01 1_2016-07-01 2_2016-08-01 ... MTFCC NAME REGION \\\n0 0.256468 0.282624 0.341025 ... G4000 Maryland 3 \n1 0.249326 0.246035 0.175100 ... G4000 Delaware 3 \n2 0.173537 0.191998 0.152416 ... G4000 Maryland 3 \n3 0.180049 0.179679 0.196984 ... G4000 Delaware 3 \n4 -0.144259 -0.164000 -0.124886 ... G4000 Maryland 3 \n5 -0.117901 -0.105460 -0.116451 ... G4000 Delaware 3 \n6 0.605813 0.644847 0.624291 ... G4000 Maryland 3 \n7 0.594031 0.602972 0.613512 ... G4000 Delaware 3 \n\n STATEFP STATENS STUSPS aspect elevation slope stat_type \n0 24 01714934 MD 159.739159 104.544272 0.859940 mean \n1 10 01779781 DE 136.077499 11.554610 0.146803 mean \n2 24 01714934 MD 106.560749 185.224468 1.530845 std \n3 10 01779781 DE 110.884244 15.832690 0.285621 std \n4 24 01714934 MD 0.000000 -0.090338 0.000000 min \n5 10 01779781 DE 0.000000 -0.042177 0.000000 min \n6 24 01714934 MD 360.000000 950.324707 16.734945 max \n7 10 01779781 DE 359.950195 131.447540 4.026423 max \n\n[8 rows x 35 columns]", | |
| "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>geometry</th>\n <th>0_2016-06-01</th>\n <th>10_2018-08-01</th>\n <th>11_2018-09-01</th>\n <th>12_2019-06-01</th>\n <th>13_2019-07-01</th>\n <th>14_2019-08-01</th>\n <th>15_2019-09-01</th>\n <th>1_2016-07-01</th>\n <th>2_2016-08-01</th>\n <th>...</th>\n <th>MTFCC</th>\n <th>NAME</th>\n <th>REGION</th>\n <th>STATEFP</th>\n <th>STATENS</th>\n <th>STUSPS</th>\n <th>aspect</th>\n <th>elevation</th>\n <th>slope</th>\n <th>stat_type</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>GEOMETRYCOLLECTION (LINESTRING (-77.52722 39.1...</td>\n <td>0.284564</td>\n <td>0.283944</td>\n <td>0.219309</td>\n <td>0.316866</td>\n <td>0.321156</td>\n <td>0.286520</td>\n <td>0.256468</td>\n <td>0.282624</td>\n <td>0.341025</td>\n <td>...</td>\n <td>G4000</td>\n <td>Maryland</td>\n <td>3</td>\n <td>24</td>\n <td>01714934</td>\n <td>MD</td>\n <td>159.739159</td>\n <td>104.544272</td>\n <td>0.859940</td>\n <td>mean</td>\n </tr>\n <tr>\n <th>1</th>\n <td>POLYGON ((-75.78902 39.65843, -75.78898 39.658...</td>\n <td>0.260565</td>\n <td>0.286552</td>\n <td>0.184872</td>\n <td>0.273753</td>\n <td>0.290885</td>\n <td>0.278545</td>\n <td>0.249326</td>\n <td>0.246035</td>\n <td>0.175100</td>\n <td>...</td>\n <td>G4000</td>\n <td>Delaware</td>\n <td>3</td>\n <td>10</td>\n <td>01779781</td>\n <td>DE</td>\n <td>136.077499</td>\n <td>11.554610</td>\n <td>0.146803</td>\n <td>mean</td>\n </tr>\n <tr>\n <th>2</th>\n <td>GEOMETRYCOLLECTION (LINESTRING (-77.52722 39.1...</td>\n <td>0.194916</td>\n <td>0.202376</td>\n <td>0.185845</td>\n <td>0.200007</td>\n <td>0.199094</td>\n <td>0.190424</td>\n <td>0.173537</td>\n <td>0.191998</td>\n <td>0.152416</td>\n <td>...</td>\n <td>G4000</td>\n <td>Maryland</td>\n <td>3</td>\n <td>24</td>\n <td>01714934</td>\n <td>MD</td>\n <td>106.560749</td>\n <td>185.224468</td>\n <td>1.530845</td>\n <td>std</td>\n </tr>\n <tr>\n <th>3</th>\n <td>POLYGON ((-75.78902 39.65843, -75.78898 39.658...</td>\n <td>0.169765</td>\n <td>0.196017</td>\n <td>0.181372</td>\n <td>0.184979</td>\n <td>0.195976</td>\n <td>0.193160</td>\n <td>0.180049</td>\n <td>0.179679</td>\n <td>0.196984</td>\n <td>...</td>\n <td>G4000</td>\n <td>Delaware</td>\n <td>3</td>\n <td>10</td>\n <td>01779781</td>\n <td>DE</td>\n <td>110.884244</td>\n <td>15.832690</td>\n <td>0.285621</td>\n <td>std</td>\n </tr>\n <tr>\n <th>4</th>\n <td>GEOMETRYCOLLECTION (LINESTRING (-77.52722 39.1...</td>\n <td>-0.136002</td>\n <td>-0.211282</td>\n <td>-0.300452</td>\n <td>-0.138128</td>\n <td>-0.119868</td>\n <td>-0.126583</td>\n <td>-0.144259</td>\n <td>-0.164000</td>\n <td>-0.124886</td>\n <td>...</td>\n <td>G4000</td>\n <td>Maryland</td>\n <td>3</td>\n <td>24</td>\n <td>01714934</td>\n <td>MD</td>\n <td>0.000000</td>\n <td>-0.090338</td>\n <td>0.000000</td>\n <td>min</td>\n </tr>\n <tr>\n <th>5</th>\n <td>POLYGON ((-75.78902 39.65843, -75.78898 39.658...</td>\n <td>-0.126619</td>\n <td>-0.121586</td>\n <td>-0.452180</td>\n <td>-0.125007</td>\n <td>-0.100369</td>\n <td>-0.110864</td>\n <td>-0.117901</td>\n <td>-0.105460</td>\n <td>-0.116451</td>\n <td>...</td>\n <td>G4000</td>\n <td>Delaware</td>\n <td>3</td>\n <td>10</td>\n <td>01779781</td>\n <td>DE</td>\n <td>0.000000</td>\n <td>-0.042177</td>\n <td>0.000000</td>\n <td>min</td>\n </tr>\n <tr>\n <th>6</th>\n <td>GEOMETRYCOLLECTION (LINESTRING (-77.52722 39.1...</td>\n <td>0.621621</td>\n <td>0.653156</td>\n <td>0.623258</td>\n <td>0.682850</td>\n <td>0.628371</td>\n <td>0.599217</td>\n <td>0.605813</td>\n <td>0.644847</td>\n <td>0.624291</td>\n <td>...</td>\n <td>G4000</td>\n <td>Maryland</td>\n <td>3</td>\n <td>24</td>\n <td>01714934</td>\n <td>MD</td>\n <td>360.000000</td>\n <td>950.324707</td>\n <td>16.734945</td>\n <td>max</td>\n </tr>\n <tr>\n <th>7</th>\n <td>POLYGON ((-75.78902 39.65843, -75.78898 39.658...</td>\n <td>0.557600</td>\n <td>0.617467</td>\n <td>0.623408</td>\n <td>0.614060</td>\n <td>0.590652</td>\n <td>0.612473</td>\n <td>0.594031</td>\n <td>0.602972</td>\n <td>0.613512</td>\n <td>...</td>\n <td>G4000</td>\n <td>Delaware</td>\n <td>3</td>\n <td>10</td>\n <td>01779781</td>\n <td>DE</td>\n <td>359.950195</td>\n <td>131.447540</td>\n <td>4.026423</td>\n <td>max</td>\n </tr>\n </tbody>\n</table>\n<p>8 rows × 35 columns</p>\n</div>" | |
| }, | |
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