#!/usr/bin/env python # coding: utf-8 #

1 Create an interactive map
2 Add basemaps
3 Add WMS and XYZ tile layers
4 Add Earth Engine data layers
5 Search Earth Engine data catalog
6 Search Earth Engine API documentation
7 Use Insector tool
8 Use Plotting tool
9 Create a split-panel map
10 Add marker cluster
11 Add customized legends
12 Use Drawing tools
13 Convert JavaScripts to Python
14 Use shapefiles
15 Create Landsat timelapse
16 Use time-series inspector
17 Export images

# In[ ]: import ee import geemap # ## Create an interactive map # In[ ]: Map = geemap.Map(center=(40, -100), zoom=4) Map # ## Add basemaps # In[ ]: Map = geemap.Map() Map # In[ ]: Map.add_basemap('HYBRID') # In[ ]: Map.add_basemap('OpenTopoMap') # In[ ]: Map = geemap.Map() Map.basemap_demo() Map # ## Add WMS and XYZ tile layers # In[ ]: Map = geemap.Map() Map # In[ ]: # https://viewer.nationalmap.gov/services/ url = 'https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}' Map.add_tile_layer(url, name='Google Satellite', attribution='Google') # In[ ]: naip_url = 'https://services.nationalmap.gov/arcgis/services/USGSNAIPImagery/ImageServer/WMSServer?' Map.add_wms_layer(url=naip_url, layers='0', name='NAIP Imagery', format='image/png', shown=True) # ## Add Earth Engine data layers # In[ ]: Map = geemap.Map() Map # In[ ]: # Add Earth Engine dataset dem = ee.Image('USGS/SRTMGL1_003') landcover = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover') landsat7 = ee.Image('LE7_TOA_5YEAR/1999_2003') states = ee.FeatureCollection("TIGER/2018/States") # Set visualization parameters. vis_params = { 'min': 0, 'max': 4000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5']} # Add Earth Eninge layers to Map Map.addLayer(dem, vis_params, 'STRM DEM', True, 0.5) Map.addLayer(landcover, {}, 'Land cover') Map.addLayer(landsat7, {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200}, 'Landsat 7') Map.addLayer(states, {}, "US States") # ## Search Earth Engine data catalog # In[ ]: Map = geemap.Map() Map # In[ ]: Map.search_locations # In[ ]: Map.search_loc_geom # In[ ]: location = Map.search_loc_geom print(location.getInfo()) # ## Search Earth Engine API documentation # In[ ]: geemap.ee_search() # In[ ]: # ## Use Insector tool # In[ ]: Map = geemap.Map() # Add Earth Engine dataset dem = ee.Image('USGS/SRTMGL1_003') landcover = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover') landsat7 = ee.Image('LE7_TOA_5YEAR/1999_2003') states = ee.FeatureCollection("TIGER/2018/States") # Set visualization parameters. vis_params = { 'min': 0, 'max': 4000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5']} # Add Earth Eninge layers to Map Map.addLayer(dem, vis_params, 'STRM DEM', True, 0.5) Map.addLayer(landcover, {}, 'Land cover') Map.addLayer(landsat7, {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200}, 'Landsat 7') Map.addLayer(states, {}, "US States") Map # ## Use Plotting tool # In[ ]: Map = geemap.Map() landsat7 = ee.Image('LE7_TOA_5YEAR/1999_2003') \ .select([0, 1, 2, 3, 4, 6]) landsat_vis = { 'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4 } Map.addLayer(landsat7, landsat_vis, "LE7_TOA_5YEAR/1999_2003") hyperion = ee.ImageCollection('EO1/HYPERION') \ .filter(ee.Filter.date('2016-01-01', '2017-03-01')) hyperion_vis = { 'min': 1000.0, 'max': 14000.0, 'gamma': 2.5, } Map.addLayer(hyperion, hyperion_vis, 'EO1/HYPERION') Map # In[ ]: Map.set_plot_options(plot_type='bar', add_marker_cluster=True) # ## Create a split-panel map # In[ ]: Map = geemap.Map() Map.split_map(left_layer='HYBRID', right_layer='ROADMAP') Map # In[ ]: Map = geemap.Map() Map.split_map(left_layer='NLCD 2016 CONUS Land Cover', right_layer='NLCD 2001 CONUS Land Cover') Map # In[ ]: nlcd_2001 = ee.Image('USGS/NLCD/NLCD2001').select('landcover') nlcd_2016 = ee.Image('USGS/NLCD/NLCD2016').select('landcover') left_layer = geemap.ee_tile_layer(nlcd_2001, {}, 'NLCD 2001') right_layer = geemap.ee_tile_layer(nlcd_2016, {}, 'NLCD 2016') Map = geemap.Map() Map.split_map(left_layer, right_layer) Map # ## Add marker cluster # In[ ]: import geemap import json import os import requests from geemap import geojson_to_ee, ee_to_geojson from ipyleaflet import GeoJSON, Marker, MarkerCluster # In[ ]: Map = geemap.Map() Map # In[ ]: file_path = os.path.join(os.getcwd(), 'us-cities.json') if not os.path.exists(file_path): url = 'https://github.com/giswqs/geemap/raw/master/examples/data/us-cities.json' r = requests.get(url) with open(file_path, 'w') as f: f.write(r.content.decode("utf-8")) with open(file_path) as f: json_data = json.load(f) # In[ ]: maker_cluster = MarkerCluster( markers=[Marker(location=feature['geometry']['coordinates'][::-1]) for feature in json_data['features']], name = 'Markers') # In[ ]: Map.add_layer(maker_cluster) # In[ ]: ee_fc = geojson_to_ee(json_data) Map.addLayer(ee_fc, {}, "US Cities EE") # ## Add customized legends # In[ ]: Map = geemap.Map() Map.add_basemap('HYBRID') landcover = ee.Image('USGS/NLCD/NLCD2016').select('landcover') Map.addLayer(landcover, {}, 'NLCD Land Cover') Map.add_legend(builtin_legend='NLCD') Map # In[ ]: Map = geemap.Map() Map.add_basemap('HYBRID') Map.add_basemap('FWS NWI Wetlands') Map.add_legend(builtin_legend='NWI') Map # In[ ]: Map = geemap.Map() legend_dict = { '11 Open Water': '466b9f', '12 Perennial Ice/Snow': 'd1def8', '21 Developed, Open Space': 'dec5c5', '22 Developed, Low Intensity': 'd99282', '23 Developed, Medium Intensity': 'eb0000', '24 Developed High Intensity': 'ab0000', '31 Barren Land (Rock/Sand/Clay)': 'b3ac9f', '41 Deciduous Forest': '68ab5f', '42 Evergreen Forest': '1c5f2c', '43 Mixed Forest': 'b5c58f', '51 Dwarf Scrub': 'af963c', '52 Shrub/Scrub': 'ccb879', '71 Grassland/Herbaceous': 'dfdfc2', '72 Sedge/Herbaceous': 'd1d182', '73 Lichens': 'a3cc51', '74 Moss': '82ba9e', '81 Pasture/Hay': 'dcd939', '82 Cultivated Crops': 'ab6c28', '90 Woody Wetlands': 'b8d9eb', '95 Emergent Herbaceous Wetlands': '6c9fb8' } landcover = ee.Image('USGS/NLCD/NLCD2016').select('landcover') Map.addLayer(landcover, {}, 'NLCD Land Cover') Map.add_legend(legend_title="NLCD Land Cover Classification", legend_dict=legend_dict) Map # In[ ]: # https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1 Map = geemap.Map() ee_class_table = """ Value Color Description 0 1c0dff Water 1 05450a Evergreen needleleaf forest 2 086a10 Evergreen broadleaf forest 3 54a708 Deciduous needleleaf forest 4 78d203 Deciduous broadleaf forest 5 009900 Mixed forest 6 c6b044 Closed shrublands 7 dcd159 Open shrublands 8 dade48 Woody savannas 9 fbff13 Savannas 10 b6ff05 Grasslands 11 27ff87 Permanent wetlands 12 c24f44 Croplands 13 a5a5a5 Urban and built-up 14 ff6d4c Cropland/natural vegetation mosaic 15 69fff8 Snow and ice 16 f9ffa4 Barren or sparsely vegetated 254 ffffff Unclassified """ landcover = ee.Image('MODIS/051/MCD12Q1/2013_01_01') \ .select('Land_Cover_Type_1') Map.setCenter(6.746, 46.529, 2) Map.addLayer(landcover, {}, 'MODIS Land Cover') legend_dict = geemap.legend_from_ee(ee_class_table) Map.add_legend(legend_title="MODIS Global Land Cover", legend_dict=legend_dict) Map # ## Use Drawing tools # In[ ]: Map = geemap.Map() Map # In[ ]: # Add Earth Engine dataset image = ee.Image('USGS/SRTMGL1_003') # Set visualization parameters. vis_params = { 'min': 0, 'max': 4000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5']} # Add Earth Engine DEM to map Map.addLayer(image, vis_params, 'SRTM DEM') states = ee.FeatureCollection("TIGER/2018/States") Map.addLayer(states, {}, 'US States') # In[ ]: Map.draw_features # ## Convert JavaScripts to Python # # You can simply copy and paste your GEE JavaScripts into a code block wrapped with trip quotes and pass it to a variable. # # For example, you can grap GEE JavaScripts from [GEE Documentation](https://developers.google.com/earth-engine/image_visualization). # In[ ]: js_snippet = """ // Load an image. var image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318'); // Define the visualization parameters. var vizParams = { bands: ['B5', 'B4', 'B3'], min: 0, max: 0.5, gamma: [0.95, 1.1, 1] }; // Center the map and display the image. Map.setCenter(-122.1899, 37.5010, 10); // San Francisco Bay Map.addLayer(image, vizParams, 'false color composite'); """ # In[ ]: geemap.js_snippet_to_py(js_snippet, add_new_cell=True, import_ee=True, import_geemap=True, show_map=True) # In[ ]: import ee import geemap Map = geemap.Map() ee.Initialize() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Define the visualization parameters. vizParams = { 'bands': ['B5', 'B4', 'B3'], 'min': 0, 'max': 0.5, 'gamma': [0.95, 1.1, 1] } # Center the map and display the image. Map.setCenter(-122.1899, 37.5010, 10); # San Francisco Bay Map.addLayer(image, vizParams, 'False color composite') Map # In[ ]: js_snippet = """ // Load an image. var image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318'); // Create an NDWI image, define visualization parameters and display. var ndwi = image.normalizedDifference(['B3', 'B5']); var ndwiViz = {min: 0.5, max: 1, palette: ['00FFFF', '0000FF']}; Map.addLayer(ndwi, ndwiViz, 'NDWI', false); """ # In[ ]: geemap.js_snippet_to_py(js_snippet) # In[ ]: import ee import geemap Map = geemap.Map() ee.Initialize() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Create an NDWI image, define visualization parameters and display. ndwi = image.normalizedDifference(['B3', 'B5']) ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']} Map.addLayer(ndwi, ndwiViz, 'NDWI', False) Map # ## Use shapefiles # In[ ]: Map = geemap.Map() Map # In[ ]: countries_shp = '../data/countries.shp' countries = geemap.shp_to_ee(countries_shp) Map.addLayer(countries, {}, 'Countries') # In[ ]: states_shp = '../data/us-states.shp' states = geemap.shp_to_ee(states_shp) Map.addLayer(states, {}, 'US States') # In[ ]: cities_shp = '../data/us-cities.shp' cities = geemap.shp_to_ee(cities_shp) Map.addLayer(cities, {}, 'US Cities') # In[ ]: geemap.ee_to_shp(countries, filename='../data/countries_new.shp') # In[ ]: geemap.ee_export_vector(states, filename='../data/states.csv') # ## Create Landsat timelapse # In[ ]: Map = geemap.Map() Map # In[ ]: label = 'Urban Growth in Las Vegas' Map.add_landsat_ts_gif(label=label, start_year=1985, bands=['Red', 'Green', 'Blue'], font_color='white', frames_per_second=10, progress_bar_color='blue') # ## Use time-series inspector # In[ ]: naip_ts = geemap.naip_timeseries(start_year=2009, end_year=2018) # In[ ]: layer_names = ['NAIP ' + str(year) for year in range(2009, 2019)] print(layer_names) # In[ ]: naip_vis = {'bands': ['N', 'R', 'G']} # In[ ]: Map = geemap.Map() Map.ts_inspector(left_ts=naip_ts, right_ts=naip_ts, left_names=layer_names, right_names=layer_names, left_vis=naip_vis, right_vis=naip_vis) Map # ## Export images # In[ ]: Map = geemap.Map() Map # In[ ]: image = ee.Image('LE7_TOA_5YEAR/1999_2003') landsat_vis = { 'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4 } Map.addLayer(image, landsat_vis, "LE7_TOA_5YEAR/1999_2003", True, 0.7) # In[ ]: # Draw any shapes on the map using the Drawing tools before executing this code block feature = Map.draw_last_feature roi = feature.geometry() # In[ ]: out_dir = os.path.join(os.path.expanduser('~'), 'Downloads') filename = os.path.join(out_dir, 'landsat.tif') # In[ ]: geemap.ee_export_image(image, filename=filename, scale=90, region=roi, file_per_band=False) # In[ ]: geemap.ee_export_image(image, filename=filename, scale=90, region=roi, file_per_band=True) # In[ ]: loc = ee.Geometry.Point(-99.2222, 46.7816) collection = ee.ImageCollection('USDA/NAIP/DOQQ') \ .filterBounds(loc) \ .filterDate('2008-01-01', '2020-01-01') \ .filter(ee.Filter.listContains("system:band_names", "N")) # In[ ]: out_dir = os.path.join(os.path.expanduser('~'), 'Downloads') # In[ ]: geemap.ee_export_image_collection(collection, out_dir=out_dir) # In[ ]:

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