Extract values

Uncomment the following line to install geemap if needed.

In [1]:

# !pip install geemap

# !pip install geemap

In [2]:

# import geemap
# geemap.update_package()

# import geemap # geemap.update_package()

Interactive extraction of pixel values¶

Import libraries¶

In [3]:

import os
import ee
import geemap

import os import ee import geemap

Create an interactive map¶

In [4]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

Out[4]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Add data to the map¶

In [5]:

landsat7 = ee.Image('LE7_TOA_5YEAR/1999_2003').select([0, 1, 2, 3, 4, 6])
landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.7}
Map.addLayer(landsat7, landsat_vis, "LE7_TOA_5YEAR/1999_2003")

Map.set_plot_options(add_marker_cluster=True)

landsat7 = ee.Image('LE7_TOA_5YEAR/1999_2003').select([0, 1, 2, 3, 4, 6]) landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.7} Map.addLayer(landsat7, landsat_vis, "LE7_TOA_5YEAR/1999_2003") Map.set_plot_options(add_marker_cluster=True)

The folium plotting backend does not support this function.

Activate the plotting tool¶

Tick the Plotting checkbox and click the mouse on the map to start displaying charts.

Export pixel values to shapefile/csv¶

In [6]:

out_dir = os.path.expanduser('~/Downloads')
# out_csv = os.path.join(out_dir, 'points.csv')
out_shp = os.path.join(out_dir, 'points.shp')

out_dir = os.path.expanduser('~/Downloads') # out_csv = os.path.join(out_dir, 'points.csv') out_shp = os.path.join(out_dir, 'points.shp')

In [7]:

Map.extract_values_to_points(out_shp)

Map.extract_values_to_points(out_shp)

The folium plotting backend does not support this function.

Interactive Region Reduction¶

Import libraries¶

In [8]:

import geemap.colormaps as cm

import geemap.colormaps as cm

Create an interactive map¶

In [9]:

m = geemap.Map()

m = geemap.Map()

Add add to the map¶

In [10]:

collection = (
    ee.ImageCollection('MODIS/006/MOD13A2')
    .filterDate('2015-01-01', '2019-12-31')
    .select('NDVI')
)

# Convert the image collection to an image.
image = collection.toBands()

palette = cm.palettes.ndvi

ndvi_vis = {'min': 0.0, 'max': 9000.0, 'palette': palette}

m.addLayer(image, {}, 'MODIS NDVI Time-series')
m.addLayer(image.select(0), ndvi_vis, 'MODIS NDVI VIS')

m

collection = ( ee.ImageCollection('MODIS/006/MOD13A2') .filterDate('2015-01-01', '2019-12-31') .select('NDVI') ) # Convert the image collection to an image. image = collection.toBands() palette = cm.palettes.ndvi ndvi_vis = {'min': 0.0, 'max': 9000.0, 'palette': palette} m.addLayer(image, {}, 'MODIS NDVI Time-series') m.addLayer(image.select(0), ndvi_vis, 'MODIS NDVI VIS') m

Out[10]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Set reducer¶

In [11]:

m.set_plot_options(add_marker_cluster=True, marker=None)
m.roi_reducer = ee.Reducer.mean()

m.set_plot_options(add_marker_cluster=True, marker=None) m.roi_reducer = ee.Reducer.mean()

The folium plotting backend does not support this function.

Export data¶

In [12]:

out_dir = os.path.expanduser('~/Downloads')
# out_csv = os.path.join(out_dir, 'points.csv')
out_shp = os.path.join(out_dir, 'ndvi.shp')
m.extract_values_to_points(out_shp)

out_dir = os.path.expanduser('~/Downloads') # out_csv = os.path.join(out_dir, 'points.csv') out_shp = os.path.join(out_dir, 'ndvi.shp') m.extract_values_to_points(out_shp)

The folium plotting backend does not support this function.

Creating training samples¶

In [13]:

import geemap

import geemap

In [14]:

geemap.show_youtube("https://youtu.be/VWh5PxXPZw0")

geemap.show_youtube("https://youtu.be/VWh5PxXPZw0")

In [15]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

Out[15]:

Make this Notebook Trusted to load map: File -> Trust Notebook

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Last update: 2022-03-25