Uncomment the following line to install geemap if needed.

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# !pip install geemap

# !pip install geemap

Interactive extraction of pixel values and interactive region reduction¶

Interactive extraction of pixel values¶

Import libraries¶

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import os
import ee
import geemap

import os import ee import geemap

Create an interactive map¶

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Map = geemap.Map()
Map

Map = geemap.Map() Map

Add data to the map¶

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landsat7 = ee.Image("LANDSAT/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")

Map.set_plot_options(add_marker_cluster=True)

landsat7 = ee.Image("LANDSAT/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") Map.set_plot_options(add_marker_cluster=True)

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¶

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

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Map.extract_values_to_points(out_shp)

Map.extract_values_to_points(out_shp)

Interactive Region Reduction¶

Import libraries¶

In [ ]:

import os
import ee
import geemap

import os import ee import geemap

Create an interactive map¶

In [ ]:

m = geemap.Map()

m = geemap.Map()

Add add to the map¶

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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()

ndvi_vis = {
    "min": 0.0,
    "max": 9000.0,
    "palette": [
        "FFFFFF",
        "CE7E45",
        "DF923D",
        "F1B555",
        "FCD163",
        "99B718",
        "74A901",
        "66A000",
        "529400",
        "3E8601",
        "207401",
        "056201",
        "004C00",
        "023B01",
        "012E01",
        "011D01",
        "011301",
    ],
}

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() ndvi_vis = { "min": 0.0, "max": 9000.0, "palette": [ "FFFFFF", "CE7E45", "DF923D", "F1B555", "FCD163", "99B718", "74A901", "66A000", "529400", "3E8601", "207401", "056201", "004C00", "023B01", "012E01", "011D01", "011301", ], } m.addLayer(image, {}, "MODIS NDVI Time-series") m.addLayer(image.select(0), ndvi_vis, "MODIS NDVI VIS") m

Set reducer¶

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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()

Export data¶

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out_dir = os.path.join(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.join(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)