KMarkert

using EarthEngine

Initialize()

# base ndvi function to do the calculation
function ndvi(n::EE.AbstractEEObject, r::EE.AbstractEEObject)
    return (n - r) / (n + r)
end

# ndvi function for image, will extract out the correct bands

KMarkert

KMarkert

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KMarkert

using PyCall, JSON, Images, Plots

# import gcloud authentication packages for interacting with the EE REST API 
service_account = pyimport("google.oauth2.service_account");
gauth = pyimport("google.auth.transport.requests");

# path to service account secret key 
# see the folowing link for details to key:
# https://developers.google.com/earth-engine/reference/Quickstart#obtain-a-private-key-file-for-your-service-account
SECRET_KEY = "<PATH_TO_SECRET_KEY>"

KMarkert

KMarkert

using PyCall, Plots, Colors, FileIO;

# import the ee Python module into Julia
# and initialize the EE API
ee = pyimport("ee");
ee.Initialize();

# produce tabular data that can be plotted by data visualization in Julia
# Fetch a Landsat image.
img = ee.Image("LANDSAT/LT05/C01/T1_SR/LT05_034033_20000913");

KMarkert

using PyCall, Plots;
theme(:bright);

# import the ee Python module into Julia
# and initialize the EE API
ee = pyimport("ee");
ee.Initialize();

# produce tabular data that can be plotted by data visualization in Julia
# Fetch a Landsat image.

KMarkert

using PyCall;

# import the ee Python module into Julia
# and initialize the EE API
ee = pyimport("ee");
ee.Initialize();

# load in ee.Image and sample a point
dem = ee.Image("USGS/SRTMGL1_003");
xy = ee.Geometry.Point([86.9250, 27.9881]...);

KMarkert

using PyCall;

# import the ee Python module into Julia
# and initialize the EE API
ee = pyimport("ee");
ee.Initialize();

# load in ee.Image and sample a point
dem = ee.Image("USGS/SRTMGL1_003");
xy = ee.Geometry.Point([86.9250, 27.9881]...);