A34d Checkpoint

Import libraries¶

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import ee
import geemap

import ee import geemap

Create an interactive map¶

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Map = geemap.Map(center=[40, -100], zoom=4)

Map = geemap.Map(center=[40, -100], zoom=4)

Add Earth Engine Python script¶

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# Add Earth Engine dataset
image = ee.Image("USGS/SRTMGL1_003")

#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      A3.4 Forest Degradation and Deforestation
#  Checkpoint:   A34d
#  Author:       Carlos Souza Jr., Karis Tenneson, John Dilger,
#                Crystal Wespestad, Eric Bullock
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

api = require("users/bullocke/coded:CODED/api")
utils = require("projects/GLANCE:ccdcUtilities/api")

# ------------------------------------------------------------------------
# CHECKPOINT
# ------------------------------------------------------------------------

# We will use the geometry of the image from the previous section as
# the study area.
studyArea = ee.Image("LANDSAT/LT05/C02/T1_L2/LT05_226068_19840411").geometry()

# Get cloud masked (Fmask) Landsat imagery.
landsat = (
    utils.Inputs.getLandsat()
    .filterBounds(studyArea)
    .filterDate("1984-01-01", "2021-01-01")
)

# Make a forest mask
gfwImage = ee.Image("UMD/hansen/global_forest_change_2019_v1_7")

# Get areas of forest cover above the threshold
treeCover = 40
forestMask = gfwImage.select("treecover2000").gte(treeCover).rename("landcover")

samples = ee.FeatureCollection(
    "projects/gee-book/assets/A3-4/sample_with_pred_hansen_2010"
)

minObservations = 4
chiSquareProbability = 0.97
training = samples
forestValue = 1
startYear = 1990
endYear = 2020
classBands = ["NDFI", "GV", "Shade", "NPV", "Soil"]
prepTraining = False

# ---------------- CODED parameters
codedParams = {
    "minObservations": minObservations,
    "chiSquareProbability": chiSquareProbability,
    "training": training,
    "studyArea": studyArea,
    forestValue: forestValue,
    forestMask: forestMask,
    "classBands": classBands,
    "collection": landsat,
    "startYear": startYear,
    "endYear": endYear,
    "prepTraining": prepTraining,
}

# -------------- Run CODED
results = api.ChangeDetection.coded(codedParams)
print(results)

# ------------------------------------------------------------------------
# CHECKPOINT
# ------------------------------------------------------------------------

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: A3.4 Forest Degradation and Deforestation # Checkpoint: A34d # Author: Carlos Souza Jr., Karis Tenneson, John Dilger, # Crystal Wespestad, Eric Bullock # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ api = require("users/bullocke/coded:CODED/api") utils = require("projects/GLANCE:ccdcUtilities/api") # ------------------------------------------------------------------------ # CHECKPOINT # ------------------------------------------------------------------------ # We will use the geometry of the image from the previous section as # the study area. studyArea = ee.Image("LANDSAT/LT05/C02/T1_L2/LT05_226068_19840411").geometry() # Get cloud masked (Fmask) Landsat imagery. landsat = ( utils.Inputs.getLandsat() .filterBounds(studyArea) .filterDate("1984-01-01", "2021-01-01") ) # Make a forest mask gfwImage = ee.Image("UMD/hansen/global_forest_change_2019_v1_7") # Get areas of forest cover above the threshold treeCover = 40 forestMask = gfwImage.select("treecover2000").gte(treeCover).rename("landcover") samples = ee.FeatureCollection( "projects/gee-book/assets/A3-4/sample_with_pred_hansen_2010" ) minObservations = 4 chiSquareProbability = 0.97 training = samples forestValue = 1 startYear = 1990 endYear = 2020 classBands = ["NDFI", "GV", "Shade", "NPV", "Soil"] prepTraining = False # ---------------- CODED parameters codedParams = { "minObservations": minObservations, "chiSquareProbability": chiSquareProbability, "training": training, "studyArea": studyArea, forestValue: forestValue, forestMask: forestMask, "classBands": classBands, "collection": landsat, "startYear": startYear, "endYear": endYear, "prepTraining": prepTraining, } # -------------- Run CODED results = api.ChangeDetection.coded(codedParams) print(results) # ------------------------------------------------------------------------ # CHECKPOINT # ------------------------------------------------------------------------

Display the interactive map¶

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Map

Map