A18e 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:      A1.6 Monitoring Gold Mining Activity using SAR
#  Checkpoint:   A16e
#  Authors:      Lucio Villa, Sidney Novoa, Milagros Becerra,
#                Andréa Puzzi Nicolau, Karen Dyson, Karis Tenneson, John Dilger
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

###########################
# / Section Four
###########################

# Define the area of study.
aoi = ee.FeatureCollection("projects/gee-book/assets/A1-8/mdd")

# Center the map.
Map.centerObject(aoi, 10)

# Create an empty image.
empty = ee.Image().byte()

# Convert the area of study to an EE image object so we can visualize
# only the boundary.
aoiOutline = empty.paint({"featureCollection": aoi, "color": 1, "width": 2})

# Select the satellite basemap view.
Map.setOptions("SATELLITE")

# Add the area of study boundary to the map.
Map.addLayer(aoiOutline, {"palette": "red"}, "Area of Study")

# Import the smap result from section 3.
changeDetect = ee.Image("projects/gee-book/assets/A1-8/smap")

# Visualization parameters.
countDates = 30
jet = ["black", "blue", "cyan", "yellow", "red"]
vis = {"min": 0, "max": countDates, "palette": jet}

# Add results to the map.
Map.addLayer(changeDetect, vis, "Change Map Unfiltered")

# Digital Elevation Model SRTM.
# https:#developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003
srtm = ee.Image("USGS/SRTMGL1_003").clip(aoi)
slope = ee.Terrain.slope(srtm)
srtmVis = {"min": 0, "max": 1000, "palette": ["black", "blue", "cyan", "yellow", "red"]}
Map.addLayer(srtm, srtmVis, "SRTM Elevation")
slopeVis = {"min": 0, "max": 15, "palette": ["black", "blue", "cyan", "yellow", "red"]}
Map.addLayer(slope, slopeVis, "SRTM Slope")

# Hansen Global Forest Change v1.8 (2000-2020)
# https:#developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2020_v1_8
gfc = ee.Image("UMD/hansen/global_forest_change_2020_v1_8").clip(aoi)
forest2020 = (
    gfc.select("treecover2000").gt(0).updateMask(gfc.select("loss").neq(1)).selfMask()
)
Map.addLayer(
    forest2020, {"min": 0, "max": 1, "palette": ["black", "green"]}, "Forest cover 2020"
)

# JRC Yearly Water Classification History, v1.3 (Updated until Dec 2020).
# https:#developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_3_GlobalSurfaceWater
waterJRC = ee.Image("JRC/GSW1_3/GlobalSurfaceWater").select("max_extent")
waterVis = {"min": 0, "max": 1, "palette": ["blue", "black"]}
Map.addLayer(waterJRC.eq(0), waterVis, "Water Bodies until 2020")

# Apply filters through masks.
alertsFiltered = (
    changeDetect.updateMask(srtm.lt(1000))
    .updateMask(slope.lt(15))
    .updateMask(forest2020.eq(1))
    .updateMask(waterJRC.eq(0))
    .selfMask()
)

# Add filtered results to the map.
Map.addLayer(
    alertsFiltered,
    {"min": 0, "max": countDates, "palette": jet},
    "Change Map Filtered",
    1,
)


# Function to filter small patches and isolated pixels.
def filterMinPatchs(alerts0, minArea0, maxSize0):
    pixelCount = alerts0.gt(0).connectedPixelCount(maxSize0)
    minPixelCount = ee.Image(minArea0).divide(ee.Image.pixelArea())
    return alerts0.updateMask(pixelCount.gte(minPixelCount))


# Apply the function and visualize the filtered results.
alertsFiltMinPatchs = filterMinPatchs(alertsFiltered, 10000, 200)

Map.addLayer(alertsFiltMinPatchs, vis, "Alerts Filtered - Minimum Patches")

# Export filtered results to the Drive.
Export.image.toDrive(
    {
        "image": alertsFiltMinPatchs,
        "description": "alertsFiltered",
        "folder": "alertsFiltered",
        "region": aoi,
        "scale": 10,
    }
)

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: A1.6 Monitoring Gold Mining Activity using SAR # Checkpoint: A16e # Authors: Lucio Villa, Sidney Novoa, Milagros Becerra, # Andréa Puzzi Nicolau, Karen Dyson, Karis Tenneson, John Dilger # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ########################### # / Section Four ########################### # Define the area of study. aoi = ee.FeatureCollection("projects/gee-book/assets/A1-8/mdd") # Center the map. Map.centerObject(aoi, 10) # Create an empty image. empty = ee.Image().byte() # Convert the area of study to an EE image object so we can visualize # only the boundary. aoiOutline = empty.paint({"featureCollection": aoi, "color": 1, "width": 2}) # Select the satellite basemap view. Map.setOptions("SATELLITE") # Add the area of study boundary to the map. Map.addLayer(aoiOutline, {"palette": "red"}, "Area of Study") # Import the smap result from section 3. changeDetect = ee.Image("projects/gee-book/assets/A1-8/smap") # Visualization parameters. countDates = 30 jet = ["black", "blue", "cyan", "yellow", "red"] vis = {"min": 0, "max": countDates, "palette": jet} # Add results to the map. Map.addLayer(changeDetect, vis, "Change Map Unfiltered") # Digital Elevation Model SRTM. # https:#developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003 srtm = ee.Image("USGS/SRTMGL1_003").clip(aoi) slope = ee.Terrain.slope(srtm) srtmVis = {"min": 0, "max": 1000, "palette": ["black", "blue", "cyan", "yellow", "red"]} Map.addLayer(srtm, srtmVis, "SRTM Elevation") slopeVis = {"min": 0, "max": 15, "palette": ["black", "blue", "cyan", "yellow", "red"]} Map.addLayer(slope, slopeVis, "SRTM Slope") # Hansen Global Forest Change v1.8 (2000-2020) # https:#developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2020_v1_8 gfc = ee.Image("UMD/hansen/global_forest_change_2020_v1_8").clip(aoi) forest2020 = ( gfc.select("treecover2000").gt(0).updateMask(gfc.select("loss").neq(1)).selfMask() ) Map.addLayer( forest2020, {"min": 0, "max": 1, "palette": ["black", "green"]}, "Forest cover 2020" ) # JRC Yearly Water Classification History, v1.3 (Updated until Dec 2020). # https:#developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_3_GlobalSurfaceWater waterJRC = ee.Image("JRC/GSW1_3/GlobalSurfaceWater").select("max_extent") waterVis = {"min": 0, "max": 1, "palette": ["blue", "black"]} Map.addLayer(waterJRC.eq(0), waterVis, "Water Bodies until 2020") # Apply filters through masks. alertsFiltered = ( changeDetect.updateMask(srtm.lt(1000)) .updateMask(slope.lt(15)) .updateMask(forest2020.eq(1)) .updateMask(waterJRC.eq(0)) .selfMask() ) # Add filtered results to the map. Map.addLayer( alertsFiltered, {"min": 0, "max": countDates, "palette": jet}, "Change Map Filtered", 1, ) # Function to filter small patches and isolated pixels. def filterMinPatchs(alerts0, minArea0, maxSize0): pixelCount = alerts0.gt(0).connectedPixelCount(maxSize0) minPixelCount = ee.Image(minArea0).divide(ee.Image.pixelArea()) return alerts0.updateMask(pixelCount.gte(minPixelCount)) # Apply the function and visualize the filtered results. alertsFiltMinPatchs = filterMinPatchs(alertsFiltered, 10000, 200) Map.addLayer(alertsFiltMinPatchs, vis, "Alerts Filtered - Minimum Patches") # Export filtered results to the Drive. Export.image.toDrive( { "image": alertsFiltMinPatchs, "description": "alertsFiltered", "folder": "alertsFiltered", "region": aoi, "scale": 10, } ) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map