A32s1 Supplemental

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:      A3.2 Mangroves
#  Section:      Synthesis (Assignment 2)
#  Author:       Aurélie Shapiro
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Index functions - many sources for Sentinel-2 are here:
# https:#www.indexdatabase.de/db/s-single.php?id=96

# Various NDVI calculations.
NDVI = S2.normalizedDifference(["nir", "red"]).rename("NDVI")
NDVI_red = S2.normalizedDifference(["red", "green"]).rename(["NDVI_red"])
NDVI_re1 = S2.normalizedDifference(["nir", "redEdge1"]).rename("NDVI_re1")
NDVI_re2 = S2.normalizedDifference(["nir", "redEdge2"]).rename("NDVI_re2")
NDVI_re3 = S2.normalizedDifference(["nir", "redEdge3"]).rename("NDVI_re3")
#  NDYI: Yellowness index
NDYI = S2.normalizedDifference(["green", "blue"]).rename("NDYI")
#  MNDWI: Modified Normalized Difference Wetness Index
MNDWI = S2.normalizedDifference(["green", "swir1"]).rename("MNDWI")
#  MNDWI: Normalized Difference Wetness Index
NDWI = S2.normalizedDifference(["blue", "red"]).rename(["NDWI"])
#  MNDWI: Normalized Difference Blue/Nir Index
NDBN = S2.normalizedDifference(["blue", "nir"]).rename(["NDBN"])
SAVI = (
    S2.select("nir")
    .subtract(S2.select("red"))
    .multiply(1.5)
    .divide(S2.select("nir").add(S2.select("red").add(0.5)))
)
OSAVI = (
    S2.select("red")
    .subtract(S2.select("nir"))
    .divide((S2.select("red")).add(S2.select("nir")).add(0.16))
    .rename(["OSAVI"])
)
LSWI = S2.normalizedDifference(["red", "nir"]).rename(["LSWI"])

ratio_swir1_nir = S2.expression(
    "swir1/(nir+0.1)", {"swir1": S2.select("swir1"), "nir": S2.select("nir")}
).rename("ratio_swir1_nir_wet")

# ratio_red_swir1
ratio_red_swir1 = S2.expression(
    "red/(swir1+0.1)", {"red": S2.select("red"), "swir1": S2.select("swir1")}
).rename("ratio_red_swir1_wet")

# FDI Forest Discrimination Index from Wang et al., 2018
FDI = S2.expression(
    "nir-(red+green)",
    {"nir": S2.select("nir"), "red": S2.select("red"), "green": S2.select("green")},
).rename("FDI_wet")

# Tasseled cap wetness.
wetTC = S2.expression(
    "(0.1509 * BLUE) + (0.1973 * GREEN) + (0.3279 * RED) + "
    + "(3406 * NIR) - (0.7112 * SWIR) - (0.4572 * SWIR2)",
    {
        "BLUE": S2.select("blue"),
        "GREEN": S2.select("green"),
        "RED": S2.select("red"),
        "NIR": S2.select("nir"),
        "SWIR": S2.select("swir1"),
        "SWIR2": S2.select("swir2"),
    },
).rename("wetTC")

# Tasseled cap greenness.
greenTC = S2.expression(
    "(-0.2848 * BLUE) - (0.2435 * GREEN) - (0.5436 * RED) + "
    + "(0.7243 * NIR) + (0.084011 * NIR) - (0.1800 * SWIR)",
    {
        "BLUE": S2.select("blue"),
        "GREEN": S2.select("green"),
        "RED": S2.select("red"),
        "NIR": S2.select("nir"),
        "SWIR": S2.select("swir1"),
        "SWIR2": S2.select("swir2"),
    },
).rename("greenTC")

# Stack all bands together
data_stack = (
    S2.addBands(ratio_swir1_nir)
    .addBands(ratio_red_swir1)
    .addBands(FDI)
    .addBands(NDVI)
    .addBands(NDVI_red)
    .addBands(NDVI_re1)
    .addBands(NDVI_re2)
    .addBands(NDVI_re3)
    .addBands(NDYI)
    .addBands(MNDWI)
    .addBands(NDWI)
    .addBands(MNDWI)
    .addBands(SAVI)
    .addBands(OSAVI)
    .addBands(wetTC)
    .addBands(greenTC)
    .addBands(LSWI)
    .addBands(wetS1)
    .addBands(wetS1)
)

print(data_stack, "data stack")

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: A3.2 Mangroves # Section: Synthesis (Assignment 2) # Author: Aurélie Shapiro # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Index functions - many sources for Sentinel-2 are here: # https:#www.indexdatabase.de/db/s-single.php?id=96 # Various NDVI calculations. NDVI = S2.normalizedDifference(["nir", "red"]).rename("NDVI") NDVI_red = S2.normalizedDifference(["red", "green"]).rename(["NDVI_red"]) NDVI_re1 = S2.normalizedDifference(["nir", "redEdge1"]).rename("NDVI_re1") NDVI_re2 = S2.normalizedDifference(["nir", "redEdge2"]).rename("NDVI_re2") NDVI_re3 = S2.normalizedDifference(["nir", "redEdge3"]).rename("NDVI_re3") # NDYI: Yellowness index NDYI = S2.normalizedDifference(["green", "blue"]).rename("NDYI") # MNDWI: Modified Normalized Difference Wetness Index MNDWI = S2.normalizedDifference(["green", "swir1"]).rename("MNDWI") # MNDWI: Normalized Difference Wetness Index NDWI = S2.normalizedDifference(["blue", "red"]).rename(["NDWI"]) # MNDWI: Normalized Difference Blue/Nir Index NDBN = S2.normalizedDifference(["blue", "nir"]).rename(["NDBN"]) SAVI = ( S2.select("nir") .subtract(S2.select("red")) .multiply(1.5) .divide(S2.select("nir").add(S2.select("red").add(0.5))) ) OSAVI = ( S2.select("red") .subtract(S2.select("nir")) .divide((S2.select("red")).add(S2.select("nir")).add(0.16)) .rename(["OSAVI"]) ) LSWI = S2.normalizedDifference(["red", "nir"]).rename(["LSWI"]) ratio_swir1_nir = S2.expression( "swir1/(nir+0.1)", {"swir1": S2.select("swir1"), "nir": S2.select("nir")} ).rename("ratio_swir1_nir_wet") # ratio_red_swir1 ratio_red_swir1 = S2.expression( "red/(swir1+0.1)", {"red": S2.select("red"), "swir1": S2.select("swir1")} ).rename("ratio_red_swir1_wet") # FDI Forest Discrimination Index from Wang et al., 2018 FDI = S2.expression( "nir-(red+green)", {"nir": S2.select("nir"), "red": S2.select("red"), "green": S2.select("green")}, ).rename("FDI_wet") # Tasseled cap wetness. wetTC = S2.expression( "(0.1509 * BLUE) + (0.1973 * GREEN) + (0.3279 * RED) + " + "(3406 * NIR) - (0.7112 * SWIR) - (0.4572 * SWIR2)", { "BLUE": S2.select("blue"), "GREEN": S2.select("green"), "RED": S2.select("red"), "NIR": S2.select("nir"), "SWIR": S2.select("swir1"), "SWIR2": S2.select("swir2"), }, ).rename("wetTC") # Tasseled cap greenness. greenTC = S2.expression( "(-0.2848 * BLUE) - (0.2435 * GREEN) - (0.5436 * RED) + " + "(0.7243 * NIR) + (0.084011 * NIR) - (0.1800 * SWIR)", { "BLUE": S2.select("blue"), "GREEN": S2.select("green"), "RED": S2.select("red"), "NIR": S2.select("nir"), "SWIR": S2.select("swir1"), "SWIR2": S2.select("swir2"), }, ).rename("greenTC") # Stack all bands together data_stack = ( S2.addBands(ratio_swir1_nir) .addBands(ratio_red_swir1) .addBands(FDI) .addBands(NDVI) .addBands(NDVI_red) .addBands(NDVI_re1) .addBands(NDVI_re2) .addBands(NDVI_re3) .addBands(NDYI) .addBands(MNDWI) .addBands(NDWI) .addBands(MNDWI) .addBands(SAVI) .addBands(OSAVI) .addBands(wetTC) .addBands(greenTC) .addBands(LSWI) .addBands(wetS1) .addBands(wetS1) ) print(data_stack, "data stack")

Display the interactive map¶

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Map

Map