A15a 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:      A1.5 Heat Islands
#  Checkpoint:   A15a
#  Author:       TC Chakraborty
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Load feature collection of New Haven's census tracts from user assets.
regionInt = ee.FeatureCollection("projects/gee-book/assets/A1-5/TC_NewHaven")

# Get dissolved feature collection using an error margin of 50 meters.
regionInt = regionInt.union(50)

# Set map center and zoom level (Zoom level varies from 1 to 20).
Map.setCenter(-72.9, 41.3, 12)

# Add layer to map.
Map.addLayer(regionInt, {}, "New Haven boundary")

# Load MODIS image collection from the Earth Engine data catalog.
modisLst = ee.ImageCollection("MODIS/006/MYD11A2")

# Select the band of interest (in this case: Daytime LST).
landSurfTemperature = modisLst.select("LST_Day_1km")

# Create a summer filter.
sumFilter = ee.Filter.dayOfYear(152, 243)

# Filter the date range of interest using a date filter.
lstDateInt = landSurfTemperature.filterDate("2014-01-01", "2019-01-01").filter(
    sumFilter
)

# Take pixel-wise mean of all the images in the collection.
lstMean = lstDateInt.mean()

# Multiply each pixel by scaling factor to get the LST values.
lstFinal = lstMean.multiply(0.02)

# Generate a water mask.
water = ee.Image("JRC/GSW1_0/GlobalSurfaceWater").select("occurrence")
notWater = water.mask().Not()

# Clip data to region of interest, convert to degree Celsius, and mask water pixels.
lstNewHaven = lstFinal.clip(regionInt).subtract(273.15).updateMask(notWater)

# Add layer to map.
Map.addLayer(
    lstNewHaven,
    {"palette": ["blue", "white", "red"], "min": 25, "max": 38},
    "LST_MODIS",
)

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: A1.5 Heat Islands # Checkpoint: A15a # Author: TC Chakraborty # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Load feature collection of New Haven's census tracts from user assets. regionInt = ee.FeatureCollection("projects/gee-book/assets/A1-5/TC_NewHaven") # Get dissolved feature collection using an error margin of 50 meters. regionInt = regionInt.union(50) # Set map center and zoom level (Zoom level varies from 1 to 20). Map.setCenter(-72.9, 41.3, 12) # Add layer to map. Map.addLayer(regionInt, {}, "New Haven boundary") # Load MODIS image collection from the Earth Engine data catalog. modisLst = ee.ImageCollection("MODIS/006/MYD11A2") # Select the band of interest (in this case: Daytime LST). landSurfTemperature = modisLst.select("LST_Day_1km") # Create a summer filter. sumFilter = ee.Filter.dayOfYear(152, 243) # Filter the date range of interest using a date filter. lstDateInt = landSurfTemperature.filterDate("2014-01-01", "2019-01-01").filter( sumFilter ) # Take pixel-wise mean of all the images in the collection. lstMean = lstDateInt.mean() # Multiply each pixel by scaling factor to get the LST values. lstFinal = lstMean.multiply(0.02) # Generate a water mask. water = ee.Image("JRC/GSW1_0/GlobalSurfaceWater").select("occurrence") notWater = water.mask().Not() # Clip data to region of interest, convert to degree Celsius, and mask water pixels. lstNewHaven = lstFinal.clip(regionInt).subtract(273.15).updateMask(notWater) # Add layer to map. Map.addLayer( lstNewHaven, {"palette": ["blue", "white", "red"], "min": 25, "max": 38}, "LST_MODIS", ) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map