A39b 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:      Chapter A3.9 Conservation Applications - Assessing the
#                spatial relationship between burned area and precipitation
#  Checkpoint:   A39b
#  Authors:      Harriet Branson, Chelsea Smith
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# ** Upload the area of interest ** #
AOI = ee.Geometry.Polygon([
    [
        [37.72, -11.22],
        [38.49, -11.22],
        [38.49, -12.29],
        [37.72, -12.29]
    ]
])
Map.centerObject(AOI, 9)
Map.addLayer(AOI, {
    'color': 'white'
}, 'Area of interest')

# ** MODIS Monthly Burn Area ** #

# Load in the MODIS Monthly Burned Area dataset.
dataset = ee.ImageCollection('MODIS/006/MCD64A1') \
    .filter(ee.Filter.date('2010-01-01', '2021-12-31'))

# Select the BurnDate band from the images in the collection.
MODIS_BurnDate = dataset.select('BurnDate')

# A function that will calculate the area of pixels in each image by date.
def addArea(img):
    area = ee.Image.pixelArea() \
        .updateMask(
            img
        ) # Limit area calculation to areas that have burned data. \
        .divide(1e6) \
        .clip(AOI) \
        .reduceRegion({
            'reducer': ee.Reducer.sum(),
            'geometry': AOI,
            'scale': 500,
            'bestEffort': True
        }).getNumber(
            'area'
        ); # Retrieve area from the reduce region calculation.
    # Add a new band to each image in the collection named area.
    return img.addBands(ee.Image(area).rename('area'))


# Apply function on image collection.
burnDateArea = MODIS_BurnDate.map(addArea)

# Select only the area band as we are using system time for date.
burnedArea = burnDateArea.select('area')

# Create a chart that shows the total burned area over time.
burnedAreaChart =
    ui.Chart.image \
    .series({
        'imageCollection': burnedArea, # Our image collection.
        'region': AOI,
        'reducer': ee.Reducer.mean(),
        'scale': 500,
        'xProperty': 'system:time_start' # time
    }) \
    .setSeriesNames(['Area']) \
    .setOptions({
        'title': 'Total monthly area burned in AOI',
        'hAxis': {
            'title': 'Date', # The x axis label.
            format: 'YYYY', # Years only for date format.
            'gridlines': {
                'count': 12
            },
            'titleTextStyle': {
                'italic': False,
                'bold': True
            }
        },
        'vAxis': {
            'title': 'Total burned area (km²)', # The y-axis label
            'maxValue': 2250, # The bounds for y-axis
            'minValue': 0,
            'titleTextStyle': {
                'italic': False,
                'bold': True
            }
        },
        'lineWidth': 1.5,
        'colors': ['d74b46'], # The line color
    })
print(burnedAreaChart)

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

# Load in the CHIRPS rainfall pentad dataset.
chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD')

# Define the temporal range
startyear = 2010
endyear = 2021

# Set the advancing dates from the temporal range.
startdate = ee.Date.fromYMD(startyear, 1, 1)
enddate = ee.Date.fromYMD(endyear, 12, 31)

# Create a list of years
years = ee.List.sequence(startyear, endyear)
# Create a list of months
months = ee.List.sequence(1, 12)

# Filter the dataset based on the temporal range.
Pchirps = chirps.filterDate(startdate, enddate) \
    .sort('system:time_start',
        False) # Sort chronologically in descending order. \
    .filterBounds(AOI) \
    .select('precipitation'); 

# Calculate the precipitation per month.
MonthlyRainfall = ee.ImageCollection.fromImages(
        y
    ) { # Using the list of years based on temporal range.

def func_fdw(m):
            w = Pchirps.filter(ee.Filter \
                    .calendarRange(y, y, 'year')) \
                .filter(ee.Filter.calendarRange(m, m,
                    'month')) \
                .sum(); 
            return w.set('year', y) \
                .set('month', m) \
                .set('system:time_start', ee.Date \
                    .fromYMD(y, m, 1).millis()
                ) # Use millis to keep the system time number. \
                .set('date', ee.Date.fromYMD(y, m,
                    1))

        return months.map(func_fdw)














    }).flatten())
# Print the image collection.
print('Monthly Precipitation Image Collection', MonthlyRainfall)

# ** Chart: CHIRPS Precipitation ** #

# Create a chart displaying monthly rainfall over a temporal range.
monthlyRainfallChart =
    ui.Chart.image \
    .series({
        'imageCollection': MonthlyRainfall.select(
            'precipitation'), # Select precipitation band
        'region': AOI,
        'reducer': ee.Reducer \
            .mean(), 
        'scale': 500,
        'xProperty': 'system:time_start' # Use system time start for x-axis
    }) \
    .setSeriesNames(['Precipitation']) \
    .setOptions({
        'title': 'Total monthly precipitation in AOI', # Add title
        'hAxis': {
            'title': 'Date',
            format: 'YYYY', # Year only date format
            'gridlines': {
                'count': 12
            },
            'titleTextStyle': {
                'italic': False,
                'bold': True
            }
        },
        'vAxis': {
            'title': 'Precipitation (mm)', # The y-axis label
            'maxValue': 450, # The bounds for y-axis
            'minValue': 0,
            'titleTextStyle': {
                'italic': False,
                'bold': True
            }
        },
        'lineWidth': 1.5,
        'colors': ['4f5ebd'],
    })
print(monthlyRainfallChart)

# 2010/2011 wet season total
year = 2010; # Adjust year
startDate = ee.Date.fromYMD(year, 11, 1); # Adjust months/days
endDate = ee.Date.fromYMD(year + 1, 5, 31); # Adjust months/days
filtered = chirps \
    .filter(ee.Filter.date(startDate, endDate))
Rains10_11Total = filtered.reduce(ee.Reducer.sum()).clip(AOI)

# 2011/2012 wet season total
year = 2011; # Adjust year
startDate = ee.Date.fromYMD(year, 11, 1); # Adjust months/days
endDate = ee.Date.fromYMD(year + 1, 5, 31); # Adjust months/days
filtered = chirps \
    .filter(ee.Filter.date(startDate, endDate))
Rains11_12Total = filtered.reduce(ee.Reducer.sum()).clip(AOI)

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: Chapter A3.9 Conservation Applications - Assessing the # spatial relationship between burned area and precipitation # Checkpoint: A39b # Authors: Harriet Branson, Chelsea Smith # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ** Upload the area of interest ** # AOI = ee.Geometry.Polygon([ [ [37.72, -11.22], [38.49, -11.22], [38.49, -12.29], [37.72, -12.29] ] ]) Map.centerObject(AOI, 9) Map.addLayer(AOI, { 'color': 'white' }, 'Area of interest') # ** MODIS Monthly Burn Area ** # # Load in the MODIS Monthly Burned Area dataset. dataset = ee.ImageCollection('MODIS/006/MCD64A1') \ .filter(ee.Filter.date('2010-01-01', '2021-12-31')) # Select the BurnDate band from the images in the collection. MODIS_BurnDate = dataset.select('BurnDate') # A function that will calculate the area of pixels in each image by date. def addArea(img): area = ee.Image.pixelArea() \ .updateMask( img ) # Limit area calculation to areas that have burned data. \ .divide(1e6) \ .clip(AOI) \ .reduceRegion({ 'reducer': ee.Reducer.sum(), 'geometry': AOI, 'scale': 500, 'bestEffort': True }).getNumber( 'area' ); # Retrieve area from the reduce region calculation. # Add a new band to each image in the collection named area. return img.addBands(ee.Image(area).rename('area')) # Apply function on image collection. burnDateArea = MODIS_BurnDate.map(addArea) # Select only the area band as we are using system time for date. burnedArea = burnDateArea.select('area') # Create a chart that shows the total burned area over time. burnedAreaChart = ui.Chart.image \ .series({ 'imageCollection': burnedArea, # Our image collection. 'region': AOI, 'reducer': ee.Reducer.mean(), 'scale': 500, 'xProperty': 'system:time_start' # time }) \ .setSeriesNames(['Area']) \ .setOptions({ 'title': 'Total monthly area burned in AOI', 'hAxis': { 'title': 'Date', # The x axis label. format: 'YYYY', # Years only for date format. 'gridlines': { 'count': 12 }, 'titleTextStyle': { 'italic': False, 'bold': True } }, 'vAxis': { 'title': 'Total burned area (km²)', # The y-axis label 'maxValue': 2250, # The bounds for y-axis 'minValue': 0, 'titleTextStyle': { 'italic': False, 'bold': True } }, 'lineWidth': 1.5, 'colors': ['d74b46'], # The line color }) print(burnedAreaChart) # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # Load in the CHIRPS rainfall pentad dataset. chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD') # Define the temporal range startyear = 2010 endyear = 2021 # Set the advancing dates from the temporal range. startdate = ee.Date.fromYMD(startyear, 1, 1) enddate = ee.Date.fromYMD(endyear, 12, 31) # Create a list of years years = ee.List.sequence(startyear, endyear) # Create a list of months months = ee.List.sequence(1, 12) # Filter the dataset based on the temporal range. Pchirps = chirps.filterDate(startdate, enddate) \ .sort('system:time_start', False) # Sort chronologically in descending order. \ .filterBounds(AOI) \ .select('precipitation'); # Calculate the precipitation per month. MonthlyRainfall = ee.ImageCollection.fromImages( y ) { # Using the list of years based on temporal range. def func_fdw(m): w = Pchirps.filter(ee.Filter \ .calendarRange(y, y, 'year')) \ .filter(ee.Filter.calendarRange(m, m, 'month')) \ .sum(); return w.set('year', y) \ .set('month', m) \ .set('system:time_start', ee.Date \ .fromYMD(y, m, 1).millis() ) # Use millis to keep the system time number. \ .set('date', ee.Date.fromYMD(y, m, 1)) return months.map(func_fdw) }).flatten()) # Print the image collection. print('Monthly Precipitation Image Collection', MonthlyRainfall) # ** Chart: CHIRPS Precipitation ** # # Create a chart displaying monthly rainfall over a temporal range. monthlyRainfallChart = ui.Chart.image \ .series({ 'imageCollection': MonthlyRainfall.select( 'precipitation'), # Select precipitation band 'region': AOI, 'reducer': ee.Reducer \ .mean(), 'scale': 500, 'xProperty': 'system:time_start' # Use system time start for x-axis }) \ .setSeriesNames(['Precipitation']) \ .setOptions({ 'title': 'Total monthly precipitation in AOI', # Add title 'hAxis': { 'title': 'Date', format: 'YYYY', # Year only date format 'gridlines': { 'count': 12 }, 'titleTextStyle': { 'italic': False, 'bold': True } }, 'vAxis': { 'title': 'Precipitation (mm)', # The y-axis label 'maxValue': 450, # The bounds for y-axis 'minValue': 0, 'titleTextStyle': { 'italic': False, 'bold': True } }, 'lineWidth': 1.5, 'colors': ['4f5ebd'], }) print(monthlyRainfallChart) # 2010/2011 wet season total year = 2010; # Adjust year startDate = ee.Date.fromYMD(year, 11, 1); # Adjust months/days endDate = ee.Date.fromYMD(year + 1, 5, 31); # Adjust months/days filtered = chirps \ .filter(ee.Filter.date(startDate, endDate)) Rains10_11Total = filtered.reduce(ee.Reducer.sum()).clip(AOI) # 2011/2012 wet season total year = 2011; # Adjust year startDate = ee.Date.fromYMD(year, 11, 1); # Adjust months/days endDate = ee.Date.fromYMD(year + 1, 5, 31); # Adjust months/days filtered = chirps \ .filter(ee.Filter.date(startDate, endDate)) Rains11_12Total = filtered.reduce(ee.Reducer.sum()).clip(AOI) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map