Visualizing and Analyzing Global Land Cover Data¶

Import libraries¶

In [ ]:

# pip install geemap

# pip install geemap

In [ ]:

import ee
import geemap

import ee import geemap

In [ ]:

geemap.ee_initialize()

geemap.ee_initialize()

Visualizing Global Land Cover Data Products¶

Creating Dynamic World Land Cover Composites¶

• App: https://www.dynamicworld.app
• App2: https://earthoutreach.users.earthengine.app/view/dynamicworld
• Paper: https://doi.org/10.1038/s41597-022-01307-4
• Model: https://github.com/google/dynamicworld
• Training data: https://doi.pangaea.de/10.1594/PANGAEA.933475
• Data: https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1
• JavaScript tutorial: https://developers.google.com/earth-engine/tutorials/community/introduction-to-dynamic-world-pt-1

In [ ]:

Map = geemap.Map()
Map.add_basemap('HYBRID')
Map

Map = geemap.Map() Map.add_basemap('HYBRID') Map

In [ ]:

# Set the region of interest by simply drawing a polygon on the map
region = Map.user_roi
if region is None:
    region = ee.Geometry.BBox(-89.7088, 42.9006, -89.0647, 43.2167)
    Map.addLayer(region, {}, 'Region')

Map.centerObject(region)

# Set the region of interest by simply drawing a polygon on the map region = Map.user_roi if region is None: region = ee.Geometry.BBox(-89.7088, 42.9006, -89.0647, 43.2167) Map.addLayer(region, {}, 'Region') Map.centerObject(region)

In [ ]:

# Set the date range
start_date = '2021-01-01'
end_date = '2022-01-01'

# Set the date range start_date = '2021-01-01' end_date = '2022-01-01'

In [ ]:

# Create a Sentinel-2 image composite
image = geemap.dynamic_world_s2(region, start_date, end_date, clip=True)
vis_params = {'bands': ['B8', 'B4', 'B3'], 'min': 0, 'max': 3000}
Map.addLayer(image, vis_params, 'Sentinel-2 image')

# Create a Sentinel-2 image composite image = geemap.dynamic_world_s2(region, start_date, end_date, clip=True) vis_params = {'bands': ['B8', 'B4', 'B3'], 'min': 0, 'max': 3000} Map.addLayer(image, vis_params, 'Sentinel-2 image')

In [ ]:

# Create Dynamic World land cover composite
landcover = geemap.dynamic_world(
    region, start_date, end_date, clip=True, return_type='class'
)

dwVisParams = {
    "min": 0,
    "max": 8,
    "palette": [
        "#419BDF",
        "#397D49",
        "#88B053",
        "#7A87C6",
        "#E49635",
        "#DFC35A",
        "#C4281B",
        "#A59B8F",
        "#B39FE1",
    ],
}

Map.addLayer(landcover, dwVisParams, 'Land Cover Class')
Map

# Create Dynamic World land cover composite landcover = geemap.dynamic_world( region, start_date, end_date, clip=True, return_type='class' ) dwVisParams = { "min": 0, "max": 8, "palette": [ "#419BDF", "#397D49", "#88B053", "#7A87C6", "#E49635", "#DFC35A", "#C4281B", "#A59B8F", "#B39FE1", ], } Map.addLayer(landcover, dwVisParams, 'Land Cover Class') Map

In [ ]:

Map.add_legend(title='Dynamic World Land Cover', builtin_legend='Dynamic_World')

Map.add_legend(title='Dynamic World Land Cover', builtin_legend='Dynamic_World')

In [ ]:

landcover = geemap.dynamic_world(
    region, start_date, end_date, clip=True, return_type='visualize'
)
Map.addLayer(landcover, {}, 'Land Cover Visualize')

landcover = geemap.dynamic_world( region, start_date, end_date, clip=True, return_type='visualize' ) Map.addLayer(landcover, {}, 'Land Cover Visualize')

In [ ]:

landcover = geemap.dynamic_world(
    region, start_date, end_date, clip=True, return_type='probability'
)
Map.addLayer(landcover, {}, 'Land Cover Probability')

landcover = geemap.dynamic_world( region, start_date, end_date, clip=True, return_type='probability' ) Map.addLayer(landcover, {}, 'Land Cover Probability')

In [ ]:

# Create Dynamic World land cover composite
landcover = geemap.dynamic_world(
    region, start_date, end_date, clip=True, return_type='hillshade'
)
Map.addLayer(landcover, {}, 'Land Cover')

# Create Dynamic World land cover composite landcover = geemap.dynamic_world( region, start_date, end_date, clip=True, return_type='hillshade' ) Map.addLayer(landcover, {}, 'Land Cover')

Comparing Global Land Cover Data Products¶

Visualizing ESA Global Land Cover.

In [ ]:

start_date = '2021-01-01'
end_date = '2022-01-01'
region = ee.Geometry.BBox(-179, -89, 179, 89)

start_date = '2021-01-01' end_date = '2022-01-01' region = ee.Geometry.BBox(-179, -89, 179, 89)

In [ ]:

Map = geemap.Map()

esa = ee.ImageCollection("ESA/WorldCover/v200").first()
esa_vis = {'bands': ['Map']}

Map.addLayer(esa, esa_vis, "ESA LC 2021")
Map.add_legend(title="ESA Land Cover", builtin_legend='ESA_WorldCover')

Map

Map = geemap.Map() esa = ee.ImageCollection("ESA/WorldCover/v200").first() esa_vis = {'bands': ['Map']} Map.addLayer(esa, esa_vis, "ESA LC 2021") Map.add_legend(title="ESA Land Cover", builtin_legend='ESA_WorldCover') Map

Visualizing ESRI Global Land Cover.

In [ ]:

Map = geemap.Map()

esri = (
    ee.ImageCollection(
        "projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m_TS"
    )
    .filterDate(start_date, end_date)
    .mosaic()
)
esri_vis = {
    'min': 1,
    'max': 11,
    'palette': [
        "#1A5BAB",
        "#358221",
        "#000000",
        "#87D19E",
        "#FFDB5C",
        "#000000",
        "#ED022A",
        "#EDE9E4",
        "#F2FAFF",
        "#C8C8C8",
        "#C6AD8D",
    ],
}

Map.addLayer(esri, esri_vis, "ESRI LC 2021")
Map.add_legend(title="ESRI Land Cover", builtin_legend='ESRI_LandCover_TS')

Map

Map = geemap.Map() esri = ( ee.ImageCollection( "projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m_TS" ) .filterDate(start_date, end_date) .mosaic() ) esri_vis = { 'min': 1, 'max': 11, 'palette': [ "#1A5BAB", "#358221", "#000000", "#87D19E", "#FFDB5C", "#000000", "#ED022A", "#EDE9E4", "#F2FAFF", "#C8C8C8", "#C6AD8D", ], } Map.addLayer(esri, esri_vis, "ESRI LC 2021") Map.add_legend(title="ESRI Land Cover", builtin_legend='ESRI_LandCover_TS') Map

In [ ]:

Map = geemap.Map()

dw_class = geemap.dynamic_world(region, start_date, end_date, return_type='class')
dw = geemap.dynamic_world(region, start_date, end_date, return_type='hillshade')

dw_vis = {
    "min": 0,
    "max": 8,
    "palette": [
        "#419BDF",
        "#397D49",
        "#88B053",
        "#7A87C6",
        "#E49635",
        "#DFC35A",
        "#C4281B",
        "#A59B8F",
        "#B39FE1",
    ],
}

Map.addLayer(dw_class, dw_vis, 'DW LC 2021', False)
Map.addLayer(dw, {}, 'DW LC Hillshade')

Map.add_legend(title="Dynamic World Land Cover", builtin_legend='Dynamic_World')
Map.setCenter(-88.9088, 43.0006, 12)
Map

Map = geemap.Map() dw_class = geemap.dynamic_world(region, start_date, end_date, return_type='class') dw = geemap.dynamic_world(region, start_date, end_date, return_type='hillshade') dw_vis = { "min": 0, "max": 8, "palette": [ "#419BDF", "#397D49", "#88B053", "#7A87C6", "#E49635", "#DFC35A", "#C4281B", "#A59B8F", "#B39FE1", ], } Map.addLayer(dw_class, dw_vis, 'DW LC 2021', False) Map.addLayer(dw, {}, 'DW LC Hillshade') Map.add_legend(title="Dynamic World Land Cover", builtin_legend='Dynamic_World') Map.setCenter(-88.9088, 43.0006, 12) Map

Comparing Dynamic World and ESA Land Cover.

In [ ]:

Map = geemap.Map(center=[39.3322, -106.7349], zoom=10)

left_layer = geemap.ee_tile_layer(esa, esa_vis, "ESA LC")
right_layer = geemap.ee_tile_layer(dw, {}, "Dynamic World LC")

Map.split_map(left_layer, right_layer)
Map.add_legend(
    title="ESA Land Cover", builtin_legend='ESA_WorldCover', position='bottomleft'
)
Map.add_legend(
    title="Dynamic World Land Cover",
    builtin_legend='Dynamic_World',
    position='bottomright',
)
Map.setCenter(-88.9088, 43.0006, 12)

Map

Map = geemap.Map(center=[39.3322, -106.7349], zoom=10) left_layer = geemap.ee_tile_layer(esa, esa_vis, "ESA LC") right_layer = geemap.ee_tile_layer(dw, {}, "Dynamic World LC") Map.split_map(left_layer, right_layer) Map.add_legend( title="ESA Land Cover", builtin_legend='ESA_WorldCover', position='bottomleft' ) Map.add_legend( title="Dynamic World Land Cover", builtin_legend='Dynamic_World', position='bottomright', ) Map.setCenter(-88.9088, 43.0006, 12) Map

Comparing Dynamic World with ESRI Land Cover.

In [ ]:

Map = geemap.Map(center=[-89.3998, 43.0886], zoom=10)

left_layer = geemap.ee_tile_layer(esri, esri_vis, "ESRI LC")
right_layer = geemap.ee_tile_layer(dw, {}, "Dynamic World LC")

Map.split_map(left_layer, right_layer)
Map.add_legend(
    title="ESRI Land Cover", builtin_legend='ESRI_LandCover_TS', position='bottomleft'
)
Map.add_legend(
    title="Dynamic World Land Cover",
    builtin_legend='Dynamic_World',
    position='bottomright',
)
Map.setCenter(-88.9088, 43.0006, 12)

Map

Map = geemap.Map(center=[-89.3998, 43.0886], zoom=10) left_layer = geemap.ee_tile_layer(esri, esri_vis, "ESRI LC") right_layer = geemap.ee_tile_layer(dw, {}, "Dynamic World LC") Map.split_map(left_layer, right_layer) Map.add_legend( title="ESRI Land Cover", builtin_legend='ESRI_LandCover_TS', position='bottomleft' ) Map.add_legend( title="Dynamic World Land Cover", builtin_legend='Dynamic_World', position='bottomright', ) Map.setCenter(-88.9088, 43.0006, 12) Map

Creating Dynamic World Time Series¶

In [ ]:

Map = geemap.Map()
Map.add_basemap('HYBRID')
Map

Map = geemap.Map() Map.add_basemap('HYBRID') Map

In [ ]:

# Set the region of interest by simply drawing a polygon on the map
region = Map.user_roi
if region is None:
    region = ee.Geometry.BBox(-89.7088, 42.9006, -89.0647, 43.2167)
    Map.addLayer(region, {}, 'Region')

Map.centerObject(region)

# Set the region of interest by simply drawing a polygon on the map region = Map.user_roi if region is None: region = ee.Geometry.BBox(-89.7088, 42.9006, -89.0647, 43.2167) Map.addLayer(region, {}, 'Region') Map.centerObject(region)

In [ ]:

# Set the date range
start_date = '2017-01-01'
end_date = '2023-01-01'

# Set the date range start_date = '2017-01-01' end_date = '2023-01-01'

In [ ]:

images = geemap.dynamic_world_timeseries(
    region, start_date, end_date, frequency='year', return_type="class"
)

images = geemap.dynamic_world_timeseries( region, start_date, end_date, frequency='year', return_type="class" )

In [ ]:

vis_params = {
    "min": 0,
    "max": 8,
    "palette": [
        "#419BDF",
        "#397D49",
        "#88B053",
        "#7A87C6",
        "#E49635",
        "#DFC35A",
        "#C4281B",
        "#A59B8F",
        "#B39FE1",
    ],
}
Map.addLayer(images.first(), vis_params, 'First image')
Map.add_legend(title="Dynamic World Land Cover", builtin_legend='Dynamic_World')
Map

vis_params = { "min": 0, "max": 8, "palette": [ "#419BDF", "#397D49", "#88B053", "#7A87C6", "#E49635", "#DFC35A", "#C4281B", "#A59B8F", "#B39FE1", ], } Map.addLayer(images.first(), vis_params, 'First image') Map.add_legend(title="Dynamic World Land Cover", builtin_legend='Dynamic_World') Map

In [ ]:

Map.ts_inspector(images, left_vis=vis_params, date_format='YYYY')

Map.ts_inspector(images, left_vis=vis_params, date_format='YYYY')

In [ ]:

Map = geemap.Map()
Map.add_basemap('HYBRID')
Map.centerObject(region)

images = geemap.dynamic_world_timeseries(
    region, start_date, end_date, frequency='year', return_type="hillshade"
)
Map.ts_inspector(images, date_format='YYYY')
Map.add_legend(title="Dynamic World Land Cover", builtin_legend='Dynamic_World')

Map

Map = geemap.Map() Map.add_basemap('HYBRID') Map.centerObject(region) images = geemap.dynamic_world_timeseries( region, start_date, end_date, frequency='year', return_type="hillshade" ) Map.ts_inspector(images, date_format='YYYY') Map.add_legend(title="Dynamic World Land Cover", builtin_legend='Dynamic_World') Map

Analyzing Global Land Cover Data¶

In [ ]:

Map = geemap.Map()
dataset = ee.ImageCollection("ESA/WorldCover/v200").first()
Map.addLayer(dataset, {'bands': ['Map']}, 'ESA Land Cover')
Map.add_legend(title='ESA Land Cover Type', builtin_legend='ESA_WorldCover')
Map

Map = geemap.Map() dataset = ee.ImageCollection("ESA/WorldCover/v200").first() Map.addLayer(dataset, {'bands': ['Map']}, 'ESA Land Cover') Map.add_legend(title='ESA Land Cover Type', builtin_legend='ESA_WorldCover') Map

In [ ]:

df = geemap.image_area_by_group(
    dataset, scale=1000, denominator=1e6, decimal_places=4, verbose=True
)
df

df = geemap.image_area_by_group( dataset, scale=1000, denominator=1e6, decimal_places=4, verbose=True ) df

In [ ]:

df.to_csv('esa2021.csv')

df.to_csv('esa2021.csv')

In [ ]:

esa_dict = {
    "10 Trees": "006400",
    "20 Shrubland": "ffbb22",
    "30 Grassland": "ffff4c",
    "40 Cropland": "f096ff",
    "50 Built-up": "fa0000",
    "60 Barren / sparse vegetation": "b4b4b4",
    "70 Snow and ice": "f0f0f0",
    "80 Open water": "0064c8",
    "90 Herbaceous wetland": "0096a0",
    "95 Mangroves": "00cf75",
    "100 Moss and lichen": "fae6a0",
}
classes = list(esa_dict.keys())
classes

esa_dict = { "10 Trees": "006400", "20 Shrubland": "ffbb22", "30 Grassland": "ffff4c", "40 Cropland": "f096ff", "50 Built-up": "fa0000", "60 Barren / sparse vegetation": "b4b4b4", "70 Snow and ice": "f0f0f0", "80 Open water": "0064c8", "90 Herbaceous wetland": "0096a0", "95 Mangroves": "00cf75", "100 Moss and lichen": "fae6a0", } classes = list(esa_dict.keys()) classes

In [ ]:

df['class'] = classes

df['class'] = classes

In [ ]:

geemap.bar_chart(
    df,
    x='class',
    y='area',
    x_label='Land Cover Type',
    y_label='Area (km2)',
)

geemap.bar_chart( df, x='class', y='area', x_label='Land Cover Type', y_label='Area (km2)', )

In [ ]:

geemap.pie_chart(df, names='class', values='area', height=500)

geemap.pie_chart(df, names='class', values='area', height=500)

In [ ]:

countries = ee.FeatureCollection(geemap.examples.get_ee_path('countries'))
Map.addLayer(countries, {}, 'Countries')
Map

countries = ee.FeatureCollection(geemap.examples.get_ee_path('countries')) Map.addLayer(countries, {}, 'Countries') Map

In [ ]:

geemap.zonal_stats_by_group(
    dataset,
    countries,
    'esa_2021_country.csv',
    statistics_type='SUM',
    denominator=1e6,
    scale=1000,
)

geemap.zonal_stats_by_group( dataset, countries, 'esa_2021_country.csv', statistics_type='SUM', denominator=1e6, scale=1000, )

In [ ]:

geemap.bar_chart(
    'esa_2021_country.csv',
    x='NAME',
    y='Class_10',
    max_rows=30,
    x_label='Country',
    y_label='Forest Area (km2)',
)

geemap.bar_chart( 'esa_2021_country.csv', x='NAME', y='Class_10', max_rows=30, x_label='Country', y_label='Forest Area (km2)', )

In [ ]:

geemap.bar_chart(
    'esa_2021_country.csv',
    x='NAME',
    y='Class_40',
    max_rows=30,
    x_label='Country',
    y_label='Cropland Area (km2)',
)

geemap.bar_chart( 'esa_2021_country.csv', x='NAME', y='Class_40', max_rows=30, x_label='Country', y_label='Cropland Area (km2)', )

In [ ]:

geemap.bar_chart(
    'esa_2021_country.csv',
    x='NAME',
    y=['Class_10', 'Class_20', 'Class_30', 'Class_40'],
    max_rows=10,
    x_label='Country',
)

geemap.bar_chart( 'esa_2021_country.csv', x='NAME', y=['Class_10', 'Class_20', 'Class_30', 'Class_40'], max_rows=10, x_label='Country', )

In [ ]:

geemap.pie_chart(
    'esa_2021_country.csv', names='NAME', values='Class_10', max_rows=30, height=500
)

geemap.pie_chart( 'esa_2021_country.csv', names='NAME', values='Class_10', max_rows=30, height=500 )

In [ ]:

geemap.pie_chart(
    'esa_2021_country.csv', names='NAME', values='Class_40', max_rows=30, height=500
)

geemap.pie_chart( 'esa_2021_country.csv', names='NAME', values='Class_40', max_rows=30, height=500 )

Forest cover change analysis¶

https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2021_v1_9

In [ ]:

Map = geemap.Map()
Map.add_basemap('HYBRID')

Map = geemap.Map() Map.add_basemap('HYBRID')

In [ ]:

dataset = ee.Image('UMD/hansen/global_forest_change_2021_v1_9')

dataset = ee.Image('UMD/hansen/global_forest_change_2021_v1_9')

In [ ]:

dataset.bandNames().getInfo()

dataset.bandNames().getInfo()

In [ ]:

first_bands = ['first_b50', 'first_b40', 'first_b30']
first_image = dataset.select(first_bands)
Map.addLayer(first_image, {'bands': first_bands, 'gamma': 1.5}, 'Year 2000 Bands 5/4/3')
Map

first_bands = ['first_b50', 'first_b40', 'first_b30'] first_image = dataset.select(first_bands) Map.addLayer(first_image, {'bands': first_bands, 'gamma': 1.5}, 'Year 2000 Bands 5/4/3') Map

In [ ]:

last_bands = ['last_b50', 'last_b40', 'last_b30']
last_image = dataset.select(last_bands)
Map.addLayer(last_image, {'bands': last_bands, 'gamma': 1.5}, 'Year 2021 Bands 5/4/3')

last_bands = ['last_b50', 'last_b40', 'last_b30'] last_image = dataset.select(last_bands) Map.addLayer(last_image, {'bands': last_bands, 'gamma': 1.5}, 'Year 2021 Bands 5/4/3')

In [ ]:

treecover = dataset.select(['treecover2000'])

treeCoverVisParam = {'min': 0, 'max': 100, 'palette': ['black', 'green']}

name1 = 'Tree cover (%)'
Map.addLayer(treecover, treeCoverVisParam, name1)
Map.add_colorbar(treeCoverVisParam, label=name1, layer_name=name1)
Map

treecover = dataset.select(['treecover2000']) treeCoverVisParam = {'min': 0, 'max': 100, 'palette': ['black', 'green']} name1 = 'Tree cover (%)' Map.addLayer(treecover, treeCoverVisParam, name1) Map.add_colorbar(treeCoverVisParam, label=name1, layer_name=name1) Map

In [ ]:

threshold = 10
treecover_bin = treecover.gte(threshold).selfMask()
treeVisParam = {'palette': ['green']}
Map.addLayer(treecover_bin, treeVisParam, 'Tree cover bin')

threshold = 10 treecover_bin = treecover.gte(threshold).selfMask() treeVisParam = {'palette': ['green']} Map.addLayer(treecover_bin, treeVisParam, 'Tree cover bin')

In [ ]:

treeloss_year = dataset.select(['lossyear'])

treeLossVisParam = {'min': 0, 'max': 21, 'palette': ['yellow', 'red']}

layer_name = 'Tree loss year'
Map.addLayer(treeloss_year, treeLossVisParam, layer_name)
Map.add_colorbar(treeLossVisParam, label=layer_name, layer_name=layer_name)

treeloss_year = dataset.select(['lossyear']) treeLossVisParam = {'min': 0, 'max': 21, 'palette': ['yellow', 'red']} layer_name = 'Tree loss year' Map.addLayer(treeloss_year, treeLossVisParam, layer_name) Map.add_colorbar(treeLossVisParam, label=layer_name, layer_name=layer_name)

In [ ]:

treeloss = dataset.select(['loss']).selfMask()
Map.addLayer(treeloss, {'palette': 'red'}, 'Tree loss')
Map

treeloss = dataset.select(['loss']).selfMask() Map.addLayer(treeloss, {'palette': 'red'}, 'Tree loss') Map

In [ ]:

treegain = dataset.select(['gain']).selfMask()
Map.addLayer(treegain, {'palette': 'yellow'}, 'Tree gain')
Map

treegain = dataset.select(['gain']).selfMask() Map.addLayer(treegain, {'palette': 'yellow'}, 'Tree gain') Map

In [ ]:

countries = ee.FeatureCollection(geemap.examples.get_ee_path('countries'))

countries = ee.FeatureCollection(geemap.examples.get_ee_path('countries'))

In [ ]:

geemap.ee_to_df(countries)

geemap.ee_to_df(countries)

In [ ]:

style = {'color': '#ffff0088', 'fillColor': '#00000000'}
Map.addLayer(countries.style(**style), {}, 'Countries')

style = {'color': '#ffff0088', 'fillColor': '#00000000'} Map.addLayer(countries.style(**style), {}, 'Countries')

Forest area analysis

In [ ]:

geemap.zonal_stats_by_group(
    treecover_bin,
    countries,
    'forest_cover.csv',
    statistics_type='SUM',
    denominator=1e6,
    scale=1000,
)

geemap.zonal_stats_by_group( treecover_bin, countries, 'forest_cover.csv', statistics_type='SUM', denominator=1e6, scale=1000, )

In [ ]:

geemap.pie_chart(
    'forest_cover.csv', names='NAME', values='Class_sum', max_rows=20, height=600
)

geemap.pie_chart( 'forest_cover.csv', names='NAME', values='Class_sum', max_rows=20, height=600 )

In [ ]:

geemap.bar_chart(
    'forest_cover.csv',
    x='NAME',
    y='Class_sum',
    max_rows=20,
    x_label='Country',
    y_label='Forest area (km2)',
)

geemap.bar_chart( 'forest_cover.csv', x='NAME', y='Class_sum', max_rows=20, x_label='Country', y_label='Forest area (km2)', )

Forest loss analysis.

In [ ]:

geemap.zonal_stats_by_group(
    treeloss,
    countries,
    'treeloss.csv',
    statistics_type='SUM',
    denominator=1e6,
    scale=1000,
)

geemap.zonal_stats_by_group( treeloss, countries, 'treeloss.csv', statistics_type='SUM', denominator=1e6, scale=1000, )

In [ ]:

geemap.pie_chart(
    'treeloss.csv', names='NAME', values='Class_sum', max_rows=20, height=600
)

geemap.pie_chart( 'treeloss.csv', names='NAME', values='Class_sum', max_rows=20, height=600 )

In [ ]:

geemap.bar_chart(
    'treeloss.csv',
    x='NAME',
    y='Class_sum',
    max_rows=20,
    x_label='Country',
    y_label='Forest loss area (km2)',
)

geemap.bar_chart( 'treeloss.csv', x='NAME', y='Class_sum', max_rows=20, x_label='Country', y_label='Forest loss area (km2)', )

Surface water change analysis¶

Surface water occurrence¶

In [ ]:

dataset = ee.Image('JRC/GSW1_3/GlobalSurfaceWater')
dataset.bandNames().getInfo()

dataset = ee.Image('JRC/GSW1_3/GlobalSurfaceWater') dataset.bandNames().getInfo()

In [ ]:

Map = geemap.Map()
Map.add_basemap('HYBRID')

image = dataset.select(['occurrence'])
region = ee.Geometry.BBox(-99.957, 46.8947, -99.278, 47.1531)

vis_params = {'min': 0.0, 'max': 100.0, 'palette': ['ffffff', 'ffbbbb', '0000ff']}

Map.addLayer(image, vis_params, 'Occurrence')
Map.addLayer(region, {}, 'ROI', True, 0.5)
Map.centerObject(region)
Map.add_colorbar(vis_params, label='Water occurrence (%)', layer_name='Occurrence')

Map

Map = geemap.Map() Map.add_basemap('HYBRID') image = dataset.select(['occurrence']) region = ee.Geometry.BBox(-99.957, 46.8947, -99.278, 47.1531) vis_params = {'min': 0.0, 'max': 100.0, 'palette': ['ffffff', 'ffbbbb', '0000ff']} Map.addLayer(image, vis_params, 'Occurrence') Map.addLayer(region, {}, 'ROI', True, 0.5) Map.centerObject(region) Map.add_colorbar(vis_params, label='Water occurrence (%)', layer_name='Occurrence') Map

In [ ]:

hist = geemap.image_histogram(
    image,
    region,
    scale=30,
    x_label='Frequency',
    y_label='Pixel Count',
    title='Water Occurrence',
    return_df=False,
)
hist

hist = geemap.image_histogram( image, region, scale=30, x_label='Frequency', y_label='Pixel Count', title='Water Occurrence', return_df=False, ) hist

Surace water monthly history¶

In [ ]:

dataset = ee.ImageCollection('JRC/GSW1_3/MonthlyHistory')
size = dataset.size()
print(size.getInfo())

dataset = ee.ImageCollection('JRC/GSW1_3/MonthlyHistory') size = dataset.size() print(size.getInfo())

In [ ]:

# dataset.aggregate_array("system:index").getInfo()

# dataset.aggregate_array("system:index").getInfo()

In [ ]:

Map = geemap.Map()

image = dataset.filterDate('2020-08-01', '2020-09-01').first()
region = ee.Geometry.BBox(-99.957, 46.8947, -99.278, 47.1531)

vis_params = {'min': 0.0, 'max': 2.0, 'palette': ['ffffff', 'fffcb8', '0905ff']}

Map.addLayer(image, vis_params, 'Water')
Map.addLayer(region, {}, 'ROI', True, 0.5)
Map.centerObject(region)
Map

Map = geemap.Map() image = dataset.filterDate('2020-08-01', '2020-09-01').first() region = ee.Geometry.BBox(-99.957, 46.8947, -99.278, 47.1531) vis_params = {'min': 0.0, 'max': 2.0, 'palette': ['ffffff', 'fffcb8', '0905ff']} Map.addLayer(image, vis_params, 'Water') Map.addLayer(region, {}, 'ROI', True, 0.5) Map.centerObject(region) Map

In [ ]:

df = geemap.jrc_hist_monthly_history(
    region=region, scale=30, frequency='month', denominator=1e4, return_df=True
)
df

df = geemap.jrc_hist_monthly_history( region=region, scale=30, frequency='month', denominator=1e4, return_df=True ) df

In [ ]:

geemap.jrc_hist_monthly_history(
    region=region, scale=30, frequency='month', denominator=1e4, y_label='Area (ha)'
)

geemap.jrc_hist_monthly_history( region=region, scale=30, frequency='month', denominator=1e4, y_label='Area (ha)' )

In [ ]:

geemap.jrc_hist_monthly_history(
    region=region,
    start_month=6,
    end_month=9,
    scale=30,
    frequency='month',
    y_label='Area (ha)',
)

geemap.jrc_hist_monthly_history( region=region, start_month=6, end_month=9, scale=30, frequency='month', y_label='Area (ha)', )

In [ ]:

geemap.jrc_hist_monthly_history(
    region=region,
    start_month=6,
    end_month=9,
    scale=30,
    frequency='month',
    y_label='Area (ha)',
    color='month',
)

geemap.jrc_hist_monthly_history( region=region, start_month=6, end_month=9, scale=30, frequency='month', y_label='Area (ha)', color='month', )

In [ ]:

geemap.jrc_hist_monthly_history(
    region=region,
    start_month=6,
    end_month=9,
    scale=30,
    frequency='year',
    reducer='mean',
    y_label='Area (ha)',
)

geemap.jrc_hist_monthly_history( region=region, start_month=6, end_month=9, scale=30, frequency='year', reducer='mean', y_label='Area (ha)', )

In [ ]:

geemap.jrc_hist_monthly_history(
    region=region,
    start_month=6,
    end_month=9,
    scale=30,
    frequency='year',
    reducer='max',
    y_label='Area (ha)',
)

geemap.jrc_hist_monthly_history( region=region, start_month=6, end_month=9, scale=30, frequency='year', reducer='max', y_label='Area (ha)', )

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Last update: 2023-04-06
Created: 2022-11-19