Land Cover Over Time

Land Cover Over Time#

How have land use and land cover changed over time?

import folium
import ee
import geemap
import geopandas as gpd
ee.Authenticate()

# Write your project ID here, in quotes
ee.Initialize(project = "emerge-lessons")

def add_ee_layer(self, ee_image_object, vis_params, name):
    """Adds a method for displaying Earth Engine image tiles to folium map."""
    map_id_dict = ee.Image(ee_image_object).getMapId(vis_params)
    folium.raster_layers.TileLayer(
        tiles=map_id_dict['tile_fetcher'].url_format,
        attr='Map Data &copy; <a href="https://earthengine.google.com/">Google Earth Engine</a>',
        name=name,
        overlay=True,
        control=True
    ).add_to(self)

folium.Map.add_ee_layer = add_ee_layer

map = folium.Map(location=[28.263363, -83.497652], tiles="Cartodb dark_matter", zoom_start=7)

We will use land cover and land use data from USFS Landscape Change Monitoring System v2024.10 (CONUS and OCONUS) on Google Earth Engine.

landcover_1985 = (
    ee.ImageCollection('USFS/GTAC/LCMS/v2024-10')
      .filterDate('1985', '1986')
      .filter('study_area == "CONUS"')
      .first()
)

palette = ['fbff97', 'e6558b', '004e2b', '9dbac5', 'a6976a', '1b1716']
visual = {'min': 1, 'max': 6, 'palette': palette}

map.add_ee_layer(landcover_1985.select('Land_Use'), visual, '1985 Land Use')

display(map)

Make this Notebook Trusted to load map: File -> Trust Notebook

landcover_2024 = (
    ee.ImageCollection('USFS/GTAC/LCMS/v2024-10')
      .filterDate('2024', '2025')
      .filter('study_area == "CONUS"')
      .first()
)

map.add_ee_layer(landcover_2024.select('Land_Use'), visual, '2024 Land Use')
folium.LayerControl(collapsed = False).add_to(map)
display(map)

Make this Notebook Trusted to load map: File -> Trust Notebook

In the upper right, you can click the checkbox next to “2024 Land Use” to turn the layer on and off and compare the changes that occured in land use from 1985 to 2024.

Color Meanings (from catalog)

\[\color{#fbff97}Agriculture\]

\[\color{#e6558b}Developed\]

\[\color{#004e2b}Forest\]

\[\color{#9dbac5}Other\]

\[\color{#a6976a}Rangeland or Pasture\]

Now, we will show land cover, as opposed to land use.

# Refresh the map
map = folium.Map(location=[28.263363, -83.497652], tiles="Cartodb dark_matter", zoom_start=7)

palette = ['004e2b', '009344', '61bb46', 'acbb67', '8b8560', 'cafd4b', 'f89a1c', '8fa55f', 'bebb8e', 'e5e98a', 'ddb925', '893f54', 'e4f5fd', '00b6f0', '1b1716']
visual = {'min': 1, 'max': 15, 'palette': palette}

# Add land cover for 1985 and 2024
map.add_ee_layer(landcover_1985.select('Land_Cover'), visual, '1985 Land Cover')
map.add_ee_layer(landcover_2024.select('Land_Cover'), visual, '2024 Land Cover')
folium.LayerControl(collapsed = False).add_to(map)
display(map)

Make this Notebook Trusted to load map: File -> Trust Notebook

\[\color{#004e2b}Trees\]

\[\color{#61bb46}Shrubs \space and \space Trees Mix\]

\[\color{#acbb67}Grass/Forb/Herb \space and \space Trees Mix\]

\[\color{#8b8560}Barren \space and \space Trees Mix\]

\[\color{#f89a1c}Shrubs\]

\[\color{#8fa55f}Grass/Forb/Herb \space and \space Shrubs Mix\]

\[\color{#bebb8e}Barren \space and \space Shrubs Mix\]

\[\color{#e5e98a}Grass/Forb/Herb\]

\[\color{#ddb925}Barren \space and \space Grass/Forb/Herb Mix\]

\[\color{#893f54}Barren or Impervious\]

\[\color{#e4f5fd}Snow or Ice\]

Create Timelapse#

landcover = (
    ee.ImageCollection('USFS/GTAC/LCMS/v2024-10')
      .filter('study_area == "CONUS"')
      .select('Land_Use')
)

point = ee.Geometry.Point(-81.660044, 28.473813)
region = point.buffer(distance=100000)

images = geemap.create_timeseries(
    landcover, '1985', '2024', region, frequency='year', reducer='first'
)
images

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    10: [-81.97062542666436, 27.616689207211685]
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    11: [-81.68818493731065, 27.57426933175896]
    0:-81.68818493731065
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    12: [-81.4035583739624, 27.602866053229775]
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    14: [-80.91480405360673, 27.85885775475632]
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    15: [-80.74906421945843, 28.066281134163564]
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    16: [-80.65502009867, 28.306246721606673]
    0:-80.65502009867
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    17: [-80.64058977761022, 28.559808412220587]
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    18: [-80.70748289852588, 28.806811872187883]
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    19: [-80.85089094321603, 29.027486614165717]
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    20: [-81.05970365932073, 29.204051281761696]
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    21: [-81.3172897506411, 29.32219623710256]
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    22: [-81.60281790465068, 29.37230459521959]
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    23: [-81.660044, 29.373699595148146]
    0:-81.660044
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    system:index:37
    system:time_start:1640995200000
  - 38: Image (1 band)
    type:Image
    bands: List (1 element)
    0: "Land_Use", unsigned int8, EPSG:4326, 4x3 px
    id:Land_Use
    crs:EPSG:4326
    crs_transform: [1, 0, 0, 0, 1, 0]
    0:1
    1:0
    2:0
    3:0
    4:1
    5:0
    data_type: unsigned int8
    type:PixelType
    max:255
    min:0
    precision:int
    dimensions: [4, 3]
    0:4
    1:3
    origin: [-84, 27]
    0:-84
    1:27
    properties: Object (4 properties)
    empty:0
    system:footprint: Polygon (24 vertices)
    type:Polygon
    coordinates: List (1 element)
    0: List (24 elements)
    0: [-81.660044, 29.373699595148146]
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    1: [-81.94836247854758, 29.337579808050535]
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    2: [-82.21316606774361, 29.23216871504045]
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    3: [-82.4329958161192, 29.066052246587347]
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    4: [-82.59027797775441, 28.85270649294279]
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    system:index:38
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Map = geemap.Map()

labels = [str(y) for y in range(1985, 2024)]

Map.addLayer(images, visual, "Land Use")
Map.add_time_slider(images, visual, time_interval=2, labels=labels)
Map.setCenter(-81.660044, 28.473813, zoom=8)
Map

In Google Colab, click the > arrow to see a timelapse of the land use over the years. This may take a few seconds to load.

fl = gpd.read_file('https://github.com/geo-di-lab/emerge-lessons/raw/refs/heads/main/docs/data/florida_boundary.geojson')[['geometry']]
region = geemap.geopandas_to_ee(fl)

timelapse = geemap.modis_ndvi_timelapse(
    region,
    out_gif='ndvi.gif',
    data='Terra',
    band='NDVI',
    start_date='2000-01-01',
    end_date='2024-12-31',
    frames_per_second=3,
    title='MODIS NDVI Timelapse',
)

geemap.show_image(timelapse)

Florida vegetation time lapse

References

Land Cover Over Time

Contents

Land Cover Over Time#

Create Timelapse#