116_land_cover_timeseries.ipynb

Kernel: Python 3

# !pip install geemap

import ee
import geemap

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

# 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.centerObject(region)

# Set the date range
start_date = "2017-01-01"
end_date = "2021-12-31"

The return_type can be hillshade, visualize, class, or probability. If you want to use the resulting images for further analysis, you should use class.

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

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

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

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

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

Map