Landsat 8 visualizations

In [1]:

# import geemap
import geemap as geemap
import ee
import numpy as np
import pandas as pd

from egis.utils import (
    vis_params_dict, default_vis_params, get_known_roi, extract_single_img, 
    extract_img_collection_metadata, extract_img_collection_properties,
    coords_to_polygon, collection_len
)

In [2]:

ee.Initialize()

Description: understanding Landsat satellite data, frequency of images and ways to visualize without clouds.

Landsat coverage¶

The satellite is circulating around the globe and taking remote sensing images. Thereby it will take consecutive snapshots from the earth as it progresses. Individual areas will be revisited regularly roughly every 2 weeks.

In [3]:

roi_coords = get_known_roi('grand_canyon')
roi_region_large = coords_to_polygon(get_known_roi('us_west_large'))
roi_region_medium = coords_to_polygon(get_known_roi('grand_canyon_rectangle'))

In [4]:

vis_params_l8 = default_vis_params('landsat')

In [5]:

dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR') \
                  .filterDate('2016-10-01', '2016-12-31') \
                  .filterBounds(roi_region_large)

The orbit of the satellite is reflected in the image tiles: vertical image neighbours are aligned more than horizontal neighbours, because there is much less time lag between vertically consecutive images.

In [ ]:

Map = geemap.Map()
Map.setCenter(roi_coords[0], roi_coords[1], 5)
Map.addLayer(dataset, vis_params_l8)
Map

Due to the time lag, horizontal neighbours can have very different weather conditions.

In [ ]:

Map = geemap.Map()
Map.setCenter(roi_coords[0], roi_coords[1], 8)
Map.addLayer(dataset, vis_params_l8)
Map