37 planetary computer

Uncomment the following line to install leafmap if needed.

In [1]:

# !pip install leafmap

# !pip install leafmap

In [2]:

import leafmap

import leafmap

Add a STAC item via an HTTP URL

In [3]:

url = "https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json"

url = "https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json"

In [4]:

leafmap.stac_assets(url)

leafmap.stac_assets(url)

Out[4]:

['pan', 'B1', 'B2', 'B3', 'B4']

In [5]:

leafmap.stac_bounds(url)

leafmap.stac_bounds(url)

Out[5]:

[-111.6453245, 60.59892389999882, -110.1583693, 61.30928879999903]

In [6]:

leafmap.stac_center(url)

leafmap.stac_center(url)

Out[6]:

(-110.90184690000001, 60.95410634999892)

In [7]:

# leafmap.stac_info(url)

# leafmap.stac_info(url)

In [8]:

# leafmap.stac_stats(url)

# leafmap.stac_stats(url)

In [9]:

m = leafmap.Map()
m.add_stac_layer(url, bands=["B3", "B2", "B1"])
m

m = leafmap.Map() m.add_stac_layer(url, bands=["B3", "B2", "B1"]) m

Out[9]:

Add a Microsoft Planetry Computer STAC item. The titiler endpoint can set in one of the ways below:

os.environ["TITILER_ENDPOINT"] = "planetary-computer"
titiler_endpoint="pc"
titiler_endpoint="planetary-computer"

In [10]:

# import os
# os.environ["TITILER_ENDPOINT"] = "planetary-computer"

# import os # os.environ["TITILER_ENDPOINT"] = "planetary-computer"

In [11]:

collection = "landsat-8-c2-l2"

collection = "landsat-8-c2-l2"

In [12]:

item = "LC08_L2SP_047027_20201204_02_T1"

item = "LC08_L2SP_047027_20201204_02_T1"

In [13]:

leafmap.stac_assets(collection=collection, item=item, titiler_endpoint="pc")

leafmap.stac_assets(collection=collection, item=item, titiler_endpoint="pc")

Out[13]:

['SR_B1',
 'SR_B2',
 'SR_B3',
 'SR_B4',
 'SR_B5',
 'SR_B6',
 'SR_B7',
 'ST_QA',
 'ST_B10',
 'ST_DRAD',
 'ST_EMIS',
 'ST_EMSD',
 'ST_TRAD',
 'ST_URAD',
 'QA_PIXEL',
 'ST_ATRAN',
 'ST_CDIST',
 'QA_RADSAT',
 'SR_QA_AEROSOL']

In [14]:

leafmap.stac_bounds(collection=collection, item=item)

leafmap.stac_bounds(collection=collection, item=item)

Out[14]:

[-124.98085491310867,
 46.35352512466258,
 -121.78788697796408,
 48.51466487533742]

In [15]:

leafmap.stac_info(collection=collection, item=item, assets="SR_B7")

leafmap.stac_info(collection=collection, item=item, assets="SR_B7")

Out[15]:

{'SR_B7': {'bounds': [-124.9808633132731,
   46.35352340750138,
   -121.78787875260963,
   48.52103509752985],
  'minzoom': 7,
  'maxzoom': 12,
  'band_metadata': [['b1', {}]],
  'band_descriptions': [['b1', '']],
  'dtype': 'uint16',
  'nodata_type': 'Nodata',
  'colorinterp': ['gray'],
  'height': 7971,
  'count': 1,
  'width': 7861,
  'overviews': [2, 4, 8, 16, 32, 64],
  'nodata_value': 0.0,
  'driver': 'GTiff'}}

In [16]:

leafmap.stac_stats(collection=collection, item=item, assets="SR_B7")

leafmap.stac_stats(collection=collection, item=item, assets="SR_B7")

Out[16]:

{'SR_B7_b1': {'min': 6912.0,
  'max': 32069.0,
  'mean': 8275.555009181073,
  'count': 673124.0,
  'sum': 5570474690.0,
  'std': 1156.664685686523,
  'median': 8046.0,
  'majority': 7295.0,
  'minority': 6912.0,
  'unique': 8681.0,
  'histogram': [[587083.0,
    79039.0,
    4498.0,
    1328.0,
    914.0,
    222.0,
    37.0,
    2.0,
    0.0,
    1.0],
   [6912.0,
    9427.7,
    11943.4,
    14459.099999999999,
    16974.8,
    19490.5,
    22006.199999999997,
    24521.899999999998,
    27037.6,
    29553.3,
    32069.0]],
  'valid_percent': 65.08,
  'masked_pixels': 361116.0,
  'valid_pixels': 673124.0,
  'percentile_98': 11233.0,
  'percentile_2': 7278.0}}

Color infrared composite.

In [17]:

m = leafmap.Map()
m.add_stac_layer(
    collection=collection,
    item=item,
    assets=["SR_B5", "SR_B4", "SR_B3"],
    name="Color infrared",
)
m

m = leafmap.Map() m.add_stac_layer( collection=collection, item=item, assets=["SR_B5", "SR_B4", "SR_B3"], name="Color infrared", ) m

Out[17]:

False color composite.

In [18]:

m = leafmap.Map()
m.add_stac_layer(
    collection=collection, item=item, assets="SR_B7,SR_B5,SR_B4", name="False color"
)
m

m = leafmap.Map() m.add_stac_layer( collection=collection, item=item, assets="SR_B7,SR_B5,SR_B4", name="False color" ) m

Out[18]:

Calculate NDVI.

In [19]:

m = leafmap.Map()
m.add_stac_layer(
    collection=collection,
    item=item,
    expression="(SR_B5-SR_B4)/(SR_B5+SR_B4)",
    rescale="-1,1",
    name="NDVI",
)
m

m = leafmap.Map() m.add_stac_layer( collection=collection, item=item, expression="(SR_B5-SR_B4)/(SR_B5+SR_B4)", rescale="-1,1", name="NDVI", ) m

Out[19]:

Calculate NDVI and add a colormap. See available colormaps at https://planetarycomputer.microsoft.com/docs/reference/data/

In [20]:

m = leafmap.Map()
m.add_stac_layer(
    collection=collection, item=item, assets="SR_B5,SR_B4,SR_B3", name="Color infrared"
)
m.add_stac_layer(
    collection=collection,
    item=item,
    expression="(SR_B5-SR_B4)/(SR_B5+SR_B4)",
    rescale="-1,1",
    colormap_name="greens",
    name="NDVI Green",
)
m

m = leafmap.Map() m.add_stac_layer( collection=collection, item=item, assets="SR_B5,SR_B4,SR_B3", name="Color infrared" ) m.add_stac_layer( collection=collection, item=item, expression="(SR_B5-SR_B4)/(SR_B5+SR_B4)", rescale="-1,1", colormap_name="greens", name="NDVI Green", ) m

Out[20]: