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Working with specific remote sensing data

Landsat

Usage

Pygaarst offers an intuitive and friendly way to access Landsat scene data and metadata as distributed as at-sensor scaled radiance data files ("Level1") in GeoTIFF format, one file per imaging band, as zipped tar archives.

The work is done by two classes: Landsatscene, which takes the name of the unzipped data directory, and Landsatband, which can be instantiated from a Landsatscene object:

>>> from pygaarst import raster
>>> basedir = "path/to/data"
>>> landsatscene = "LE70690142004201EDC01"
>>> sc = raster.Landsatscene(os.path.join(basedir, landsatscene))
>>> type(sc)
<class 'pygaarst.raster.Landsatscene'>
>>> b2 = sc.band2
>>> type(b2.data)
<type 'numpy.ndarray'>
>>> b2.data.shape
(8021, 8501)

The Landsatscene knows how to calculate often used radiometric quantities such as vegetation indices, or access the Landsat metadata. Both old and new metadata format is supported:

>>> ndvi = sc.NDVI
>>> type(ndvi)
<type 'numpy.ndarray'>
>>> sc.meta['IMAGE_ATTRIBUTES']['SUN_ELEVATION']
44.26873508
>>> sc.meta['PRODUCT_METADATA']['DATE_ACQUIRED']
datetime.date(2004, 7, 19)

Landsatscene also supports a filename infix in case all scene files have been pre-processed. A typical example would be if the GeoTIFF band files have been sub-setted and saved under a new file name that adds a label at the end of the filename, before the extension. For example:

>>> b2.data.shape (8021, 8501) >>> sc.infix = '_CLIP' >>> b3 = sc.band3 >>> b3.data.shape (347, 373) >>> b2.filepath 'path/to/data/LE70690142004201EDC01_B2.TIF' >>> b3.filepath 'path/to/data/LE70690142004201EDC01_B3_CLIP.TIF'

The Landsatband object knows its own geographic attributes and can translate the raw data into radiance and reflectance. For thermal IR bands, calculation of radiant (brightness) temperatures is supported:

>>> b3.radiance
array([[ 23.59606299,  24.83937008,  23.59606299, ...,  29.81259843,
         29.81259843,  29.81259843],
       [ 25.46102362,  24.21771654,  23.59606299, ...,  30.43425197,
         30.43425197,  29.19094488],
       ..., 
       [ 41.0023622 ,  39.75905512,  40.38070866, ...,  90.11299213,
         91.35629921,  92.5996063 ],
       [ 40.38070866,  38.51574803,  41.0023622 , ...,  90.11299213,
         90.73464567,  90.73464567]])
>>> b3.Lat
array([[ 65.23978665,  65.23979005,  65.23979346, ...,  65.24086228,
         65.24086467,  65.24086706],
       [ 65.2400558 ,  65.2400592 ,  65.24006261, ...,  65.24113144,
         65.24113383,  65.24113622],
       ..., 
       [ 65.33291157,  65.332915  ,  65.33291841, ...,  65.3339918 ,
         65.3339942 ,  65.3339966 ],
       [ 65.33318072,  65.33318414,  65.33318756, ...,  65.33426096,
         65.33426336,  65.33426576]])
>>> b6 = sc.band6L
>>> b6.tKelvin
array([[ 298.51857637,  298.51857637,  299.01776653, ...,  301.97175896,
         301.48420756,  301.48420756],
       [ 297.51409689,  298.01736166,  298.51857637, ...,  302.45745107,
         301.97175896,  301.48420756],
       ..., 
       [ 294.96609241,  294.96609241,  294.96609241, ...,  290.23752626,
         290.77248753,  290.23752626],
       [ 294.96609241,  294.96609241,  294.96609241, ...,  290.77248753,
         290.77248753,  290.77248753]])

All parameters are, where provided, taken from the scene-specific metadata. Where not, they are taken from the published literature.

The Landsatscene class

pygaarst.raster.Landsatscene

The Landsatband class

pygaarst.raster.Landsatband

EO-1 Hyperion, ALI and general USGS Level 1 data

Hyperion and ALI

Hyperion imaging spectroscopy data and ALI multi-spectral imagery, processed to a Level 1 product, are distributed by the USGS in a format very similar to Landsat data. Pygaarst provides classes similar to the Landsat-specific classes for these sensors.

pygaarst.raster.Hyperionscene

pygaarst.raster.Hyperionband

pygaarst.raster.ALIscene

pygaarst.raster.ALIband

The USGSL1 parent classes

All of them inherit from parent classes called USGSL1scene and USGSL1band respectively.

pygaarst.raster.USGSL1scene

pygaarst.raster.USGSL1band

The metatadata parser

All these USGS-provided satellite remote sensing data use essentially the same metadata format. It is provided in a text file that can be recognized by the letters MTL in the filename. The data structure is nested naturally maps onto a dictionary of dictionaries, with unlimited nesting depth. Unfortunately it appears to be quite badly documented, or in any event I have been unable to locate a data description or specification.

For objects of type pygaarst.raster.USGSL1scene or its children (pygaarst.raster.Landsatscene, pygaarst.raster.ALIscene or pygaarst.raster.Hyperionscene), the parsed metadata dictionary is provided in the meta attribute:

>>> print sc.meta
{'IMAGE_ATTRIBUTES': {'GEOMETRIC_RMSE_MODEL': 2.868, 'CLOUD_COVER': 31.0, 'GEOMETRIC_RMSE_MODEL_X': 1.73, 'SUN_AZIMUTH': 162.9104406, 'SUN_ELEVATION': 44.26873508, 'GROUND_CONTROL_POINTS_MODEL': 210, 'IMAGE_QUALITY': 9, 'GEOMETRIC_RMSE_MODEL_Y': 2.287}, 'RADIOMETRIC_RESCALING': {'RADIANCE_MULT_BAND_7': 0.044, 'RADIANCE_MULT_BAND_5': 0.126, 'RADIANCE_MULT_BAND_4': 0.969, ...

The parser is implemented via the module pygaarst.mtlutils. An arbitrary MTL file can be parsed by calling pygaarst.mtlutils.parsemeta():

pygaarst.mtlutils

MODIS swath data

[This is a little harder...]

Suomi/NPP VIIRS

pygaarst.raster.VIIRSHDF5

MODAPS data download API client

This is a REST-full web service client that implements the NASA LAADSWEB data API (http://ladsweb.nascom.nasa.gov/data/api.html). Usage:

>>> from pygaarst import modapsclient as m
>>> a = m.ModapsClient()
>>> retvar = a.[methodname](args)

pygaarst.modapsclient