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Geoprocessing with GDAL and Numpy in Python

Geoprocessing with GDAL and Numpy in Python. Delong Zhao 11-03-2011. Geoprocessing with GDAL and Numpy in Python. GDAL - Geospatial Data Abstraction Library Numpy - the N-dimensional array package for scientific computing with Python. Both of them are open source software.

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Geoprocessing with GDAL and Numpy in Python

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  1. Geoprocessing with GDAL and Numpy in Python Delong Zhao 11-03-2011

  2. Geoprocessing with GDAL and Numpy in Python • GDAL - Geospatial Data Abstraction Library • Numpy - the N-dimensional array package for scientific computing with Python. • Both of them are open source software Read raster dataset using GDAL Do some calculation using Numpy Output to geospatial dataset using GDAL

  3. GDAL • Supports about 100 raster formats • ArcInfo grids, ArcSDE raster, Imagine, Idrisi, • ENVI, GRASS, GeoTIFF • HDF4, HDF5 • USGS DOQ, USGS DEM • ECW, MrSID • TIFF, JPEG, JPEG2000, PNG, GIF, BMP • See http://www.gdal.org/formats_list.html

  4. Numpy • the fundamental package needed for scientific computing with Python. • a powerful N-dimensional array object • sophisticated (broadcasting) functions • tools for integrating C/C++ and Fortran code • useful linear algebra, Fourier transform, and random number capabilities. • http://numpy.scipy.org/

  5. Installation • 1. Enthoughtpython scientific computing package, includes numpy • http://www.enthought.com/ • 2. GDAL - Geospatial Data Abstraction Library • http://www.lfd.uci.edu/~gohlke/pythonlibs/ • Or all of these has been installed on EOMF and Cybercommons servers

  6. Tutorial • http://itmetr.net/itmetr.cgi/PyIntro • http://www.gis.usu.edu/~chrisg/python/2009/ • http://www.scipy.org/NumPy_for_Matlab_Users • https://www.cfa.harvard.edu/~jbattat/computer/python/science/idl-numpy.html

  7. Sample 1 • Read two tif files (red band and nir band) • Calculate • Output NDVI in same projection and georeferenceas the input file. • Numpy example

  8. Algorithm development for global cropping intensity from 2000-2011 1-crop per year 2-crop per year 3-crop per year

  9. Dynamics of winter wheat and paddy rice fields in Nanjing, Jiangsu, China (c) 7/3/99 2-weeks after rice transplanting paddy rice winter wheat (d) 9/6/99 rice plant heading (b) 6/11/99 rice field preparation

  10. MODIS 8-day composites of surface reflectance product (MOD09A1) NDSI NDVI, EVI, LSWI Snow mask Evergreen vegetation mask Permanent water mask Cloud mask Temporal profile analysis of individual pixels Cropping intensity ( # of crops per year) Crop calendar (planting & harvesting dates) Inundation and paddy rice

  11. Global Mapping of Croplands Cropping Intensity map in 2004

  12. Python multiprocessing • http://docs.python.org/library/multiprocessing.html • import multiprocessing • pool = multiprocessing.Pool(processes=multiprocessing.cpu_count()) • pool.map(doprocess,findfiles(root_dir))

  13. Benchmark • I did some benchmark. By using all 8 cpu and 16G memory on one eomf server • can finish 1 MODIS tile NDVI, EVI, CLOUD,SNOW, LANDWATER, FLOOD, Drought  products in 15 minutes. • This means we can finish global 296 tiles 2000-2011 MODIS data in 786 hours (32 days) with one server. • And we have 6 computation servers, wecan improve it to 6 days if all the servers can do the job.

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