Geoprocessing with GDAL and Numpy in Python Delong Zhao

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

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

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.

Installation 1. Enthought python scientific computing package, includes numpy – GDAL - Geospatial Data Abstraction Library – Or all of these has been installed on EOMF and Cybercommons servers

Tutorial / / ers ers er/python/science/idl-numpy.html er/python/science/idl-numpy.html

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

Algorithm development for global cropping intensity from crop per year 2-crop per year 3-crop per year

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

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

Cropping Intensity map in 2004 Global Mapping of Croplands

Python multiprocessing g.html g.html import multiprocessing pool = multiprocessing.Pool(processes=multiprocessing.cpu_count()) pool.map(doprocess,findfiles(root_dir))

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 MODIS data in 786 hours (32 days) with one server. And we have 6 computation servers, we can improve it to 6 days if all the servers can do the job.