This document describes a tool created using Python to calculate snow cover area from MODIS Terra datasets and plot the change over time. It uses PyQt4 and PySide for the GUI, GDAL for image processing, NumPy for arithmetic and file listing, and Matplotlib for plotting. The tool allows the user to select input and output folders, runs snow cover calculations on the MODIS files, and displays the results as a graph of snow cover area over time. Future improvements could involve using more GDAL and OGR capabilities to generate output maps of permanent and seasonal snow cover.

1. SUBMITTED BY:
A N T A R A * , A K S H A R A , S A P N A & M E E N A K S H I
SUPERVISED BY:
P R A S U N K . G U P T A #
GEOINFORMATICS DEPARTMENT
INDIAN INSTITUTE OF REMOTE SENSING
SNOW COVER ASSESSMENT
TOOL USING PYTHON
* antaradasgupta26@gmail.com # prasun@iirs.gov.in

2. INTRODUCTION
 AIM:
To create a tool using PYTHON which calculates the
snow cover area using MODIS Terra Temporal
Datasets and plots a graph of the change in the
same over time.
 DATASETS USED:
MOD10A2-L8 (8-day composite data)
Subdataset used - Maximum Snow Extent over the
8 day period

3. MATERIALS AND METHODS
 SOFTWARES USED:
Enthought Canopy
Qt Designer
 PYTHON LIBRARIES USED:
For UI design - PyQt4 and PySide
For image processing – GDAL
For the arithmetic and file listing functions –
numpy, glob
For the graph - pylab

4. MATERIALS AND METHODS(2)
 METHODOLOGY FOLLOWED
 Create a GUI using QtDesigner and save the
.ui file.
 Export it to a .py file using the in built utility
in PyQt4 – pyuic4.
 Open the .py file in Canopy, define the
required functions and connect the buttons
to the requisite functions

5. PYQT4 & PYSIDE
 PyQt is a Python binding of the cross-platform GUI toolkit Qt. It is
one of Python's options for GUI programming.
 PyQt implements around 440 classes and over 6,000 functions and
methods including:
a substantial set of GUI widgets
classes for accessing SQL databases (MYSQL, ORACLE), etc.
 Notable applications that use PyQt :
Orange, a data mining and visualization framework
QGIS, a free software desktop GIS, etc.
 PySide is the Canopy alternative for PyQt4.

6. GDAL
 GDAL (Geospatial Data Abstraction Library) is
a library for reading and writing raster geospatial
data formats.
 The related OGR library (OGR Simple Features
Library), which is part of the GDAL source tree,
provides a similar capability for simple
features vector data.
 Software that uses GDAL/OGR are: ARCGIS,
GOOGLE EARTH, MAPSERVER, GRASS GIS, QGIS,
etc.

7. NUMPY
o NumPy is the fundamental package for scientific
computing with Python. It is an extension to
the Python programming language.
o It contains :
 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

8. PYLAB
 Pylab is the module for scientific computing with
Python.
 Pylab is part of matplotlib (in matplotlib.pylab)
and tries to give a MatLab like environment.
 matplotlib.pylab which allow the user to create
plots with code quite similar to MATLAB figure
generating code.

9. GLOB
 The glob module finds all the pathnames matching a
specified pattern according to the rules used by the
Unix shell. No tilde expansion is done, but *, ?, and
character ranges expressed with [] will be correctly
matched.
 glob.glob(pathname):
Return a possibly-empty list of path names that
match pathname, which must be a string containing a
path specification. pathname can be either absolute
(like /usr/src/Python-1.5/Makefile) or relative
(like ../../Tools/*/*.gif), and can contain shell-style
wildcards.

10. THE GUI

11. FUNCTIONS USED

12. EXECUTION

13. OUTPUT GRAPH OF CHANGE IN SNOW
COVER AREA

14. FUTURE SCOPE
 The program is currently just generating the
area numerically, in future it could be
modified using GDAL to generate the output
binary files for snow cover and generate
permanent and seasonal snow cover maps
using OGR capabilities.
 Such a platform could also be created for the
processing of AMSR-E Data for Soil Moisture
Studies.

15. THANK
YOU!