Main aim of the project was to analyze the MODIS Satellite data for LST, NDVI and Snow Cover over Uttarkhand for 15 years from 2001-2015.

1. Title
Temporalvariationofvegetation,snowcoverandtemperatureover
UttarakhandusingMODISTerraproducts

2. Objective
Seasonal variations of vegetation, snow cover and temperature over the
Uttarakhand are examined using the data acquired from the Moderate
Resolution Imaging Spectro-radiometer (MODIS) on the Terra spacecraft. In this
study, we will perform
•Temporal analysis will be performed over the MODIS snow data, vegetation
and temperature during 2001–2015.
•Establish correlation between snow cover, vegetation and temperature.

3. DATA
PREPARATION
DATA
EXTRACTION
DATA
VISUALIZATION
DATA
INTERPRETATION
Methodology: Over View

4. Data Preparation :
•Shapefile preparation of study area ie Uttarakhand
•Identifying MODIS product for study
• Temperature : MOD11A2(V005)
• Vegetation : MOD13A1(V005)
• Snow cover : MOD10A2(V005)
•Size of Data
• MOD11A2(V005) : ~ 23 GB
• MOD13A1(V005) : ~ 85 GB
• MOD10A2(V005) : ~ 1.2 GB
•Layers to be extracted
• MOD11A2(V005) : Layer 0
• MOD13A1(V005) : Layer 0
• MOD10A2(V005) : Layer 0, Layer 1
Info : Very important to
read hdf file format and
file naming convention

5. Data Extraction
• Problems
Data present Required
HDF-EOS file format TIFF file format
File naming convention(example :
MOD10A2.A2013001.h24v06.005.201301701
5045)
Small name example
SNOW13001
Projection: Sinusoidal WGS-84
Data in 4 tiles(1º X 1º) Need Mosaicked data

6. How Did We Do
Snow HDF Temperature HDF NDVI HDF
LPDAAC ftp
wget –i filename wget –i filename wget –i filename
Extraction Extract_mosiac_clip
_modified_snow.py
Extract_mosiac_clip
_modified_temp.py
Extract_mosiac_clip
_modified_ndvi.py
Snow Extent
TIFF
NDVI TIFFTemperature
TIFF
Snow Days
TIFF

7. filteringNDVI.py
geotiff_floating_c
onversion_ndvi.py
Scaling
PixelCount
pixelStatisticsCoun
t.py
pixelStatisticsCoun
t.py
pixelStatisticsCoun
t.py
pixelStatisticsCoun
t.py
Categorisation
filteringTemp.py
geotiff_floating_co
nversion_temp.py
pixelSNOWEx
tent.txt
pixelNDVI.txtpixelTemp.txtpixelSNOWD
yas.txt
Snow Extent
TIFF
NDVI TIFFTemperature
TIFF
Snow Days
TIFFA

8. Snow Extent
TIFF
NDVI TIFF
(Categorised)
Temperature TIFF
(Categorised)
Snow Days
TIFFB
Elevation Wise
Satistic
demwiseStatisticSn
owExtent.py
demwiseStatisticSn
owDay.py
demwiseStatisticTe
mp.py
demwiseStatisticN
DVI.py
Snow Extent
Excel
NDVI ExcelTemperature
Excel
Snow Days
Excel
In total 15 different scripts are used

9. Data Visualization and Interpretation
Required Tables
•8 or 16 days snow extent pixels
•Monthly snow extent and snow days
•Monthly Elevation wise data(10 bins
from 2000m to 6000m and above)
•Vegetation 16 days
•Monthly Vegetation(Elevation wise)
•Temperature 16 days
•Monthly Vegetation(Elevation wise)
FileName Year Days
extentTYPE(s
now)
Number of
snow Pixels
extentType(n
o-snow)
Number of no
snow Pixels
SNOW01001.tif 20011 200 55948 25 113260
SNOW01009.tif 20019 200 47300 25 120861
SNOW01017.tif 200117 200 53978 25 113503
SNOW01025.tif 200125 200 39432 25 130104
SNOW01033.tif 200133 200 34034 25 135686
Sample Table : Snow Extent

10. Data Visualization and Interpretation

11. Data Visualization and Interpretation

12. To be done:
• Trend of all parameters
• Correlation between 3 parameters
• Elevation wise trend analysis and correlation with parameters