This document summarizes a study monitoring vegetation cover in Sargodha district, Pakistan using remote sensing. Remote sensing was used to obtain data from SPOT satellite images over three dates in April and May. Two methods were applied: 1) calculating the NDVI index to assess vegetation greenness and 2) supervised classification to identify vegetative and non-vegetative cover types. The results of both methods along with analysis of canal networks showed vegetation is in better condition near canals with available water compared to areas without canals or at their ends with less water. In conclusion, remote sensing techniques proved useful for monitoring vegetation cover and its relationship to water availability in the study area.

1. Vegetation cover monitoring
Presented by: Saman Jamil
BS III ( environmental
science)
By Remote Sensing

2. Remote sensing
• Remote sensing is the science of
obtaining information about objects or
areas from a distance, typically from
aircraft or satellites.

3. Case study:
• Monitoring of vegetation cover in
Sargodha district, Punjab: Pakistan

4. Pakistan: An agricultural
country
• Pakistan is an agricultural country
• (70 % population is linked with
agriculture)
• Country’s economic development is
directly based on agriculture,
vegetative cover monitoring is an
important aspect to study the
agricultural situation in this area.

5. Sargodha: Chaj doab
• The study area lower Chaj Doab consisting of an
area about 7000 km2
• The temperature ranges from 35oC to 48oC in
summer and 0 to 15 o in winter. Frost may
occur for a short period of two to three weeks
in December and January.
• The mean annual precipitation ranges from 700
mm in the North to 250 mm in the South.

6. Indus plain
Chaj doab
Jhelum
river
Chenab
river

7. Data sets used:
• SPOT images: 20 m resolution of three
bands: green ,red and NIR
• Eight images of three different dates were
taken:17th April, 3rd may and 13th may.

8. Methodology
• Two different methods were used
• 1) NDVI
• 2) supervised classification
• Software: The input data were manipulated and
analyzed using ERDAS IMAGINE 9.1 and ArcGIS
9.2 software.

9. NDVI
• Standardized method for
comparing the vegetation
greenness from satellite
• NDVI used red and NIR band of
SPOT-XS
• NDVI = (NIR) - (RED) / (NIR) +
(RED).
• Density of vegetation
• The NDVI values tentatively range
between -1 to +1, the values close
to +1 denotes the good vegetation
condition.

10. 2) Supervised classification
• The supervised classification proved best for
identification of vegetative and non-vegetative cover
• SPOT satellite data of 20 m cell size having three bands
green, red and near infrared mosaic in ERDAS IMAGINE
9.1.
• Visual interpretation
• The spectral responses of different spectral bands in the
form of digital number (DN) values were generated
from these samples for supervised classification.
• Spectral signatures showed that vegetation have great
response in NIR band while in red band the response of
vegetation is suppressed.

11. Results
• NDVI

12. Supervised classification

13. Comparative analysis

14. Conclusion
• The study has explored the utility and potentials of remote
sensing techniques for vegetation cover monitoring in the
lower Chaj Doab. The thematic maps study along with canal
network clearly showed that where there is no canal or end
of the canal the vegetation is in worst condition while where
water of the canals are available vegetation is in better
condition.