International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 8-April 2015
Application of Geospatial Technique in Studying
Forest Cover Conditions of Twelve Districts in
Jharkhand, India
Jai kumar1, Paras Talwar*2, A. P. Krishna#3
#1
Department of Remote Sensing, Birla Institute of Technology Ranchi, India
Abstract— In this article geospatial techniques were used
to measure forest cover in twelve major districts of
Jharkhand. A Landsat 8 satellite image is used for
preparing forest cover mapping and derives the
Normalized Difference Vegetation Index (NDVI) in ArcGIS 10. The Supervised Classification on Landsat 8 image
was done whose Kappa statistics are carried out in
ERDAS Image 9.2 i.e. 0.93. The correlation is calculated
between Forest cover Density and mean NDVI (using R
statistical software), which comes out to be 0.82 also
between average Elevation (which comes out through
Aster DEM data) and Forest cover Density comes out to be
0.36.
Keywords
— Remote sensing, Deforestation, Supervised
classification, NDVI, FCD, Elevation, Correlation
I.
The use of remote sensing helps us to map the different
species/forest so that to conserve the forest area, which is
disturbed by various human activities which cause negative
effect on the environment in term of temperature variation
result in global warming, disturbing water cycle balance, air
pollution etc.
II.
OBJECTIVE:
The main objective of our study is to verify the relation
among various identities such as Mean Elevation, Mean
NDVI and Forest cover density of twelve districts of
Jharkhand.
III.
STUDY AREA
The study area is covered by forest plantation of twelve
district areas in Jharkhand i.e. Ranchi, lohardaga, Gumla,
Simdega, Palamu,Latehar ,Garhwa, West Singhbum, Saraikla,
East Singhbum , Dumka , Jamtara.
INTRODUCTION
Forest plays an important role in our society by manufacturing
the assets i.e. fuel, food (for animals) and medical activities
and so on. The forest is considered as a backbone in India so
the pressure on India is forests are high because of high
population. The quick growth in the interests, money, and
goods of the country in the last one ten-years stage has also
put an added request on the forest for roads and systems
development getting rightly of forest relation between mass
and size in keeping safe areas, in this way, plays a chief
undertakings in this makes sense clearer.
Fig.1 Study area (Jharkhand)
In earlier times, the Forest mapping through is far away, but
through the use Remote sensing and Geographic information
system (GIS) technologies make ready such chance to make
accurate, timely and forest management.
The satellite-derived Normalized Difference Vegetation Index
(NDVI) is also a widely used way of going to learn and
observe the plants.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 8-April 2015
IV.
METHODOLOGY:
B.
Altitudinal Calculation
a)
Now we have to take Aster DEM data and
clipped it out our study area/
b) Contour map is generated in Arc-GIS to
determine the altitude of each district (12) in
Jharkhand.
C.
Normalized Difference Vegetation Index (NDVI)
The NDVI ratio is calculated by dividing the
difference in the near-infrared (NIR) and red
color bands by the sum of the NIR and red colors
bands for each pixel in the image as follows.
NDVI= (NIR-RED)/ (NIR + RED)
1) First we have to take data i.e. Landsat 8 from the
USGS site
2) Now we have to clip our study area i.e Jharkhand
state from satellite image.
3) To determine the correlation Between forest cover
density, Altitudinal and Normalized Difference
Vegetation Index(NDVI) we have to take some step:
D.
Correlation in R Statistical Software
1)
Forest Density with Mean NDVI:
The following steps are followed in R software
window i.e.
2)
A.
Forest Density with Elevation:
Forest Cover Density
a)
Forest classification(Supervised Classification)
is carried out by digital image processing
techniques in Erdas Imagine 9.2 .The classified
image has four classes
i.e. Forest(Green), Fallow Land(Cattleya Orchid)
, Agriculture Land(Yellow) ,Water Body(Blue),
BuiltUp/Mining(Red).
b) The classified image is now going through
Kappa statistical test i.e Accuracy assessment
using Erdas Imagine 9.2
c) After that we have calculated the Forest Area in
SqKm.
d) The classified image is then used in Arc-GIS 10
to represent the forest cover through Map.
e) At the end we have calculated Forest Cover
Density.
V.
RESULT & DISCUSSION:
Forest Cover Density (%) = (Forest Cover
Area/Total Land Area)*100
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Fig.2.1
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 8-April 2015
Fig2.2
Fig2.5
Fig 2.6
Fig 2.3
Fig2.4
Fig 2.7
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 8-April 2015
Fig2.11
Fig2.8
Fig2.9
Fig2.12
Fig2.10
Fig2.13
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 8-April 2015
The above result clarifies that they are positive (0.82) relation
between Forest cover density and NDVI whereas the relation
between Elevation and Forest Cover density is not up to our
expected value (0.36)
VI.
CONCLUSION:
[9] Ilayaraja, K. "Vegetation response of Chennai City using Normalized
difference vegetation index (NDVI) from Landsat images." IJEET Vol.2
Issue3No.1-Nov2011
[10] Ilayaraja, K. "Normalized difference vegetation index (NDVI) of
surrounding regions of Neyveli, Cuddalore District using Landsat
images."IJEET Vol.2 Issue1No.1-Jul2011
Forest cover density observed that the NDVI variation
increase with forest density. Hence it indicates the healthy
forest in our study area. It was also observed that the elevation
is not compatible with the forest cover density due to
fluctuation in elevation range.
ACKNOWLEDGMENT
Words fall inadequate in express the gratitude to A.P.Krishna,
Sir Professor & Head, Department of Remote Sensing, BIT,
Mesra, Ranchi for reviewing the manuscript in detail and
extending his full support during the course of the study and
help me at every step and making the doubts clear and telling
me the right approach.
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