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African Journal of Geo-Science Research, 2014, 2(3): 19-22
ISSN: 2307-6992
Available Online: http://ajgr.rstpublishers.com/
ESTIMATION OF FOREST COVER DENSITY IN KODAIKANAL REGION.
Rajeshwari.A1 and Mani N.D2
E-mail: [email protected]
Received: 26, Mar,2014
Accepted:27,June,2014.
Abstract
Forest was one of the major elements of earth’s ecosystem. Dindigul district in Tamil Nadu state has a few major hills like Kodaikanal, Palani,
Sirumalai and Karanthamalai. These hills in the district are having forest cover, of these hills, Kodaikanal has a major area under forest. At the
centre of the hills, Kodaikanal town is located. It is a tourist place. The area under forest cover in Kodaikanal hills was decreasing day to day
due to many anthropogenic activities like construction of settlements, hotels, resorts and laying roads. Hence, in this study an attempt was made
to study the changes in forest cover of Kodaikanal region that falls above 500 m of altitude in Dindigul District.
LANDSAT ETM+ image of 2006 with spatial resolution of 30 m was classified in ERDAS Imagine software and the forest cover was reclassified
from the output. A grid of 0.25 Km x 0.25 Km was overlaid upon the forest cover map and the density of forest cover in each 0.625 Km2 were
calculated and the output revealed that Kodaikanal hill was dominated by moderately dense vegetation. For assessing the accuracy level of
supervised classification the forest density map was correlated with NDVI map with the use of correlation technique in ArcGIS. There was high
positive correlation value of 0.89 derived between these two maps. This confirms the accuracy of supervised classification technique. Forest
cover density was calculated for four different altitude zones viz., 500 to 1000 m, 1000 to 1500 m, 1500 to 2000 m and above 2000 m and it was
categorized into very dense vegetation (VDV), moderately dense vegetation (MDV), open vegetation (OV) and others as per the guidelines of
Forest Survey of India. The altitude zone-wise analysis showed that 1000 to 1500 m altitude was with high forest cover density.
Key words: Ecosystem, tourist place, anthropogenic activities, Kodakanal and Tamil Nadu.
INTRODUCTION
Forest is one of the important natural resources of a country. It
acts as a storehouse of various biodiversity features, livelihood for
people depending on it, habitat of wild animals, consists of medicinal
plants and it brings rain to the earth. As the extent of area under
forest decreases, the entire biodiversity of the area gets disturbed.
Population pressure on forest is reason. The people clear the forest
area for cultivation, build roads and houses. This leads to reduction
in rainfall, high temperature and wild animals roam into human
habitats. Therefore, the dwindling forest resources have to be
assessed in terms of its density.
For better management of forest resources, it is essential to
estimate the density of forest and its changes. Forest cover density
is an important parameter for identifying the deforested area, for plan
preparation and implementation of afforestation activities. Remote
sensing is an effective way to monitor and estimate the natural
resources. It is cost effective and less time consuming. Geographical
Information System (GIS) is a spatial tool, which can process large
amount of data and capable to perform software analysis. Hence,
many researchers had utilized satellite images and GIS to estimate
the forest cover density.
Forest canopy density was calculated by combining the three
vegetative indices viz., canopy shadow index, advanced vegetation
index and bar soil index. The output revealed an accuracy of 84.4
percent (Azizi.Z et al., 2008; Roy P.S. et al., 1996). The NDVI output
was observed for Idle Agricultural Lands (IAL). The output was
plotted and compared between 2003 and 2005 (Chaichoke Vaiphasa
et al., 2011). The tree canopy density was estimated by calibrating
the canopy density derived from orthophotos and Landsat spectral
data. Linear regression and regression tree techniques were used
and it was found that the regression tree technique was forceful than
linear regression technique (Chengquan Huang et al., 2001).
Satellite image was classified using hybrid classification technique
and a mesh of 500 m x 500 m was evolved for the study area. The
classified image was overlaid with the grid and density of forest was
calculated. The output was verified with NDVI and a correlation of
0.99 was derived (Kumar et al., 2007). Forest density map was
prepared using supervised classification for two different images and
changes over period were distinguished (Majid Farooq et al., 2010).
Forest cover degradation was monitored for 12 years through
change detection and NDVI difference technique (Pavan Kumar et
al., 2010). Forest cover map was prepared using satellite images
from 1987 to 2001. The forest canopy density was estimated and the
output was classified into three groups viz., dense forest (> 40%),
open-forest (40 – 70%) and scrub forest (<10%) (Rawat.J.K et al.,
2003). Biophysical Spectral Response Modeling was used to prepare
forest density map. An accuracy of 83 percent was derived by
classifying the satellite image through this technique (Saei
Jamalabad et al., 2002). Classified image was overlaid on a grid of 1
km x 1 km and forest cover density was calculated. The output was
regrouped based on the criteria specified by Forest Survey of India.
The output was correlated with the NDVI image with a positive
correlation of 0.93 (Vandana Kumari Chauchan et al., 2012).
A few researchers had done grid analysis and used either 1 Km or
500 m as grid resolution and density was calculated for the entire
study area. In this present study a grid of 250 m was generated to
get more accurate results and forest cover density was estimated
altitude zone-wise for better understanding of the distribution of
forest cover and its density. It was further reclassified as per the
guidelines of Forest Survey of India (FSI, 2013) criteria. The output
Rajeshwari et al.
20
was correlated with NDVI image for validation.
cell was generated and forest cover density was calculated using the
following formula.
STUDY AREA AND DATA USED
Dindigul district was a drought prone district in Tamil Nadu state,
India. It is located in the rain-shadow region of the State. The total
geographical area (TGA) of the district is 5580 sq.km and forest
occupies 1489 sq.km (26.89 percent). The area selected for the
present study was Kodaikanal hills, Dindigul District. The area under
study was restricted to the region above 500 m of altitude in Dindigul
District. Kodaikanal is one of the major tourist places in Tamil Nadu.
It is located at an elevation of 2194.56 m above the Mean Sea Level
(MSL) in Kodaikanal forest. The maximum and minimum
temperature is 30oC and 8oC respectively.
Forest Cover Density = (Total area under forest cover/ TGA) x 100(in
percent) Based on the criteria provided by Forest Survey of India, it
was classified into very dense vegetation (VDV) (>70%), moderate
dense vegetation (MDV) (40 to 70%), and open vegetation (OV)
(>40%) (FSI, 2013).
Normalized Difference Vegetative Index (NDVI) Analysis
NDVI provides the area under biomass. Red and Near Infra-red
(NIR) bands of ETM+ image were used for calculating NDVI. The
NDVI is calculated employing the following index NDVI = (NIR –
Red)/ (NIR + Red). The output value ranges between -1 to 1. -1 to 0
range indicated the area under barren rock, sand and snow, 0 to 1
portrayed the area under biomass.
Satellite
Image 2006
Supervised
Classification
Reclassific
ation
Grid of 250 x
250 m
Forest
Cover
SRT
M data
Raster to
Vector
The spatial data used for the present study were downloaded from
GLCF website and the metadata was furnished for the downloaded
images in table (1).
Table (1)Metadata of the Satellite Images Used
Satellite
Landsat 7
Sensor
Enhanced
Thematic
Mapper
Plus (ETM+)
Satellite
Sensor
Space
Shuttle
Endeavour
(SSE)
Shuttle Radar
Topographic
Mission
(SRTM)
Resolution
Area and Density
Calculation
Path/Row
Year
30 m
143/053
2006
Resolution
Lat/ Long
Origin
Year
10oN/77oE
2000
Altitude
Zones
Intersect
Overlay
Vector to
Raster
NDVI
Corr
elation
I
nter
sect
Density Altitude
Zone-wise
Correlation
90 m
MATERIALS AND METHODS
Supervised Classification
Bands 1, 2, 3 and 4 were used to create a false colour composite
image of the study area. The image was classified into forest and
non-forest region using Maximum Likelihood classifier in ERDAS
Imagine. The output was recoded to generate forest cover image.
Grid-wise Forest Cover Density Calculation
A grid of 250 mts x 250 mts was generated using Hawth’s Tool,
an open source ArcGIS extension tool. The forest cover map was
overlaid with the grid generated. The area under forest cover in each
The grid-wise forest cover density was converted to raster based
on the density field and it was correlated with the output of NDVI
image in ArcGIS using Band Collection Statistics tool. The output
confirmed that there was a positive correlation (=0.89) exists
between forest cover density and NDVI maps.
Altitude Zone-wise Analysis
SRTM data was grouped into four classes viz., Zone I (500-1000
m), Zone II (1000-1500 m), Zone III (1500-2000 m) and Zone IV
(more than 2000 m). Based on the classification, it was converted
into a vector layer. Altitude zone-wise forest cover map was
extracted and forest cover density was estimated.
ANALYSIS OF THE STUDY
TGA of Kodaikanal hills was 1351.59 sq.km and the extent of area
under forest cover was 830.87 sq.km (61 percent). Kodaikanal town
was located at the centre part of the hill and it attracts large number
African Journal of Geo-Science Research, 2014,2(3):19-22
21
of tourists from throughout the world almost all through the year.
Hence, the forest cover was found only in the peripheral region of the
hill and natural forest was sparse in the central part of the hill (Map1).
Map – 3
Table(2) Area under Forest Cover at Different Altitude
(Area in sq.km)
Map – 1
Major concentration of forest was seen in the eastern and
southern parts of the hill. Forest cover was less in the western part of
the hill due to the presence of Mannavanur and Poodi villages. From
the north towards the centre, the extent of area under forest cover is
less, as the area was more populated, while comparing to the fringes
of the hill. Thus, except the area in and around Kodaikanal town,
other area was under forest. The town was the major stress for the
depletion of forest cover in the region.
A gird of 250 x 250 m was used to calculate the density of forest
in each 62,500 m2. Map-2 showed the forest cover density in 2006.
From the map it was clear that forest cover and its density were
restricted by altitude. The area under very dense vegetation was
found more in 1000 to 1500 m altitude zone and particularly more in
the eastern, southern and north western parts. The peripheral region
of the very dense forest area had moderately dense forest region. In
and around Kodaikanal area and eastern and south eastern foot hill
area showed open vegetation as the area was easily accessible by
the people from the nearby villages located in the plains. Map – 3
portrayed that steep areas in the hill were untouched by the people;
hence the VDV was in those regions and MDV/ OV in gentle slope
and nearly level area. Thus, from the map it was clear that the
density was very high, where movement of the people was restricted.
Map – 2
Altitude
Zone
VDV
MDV
Zone I
14.61
Zone II
80.64
Zone III
Zone IV
Total
OV
Others
Total
140.03
104.94
129.44
389.03
149.73
84.53
150.20
465.10
12.78
36.98
51.37
156.02
257.16
4.86
26.10
124.28
85.06
240.30
112.90
352.84
365.13
520.72
1351.59
Table(2) described the categories of forest cover and their
variation as the altitude zone increases. NDVI image was classified
based on its spectral range (NDVI, ref.8; Eshen Sahebjalal, 2013).
The area under forest cover at altitude zone was calculated using the
classified NDVI.
The extent of area under forest cover was more in the altitude
zone II and it decreased as the altitude zone increases. In zone I and
II, MDV was found in large area followed by others and OV. As far as
Zone III was considered, the largest area was occupied by other
vegetation (156.02 sq.km) and 51.4 sq.km of area by OV. Where as
in Zone IV OV occupied larger area of 124.3 sq.km. In this zone VDV
was found in very least area of 4.86 sq.km.
OV occupies larger area under forest cover followed by
moderately dense vegetation and very dense vegetation. VDV was
seen in all the categories and it was high in zone II (80.64) followed
by zone I, zone III and zone IV. Similarly MDV is high in zone II
(149.73) and zone I (140.03). OV had occupied considerable area in
all the zones, however it was more in zone IV (124.28) and zone I
(104.94).
Figure – 1
Fig(1) portrayed altitude zone-wise forest cover in 2006. The
22
suitable altitude for MDV ranged from 500 to 1500 m. In Kodaikanal,
VDV was found in 1000 to 1500 m of altitude. Open vegetation
showed a fluctuation across the altitude zones. It decreased
gradually from 500 to 1000 m till 1500 to 2000 m, whereas in 2000 m
and above, OV abruptly increased to a peak.
CONCLUSION
Forest cover density and NDVI provided a positive correlation of
0.93.The altitude zone-wise density analysis proved that the area
under moderately dense forest was more in the altitude zone of 1000
to 1500 m. Very dense vegetation was found in the same altitude
zone. Its density decreases as the altitude increases/ decreases.
This was due to easy accessibility of the people to the forest region.
Open vegetation was seen more in all zones except zone III.
References
1)Azizi.Z, Najazi. A and Sohrabi.H, Forest Canopy Density
Estimation, using Satellite Images, The International Archives of the
Photogrammetry, Remote Sensing and Spatial Information Sciences.
Vol. XXXVII. Part B8. Beijing 2008, Pp 1127-1130,
http://www.isprs.org/proceedings/XXXVII/congress/8_pdf/11_WGVIII-11/21.pdf
2)Chaichoke Vaiphasa, Supawee Piamduaytham, Tanasak Vaiphasa
and Andrew K. Skidmore, A Normalized Difference Vegetation Index
(NDVI) time-Series of Idle Agriculture Lands: A Preliminary Study,
Engineering Journal, Vol. 15, No 1 (2011). Pp 9-16.
3)Chengquan Huang, Limin Yang, Bruce Wylie and Collin Homer, A
Strategy for Estimating Tree Canopy Density using LANDSAT 7
ETM+ and High Resolution Images Over Large Areas, USGS
Science for a Changing World, Third International Conference on
Geospatial Information in Agriculture and Forestry held in Denver,
Colorado, 5 - 7 November, 2001 http://digitalcommons.unl.edu/cgi/
viewcontent.cgi? article =1110&context=usgspubs.
4)Ehsan Sahebjalal and Kazem Dashtekian, Analysis of Land UseLand Covers Changes using Normalized Difference Vegetation Index
(NDVI) Differencing and Classification Methods, African Journal of
Agricultural research, Vol.8(37), Pp 4614-4622, 2013, Pp 4614-4622,
http://www.academicjournals.org/article/article1380905
222_Sahebjalal%20 and%20Dashtekian.pdf
5)Forest Survey of India Report, 2013,http://www. fsi.org.in /sfr_2011.
htm.
6)Hawth’s tool download link, http://www. spatialecology. com/htools
/tooldesc.php.
Rajeshwari et al.
7)Introduction to Kodaikanal, http://www.kodaikanal.com/.
8)Kumar.A, Uniyal and K.K, Lal.B, Stratification of Forest Density
and its Validation by NDVI Analysis in a Part of Western Himalaya,
India using Remote Sensing and GIS Techniques, International
Journal of Remote Sensing, Vol 28, Issue 11, 2007.
9)Majid Farooq and Humayun Rashid, Spatio-temporal Change
Analysis of Forest Density in Doodhganga Forest Range, Jammu &
Kashmir, International Journal of Geomatics and Geosciences, Vol 1,
No.2, 2010, Pp 132-140, http://www.ipublishing.co.in/jggsvol1
no12010/ EIJGGS1013.pdf.
10)Normalized Difference Vegetative Index (NDVI) Analysis for
Forestry
and
Crop
Management,
p4
http://simwright.com/downloads/SimWright_NDVI.pdf
11)Pavan Kumar, Meeni Rani, P.C.Pandey, Arnab Majumdar and
M.S.Nathawat, Monitoring of Deforestation and Forest Degradation
using Remote Sensing and GIS: A Case Study of Ranchi in
Jharkhand (India), 2010;2(4), Pp 14–20, http://www.sciencepub.net
/report/report0204/03_2578_report0204_14_20.pdf
12)Rawat.J.K, Alok Saxena and Sudhanshu Gupta, Remote Sensing
based Forest Cover Mapping: Some Recent Developments, Indian
Cartographer, 2003, Pp 195- 198, http://www.incaindia.org/INCAPDF/vol23/MAPCulture/Remote%20Sensing% 20 Satellite.pdf.
13)Roy.P.S, Shrama.K.P and Jain.A, Stratification of Density in Dry
Deciduous Forest using Satellite Remote Sensing Digital Data – An
Approach based on Spectral Indices, J. Biosci., Vol. 21, Number 5,
September 1996, Pp 723–734,http://www.ias.ac.in /jarch/jbiosci
/21/723-734.pdf
14)Sae Jamalabad.M and Abkar.A.A, Forest Canopy Density
Monitoring
using
Satellite
Images,
http://www.isprs.org/
proceedings/xxxv/congress/comm7/papers/48.pdf
15)Vandana Kumari Chauhan, Ruchika Acharya, Amit Kumar and
R.D.Singh, Application of Remote Sensing in Studying Forest Cover
Conditions of Protected Area in Himachal Pradesh, India, India
Geospatial
Forum,
7-9
February,
2012,
Pp
1-5,
http://indiageospatialforum.org/2012/proceedings/ppt/vandana.pdf
16)Vikram Agone and Bhamare. S.M, Change Detection of
Vegetation Cover using Remote Sensing and GIS, Journal of
Research and Development, Vol.2 (Issue 4), 2012,
http://www.academia.edu/2505794/Change_detection_of_vegetation
_cover_ Using_Remote_Sensing_and_GIS

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