Paper id 71201910

International Journal of Research in Advent Technology, Vol.7, No.1, January 2019
E-ISSN: 2321-9637
Available online at www.ijrat.org
307
A Geoinformatics Approach to Assess Land Use
Dynamic and Landscape Fragmentation Due to Opencast
Coal Mining in Raniganj Coalfield, India
Amit Sarkar
Senior Research Fellow, Department of Geography, University of Calcutta, Kolkata-700019, India
Email: iamitsarkar91@gmail.com
Abstract- Land use dynamics and landscape patterns have always been prime themes of global change research
(R.H.U. & Suocheng, D. 2013). Anthropogenic changes in the form of opencast mining make the ecosystem more
sensitive and fragmented. Miscellaneous mining aids like dumper, dozer and dragline caused enormous amount of
land degradation. Indeed the degraded land covers are fragmented in due course of time. Raniganj coalfield area has
a complex land use land cover fragmentation scenario due to opencast mining and associated development activities
since 1960 (Das, G. & Das, R. 2016). Arc GIS and fragstats software is used in order to measure the class level and
landscape level fragmentation of different landscapes in Raniganj coalfield and adjoining area since last 40 years.
Results denote that most of the landscape classes have become fragmented and isolated. The areas of forest,
agricultural land, excavated land and urban tend to be complex in their shape and spatial clustering. The
shapes of other land class patches have become less complex, but overall landscape fragmentation has increased
during last 25 years. Contrary landscape diversity and heterogeneity have also been increased within only 20 years
especially in Sonepur Bazari, Satgram, Sripur and Khottadih area. Therefore it is urgently essential to understand
and compute the land use fragmentation process in Raniganj mining area.
Keywords: opencast mining, class and landscape level fragmentation, fragstats and Arc GIS
1. INTRODUCTION
The researchers strive to interpret land use land cover
fragmentation in the focal areas of Raniganj coalfield
and its surroundings using remotely sensed data.
Opencast coal mining affects the local landscape,
adversely causes widespread environmental decay
especially land alteration and fragmentation
(Maitima, J.M., Mugatha, S.M., Reid, R.S.,
Gachimbi, L.N., et al. 2009). Therefore preparation
and identification of land use land cover
fragmentation in temporal manner for any particular
area is very crucial nowadays in earth science in
order to detect the temporal changes in land use land
cover (State, J., Kumar, A. & Pandey, A.C. 2013).
These fragmentation process put forwarded by
mining and associated development activities are
measured by computing the 11 class level and 2
landscape level fragmentation indices.
2. RESEARCH AREA
Raniganj coalfield is located within four districts of
West Bengal i.e. Burdwan (71%), Birbhum (9%),
Bankura (8%) and Purulia (7%) shown in figure 2.1.
This coalfield is elliptical in shape. East-west
extension is about 75 km and north-south extension is
about 35 km. It has an area of about 1530 sq km and
falls within latitudes 23° 30Ꞌ N to 23° 52Ꞌ N and
longitudes 86° 38Ꞌ E to 87° 23Ꞌ E. As on 2016 this
coalfield has 17 running OCPs and 21abandoned
OCPs within 11 areas (ECL 2015). Mean elevation is
98.45m with broad undulation (Sarkar, A., 2016).

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Figure 2.1: Location of Research Area (Source: ECL & CMPDI)
3. MATERIALS AND METHODS
3.1 Remote Sensing Data Sources
Five temporal cloud free satellite data is gleaned
from USGS Earth Explorer portal in order to prepare
land use land cover map from 1973 to 2015.
Information about satellite data is shown in table
3.1.1. Topographical maps with a scale of 1:50000
namely 73M/1, 73M/2, 73M/5, 73M/6, 73I/13 and
73I/14 from Survey of India is applied to build the
base layer of these satellite data.
Table 3.1.1: Details of remote sensing satellite data, Raniganj coalfield
Year Date of Acquisition Path/Row Spatial
Resolution
Description Projection
1973 18th
& 20th
March 149/44 & 150/43 60 m Landsat MSS World
Geological
Survey 84/
UTM, Zone
45
1992 15th
March 139/44 30 m Landsat 5 (TM)
2002 19th
2010 25th
2015 15th
March 139/44 30 m Landsat 8 (ETM+)
3.2 Data Processing
Data processing tasks are done from spatial and
spectral enhancement menu of image interpreter tab
in ERDAS Imagine software. In order to extract the
entire research area for the year 1973 image
stretching is performed using mosaic tool from data
East
Burdwan
Birbhum
Jharkhand
Puruliya
Bankura

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preparation tab (Sarkar, A., 2017). Image
enhancement techniques like histogram equalization,
contrast stretching and tail trimming are
accomplished in order to improve the visual
interpretability of remotely sensed image. RGB to
IHS and the reverse IHS to RGB colour space
transformation functions are also accomplished for
the year 2002 and 2010 to extract more information.
3.2 Classification Scheme
Beyond of visual interpretation signatures are
selected by using AOI tool followed by parametric
statistical method from the signature editor menu bar
with region growing properties in Erdas Imagine.
Signatures are collected from multiple areas
throughout the image for a single class and are
merged which belong to the same class and renamed
after a land use land cover class (Lillesand et al.,
2004). In this manner ten distinctive land use land
cover classes are captured. These are— forest,
agricultural land, fallow land, river, river sand, water
body, exposure, mining lagoon, urban and excavated.
On average 125 forest signatures, 153 agriculture
signatures, 165 fallow signatures, 60 river signatures,
120 river sand signatures, 75 water body signatures,
87 exposure signatures, 50 lagoon signatures, 155
urban signatures and 80 excavated signatures are
determined for one temporal image. Signature alarm
and contingency matrix utility is also used to evaluate
signatures that have been created from AOI in the
image. After all these evaluations, supervised
classification is performed with a distance file from
classification tab/signature editor menu
bar/classify/supervised to perform a supervised
classification. Under parametric decision rule,
maximum likelihood is selected. Then ok is clicked
in the supervised classification dialog to classify the
image (Sapena, M. & Ruiz, L.A. 2015). Post
classification filtering is applied from the viewer
menu bar/select raster/filtering/statistical filtering
(median filter) to remove unwanted discrete pixels
from the thematic image and to producing
homogeneous region permanently. The classified
maps for 1973, 1992, 2002, 2010 and 2015 are shown
in figure 3.2.1, 3.2.2 and 3.2.3 respectively
Figure 3.2.1: Landscapes map of 1973 and 1992 (clock wise), Raniganj coalfield

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Figure 3.2.2: Landscapes map of 2002 and 2010 (clock wise), Raniganj coalfield
Figure 3.2.3: Landscapes map of 2015, Raniganj coalfield
3
.3 Ground Truth and Training Data Map and
Photographs
With the help of Google Earth and GPS device
(Garmin 72H) 950 ground truth data has been
gathered for full and accurate characterization of
ground truth geographic coordinates, land use and
land cover attributes, species information of different
landscapes using stratified random sampling method
(Steven, 1987). 950 in-situ GPS waypoints are
plotted in the study region according to its
geographical coordinate value shown in figure 3.3.1.
Photographs of different landscapes are collected and
linked to the classified map according to its
geographical coordinate system with the help of
Google Earth in an aim to verify the classified land
use land cover map with the actual surface features
(Sarkar, A., 2017). Forest and excavated quarry land
photograph is taken from Purusattampur area
(23°42'10''N and 87°16' 16''E) and Sonepur Bazari
area (23°39'40''N and 87°11'0''E) respectively shown
in Figure 3.3.2. Photograph of agriculture and urban
is taken from Kandra area (23°25'42''N and
86°42'41''E) and near Barakar railway station and bus
stand area (23°54'22''N and 87°03'34''E) respectively
shown in figure 3.3.3. Fallow and river sand
photograph is taken from Mithali area (23°43'00'' N
and 87°03'00'' E) and river bed of Ajay (23°32'25''N
and 86°20'15''E) respectively shown in figure 3.3.4.
Photograph of exposure land and water body is
captured from Sonepur Bazari area (23°38'26''N and
87°12'12''E) and Dalurband area (23°42'36''N and
87°01'52''E) respectively shown in figure 3.3.5.
Photograph of river and mining lagoon is captured
from Barakar river (23°59'12''N and 87°04'10''E) and
Poidih abandoned mine (23°29'30''N and 87°12'25''E)
respectively shown in figure 3.3.6.

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Figure 3.3.1: Ground truth verification waypoints map, Raniganj coalfield
Figure 3.3.2: Landscape photograph of forest and quarry (clock wise), Raniganj coalfield
Figure 3.3.3: Landscape photograph of agriculture and urban (clock wise), Raniganj coalfield

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Figure 3.3.4: Landscape photograph of fallow and river sand (clock wise), Raniganj coalfield
Figure 3.3.5: Landscape photograph of exposure and water body (clock wise), Raniganj coalfield
Figure 3.3.6: Landscape photograph of river and mining lagoon (clock wise), Raniganj coalfield
3.4 Accuracy Assessment
This work is done in ERDAS Imagine software
followed by classification tab and accuracy
assessment tool. The accuracy assessment algorithms
are shown in table 3.4.1. Firstly stratified random
sampling method is used to furnish the 878 ground
truth reference data. These ground truth points are
overlain on the land use land cover map and value is
extracted (Kuemmerle, et al., 2006). After that a
confusion matrix is generated and placed such that
class membership determined by ground truth values
are along the x-axis, and class membership
determined by image classification is along the y-axis
(Green, 1999). When placed this way, correct values
fall along the major diagonal of the matrix
(Doktoringenieur, G., Buchroithner, M., Dresden,
T.U. & Prof, K. 2010). Incorrectly classified values
lie in the off diagonal areas of the matrix; such that it
is apparent which class they are confused with (Neill,
R.V.O., Krummel, J.R., Gardner, R.H., Sugihara, G.,

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et al. 1988). Field data for validation is not available
for 1973, 1992, 2002 and 2010. So it is assumed that
a similar accuracy is achieved using the same
methods from the 2015 land use land over map
(Green et al., 1994). Accuracy result for the land use
land cover map of 1973, 1992, 2002, 2010 and 2015
is shown in table 3.4.2, 3.4.3, 3.4.4, 3.4.5 and 3.4.6
respectively.
Table 3.4.1: Algorithms for accuracy assessment, Raniganj coalfield
Overall Accuracy Total number of correctly classified pixels (diagonal)
—————————————————————— ×
100
Total number of reference pixel
User Accuracy Number of correctly classified pixels in each category
————————————————————————
× 100
Total number of classified pixels in that category (row total)
Producer Accuracy Number of correctly classified pixels in each
category
———————————————————————
—— × 100
Total number of classified pixels in that category
(column total)
Kappa Coefficient (T) Total Sample × Total Corrected Sample) ˗ ∑ (Column total × Row
total)
—————————————————————————————
Total Sample2
˗ ∑ (Column total × Row total)
Table 3.4.2: Confusion matrix for accuracy assessment of LULC map 1973, Raniganj coalfield
ClassifiedData
Reference Data Users
accuracy
(%)
LULC
Classes
F AL FL R RS WB E L U
EL Total
F
AL
FL
R
RS
WB
E
L
U
EL
Total
1120 65 32 3 21 1241
1356
1057
629
606
510
408
486
1593
552
8408
90.24
92.18
87.04
76.31
86.47
93.0
69.85
86.63
91.46
98.73
15
45
11
6
15
18
5
1235
1250 65 4 5 7 6
2 2
21
12
3
25
30
23
5
1434
920 34 37
5
42
25
3
1092
480 9 102 10
9
8
6
507
524 16 6
4
545
425 8
19
603
285 6
4
4
326
421 20
466
1467
7
1508
545
551
Producer
Accuracy (%)
90.69 87.17 84.25 94.67 96.15 70.48 87.42 90.34 97.28
98.91
Overall Accuracy: 88.46%
Kappa Coefficient: 0.81
Cl
as
sif
ie
d
Da
ta
accuracyLULC F AL FL R RS WB E L U

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Classes EL Total (%)
F
AL
FL
R
RS
WB
E
L
U
EL
Total
1420 32 36 5 23 1516
1146
1052
760
901
530
481
567
1438
466
8850
93.67
89.01
86.12
80.26
91.68
86.42
68.61
87.83
99.09
98.28
21
54
2
4
8
20
9
1538
1020 65 6 8 11 8
2 5
31
15
4
21
36
15
4
1178
906 36 25
5
42
22
5
1081
610 9 98 21
5
8
6
635
826 15 5
6
854
458 7
34
652
389 3
8
9
450
498 25
523
1425
8
1468
458
471
Producer
Accuracy (%)
92.32 86.59 86.9 83.81 96.72 69.60 86.44 95.22 97.07
97.24
ClassifiedData
accuracy
(%)
LULC
Classes
EL Total
F
AL
FL
R
RS
WB
E
L
U
EL
Total
1725 52 29 8 25 1839
1337
1059
789
749
689
467
548
1398
594
9469
93.80
92.00
85.74
79.21
92.12
87.23
82.44
85.58
96.92
99.16
25
69
11
4
5
21
36
1896
1230 50 10 12 4 3
2 1
25
32
4
32
20
15
7
1417
908 32 25
5
25
36
4
1057
625 9 102 5
2
5
9
651
690 21 3
5
724
601 5
36
796
385 7
6
7
425
469 36
519
1355
5
1408
589
596
Producer
Accuracy (%)
90.98 86.80 85.90 96.00 95.30 75.50 90.59 90.37
96.24 98.83
ClassifiedData
accuracy
(%)
LULC
Classes
EL Total
F
AL
FL
R
RS
WB
E
L
1654 59 36 6 25 1780
1397
1257
592
973
502
382
653
1451
92.92
88.40
89.10
71.24
87.98
84.66
74.60
29
42
11
13
2
7
1235 68 11 21 3 9
8 13
25
16
3
14
30
29
1120 24 46
2
42
36
13
421 15 102 25
12
9
8
856 35 12
6 425 10
36 285 1
2

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U
EL
Total
13
1771
12
1423 1317 461 904 625
8
352
589 20 591
9578
90.20
98.28
98.48
645
1426
9
1476
582
597
Producer
Accuracy (%)
93.39 86.79 85.04 91.32 94.69 68 80.97 91.32 96.61
97.49
ClassifiedData
accuracy
(%)
LULC
Classes
EL Total
F
AL
FL
R
RS
WB
E
L
U
EL
Total
1612 48 21 4 17 1702
1290
1146
695
740
544
408
466
1593
702
9286
94.7
91.5
88.0
75.3
96.8
93.0
77.2
90.8
95.5
97.2
20
56
9
5
9
13
8
1732
1180 57 5 7 8 5
5 3
19
20
2
17
35
25
8
1354
1008 29 34
3
36
28
7
1160
523 9 124 10
8
2
4
542
716 12 2
2
734
506 4
23
677
315 2
5
6
355
423 20
437
1522
3
1603
682
692
Producer
Accuracy (%)
93.1 87.1 86.9 96.5 97.5 74.7 88.7 96.8
94.9 98.6
3.5 Landscape Fragmentation
Entire tasks are done in Arc GIS 10.2 and Fragstats
4.2 software. Individual land use land cover map is
converted from raster to polygon in Arc GIS
following arc tool box/3d analyst
tool/conversion/from raster algorithm. Then
fragmentation is computed using class analyzing tool
and landscape analyzing tool respectively from patch
analyst tool following spatial statistics/ analyzed by
class or landscape. In Fragstats software these work
is done using grid method followed by input layer
and analysis tab. Patch metrics, class metrics and
landscape metrics indices are calculated. Area edge,
shape, core area, contrast, aggregation and diversity
are also calculated for different indices. The selected
landscape metrics are shown in table 3.5.1.
Table 3.5.1: Selected landscape indices for Raniganj coalfield area
Landscape
Indices
Algorithm Description
Number of
Patches
NumP = ni This describes number of patches in each class type and the
growth of particular patches in the region (Ng, 2006).
Class Area n
CA= ∑ aij
J=1
This is the total class area for individual land use land cover.
It also measures land use land cover combination (Suocheng,
D., 2013)
Percentage of
Land
CA
PLAND=——
TA
This is the measure of the landscape composition (Ruishan,
H., 2013). It describes sum of the areas of all patches divided
by total landscape area.

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Largest Patch
Index
n
max (aij)
j=1
LPI= ————
A
This is single largest patch at the class level which gives
indication of fragmentation, homogeneity, dominance and
changes within a landscape (Yu, 2006). 100 describe
landscape consists of a single patch while 0 denotes largest
patch type is absent.
Patch Density ni
PD= —— (10000) (100)
A
This is a measure of fragmentation of a patch type or
landscape (Mairota, 2013). higher value indicates fragmented
growth and vice versa.
Total Edge Length M
TE= ∑ eik
k=1
This is an absolute measure of total edge length for particular
patch type. Higher value indicates larger continuous patches
and vice versa (Lion, 2004).
Edge Density ∑ L
ED= ———×10000
TA
This is the ratio of total edge distance to the total Area
(Poorva, 2006). Zero edge density indicates no class of the
landscape.
Mean Patch Size NP
MPS= ———
TA
This is the average size of patches of a land use class and
denotes landscape configuration and fragmentation
(Millington et al., 2003).
Patch Size
Coefficient of
Variance
PSSD
PSCoV= ———
MPS
This is the coefficient of variation of patches. It measures
relative variability about the percentage of the mean not
absolute variability (Hu, 1970).
Patch Size
Standard
Deviation
This is an absolute standard deviation measure of mean patch
areas and difference in patch size among patches (NG, 2006).
Mean Shape Index PA
MSI= ∑———
A
This is a measure of average patch shape for a particular
patch type or for all patches in the landscape (Li et al., 2004).
Area Weighted
Mean Shape Index
MSI
AWMSI=———
PA
This is mean patch shape complexity, weighted by patch area
(Sadhu K., 2012). 1 AWMSI indicates all patches are circular
while increasing value indicates patches become complex in
shape.
Mean Perimeter
Area Ratio
∑ PA
MPAR= ———× NumP
AR
This is the measure of shape complexity It is the function of
sum of each patch perimeter, area ratio and number of
patches (Mairota, 2013).
The Shannon
Diversity Index
M
SHDI= - ∑ (pi .1n pi)
I=1
This is relative measure of patch diversity, heterogeneity and
fragmentation at class level of the community (Mairota et al.,
2013). Higher index value indicates more diverse landscape.
Shannon Evenness
Index
M
SHEI= -∑(pi .1n pi)/1n m
i=1
This is a measure of patch distribution or abundance and
only available at the landscape level (Li et al., 2004). 0 SEI
indicates low patch distribution while 1 denotes more even
distribution
13 class level indices and 2 landscape level indices for Raniganj opencast mining area are computed and displayed
in table 3.5.2 and 3.5.3 respectively.
Table 3.5.2: Class level indices from 1973 to 2015, Raniganj coalfield
Landscap
e
Indices
Yea
r
Forest Agricultur
al
Land
Fallow
Land
River River
Sand
Water
Body
Exposur
e
Lagoon Urban Excavat
ed
Land
Class
Area
197
3
38143.1 55253.65 35256.5
2
4327.42 4165.68 925.82 3917.34 692.27 3552.24 7264.53

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199
2
29708.9 62113.8 32124.9 4018.95 4963.41 768.24 6723.72 884.7 8895.65 10764.5
200
2
27288.6 49115.6 34421.4 1838.07 8491.05 766.53 9862.74 1101.6 11219.5 15067.2
201
0
28193.1 38693.7 41045.9 961.02 8043.3 762.48 9575.28 1297.77 16020 10854
201
5
22096.8 44858.6 45843.2 717.75 8870.67 712.71 5232.68 1678.69 19648.7
2
13764.6
Number
of
Patches
197
3
22216 33061 13080 6408 689 2794 14388 1917 7658 9441
199
2
41415 32735 19688 5952 821 2319 17994 2450 19178 13804
200
2
44170 31855 22923 1462 3454 2611 24696 3163 21226 17147
201
0
53172 36730 25175 1242 6043 3104 34197 1873 32263 26033
201
5
59981 37166 26290 1056 7868 3090 25239 2108 32603 17147
Percenta
ge of
Land
197
3
25.12 36.31 23.64 3.24 3.63 1.03 3.34 0.23 2.68 5.42
199
2
18.47 38.58 19.97 2.50 3.08 0.48 4.18 0.55 5.52 6.68
200
2
17.14 30.86 21.63 1.15 5.33 0.48 6.20 0.69 7.05 9.47
201
0
18.13 24.90 26.41 0.62 5.17 0.49 6.15 0.83 10.31 6.98
201
5
13.52 27.45 28.05 0.44 5.43 0.44 3.20 1.03 12.03 8.42
Patch
Density
197
3
38.236 28.256 19.256 6.526 0.652 2.102 13.654 1.201 5.625 12.201
199
2
32.564 30.215 17.624 5.216 0.846 1.956 18.256 1.950 16.256 15.265
200
2
40.231 26.364 16.102 1.365 6.126 2.620 28.245 2.695 26.358 19.856
201
0
34.125 27.698 12.365 1.102 4.021 2.056 14.652 1.186 26.254 7.658
201
5
17.259 29.652 20.362 0.980 6.856 2.421 3.654 1.265 16.859 14.521
Largest
Patch
Index
197
3
18.201 9.526 7.269 1.201 0.039 0.524 2.654 0.216 1.624 2.126
199
2
14.256 11.246 6.387 0.952 0.080 0.236 6.912 0.310 4.325 4.987
200
2
20.354 7.246 4.215 0.547 1.210 0.856 8.856 0.895 8.654 7.568
201
0
16.325 7.698 2.321 0.432 0.562 0.123 3.568 0.120 8.452 1.268
201
5
6.248 10.564 7.658 0.215 1.625 0.756 1.003 0.256 5.846 2.689
Total
Edge
197
3
1734175
6.1
18226821.
49
998899
2.8
1787515.
88
583497.
05
63047
8
274429
8.5
415808.
8
136248
4.1
356654
7.8
199
2
1350714
8.7
20489816.
40
910173
2.4
1660097.
41
695237.
13
52316
7
471031
2.4
531391.
71
341198
3.9
528487
1.4
200 1591350 17846220 938502 709140 202992 63000 772182 709260 778008 814200

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2 0 0 0 0 0 0 0
201
0
1525269
8.3
13695074.
47
720990
1.9
640936.7
75
137764
7.8
53229
1
466769
1.9
417124.
10
867512
0.1
338549
3.5
201
5
7968584.
76
17937756.
08
127741
15
383651.0
1
253339
3.8
58460
5
132644
4.8
401368.
93
539948
0.7
491502
8.1
Edge
Density
197
3
55.335 58.168 31.826 5.696 1.854 2.000 8.756 1.322 4.344 11.382
199
2
43.106 65.391 29.0473 5.298 2.218 1.669 15.032 1.695 10.889 16.866
200
2
50.3586 56.474 29.699 2.244 6.423 1.993 24.435 2.244 24.620 25.765
201
0
48.267 43.338 22.815 2.028 4.3596 1.684 14.771 1.32 27.4526 10.713
201
5
25.115 56.535 40.261 1.209 7.984 1.842 4.180 1.265 17.017 15.491
Mean
Patch
Size
197
3
0.5824 0.5983 0.3709 1.4807 0.1653 3.0178 3.6729 2.7691 2.1558 1.2996
199
2
1.3940 0.5270 0.6128 1.4809 0.1654 3.0185 2.6761 2.7693 2.1558 1.2823
200
2
1.6186 0.6485 0.6659 0.7953 0.4067 3.4062 2.5039 2.8712 1.8918 1.1380
201
0
1.8859 0.9492 0.6133 1.2923 0.7513 4.0709 3.5713 1.4432 2.0139 1.8846
201
5
2.7144 0.8285 0.5734 1.4712 0.8869 4.3355 4.8233 1.2557 1.6592 1.8913
Mean
Shape
Index
197
3
1.296 1.183 1.370 1.356 1.261 1.263 1.252 1.246 1.256 1.272
199
2
1.323 1.332 1.342 1.255 1.415 1.274 1.281 1.270 1.242 1.280
200
2
1.243 1.346 1.317 1.367 1.255 1.221 1.249 1.229 1.243 1.281
201
0
1.317 1.330 1.344 1.376 1.307 1.258 1.297 1.259 1.289 1.299
201
5
1.334 1.362 1.343 1.317 1.291 1.211 1.265 1.21 1.273 1.267
Area
Weighte
d Mean
Shape
Index
197
3
8.293 22.181 10.162 8.226 2.501 2.603 1.666 2.558 0.877 3.792
199
2
6.4555 28.661 9.256 7.641 2.982 2.158 2.856 3.271 2.197 5.619
200
2
6.214 26.609 7.368 7.283 8.480 1.597 2.046 2.708 3.653 5.221
201
0
5.321 19.609 10.573 4.404 6.179 1.646 2.474 2.016 3.095 6.654
201
5
9.052 24.411 13.652 3.454 5.099 1.528 5.874 1.638 4.155 9.030
Patch
Size
Coefficie
nt of
Variance
197
3
2363.65 8340.468 3657.92 3033.215 482.584 489.27
7
836.635 752.756 323.052 1301.80
4
199
2
1841.280 9376.264 3333.11
8
2817.434 575.333 406.53
8
1436.46
0
962.866 809.257 1929.67
6
200
2
2122.029 5178.708 1569.06
6
1456.741 3804.18
4
213.05
2
431.978 962.265 1195.01
5
1309.01
0
201
0
1427.099 2731.143 2560.32
5
819.279 2217.86
7
262.77
4
868.819 453.559 833.837 2025.09
2

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201
5
1132.689 3088.358 3190.55
2
655.091 1959.53
8
313.48
0
2880.01
9
467.412 1228.51
0
3381.01
0
Patch
Size
Standard
Deviatio
n
197
3
16.690 140.549 50.484 19.381 28.535 1.578 2.114 2.629 0.674 6.748
199
2
13.162 158.742 46.440 18.965 34.849 1.313 3.635 3.362 1.690 10.040
200
2
10.361 79.847 23.447 18.314 55.842 0.527 1.245 3.351 4.112 7.576
201
0
11.732 32.472 67.877 13.612 51.568 0.687 4.513 1.649 4.071 21.604
201
5
11.138 42.216 55.831 5.938 20.969 0.762 21.266 1.457 5.535 27.076
Mean
Perimete
r Area
Ratio
197
3
1315.82 1344.857 1437.41
0
1273.875 1126.56
1
1320.1
1
1502.46
1
1389.40
5
1402.14
6
1382.71
6
199
2
1274.110 1287.594 1309.73
3
1183.072 1081.36
3
1273.5
2
1432.27
8
1359.82
6
1350.15
4
1308.82
4
200
2
1151.218 1073.240 1079.85
0
1073.070 1116.73
8
1136.8
3
1126.80
7
1146.74
0
1143.48
6
1109.61
9
201
0
1225.643 1183.373 1199.93
3
1164.910 1286.98
3
1257.6
3
1278.14
5
1267.19
5
1231.21
5
1146.51
7
201
5
1207.298 1212.615 1215.39
8
1198.112 1260.59
7
1192.4
7
1330.56
4
1278.06
0
1257.13
9
1258.92
8
Table 3.5.3: Landscape level indices from 1973 to 2015, Raniganj coalfield
Year Shannon Diversity Index Shannon Evenness Index
1973 1.453 0.606
1992 1.524 0.635
2002 1.536 0.640
2010 1.610 0.671
2015 1.612 0.672
4. DISCUSSION AND FINDINGS
Detailed statistical information of class area and
percentage of land is shown in figure 4.1. Class area
clearly indicates that there is a major temporal
change in forest followed by agriculture and fallow.
There is reduction in forest, agriculture, water body
and river in 1973 to 2015. Contrary, area under
fallow, river sand, exposure, lagoon, urban and
excavated is increased in the abovementioned time
period in northern and eastern parts of this coalfield
namely Purushottompur, Sonepur and Pandaveswar
area. Therefore it can be said that the part of forest
cover transformed into agriculture or fallow or
excavated land. Result of percentage of land indicates
that agricultural land occupies highest percentage of
land followed by fallow and forest. Contrary, water
body occupies lowest percentage of land followed by
river and lagoon. In 1973, the forest class covered an
area of 25.23%, but in 2015 it decreased to 13.52%.
The area covered by agricultural land also decreased
from 36.14% in 1973 to 27.44% in 2015. The river is
decrease from 3.02% in 1973 to 0.43% in 2015.
Fallow land is increased by 10.23% in past 50 years.
This shows that the area under the forest is turn in to
the fallow, agriculture and urban land. Urban shows
the increase by 2.32% in 1973 to 12.02% in 2015.
West and North of this coalfield Satgram, Kajora and
Sripur exhibits maximum agriculture and fallow land.

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0
5
10
15
20
25
30
35
40
45
F AL FL R RS WB E L U EL
Areain%
Land use type
1973
1992
2002
2010
2015
Figure 4.1: CA and PLAND 1973 to 2015 (clock wise), Raniganj Coalfield
Detailed statistical information of number of patch
and patch density is shown in figure 4.2. Numbers of
forest patches have increased from 22216 in 1973 to
59981 in 2015. Many forest patches isolated and
converted into agriculture, fallow, quarry or urban in
recent times near north-western area i.e. Sodepur,
Slanpur and Pandaveswar. Number of patches for
agriculture is increased from 33061 to 36730 during
1973 to 2015 is due to conversion of farmland into
grassland. Number of fallow patches increased 25175
to 26290, river sand patches increased 689 to 7868
and urban patches increased 7658 to 212226 in 1973
to 2015 respectively near Asansol and Raniganj.
Agriculture patches have increased slightly because
of aggregation of smaller fields into larger ones.
Urban and excavated patches have increased due to
growing urbanization and mining activities
(Batistella, M., Robeson, S. & Moran, E.F. 2003).
Only river patches are decreased from 6408 to 1056
in 1973 to 2015 near river Ajay and Damodar. Acute
increase in the number of patches indicates
fragmentation. Patch density for forest, agriculture,
river, exposure is significantly decreased with time.
The major decreased is observed in forest 38.236 to
17.259 and river 6.526 to 0.980 since 1973 to 2015.
There is a little increase in patch density for
agriculture 28.256 to 29.652 and fallow 19.256 to
20.262. Patch density drastically accelerated for river
sand 0.652 to 6.856, urban 5.626 to 16.859 and
excavated land 12.201 to 14.521. Higher patch
density indicates low fragmentation within these land
uses.
Figure 4.2: NP and PD 1973 to 2015 (clock wise), Raniganj Coalfield
Figure 4.3 shows temporal changes and detailed
statistical information in total edge and edge density.
There is a significant increase in total edge for fallow
land, river sand, urban and excavated. Fallow
increased 9988999.8 hectare to 127774115.2 hectare
where urban increased 1362484.1 hectare to
5399480.7 hectare during 1973 to 2015 near
Kunstoria, Sripur, Satgram and Sodepur area.
0
10000
20000
30000
40000
50000
60000
70000
F Al FL R RS WB E L U EL
Areainhectare
Land use type
1973
1992
2002
2010
2015
0
10000
20000
30000
40000
50000
60000
Number
Land use type
1973
1992
2002
2010
2015
0
10
20
30
40
50
Patchperhectare
Land use type
1973
1992
2002
2010
2015

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Agriculture, exposure and lagoon do not exhibit
much difference in total edge. During the period
forest, river and exposure edges have decreased
drastically. Forest decreased 17341756.1 hectare to
7968584 hectare, river 1787515.8 hectare to
383651.1 hectare and exposure decreased 2744298.5
hectare to 13264448.6 hectare in 1973 to 2015
respectively.
There is an acute decrease of edge density for forest,
agriculture, river, water body and exposure. Forest
declined from 55.33 meter/hectare to 25.11
meter/hectare, river decreased from 5.69
meter/hectare to 1.20 meter/hectare and exposure
declined from 8.75 meter/hectare to 4.18
meter/hectare during 1973 to 2015 respectively. This
result denotes fragmentation in these land uses due to
opencast mining and human pressure on existing
land. Some land use exhibits sharp increase in edge
density. Like fallow 31.82 meter/hectare to 40.26
meter/hectare, river sand 1.85 meter/hectare to 7.98
meter/hectare, urban 4.34 meter/hectare to 17.01
meter/hectare during 1973 to 2015 respectively. It is
happened due to rapid urbanization process and
industrial development in Raniganj coalfield area
near Kulti, Asansol, Raniganj, Barakar and
Disergarh.
Figure 4.3: TE and ED 1973 to 2015 (clock wise), Raniganj Coalfield
Class level largest patch index and mean patch size is
shown in figure 4.4. Forest is the largest patch in
each class as a percentage of the total landscape.
In 1973, largest patch index for forest was 18.201%
of total area and then it decreased to 6.248% of total
area in 2015. In 1973, the patches of forest were large
and continuous. However, in 2015, the patches are
scattered compared to 1992, 2002 and 2010. It is the
sign of more prominent fragmentation. Urban largest
patch index was 1.624% of total area in 1973 and it
increased to 5.846% of total area in 2015. It suggests
that urban is increasingly becoming a dominant land
use within the landscape. Urban expansion is mainly
at the southern part of the study area bordering to
Asansol, Durgapur, Barakar, Kulti, Raniganj city
area. Largest patch index for agriculture and river
sand is increased by about 1%. Largest path index for
fallow land is decreased from 7.269% in 1973 to
2.321% in 2010. But it increased to 7.658% in 2015.
In 2010 largest patch index of all land uses are
decreased. Henceforth in 2015 the largest patch index
for agriculture, river sand, water body, mining lagoon
and excavated slightly increased and landscape
becomes more complex with comparison of simple
shape in 1973. Decrease in the largest patch size and
increase in patches above mentioned land use land
cover classes clearly indicate that patches getting
clumped and tries to form a single patch therefore
landscape is becoming fragmented with an acute rate.
Areas covered with vegetation observed a decrease in
mean patch size from 2.71 hectare to 0.51 hectare in
1973 to 2015. Contrary urban area witnessed an acute
increment from 1.65 hectare to 2.45 hectare in 1973
to 2015. Forest, agriculture, river, water body, lagoon
are undergoing drastically conversion and
fragmentation into quarry, fallow and urban near
Jhanjra, Bankola, Kenda and Sonepur Bazari.
Subsequent decrease in mean patch size in major land
0
5000000
10000000
15000000
20000000
25000000
Hectare
Land use type
1973
1992
2002
2010
2015
0
10
20
30
40
50
60
70
Meterperhectare
Land use type
1973
1992
2002
2010
2015

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use classes which are associated with increased
fragmentation (Li et al. 2004). It has been concluded
that mean patch size is likely the most crucial
indicator of fragmentation (McGarigal and Marks,
1995), therefore I suggest Fragmentation has
increased in Raniganj coalfield area since 1973.
Figure 4.4: LPI and MPS 1973 to 2015 (clock wise), Raniganj Coalfield
Detailed about mean shape index and area weighted
mean shape index is shown in figure 4.5. Result
suggests that there is little change in mean shape
index for all land uses. Forest increased from 1.29 to
1.33, agriculture 1.18 to 1.36, river sand 1.26 to 1.29,
urban 1.25 to 1.27 in 1973 to 2015 which indicates
patch shape irregularity and fragmentation. Fallow,
river and mining lagoon exhibits a decrease of 1.37 to
1.34, 1.35 to 1.31 and 1.24 to 1.21 in 1973 to 2015
respectively which are less fragmented and less
irregular. The area weighted mean shape index shows
small change for forest from 8.29 in 1973 to 9.05 in
2015 which suggest increasing trend in shape
complexity and fragmentation. The area weighted
mean shape index for agriculture, fallow, river sand,
excavated land and urban has increased in north and
western area. Such changes indicate an expansion of
these land uses around urban and coalfield areas of
Raniganj coalfield. Agriculture increased from 22.18
to 24.41 and urban from 0.87 to 4.15 in 1973 to 2015
respectively. It expresses that inter patch connectivity
among these land uses have decreased. This also
indicates increase in shape complexity. AWMSI for
mining lagoon, water body and river have declined
from 2.55 to 1.63, 2.60 to 1.52 and 8.22 to 3.45 in
1973 to 2015 respectively. It indicates that these
landscapes tend to be circular in shape and decrease
in shape complexity.
Figure 4.5: MSI and AWMSI 1973 to 2015, Raniganj coalfield
0
5
10
15
20
25
%
Land use type
1973
1992
2002
2010
2015
1.05
1.1
1.15
1.2
1.25
1.3
1.35
1.4
1.45
MSI
Land use type
1973
1992
2002
2010
2015
0
1
2
3
4
5
6
Hectare
Land use type
1973
1992
2002
2010
2015
0
5
10
15
20
25
30
35
AWMSI
Land use type
1973
1992
2002
2010
2015

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Figure 4.6 shows patch size standard deviation and
patch size coefficient of variation. These can be
misleading with regards to landscape structure in the
absence of information on the number of patches or
patch density (Baldi, G. & Paruelo, J.M. 2008). Patch
size standard deviation for forest, river, river sand,
water body and mining lagoon decreased in tiny rate.
While fallow exhibits huge decreased in PSSD from
140.54% in 1973 to 42.21% in 2015. This indicates
fragmentation growth in these landscapes. There is a
huge increase of PSSD for urban and excavated land
from 0.67% to 5.53% and 2.11% to 21.26%
respectively in the above mentioned study period.
This means urban and excavated landscapes
becoming dominant in Raniganj coalfield area. Patch
size coefficient of variation is computed due to some
drawback of patch size standard deviation. Patch size
coefficient of variation is acutely declined for forest
2363.65 hectare to 1132.68 hectare, agriculture
8340.46 hectare to 3088.35 hectare, and river
3033.21 hectare to 655.09 hectare. Water body and
mining lagoon decreased in a small rate. There is a
sharp increase of PSCoV for excavated land, urban
and river sand. Urban area dominantly increased
from 323.05 to 1228.25 during the period.
Figure 4.6: PSSD and PSCoV 1973 to 2015 (clock wise), Raniganj coalfield
Mean perimeter area ratio is shown in figure 4.7. Except of river sand, MPAR is declined for all
land use categories. It indicates whole landscape has become more complicated and fragmented. Fallow exhibits
acute decreased from 1437.41 hectare in 1973 to 1215.39 hectare in 2015. There is an increase of mean perimeter
area ratio for river sand from 1126.56 hectare in 1973 to 1260.59 hectare in 2017. It can be said that river sand will
be increased in Raniganj coalfield area in terms of mean perimeter area ratio.
Figure 4.7: MPAR 1973 to 2015, Raniganj coalfield
0
20
40
60
80
100
120
140
160
180
%
Land use type
1973
1992
2002
2010
2015
1000
1100
1200
1300
1400
1500
1600
Hectare
Land use type
1973
1992
2002
2010
2015
0
2000
4000
6000
8000
10000
Hectare
Land use type
1973
1992
2002
2010
2015

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Shannon diversity index and Shannon evenness index
is only computed landscape level in order to measure
the fragmentation process as a whole shown in figure
4.8. Indeed SDI and SEI is likely skewed due to
many small patches like water body, excavated land,
exposure and mining lagoon in Raniganj coalfield
area (Areendran, G., Rao, P., Raj, K., Mazumdar, S.,
et al. 2013). The increase in the Shannon diversity
index from 1.45 in 1973 to 1.61 in 2015 indicates that
landscape heterogeneity and diversity increased in
Raniganj coalfield. It also means proportional
distribution of different patch in the landscape
therefore landscape becomes evenly distributed and
fragmented. Shannon evenness index increased from
0.60 in 1973 to 0.67 in 2015. The increase in the SEI
indicates that the distribution of landscape patches is
more even in 2015 than it was in 1973. Outcome of
these two indices clearly suggest pattern of spatial
change in successive years. If these trends will carry
on therefore landscape of Raniganj and adjoining
become more heterogeneous and scattered.
Figure 4.8: SDI and SEI 1973 to 2015 (clock wise), Raniganj coalfield
5. CONCLUSION
In recent time opencast mining and associated
development activities are recognized as the most
significant factor in order to widespread
fragmentation of the landscape in Raniganj coalfield
area. After assessing above mentioned results it is
clear that forest patches have converted into
numerous small patches and isolated in recent times.
Contrary urban, fallow and excavated land will be
dominated because of their increasing patch size.
This huge level of land use fragmentation in Raniganj
coalfield area is occurred due to extension of mining
areas, development of infrastructure and residential
complexes of mining industry and thermal power
plants (Ampofo, S., Sackey, I. & Ampadu, B. 2016).
This intensive analysis indicates that forest, water
body and river are acutely fragmented land use.
These land uses are more affected in past 40 years
because of declining class area, percentage of land,
patch density and largest patch size. Forest and river
are most prone to future fragmentation. Contrary
fallow, agriculture, river sand is moderately
fragmented land use because of moderate patch
density, largest patch index and percentage of land.
Urban is acutely effective land use in Raniganj
coalfield area because of inclining class area, patch
density and percentage of land in past 40 years. In the
conclusion it can be said that agriculture and urban
will dominate this area in near future. Large scale
opencast mining, agricultural expansion, land
abandonment, illegal small scale mining,
deforestation and rapid urbanization predominately
occurred in and around the periphery of Raniganj
mining area will make the landscape or ecosystem
more complex and fragmented in near future (Dupin,
L., Nkono, C., Burlet, C., Muhashi, F., et al. 2013).
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