INTRODUCTION:

Opencast mining adversely causes widespread environmental decay especially land alteration and mutation. In order to compute the LULC changes since last five decades five spatial maps are prepared with ten different classes using supervised image classification method followed by maximum likelihood algorithm in ERDAS Imagine software. Agricultural area is declining from 55253.65 hectare in 1973 to 44858.6 hectare in 2015 and exhibits a negative growth rate. On the other hand, urban area sharply indicates the inverse trend. The area is increased from 3552.24 hectare in 1973 to 19648.72 hectare in 2015.

2. Research Area:

Raniganj coalfield is the birth place of coal mining in India is located in Burdwan district of West Bengal. About 95% of the total area of the coalfield is covered by four districts (Burdwan 71%, Birbhum 9%, Bankura 8% and Purulia 7%) of West Bengal. It is almost elliptical in shape and covers an area of about 1530 sq km. included within latitudes 23° 30Ꞌ N to 23° 52Ꞌ N and longitudes 86° 38Ꞌ E to 87° 23Ꞌ E. The coalfield has an east-west stretch of 75 km and north-south stretch of 35 km. This coalfield mainly consists 10 seams viz R-I to R-X where R – denotes to Raniganj formation and roman one is the bottom most seam whereas roman ten is the top most seam. Number of running OCP is 17 and abandoned is 21. The area presents a nearly flat topography with broad undulation. Mean elevation is 98.45m (ECL, 2015).

Figure 1: Location of research are (Source:CMPDI and ECL)

3. MATERIALS AND METHODS:

To perceive the impression of anthropogenic actions on landscapes following methods has been used—

3.1 Remote Sensing and Other Data Sources:

The satellite data were gathered from USGS Earth Explorer portal. Five temporal satellite images are applied for preparation of land use maps from 1973 to 2015. Six individual topographic maps on 1:50000 scales are gathered namely 73M/1, 73M/2, 73M/5, 73M/6, 73I/13 and 73I/14 from Survey of India to built the base layer of the satellite data. Details of the satellite data are given below in table 1.

Table 1: Details of remote sensing satellite data

Year	Acquisition Date	Path/Row	Spatial Resolution	Description	Projection
1973	18^th & 20^th March	149/44 & 150/43	60 m	Landsat MSS	World Geological Survey 84/ UTM, Zone 45
1992	15^th March	139/44	30 m	Landsat 5 (TM)
2002	19^th March	139/44	30 m	Landsat 7 (TM)
2010	25^th March	139/44	30 m	Landsat 7 (TM)
2015	15^th March	139/44	30 m	Landsat 8 (ETM+)

3.2 Data Processing:

Entire tasks are done from spatial and spectral enhancement menu of image interpreter tab in ERDAS Imagine software. Contrast stretching and tail trimming algorithms are performed to improve the visual interpretability of the image. Colour space transformation RGB to IHS function and the reverse IHS to RGB functions are applied for the year 2002 and 2010 to enhance the image information. To extract the entire research area for the year 1973 image stretching is performed using mosaic tool from data preparation tab.

3.3 Classification Scheme:

Firstly satellite images are visually interpreted. Signatures are collected using AOI tool from multiple areas throughout the image for a single class. Later these signatures are merged which belong to the same class and renamed after a land use class. In this manner ten distinctive LULC classes are captured. Signature alarm and contingency matrix is also used to evaluate signatures that have been created from AOI. After all these evaluations, supervised classification is applied with distance file from classification tab followed by signature editor menu bar; select Classify/supervised to perform a supervised classification. The distance file is used for post classification purposes. Option dialog is also utilized to define the statistical information like minimum, maximum, mean and standard deviation for the signature so that the signatures in the output thematic raster layer have this statistical information. Under parametric decision rule, Maximum likelihood is selected. 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 maps and to producing homogeneous region permanently (Sarkar, A., 2017). The classified maps of 1973, 1992, 2002, 2010 and 2015 are shown in figure 2, 3 and 4 respectively.

Figure 2: Landscapes map of 1973 and 1992 (clock wise), Raniganj coalfield

Figure 3: Landscapes map of 2002 and 2010 (clock wise), Raniganj coalfield

Figure 4: Landscapes map of 2015, Raniganj coalfield3.4 Ground Truth and Training Data:

Ground truth data has been collected through extensive filed survey during May 2015 and December 2016 for better understanding of the LULC map shown in figure 5.

Figure 5: Ground truth verification waypoints map, Raniganj coalfield

A GPS device was employed across the entire study region representing all land cover classes for in-situ collection of ground truth geographic coordinates and locations; land use and land cover attributes, species information, and other general notes using stratified random sampling method. Google Earth has also been used extensively for collecting and plotting 950 ground truth data (Sarkar, A., 2017).

3.5 Accuracy Assessment:

First of all reference pixels are randomly selected. Then accuracy is measured for each classification using a confusion matrix. 720 ground truth points are overlain on the land use land cover maps and the land cover value is extracted. After values are extracted a confusion matrix is generated for accuracy assessment. The confusion matrix is used for the following—

Table 2: 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: Confusion matrix for accuracy assessment of LULC map 1973, Raniganj coalfield

Classified Data	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

Table 4: Confusion matrix for accuracy assessment of LULC map 1992, Raniganj coalfield

Classified Data	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	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
Overall Accuracy: 88.14%
Kappa Coefficient: 0.86

Table 5: Confusion matrix for accuracy assessment of LULC map 2002, Raniganj coalfield

Classified Data	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	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
Overall Accuracy: 90.04%
Kappa Coefficient: 0.88

Table 6: Confusion matrix for accuracy assessment of LULC map 2010, Raniganj coalfield

Classified Data	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	1654	59 36 6 25									1780 1397 1257 592 973 502 382 653 1451 591 9578	92.92 88.40 89.10 71.24 87.98 84.66 74.60 90.20 98.28 98.48
		29 42 11 13 2 7 13 1771	1235	68 11 21 3 9 8 13
			25 16 3 14 30 29 12 1423	1120	24 46
				2 42 36 13 1317	421	15 102 25
					12 9 8 461	856	35 12
						6 904	425	10
							36 625	285	1 2
								8 352	589	20
									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
Overall Accuracy: 88.424%
Kappa Coefficient: 0.84

Table 7: Confusion matrix for accuracy assessment of LULC map 2015, Raniganj coalfield

Classified Data	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	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
Overall Accuracy: 90.74%
Kappa Coefficient: 0.92

3.6 Microsoft Excel:

To display the proportion of area of different land use Pie diagram and simple bar graph are prepared in Microsoft excel using the data gathered from LULC maps. To compute the percent area and rate of land use change the below mentioned algorithms are used —

Area (%) = individual class area / total area * 100

Rate of land use change (%) =

((present LULC value – previous LULC value) / previous LULC value) * (1 / 10) * 100

Prediction of future values in excel is calculated from layout tab/trend line option. Here exponential trend line is prepared with three periods forward forecasting algorithm.

4. RESULT AND DISCUSSION:

In order to detect the transformation and current status of land use land cover in Raniganj coalfield area different spatial map of 1973, 1992, 2002, 2010 and 2015 is analyzed. Land use land cover is classified into ten classes— forest, agricultural land, fallow land, river, river sand, water body, exposure, lagoon, urban and excavated land. Areal proportion of different land use is displayed in figure 5, 6 and 7. Forest displayed an acute decline in their areal extent from 38143.1 hectare in 1973 to 22096.8 hectare in 2015. Agriculture dominates among all land uses, but the area is declining continuously from 55253.65 hectare in 1973 to 44858.6 hectare in 2015. River is almost at the stage of elimination, which comes down from l 4327.42 hectare in 1973 to 717.75 hectare in 2015. The areal coverage of water body is minimum and also decreasing in a rapid rate from 925.82 hectare in 1973 to 712.71 hectare in 2015. After these analysises it is clear that there is gradual decrease of dense or open forest, agricultural land, water body and river. In due course of time considerable portion of forest and agricultural land is converted into open cast quarry, construction, mining lagoon, road and fallow land.

Figure 6: Land use land cover distribution in 1973 and 1992 (clock wise), Raniganj coalfield

Figure 7: Land use land cover distribution in 2002 and 2010(clock wise), Raniganj coalfield

During the ground truth survey it was noticed that in Satgram, Pandaveswar, Kunstoria, Kajora, Bankola, Jhanjraand Sonepur Bazari area most of the forest and agricultural land is lost due opencast miing activities including land excavation, construction of new houses and buildings, mining quarry located in the South, East and Western part of the area. Rapid growth of population and urbanization accelerate the land use land cover changes. These are being accelerated due to open cast mining activities. As an outcome there is a loss of 16046.2 hectare forest, 10394.4 hectare agriculturall and and 3609.25 hectare river land and 213.11 hectare water body.

There is an increase of 10586.48 hectare fallow land, 4704.32 hectare river sand, 16095.76 hectare urban and 6499.47 hectare excavated land. Fallow land is the second largest land cover in the study area which is continuously increasing from 35256.52 hectare in 1973 to 45843.2 hectare in 2015. During opencast mining virgin land is being extracted to meet the coal. The virgin land can be either forest or agriculture. After abandonment of mining operation this land turned into fallow land. For instance there is a vast transformation of forest and agriculture into fallow land located in the South East and North West parts of the research area i.e. at Purusattampur, Sripur and Dabor area. Initially this area is renowned for mining activity. Therefore it is assumed that opencast mining is increasing decade by decade which leads to more quarry land, mining lagoon and mining exposure land. Quarry land is being created at the time of digging of land to extract coal and overburden. When extraction and production of coal is exhausted mined areas are left abandoned, which in due course of time are covered mostly with vegetation and water. Mining lagoon is that waterlogged abandoned opencast project. Contrary exposure is the vegeted land. Dense forest, agriculture and water body of Sodepur, Mahalaxmi, Slanpur and Sonepur Bazari area converted to excavated land or quarry or mining lagoon located in the Western, Central and southern part of the study area. Mining quarry which increas from 7264.53 hectare in 1973 to 13764.6 hectare in 2015. Mining lagoon which increases from 692.27 hectare in 1973 to 1678.69 hectare in 2015. Exposure land is increased from 3917.34 hectare in 1973 to 5232.68 hectare in 2015 respectively.

River sand acutely increasded due to ceasation of river, rising population pressure, agricultural activity on river bed etc. Some segments of Damodar and Ajay river turned into river sand. It is increasded from 4165.68 hectare in 1973 to 8870.67 hectare in 2015. Durgapur and Asansol are two mighty cities in this area. These two cities are industrially advanced especially based on coal. Therefore this area is urbanizing in rapid rate. Urban land is increasded from 3552.24 hectare in 1973 to 19648.72 hetare in 2015. South, central and Western parts of this area is acutely urbanizing. These area are Barakar, Dishergarh, Sanctoria, Panjabi more, etc. In Purusattampur, Dalmiya, Kenda and Shibpur abandoned quarry and mining overburden is transformed into sparse forest, fallow land, settlement located in the eastern and South western parts of the study area of the study area.

Figure 8: Land use land cover distribution in 2015, Raniganj coalfield

Percentage of area and trend and rate of land use change is computed and shown in figure 9 and figure 10 respectively. From which it is understood that there is subsequent decrease and increase in land uses. Forest was 25% in 1973 but it decreased into 13.52% in 2015. Highly affected forest cover is mostly transformed into fallow, agriculture and quarry. The rate of decrease in forest was found to rise from -2% during 1973 and1992 to -2.16% during 2010 and 2015. Initially from 1973 to 1992 agriculture is increased 36.21% to 38.58% at the rate of 1% due to transformation of forest land into it. After 1992 this land use is continuously decreasing from 38.58% in 1992 to 24.89% in 2010 at a rate of -2.12%. In last five years agriculture exhibits tiny increase 24.89% in 2010 to 27.45% in 2015 with a rate of 1.59%. Therefore it can be said that agriculture retains its own land and moderately affected. During 1973 to 2002 fallow land was decreased from 23.24% to 21.62%. Later fallow land is increased rapidly from 21.62% in 2002 to 28.05% in 2015 with a rate of 1.92%. Mostly forest and quarry was transformed into fallow.

Figure 9: Percentage area of various land use land cover in 1973 to 2015, Raniganj Coalfield

Percentage of river is continuously declined from 3% in 1973 to 0.43% in 2015. Most of river land was transformed into river sand at a rate of -5.42% in 1992 to 2002. River sand was 3.12% in 1973 but it increased to 5.42% in 2015. River sand changed at the rate of 7.10% in 1992 to 2002 but it reduced to 1.02% in 2010 to 2015 and was transformed into crop area, river water and fallow area. Water body was 1.02% in 1973 and declined into 0.43% in 2015 at the rate of -2% in 1973 to 1992. Later the rate of change comes in about 0.52%. Exposure, lagoon and excavated land are the outcome of opencast coal mining and have been discussed earlier. These land uses were increasing since 1973. Exposure land was 3% in 1992 and 6.19% in 2002. After that the areal cover declined to 3.20% in 2015. The rate of change was 7% in 1973 to 1992 and -4.53% in 2010 to 2015. Mining lagoon has inclined almost 0.2% in 1973 to 1.03% in 2015. The rate of change was 3.14% in 1973 to 1992. Area of excavated land was 5.02% in 1973, it increased to 9.47% in 1992 but later it declined to 8.42% in 2015. The rate of change was 5% in 1973 to 1992 and 2.68% in 2010 to 2015. Urban land was 2.14% in 1973 but it increased to 12.02% in 2015. The increase rate of urbanization was mainly due to transformation of fallow land into urban and construction of buildings. The acute rate of urbanization was 15% in 1973 to 1992 and 18.80% in 1992 to 2002. After 2002 the rate of urbanization was declined into 2.27% in 2010 to 2015.

Figure 10: Rate of change in land use categories in 1973 to 2015, Raniganj Coalfield

The open cast mining affects the land use and land cover of Raniganj coalfield area decade by decade in an accelerating rate. To know these impacts in near future, forecasting of trends of land uses are computed using 3 periods forward forecasting algorithm with R squared value. The trend and predicted individual land uses in the form of exponential curve are shown in figure 11, 12, 13, 14 and 15. Test results shows that rate of reduction of forest and agricultural land will follow negative trend between 2016 and 2018.

Figure 11: Trend analysis and prediction of forest & agriculture in 1973 to 2018 (clock wise), Raniganj Coalfield

Compare to agriculture, the forest area will be reduced more with greater rate of -2.16% in terms of the change in annual percentage. It is predicted that area of river and water body will also be reduced at the rate of now. Fallow land and river sand will be increased at the rate of above 1.16% and 1.02% respectively in terms of the change in annual percentage. Exposure, lagoon and excavated areas are expected to increase slightly at their present rate of change. The most important forecasting is about the urban land.

Figure 12: Trend analysis and prediction of fallow & river in 1973 to 2018 (clock wise), Raniganj Coalfield

Figure 13: Trend analysis and prediction of river sand & water body in 1973 to 2018 (clock wise) Raniganj Coalfield

Figure 14: Trend analysis and prediction of exposure & lagoon in 1973 to 2018 (clock wise), Raniganj Coalfield

It is predicted that the urban land will increase acutely with greater rate of 2.26% in terms of the change in annual percentage. The increase in urban land is mainly based on reductions in forest and agriculture. The rate at which the urban area will increase from 2015 to 2018 is lower than during 1973 to 2002 in terms of the change in annual percentage. Highly developed economy and high human population density will also caused more urbanization in Raniganj area. It should be remembered that the forecasting system in excel is an art not a science. Therefore the computed prediction is also just gives us a concept not actual value. From the above discussion about the trend analysis and forecasting of land use land cover in Raniganj coalfield area from 1973 to 2018 it can be said that opencast mining activity adversely affects the landscape.

Figure 15: Trend analysis and prediction of urban & excavated land in 1973 to 2018 (clock wise), Raniganj Coalfield

6. CONCLUSION:

The land use land cover analysis showed an increase in the non forest area that is increase in man-made land use. From the above result and discussion it can be concluded that opencast mining has disastrous to these above discussed land uses. If the rate of land use transformation is occured with the present rate it certainly leads to deforestation, destruction of fertile soil and more mine abandoned land. It is observed that because of changes in land use and land cover pattern in Raniganj coalfield area Shibpur, Pandaveswar, Damaliya and Purusattampur, Sonepur Bazari, Khottadih, Bomjemehri and Dalurband area is affected vastly. In order to reduce the impact of mining activitry on landscape scientific approach during and after mining activities has to be taken like proper backfilling, storage of top soil seperately, forestation and monitoring the spoil ersion from dumped overburden. Land use land cover degradation which is associated to opencast mining project and developmental project in Raniganj coaal field area are bringing a cronic challenge to the landscape. Due to larger size of the study area it is very difficult to allocate the land uses adequately at small scale. Furthermore, the accuracy through visually interpreted process is not good enough. These are the limitations of this study (Rao, P. 2014).

7. REFERENCES:

1. Areendran, G., Rao, P., Raj, K., Mazumdar, S., et al. (2013) Land use / land cover change dynamics analysis in mining areas of Singrauli district in Madhya Pradesh , India. 54 (2), 239–250.

2. Bianche, V.B. (2002) Automatic Land Use / Land Cover Classification System with Rules Based Both on Objects Attributes and Landscape Indicators.

3. Doktoringenieur, G., Buchroithner, M., Dresden, T.U. & Prof, K. (2010) Assessing processes of long-term land cover change and modelling their effects on tropical forest biodiversity patterns – a remote sensing and GIS-based approach for three landscapes in East Africa

4. Garg, M., Joshie, A. & Choudhary, S. (2013) Land Use Land Cover Classification in Jamuna- Kotma Coal Field Region , Anuppur District MP. 6 (2), 38–43.

5. Kindu, M., Schneider, T., Teketay, D. & Knoke, T. (2013) Land Use / Land Cover Change Analysis Using Object-Based Classification Approach in Munessa-Shashemene Landscape of. [Online] 2411–2435. Available from: doi:10.3390/rs5052411.

6. Kumar, R., Nandy, S., Agarwal, R. & Kushwaha, S.P.S. (2014) Forest cover dynamics analysis and prediction modeling using logistic regression model. Ecological Indicators. [Online] 45, 444–455. Available from: doi:10.1016/j.ecolind.2014.05.003.

7. Limited, M.C. (2014) Report on vegetation cover mapping of Raniganj coalfield based on satellite data of the year 2013. (March).

8. Maitima, J.M., Mugatha, S.M., Reid, R.S., Gachimbi, L.N., et al. (2009) The linkages between land use change , land degradation and biodiversity across East Africa. 3 (10), 310–325.

9. Morgado, P., Toger, M., Abrantes, P. & Fiegel, J. (2012) A Bottom Up Approach to Modeling Habitat Connectivity Dynamics Through Networks Analysis.

10. Mundia, C.N. & Murayama, Y. (2009) Analysis of Land Use / Cover Changes and Animal Population Dynamics in a Wildlife Sanctuary in East Africa. [Online] 952–970. Available from: doi:10.3390/rs1040952.

11. Pradesh, H., Ramachandra, T. V, Kumar, U. & Joshi, N. V (2005) Landscape Dynamics in Western Himalaya - Mandhala.

12. Reddy, C.S., Satish, K. V, Pasha, S.V., Jha, C.S., et al. (2016) Assessment and monitoring of deforestation and land-use changes ( 1976 – 2014 ) in Andaman and Nicobar Islands , India using remote sensing and GIS. 111 (9).

13. R.H.U. & Suocheng, D. (2013) Land Use Dynamics and Landscape Patterns in Shanghai , Jiangsu and Zhejiang. [Online] 4 (2), 141–148. Available from: doi:10.5814/j.issn.1674-764x.2013.02.006.

14. Samanta, P., Technical, S., Manager, P. & Consultants, T. (2015) Impact Assessment and Changes Analysis of Land Use / Land Cover Due to Open Cast Coal Mining Activity : A Case Study of Raniganj Coal Field Area. 4 (5), 17–27.

15. Sapena, M. & Ruiz, L.A. (2015) Analysis Of Urban Development By Means Of Multi-Temporal Fragmentation Metrics From Lulc Data. [Online] XL (May), 11–15. Available from: doi:10.5194/isprsarchives-XL-7-W3-1411-2015.

16. Srivastav, S., Roy, S.K. & Krishnamurthy, P.S. (2016) An Analysis Of Land Use And Land Cover Dynamics And Causative Drivers In A Thickly Populated Yamuna River Basin Of India. 14 (3), 773–792.

17. State, J., Kumar, A. & Pandey, A.C. (2013) Evaluating Impact of Coal Mining Activity on Landuse / Landcover Using Temporal Satellite Images in South Karanpura Coalfields and. 2 (1), 183–197.

18. Sarkar, A., (2017) Remote Sensing Approach To Define Landscape Dynamics In Raniganj Coalfield Area Through Trend Line Analysis, Vol. 08, Issue, 09, pp.5546-5555, September, 2017.

Received on 27.04.2018 Modified on 29.05.2018

Research J. Science and Tech. 2019; 11(1):27-37.

DOI: 10.5958/2349-2988.2019.00004.4