FOR CLIENT USE ONLY

Land Use / Vegetation Cover Mapping of Mand Coalfield based on Satellite Data for the Year 2014

Dharamjaygarh

MAND RAIGARH COALFIELD

Ghargoda

Tamnar

RAIGARH

CMPDI A Miniratna Company

For client use only

Report on Land Use/ Vegetation Cover Mapping of based on Satellite date of the year 2014

Submitted to South Limited Bilaspur

December - 2014

Remote Sensing Cell Geomatics Division CMPDI (HQ), Ranchi

Document Control Sheet

(1) Job No. RSC-561410027

(2) Publication Date December 2014 (3) Number of Pages 37 (4) Number of Figures 06 (5) Number of Tables 05 (6) Number of Plates 01 (7) Number of Drawings 01

Land use/ Vegetation Cover mapping of Mand Raigarh (8) Title of the Report Coalfield using satellite data of the year 2014.

Preparation of land use/vegetation cover map of Mand (9) Aim of the Report Raigarh Coalfields on 1:50,000 scale based on LandSAT8 Satellite data.

Remote Sensing Cell (10) Executing Unit Geomatics Division Central Mine Planning & Design Institute Ltd. Gondwana Place, Kanke Road, Ranchi

(11) User Agency Limited, Bilaspur

(12) Author Rakesh Ranjan, Senior Manager (RSC)

(13) Security Restriction Restricted Circulation

(14) No. of Copies 8

(15) Distribution Statement Official

List of Figures

1. Map of showing the location of Mand Raigarh Coalfield. 2. Remote Sensing Radiation System. 3. Electromagnetic Spectrum 4. Expanded Diagram of the visible and infrared regions. 5. Methodology of Land use/ Vegetation cover mapping. 6. Geoid-Ellipsoid Projection relationship.

List of Tables

1. Electromagnetic Spectral Regions 2. Characteristics of Satellite Sensor 3. Classification Accuracy Matrix 4. Land use / Vegetation Cover classes identified in Mand Raigarh Coalfield. 5. Status of Land Use/ Vegetation Cover pattern in Mand Raigarh Coalfield. 6. Block wise Land Use / Cover details in Mand Raigarh Coalfield

List of Plates

1. Location Map 2. FCC (Band 3, 2, 1) Map of Mand Raigarh Coalfield based on Landsat 8 Satellite data of the year 2014. 3. Land use/ Vegetation cover map of Mand Raigarh Coalfield based on Landsat 8 Satellite data of the year 2014.

List of Drawings 1. Land use/ Vegetation cover map of Mand Raigarh Coalfield based on Landsat 8 Satellite data of the year 2014.

Contents

Description Page No.

Document Control Sheet i List of Figures ii List of Tables ii List of Plates ii

1.0 Introduction 1-4

1.1 Project Reference 1.2 Objective 1.4 Location of the Area & Accessibility 1.5 Topography & Drainages 1.6 Coal Resources

2.0 Remote Sensing Concept & Methodology 5-19

2.1 Remote Sensing 2.2 Electromagnetic Spectrum 2.3 Scanning System 2.4 Data Source 2.5 Characteristics of Satellite/Sensor 2.6 Data Processing 2.6.1 Geometric Correction, rectification & geo-referencing 2.6.2 Image enhancement 2.6.3 Training set selection 2.6.4 Signature generation & classification 2.6.5 Creation / Overlay of vector database in GIS 2.6.6 Validation of classified image

3.0 Land Use/ Cover Mapping 20-29 3.1 Introduction 3.2 Land use/ Vegetation cover Classification 3.3 Data Analysis in Mand Raigarh Coalfield 3.3.1 Vegetation Cover 3.3.2 Mining Area 3.3.3 Agriculture 3.3.4 Wasteland 3.3.5 Settlement 3.3.6 Water Bodies

4.0 Conclusion and Recommendations 30

4.1 Conclusion 4.2 Recommendations

CMPDI

Chapter 1

Introduction

1.1 Project Reference

Coal India Limited requested CMPDI to take up the study based on remote sensing satellite data for creating the geo-environmental data base of coalfields for monitoring the impact of coal mining on land use and vegetation cover. Accordingly, a road map for implementation of the project was submitted to Ltd. for land use and vegetation cover mapping of 28 major coalfields for creating the geo-environmental data base and subsequent monitoring of impact of coal mining land environment at a regular interval of three years. In pursuant to the work order no.CIL/WBP/Env/2009/2428 dated 29.12.2009; issued by CIL. Subsequently, a revised work order was issued vide letter no. CIL/WBP/Env/2011/4706 dated 12.10.2012 from Coal India Limited for the period 2012-13 to 2016-17 for land reclamation monitoring of all the opencast projects as well vegetation cover monitoring of 28 major coalfields including Mand Raigarh Coalfield as per a defined plan for monitoring the impact of mining on Vegetation Cover.

1.2 Project Background

South Eastern coalfield Ltd. is a Mini Ratna Company, dedicated for maintaining the ecological balance in the region has initiated a massive plantation programme on backfilled area, OB dumps and wasteland. The advent of high resolution, multispectral satellite data has opened a new avenue in the field of mapping and monitoring of vegetation cover. The present study has been taken up to access the impact of coal mining on land use and

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CMPDI vegetation cover in Mand Raigarh Coalfield with respect to the earlier study carried out in the year 2011.

1.3 Objective The objective of the present study is to prepare a regional land use and vegetation cover map of Mand Raigarh coalfield on 1:50,000 scale based on satellite data of the year 2014, using digital image processing technique for assessing the impact of coal mining and other industrial activities on land use and vegetation cover in the coalfield area.

1.4 Location of the Area & Accessibility The Mand Raigarh coalfield falls in the of state. Dharamjaygarh is an important town located in the north of the coalfield. It is connected by all weather roads with Raigarh (75 km) and (60 km) Railway Stations on the Howrah-Nagpur section of South Eastern Railway. The coalfield boundary is connected with in the east and in the west. The nearest airports for Raigarh are , and Bilaspur. The nearest city is Raigarh lies in the southern part of the coalfield. National Highway 74 passes through the north south part of the Mand Raigarh coalfield.

The study area is bounded between North Latitudes 22041’09” to 21 0 49’23” and East longitudes 82049’36” to 830 41’50” and is covered by Survey of India j j j N N N O N (SoI) topo-sheet Nos. 64 /14, 64 /15, 64 /16, 64 /2, 64 /3,64 /4, 64 /1, 64 /6, 64 N N O N N 0 /7, 64 /8, 64 /5,64 /11, 64 /12 and 64 /9. The location map of study area is shown in Figure 2.1. The aerial extends ranges 86 km in north-south direction and 70 km in east-west direction encompassing an area of about 3447 sq. kms.

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1.5 Topography and Drainage

The area is characterized by rolling topography inter-spread with a few hills. The average height of plain is 260 to 270 m above MSL. The hills rise about 450 m above the ground level. The southerly flowing perennial with its tributaries Kurket constitute the main drainage of the area. The Kelo River which drains the eastern part of the coalfield is a tributary of River.

The Mand-Raigarh coalfield receives an average annual rainfall of 1530 mm, the bulk of which precipitates between June to October. The maximum temperature in summer reaches up to 46OC while in the winter the minimum temperature drops down to 11OC. Part of the coalfield falls within Raigarh Forest Division and has luxuriant growth of Sal and Bamboo trees.

1.6 Coal Resources

The Mand-Raigarh coalfield comprises extensive spread of Lower Gondwana formations, extending from Hasdo Arand coalfield through Raigarh Basin to Ib Valley coalfield in Sambalpur district of Orissa. The state boundary between Chhattisgarh and Orissa states arbitrarily demarcates the limits of Mand Raigarh coalfield from the Ib Valley coalfield. Korba coalfield is the western extension of Mand-Raigarh coalfield.

There are 12 coal seams in Mand-Raigarh coalfield which are largely thin and persistent. The Ash content varies from 15 to 35% and the coals are largely power grade coals. Geological Survey of India (GSI) has assessed coal resources of 21,117 Mt in this coalfield.

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Fig 1.1 : Map of India Showing the Location of Mand Raigarh Coalfields

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Chapter 2

Remote Sensing Concepts and Methodology

2.1 Remote Sensing

Remote sensing is the science and art of obtaining information about an object or area through the analysis of data acquired by a device that is not in physical contact with the object or area under investigation. The term remote sensing is commonly restricted to methods that employ electro- magnetic energy (such as light, heat and radio waves) as the means of detecting and measuring object characteristics.

All physical objects on the earth surface continuously emit electromagnetic radiation because of the oscillations of their atomic particles. Remote sensing is largely concerned with the measurement of electro- magnetic energy from the SUN, which is reflected, scattered or emitted by the objects on the surface of the earth. Figure 2.1 schematically illustrate the generalised processes involved in electromagnetic remote sensing of the earth resources.

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2.2 Electromagnetic Spectrum

The electromagnetic (EM) spectrum is the continuum of energy that ranges from meters to nanometres in wavelength and travels at the speed of light. Different objects on the earth surface reflect different amounts of energy in various wavelengths of the EM spectrum.

Figure 2.2 shows the electromagnetic spectrum, which is divided on the basis of wavelength into different regions that are described in Table 2.1. The EM spectrum ranges from the very short wavelengths of the gamma-ray region to the long wavelengths of the radio region. The visible region (0.4-0.7µm wavelengths) occupies only a small portion of the entire EM spectrum.

Energy reflected from the objects on the surface of the earth is recorded as a function of wavelength. During daytime, the maximum amount of energy is reflected at 0.5µm wavelengths, which corresponds to the green band of the visible region, and is called the reflected energy peak (Figure 2.2). The earth also radiates energy both day and night, with the maximum energy 9.7µm wavelength. This radiant energy peak occurs in the thermal band of the IR region (Figure 2.2).

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Table 2.1 Electromagnetic spectral regions

Region Wavelength Remarks Gamma ray < 0.03 nm Incoming radiation is completely absorbed by the upper atmosphere and is not available for remote sensing. X-ray 0.03 to 3.00 nm Completely absorbed by atmosphere. Not employed in remote sensing. Ultraviolet 0.03 to 0.40 µm Incoming wavelengths less than 0.3mm are completely absorbed by Ozone in the upper atmosphere. Photographic UV 0.30 to 0.40 µm Transmitted through atmosphere. Detectable with band film and photo detectors, but atmospheric scattering is severe. Visible 0.40 to 0.70 µm Imaged with film and photo detectors. Includes reflected energy peak of earth at 0.5mm. Infrared 0.70 to 100.00 µm Interaction with matter varies with wavelength. Absorption bands separate atmospheric transmission windows. Reflected IR band 0.70 to 3.00 µm Reflected solar radiation that contains no information about thermal properties of materials. The band from 0.7-0.9mm is detectable with film and is called the photographic IR band. Thermal IR band 3.00 to 5.00 µm Principal atmospheric windows in the thermal 8.00 to 14.00 µm region. Images at these wavelengths are acquired by optical-mechanical scanners and special Videocon systems but not by film. Microwave 0.10 to 30.00 cm Longer wavelengths can penetrate clouds, fog and rain. Images may be acquired in the active or passive mode. Radar 0.10 to 30.00 cm Active form of microwave remote sensing. Radar images are acquired at various wavelength bands. Radio > 30.00 cm Longest wavelength portion of electromagnetic spectrum. Some classified radars with very long wavelength operate in this region.

The earth's atmosphere absorbs energy in the gamma-ray, X-ray and most of the ultraviolet (UV) region; therefore, these regions are not used for remote sensing. Details of these regions are shown in Figure 2.3. The horizontal axes show wavelength on a logarithmic scale; the vertical axes show percent atmospheric transmission of EM energy. Wavelength regions with high transmission are called atmospheric windows and are used to acquire remote sensing data. The major remote sensing sensors records energy only in the visible, infrared and micro-wave regions. Detection and measurement of the recorded energy enables identification of surface objects (by their characteristic wavelength patterns or spectral signatures), both from air-borne and space-borne platforms.

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2.3 Scanning System The sensing device in a remotely placed platform (aircraft/satellite) records EM radiation using a scanning system. In scanning system, a sensor, with a narrow field of view is employed; this sweeps across the terrain to produce an image. The sensor receives electromagnetic energy radiated or reflected from the terrain and converts them into signal that is recorded as numerical data. In a remote sensing satellite, multiple arrays of linear sensors are used, with each array recording simultaneously a separate band of EM energy. The array of sensors employs a spectrometer to disperse the incoming energy into a spectrum. Sensors (or detectors) are positioned to record specific wavelength bands of energy. The information received by the sensor is suitably manipulated and transported back to the ground receiving station. The data are reconstructed on ground into digital images. The digital image data on magnetic/optical media consist of picture elements arranged in regular rows and columns. The position of any picture element, pixel, is determined on an x-y co-ordinate system. Each pixel has a numeric value, called digital number (DN), which records the intensity of electromagnetic energy measured for the ground resolution cell represented by that pixel. The range of digital numbers in an image data is controlled by the radiometric resolution of the satellite’s sensor system. The digital image data are further processed to produce master images of the study area. By analysing the digital data/imagery, digitally/visually, it is possible to detect, identify and classify various objects and phenomenon on the earth surface.

Remote sensing technique provides an efficient, speedy and cost-effective method for assessing the changes in vegetation cover certain period of time due to its inherited capabilities of being multi-spectral, repetitive and synoptic aerial coverage.

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2.4 Data Source

The following data are used in the present study:

y Primary Data –Raw satellite data, obtained from National Remote Sensing Centre (NRSC), Hyderabad, was used as primary data source for the study. LANDSAT 8; Sensor – OLI, Band 2, 3, 4, 5; Path # 141, Row # 044, Path # 141, Row # 045, Path # 142, Row # 044, Path # 142, Row # 045 ; Date of pass 06.02.2014*. The detail specification of the data is also given in Table 2.2. y Secondary Data Secondary (ancillary) and ground data constitute important baseline information in remote sensing, as they improve the interpretation accuracy and reliability of remotely sensed data by enabling verification of the interpreted details and by supplementing it with the information that cannot be obtained directly from the remotely sensed data.

2.5 Characteristics of Satellite/Sensor

The basic properties of a satellite’s sensor system can be summarised as: (a) Spectral coverage/resolution, i.e., band locations/width; (b) spectral dimensionality: number of bands; (c) radiometric resolution: quantisation; (d) spatial resolution/instantaneous field of view or IFOV; and (e) temporal resolution. Table 2.2 illustrates the basic properties of LandSAT 8 satellite / sensor that is used in the present study.

Table 2.2 Characteristics of the satellite/sensor used in the present project work Radiometric Spatial Temporal Platform Sensor Spectral Bands in µm Country Resolution Resolution Resolution

LANDSAT 8 OLI B2 0.45 - 0.51 Blue 16-bit 30 m 16 days USA B3 0.53 - 0.59 Green (55,000-grey 30 m B4 0.64 - 0.67 Red levels) 30 m B5 0.85 - 0.88 NIR 30 m

NIR: Near Infra-Red

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2.6 Data Processing The methodology for data processing carried out in the present study is shown in Figure 2.4. The processing involves the following major steps:

(a) Geometric correction, rectification and geo-referencing; (b) Image enhancement; (c) Training set selection; (d) Signature generation and classification; (e) Creation/overlay of vector database; (f) Validation of classified image; (g) Layer wise theme extraction using GIS (g) Final vegetation map preparation.

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Basic Data Data Source Secondary Data

LANDSAT 8 Surface Plan (OLI Image) (Scale 1:50,000)

Pre-processing, geometric correction, Creation of Vector Database rectification & (Drainage, Road network georefrencing Railway network)

Image Enhancement Geocoded FCC Generation

Training set Identification

Signature Generation

Training Set

Pre-Field Refinement Classification

Validation through Fail Ground Truthing Report Preparation

Pass Integration of Final Land Use/ Thematic Vegetation Cover Information Map using GIS

Fig 2.4: Methodology for Land Use / Vegetation Cover Mapping

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2.6.1 Geometric correction, rectification and georeferencing

Inaccuracies in digital imagery may occur due to ‘systematic errors’ attributed to earth curvature and rotation as well as ‘non-systematic errors’ attributed to intermittent sensor malfunctions, etc. Systematic errors are corrected at the satellite receiving station itself while non-systematic errors/ random errors are corrected in pre-processing stage.

In spite of ‘System / Bulk correction’ carried out at supplier end; some residual errors in respect of attitude attributes still remains even after correction. Therefore, fine tuning is required for correcting the image geometrically using ground control points (GCP).

Raw digital images contain geometric distortions, which make them unusable as maps. A map is defined as a flat representation of part of the earth’s spheroidal surface that should conform to an internationally accepted type of cartographic projection, so that any measurements made on the map will be accurate with those made on the ground. Any map has two basic characteristics: (a) scale and (b) projection. While scale is the ratio between reduced depiction of geographical features on a map and the geographical features in the real world, projection is the method of transforming map information from a sphere (round Earth) to a flat (map) sheet. Therefore, it is essential to transform the digital image data from a generic co-ordinate system (i.e. from line and pixel co-ordinates) to a projected co-ordinate system. In the present study geo-referencing was done with the help of Survey of India (SoI) topo-sheets so that information from various sources can be compared and integrated on a GIS platform, if required.

An understanding of the basics of projection system is required before selecting any transformation model. While maps are flat surfaces, Earth however is an irregular sphere, slightly flattened at the poles and bulging at the Equator. Map projections are systemic methods for “flattening the orange peel” in measurable

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CMPDI ways. When transferring the Earth and its irregularities onto the plane surface of a map, the following three factors are involved: (a) geoid (b) ellipsoid and (c) projection. Figure 2.5 illustrates the relationship between these three factors. The geoid is the rendition of the irregular spheroidal shape of the Earth; here the variations in gravity are taken into account. The observation made on the geoid is then transferred to a regular geometric reference surface, the ellipsoid. Finally, the geographical relationships of the ellipsoid (in 3-D form) are transformed into the 2-D plane of a map by a transformation process called map projection. As shown in Figure 2.5, the vast majority of projections are based upon cones, cylinders and planes.

Fig 2.5: Geoid – Ellipsoid – Projection Relationship

In the present study, Polyconic projection along with Modified Everest (1956) Ellipsoidal model was used so as to prepare the map compatible with the SoI topo-sheets. Polyconic projection is used in SoI topo-sheets as it is best suited for small-scale mapping and larger area as well as for areas with North-South orientation (viz. India). Maps prepared using this projection is a compromise of many properties; it is neither conformal perspective nor equal area. Distances, areas and shapes are true only along central meridian. Distortion increases away

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CMPDI from central meridian. Image transformation from generic co-ordinate system to a projected co-ordinate system was carried out using ERDAS Imagine 9.3 digital image processing system.

2.6.2 Image enhancement

To improve the interpretability of the raw data, image enhancement is necessary. Most of the digital image enhancement techniques are categorised as either point or local operations. Point operations modify the value of each pixel in the image data independently. However, local operations modify the value of each pixel based on brightness value of neighbouring pixels. Contrast manipulations/ stretching technique based on local operation were applied on the image data using PCI Geomatica v10.1 software.

2.6.3 Training set selection The image data were analysed based on the interpretation keys. These keys are evolved from certain fundamental image-elements such as tone/colour, size, shape, texture, pattern, location, association and shadow. Based on the image- elements and other geo-technical elements like land form, drainage pattern and physiography; training sets were selected/ identified for each land use/cover class. Field survey was carried out by taking selective traverses in order to collect the ground information (or reference data) so that training sets are selected accurately in the image. This was intended to serve as an aid for classification. Based on the variability of land use/cover condition and terrain characteristics and accessibility, 90 points were selected to generate the training sets.

2.6.4 Signature generation and classification

Image classification was carried out using the minimum distance algorithm. The classification proceeds through the following steps: (a) calculation of statistics

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[i.e. signature generation] for the identified training areas, and (b) the decision boundary of maximum probability based on the mean vector, variance, covariance and correlation matrix of the pixels.

After evaluating the statistical parameters of the training sets, reliability test of training sets was conducted by measuring the statistical separation between the classes that resulted from computing divergence matrix. The overall accuracy of the classification was finally assessed with reference to ground truth data. The aerial extent of each land use class in the coalfield was determined using PCI Geomatica v10.1 s/w. The FCC map (Band 3, 2, 1) of Mand Raigarh Coalfield based on satellite imagery is enclosed in report as Drawing No. HQREM2A01401. The classified image for the year 2014 for Mand Raigarh Coalfield is shown in Drawing No. HQREM2A01402.

2.6.5 Creation/overlay of vector database in GIS Plan showing leasehold areas of mining projects supplied by SECL are superimposed on the image as vector layer in the GIS database. Road network, rail network and drainage network are digitised on different vector layers in GIS database. Layer wise theme extraction was carried out using ArcGIS s/w and imported the same on GIS platform for further analysis.

2.6.6 Validation of classified image Ground truth survey was carried out for validation of the interpreted results from the study area. Based on the validation, classification accuracy matrix was prepared. The overall classification accuracy for the year 2014 was found to be 88.59%.

Final Land Use/vegetation cover maps were printed using HP Design jet 4500 PS Colour Plotter. Due to paper size limitation in the HP plotter, the final print output has been adjusted to 1:92,000 Scale.

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Table 2.3: Classification Accuracy Matrix for Mand Raigarh Coalfield in the year 2014

Vegetation\Land use Total no. of % of % of Built-up Vegetation Mining Water Sl.# classes as observed Agriculture Wasteland observation observation classification % of omission land Cover Area Bodies in the field points (Z) points accuracy Land use/vegetation cover Classes based on Satellite Data

(b) Vegetation Cover 16 2 +` 18 20.00 88.89 11.12

(g) Mining Area 1 7 8 8.89 87.5 12.5

(c) Agriculture 2 18 20 22.22 90.00 10.00

(d) Wasteland 1 24 1 26 28.89 92.31 7.69

(a) Built-up land 13 1 14 15.56 92.86 7.14

(h) Water Bodies 1 4 5 5.56 80.0 20.0

Total no. of 14 18 20 26 8 5 90 - 88.59 - observation points (X)

% of Commission 7.14 11.11 10.00 7.69 12.5 20.0

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Plate – 1: FCC Map of Mand Raigarh Coalfield based on Satellite Data of the Year 2014.

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Plate – 2: Classified Land Use / Vegetation Cover Map of Mand Raigarh Coalfield based on Satellite Data of the Year 2014

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Chapter 3

Land Use/ Vegetation Cover Monitoring

3.1 Introduction

Land is one of the most important natural resource on which all human activities are based. Therefore, knowledge on different type of lands as well as its spatial distribution in the form of map and statistical data is vital for its geospatial planning and management for optimal use of the land resources. In mining industry, the need for information on land use/ vegetation cover pattern has gained importance due to the all-round concern on environmental impact of mining. The information on land use/ cover inventory that includes type, spatial distribution, aerial extent, location, rate and pattern of change of each category is of paramount importance for assessing the impact of coal mining on land use/ cover.

Remote sensing data with its various spectral and spatial resolutions, offers comprehensive and accurate information for mapping and monitoring of land use/cover over a period of time. By analysing the data of different cut-off dates, impact of coal mining on land use and vegetation cover is determined.

3.2 Land Use / Vegetation Cover Classification

The array of information available on land use/ vegetation cover requires be arranging or grouping under a suitable framework in order to facilitate the creation of database. Further, to accommodate the changing land use/vegetation cover pattern, it becomes essential to develop a standardised classification system that is not only flexible in nomenclature and definition, but also capable of

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The present framework of land use/cover classification has been primarily based on the ‘Manual of Nationwide Land Use/ Land Cover Mapping Using Satellite Imagery’ developed by National Remote Sensing Centre, Hyderabad, which has further been modified by CMPDI for coal mining areas. Land use/vegetation cover map was prepared on the basis of image interpretation carried out based on the satellite data for the year 2014. Following land use/cover classes are identified in the Mand Raigarh coalfield region (Table 3.1).

Table 3.1 Land use / Vegetation Cover classes identified in Mand Raigarh Coalfield

LEVEL –I LEVEL-II 1.1 Dense Forest 1.2 Open Forest 1.3 Scrub 1 Vegetation Cover 1.4 Plantation under Social Forestry 1.5 Plantation on OB Dumps 1.6 Plantation over Backfill 2.1 Coal Quarry 2.2 Advance Quarry Site 2.3 Barren OB Dump 2 Mining Area 2.4 Barren Backfilled Area 2.5 Coal Dump 2.6 Water Filled Quarry 3.1 Crop Land 3 Agricultural Land 3.2 Fallow Land 4.1 Waste upland with/without scrubs 4 Wasteland 4.2 Fly Ash Pond 4.3 Barren Rocky Land 5.1 Urban 5 Settlements 5.2 Rural 5.3 Industrial

6 Water Bodies 6.1 River/Streams /Reservoir

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3.3 Data Analysis Satellite data of the year 2014 was processed using PCI Geomatica v.10.1 image processing s/w in order to interpret the various land use and vegetation cover classes present in the Mand Raigarh coalfield. The analysis was carried out for entire coalfield covering 3445.77 sq. km. area.

The area of each class was calculated and analysed using PCI Geomatica Digital Image Processing s/w. Analysis of land use / vegetation cover pattern in Mand Raigarh Coalfield for the year 2014 was carried out and details of the analysis are and shown in table 3.2. A similar study for Mand Raigarh coalfield was also done in the year 2011 based on the Satellite data. Table 3.2 also contains the comparative analysis of the results of the year 2011 and 2014. Moreover, Block wise details of land use / vegetation cover pattern in Mand Raigarh Coalfield for the year 2014 has also been tabulated and presented in table 3.3.

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TABLE – 3.2

STATUS OF LAND USE & VEGETATION COVER PATTERN IN MAND RAIGARH COALFIELD IN THE YEAR 2011 & 2014 Area in Sq. km AREA STATISTICS Changes in Land Use / Cover Remarks LAND USE / COVER CLASSES YEAR 2011 YEAR 2014 VEGETATION COVER Area % Area % Area % Dense forest 727.68 21.12 726.37 21.08 (-) 01.31 0.04 Decrease in Forest Cover due to Biotic interference and Open Forest 1159.39 33.65 1155.48 33.53 (-) 03.91 0.12 natural degradation. Sub Total (Forest) 1887.07 54.77 1881.85 54.61 (-) 05.22 0.16

Scrubs 303.46 8.81 305.62 8.87 (+) 02.16 0.06

Plantation under Social Forestry 0.57 0.02 0.87 0.03 (+) 00.29 0.01 Increase in Plantation is a result of plantation done by Plantation on OB Dump 0.04 0.00 0.16 0.00 (+) 00.12 0.00 SECL in mining areas. Sub Total (Plantation) 0.61 0.02 1.03 0.03 (+) 00.41 0.01 Sub Total (Vegetation Cover) 2191.14 63.60 2188.50 63.51 (‐) 2.64 0.08 MINING AREA Coal Quarry/Active Mining Area 5.28 0.16 10.04 0.29 (+) 04.76 0.14 Advance Quarry Site 0.16 0.00 0.11 0.00 (-) 00.05 0.00 Coal Dump 0.30 0.01 0.20 0.01 (-) 00.10 0.00 Coal Face 0.05 0.00 0.07 0.00 (+) 00.02 9.00 Expansion of existing mining projects and increase in Barren OB Dump 1.35 0.04 2.82 0.08 (+) 01.47 0.04 production to meet the high coal demand. Barren Backfill 0.00 0.00 0.60 0.02 (+) 00.60 0.02 Water Filled Quarry 0.15 0.00 0.39 0.01 (+) 00.24 0.01 Sub Total 7.29 0.21 14.23 0.41 (+) 06.94 0.20 AGRICULTURAL LAND Crop Land 75.61 2.19 75.42 2.18 (-) 00.19 0.01 Decrease in agricultural area is due to increase in Fallow Land 1019.38 29.58 1011.69 29.36 (-) 07.69 0.22 industrial & mining activities. Sub Total 1094.99 31.77 1087.11 31.55 (‐) 07.88 0.23 WASTELAND Waste upland 90.33 2.62 88.54 2.58 (-) 01.79 0.05 Fly-Ash Pond 1.34 0.04 1.87 0.05 (+) 00.53 0.02 Decrease in wasteland area is due to increase in Sand Body 6.00 0.17 6.00 0.17 00.00 0.00 industrial & mining activities. Sub Total 97.67 2.83 96.41 2.80 (‐) 01.26 0.03 SETTLEMENTS Urban 0.68 0.02 1.01 0.03 (+) 00.33 0.01 Rural 8.03 0.23 10.15 0.29 (+) 02.12 0.06 Increase in settlement area is due to increase in industrial Industrial 11.56 0.34 11.65 0.34 (+) 00.09 0.00 activities as well as other socio-economic reasons. Sub Total 20.27 0.59 22.81 0.66 (+) 02.54 0.07 WATER BODIES 34.41 1.00 36.71 1.07 (+) 02.30 0.07 TOTAL 3445.77 100.00 3445.77 100.00

Note: 1. All data are fixed to two decimal digits. 2. (+) indicates positive trend whereas (-) indicates negative trend.

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Table 3.3

BLOCKWISE LAND USE/COVER DETAILS OF COAL BLCOKS IN MAND RAIGARH COALFIELD FOR THE YEAR 2014

(Area in Hectare) Land Use / Cover Class Distribution Sl. Block Name Settlement Vegetation Cover Agricultural Land Waste Land Mining Area Waterbody TOTAL Area % Area % Area % Area % Area % Area % Area

1AMGAON‐KHONGHA 0.00 0.00 948.40 53.75 699.94 39.67 70.58 4.00 0.00 0.00 45.52 2.58 1764.44 2AREA BETWEEN GARE & KURUMKELA I BHALUMURA CMPDI 19.46 1.27 81.05 5.28 1346.65 87.68 73.37 4.78 0.00 0.00 15.39 1.00 1535.92 3AREA between GARE & KUDUMKELA‐II (BANAI BLOCK) 0.00 0.00 253.17 15.69 1294.52 80.23 56.03 3.47 0.00 0.00 9.79 0.61 1613.50 4BAISI BLOCK 3.17 0.53 47.97 8.01 514.58 85.95 32.96 5.51 0.00 0.00 0.00 0.00 598.68 5BAROD BLOCK 0.00 0.00 148.25 40.01 37.94 10.24 28.49 7.69 153.55 41.44 2.32 0.63 370.54 6BARPALI‐ KARMITIKRA (EAST) 0.00 0.00 125.87 32.21 209.32 53.57 11.36 2.91 0.00 0.00 44.19 11.31 390.74 7BARPALI‐ KARMITIKRA (WEST) 0.00 0.00 262.28 58.67 152.71 34.16 8.10 1.81 0.00 0.00 23.99 5.36 447.08 8BASIN FATEPUR SOUTH EXTN (MECL) 0.00 0.00 88.74 13.91 475.11 74.45 52.63 8.25 0.00 0.00 21.65 3.39 638.12 9 BATATI CENTRAL 0.00 0.00 1917.92 79.95 457.81 19.08 16.20 0.68 0.00 0.00 7.00 0.29 2398.93 10 BATATI KOLGA‐NE‐A 0.00 0.00 770.54 76.68 200.39 19.94 7.36 0.73 0.00 0.00 26.64 2.65 1004.92 11 BATATI KOLGA‐NE‐B 0.00 0.00 743.02 66.23 324.65 28.94 26.51 2.36 0.00 0.00 27.63 2.46 1121.81 12 BATATI KOLGA‐NE‐C 0.00 0.00 749.72 91.85 44.01 5.39 0.95 0.12 0.00 0.00 21.53 2.64 816.21 13 BATATI WEST 0.00 0.00 1706.49 89.74 192.40 10.12 0.54 0.03 0.00 0.00 2.25 0.12 1901.68 14 BATATI‐EAST 0.00 0.00 1553.06 67.14 633.76 27.40 126.45 5.47 0.00 0.00 0.00 0.00 2313.27 15 BIJARI BLOCK 3.29 1.48 16.22 7.29 179.51 80.64 6.75 3.03 16.83 7.56 0.00 0.00 222.59 16 CHAINPUR 0.00 0.00 817.83 41.42 1083.74 54.89 56.72 2.87 0.00 0.00 16.02 0.81 1974.31 17 CHHAL 3.78 0.53 247.50 34.79 143.78 20.21 127.60 17.94 166.96 23.47 21.74 3.06 711.34 18 CHIMTAPANI BLOCK 0.00 0.00 513.25 62.48 298.78 36.37 9.16 1.11 0.00 0.00 0.32 0.04 821.50 19 CHIMTAPANI EXTN. BLOCK 0.00 0.00 969.73 75.28 314.10 24.38 4.34 0.34 0.00 0.00 0.00 0.00 1288.17 20 CHIRA NORTH 5.67 0.23 1802.05 72.09 681.66 27.27 10.17 0.41 0.00 0.00 0.00 0.00 2499.55 21 CHIRA NORTH EAST‐A 0.00 0.00 747.56 83.45 128.14 14.30 7.11 0.79 0.00 0.00 13.01 1.45 895.82 22 CHIRA NORTH EAST‐B 0.00 0.00 831.42 90.24 86.11 9.35 1.49 0.16 0.00 0.00 2.34 0.25 921.35 23 CHIRA SOUTH CENTRAL 0.00 0.00 618.19 72.94 224.73 26.52 4.61 0.54 0.00 0.00 0.00 0.00 847.53 24 CHIRA SOUTH EAST 30.22 3.10 541.55 55.50 368.82 37.80 29.54 3.03 0.00 0.00 5.56 0.57 975.69 25 DIP SIDE OF BAISI BLOCK 0.00 0.00 38.99 4.71 711.83 86.03 61.38 7.42 0.00 0.00 15.23 1.84 827.44 26 DOLESARA 14.27 0.92 255.92 16.53 1160.03 74.93 112.73 7.28 0.00 0.00 5.18 0.33 1548.11 27 DUMIDIH 0.00 0.00 1549.49 75.50 476.53 23.22 3.96 0.19 0.00 0.00 22.43 1.09 2052.41 28 DURGAPUR SHAHPUR 57.78 4.58 21.87 1.73 1043.24 82.70 138.53 10.98 0.00 0.00 0.00 0.00 1261.42 29 EAST OF DHARMJAYGARH ‐ I 0.00 0.00 1771.70 81.36 358.67 16.47 47.14 2.16 0.00 0.00 0.00 0.00 2177.51 30 EAST OF DHARMJAYGARH ‐ II 0.00 0.00 2036.81 84.79 347.00 14.44 18.47 0.77 0.00 0.00 0.00 0.00 2402.28 31 EAST OF DHARMJAYGARH‐III (BAGDAHI WEST BLOCK MECL) 9.02 0.53 1274.33 74.68 377.78 22.14 45.18 2.65 0.00 0.00 0.00 0.00 1706.31 32 EAST OF DHARMJAYGARH‐IV (POTIYA‐MECL) 0.00 0.00 1190.41 89.29 136.53 10.24 6.32 0.47 0.00 0.00 0.00 0.00 1333.26 33 ELONG 9.59 0.36 1942.40 72.48 712.19 26.58 15.71 0.59 0.00 0.00 0.00 0.00 2679.89 34 FATEPUR 0.00 0.00 736.83 92.34 61.16 7.66 0.00 0.00 0.00 0.00 0.00 0.00 797.99 35 FATEPUR EAST ‐ CAPTIVE 0.00 0.00 563.00 35.10 996.93 62.16 42.46 2.65 0.00 0.00 1.37 0.09 1603.76 36 FATEPUR SOUTH 0.00 0.00 882.25 58.90 578.18 38.60 37.19 2.48 0.00 0.00 0.34 0.02 1497.96 37 GARE I 271.78 4.46 716.67 11.75 4527.18 74.24 490.64 8.05 0.00 0.00 91.96 1.51 6098.22 38 GARE II 80.53 3.21 309.94 12.36 2010.67 80.21 73.94 2.95 16.04 0.64 15.62 0.62 2506.73 39 GARE III 8.30 1.30 272.09 42.52 353.79 55.29 2.36 0.37 0.00 0.00 3.38 0.53 639.92 40 GARE IV/1 30.17 3.46 135.70 15.58 238.30 27.36 205.13 23.55 256.45 29.44 5.38 0.62 871.12 41 GARE IV/2 1.37 0.28 46.40 9.51 41.27 8.46 115.83 23.74 282.97 58.01 0.00 0.00 487.83 42 GARE IV/3 15.68 2.19 99.90 13.98 313.67 43.88 43.11 6.03 216.82 30.33 25.65 3.59 714.83 43 GARE IV/4 5.54 0.63 518.42 59.16 191.52 21.86 71.62 8.17 82.82 9.45 6.39 0.73 876.31 44 GARE IV/5 8.98 1.07 471.38 56.13 284.47 33.87 41.36 4.92 0.00 0.00 33.66 4.01 839.84 45 GARE IV/6 0.00 0.00 103.68 27.19 272.00 71.33 4.91 1.29 0.00 0.00 0.74 0.19 381.33 46 GARE IV/7 9.00 2.22 52.34 12.89 226.31 55.73 26.30 6.48 92.12 22.69 0.00 0.00 406.06 47 GARE IV/8 0.07 0.01 345.42 70.51 141.77 28.94 2.25 0.46 0.00 0.00 0.36 0.07 489.87 48 GIRARI 0.00 0.00 1454.87 69.15 637.02 30.28 11.93 0.57 0.00 0.00 0.00 0.00 2103.82 49 GITKUNWARI 0.00 0.00 1153.19 71.49 394.61 24.46 33.80 2.10 0.00 0.00 31.52 1.95 1613.12 50 JHARPALAM‐TANGARGHAT 2.99 0.28 182.21 17.30 794.25 75.43 73.51 6.98 0.00 0.00 0.00 0.00 1052.96 51 JILGA ‐ BARPALI (GSI) 5.18 0.20 807.89 30.99 1598.90 61.33 130.14 4.99 0.00 0.00 64.73 2.48 2606.83 52 KUSUMGHAT 0.00 0.00 74.39 15.57 378.83 79.28 24.62 5.15 0.00 0.00 0.00 0.00 477.83 53 NAWAGAON 0.00 0.00 1040.47 59.90 652.37 37.55 44.30 2.55 0.00 0.00 0.00 0.00 1737.14 54 NAYADIH 0.00 0.00 876.29 59.44 554.83 37.64 34.11 2.31 0.00 0.00 9.00 0.61 1474.22 55 ONGANA‐POTIA 0.00 0.00 1772.30 66.63 825.98 31.05 61.56 2.31 0.00 0.00 0.00 0.00 2659.84 56 PELMA BLOCJK 0.00 0.00 808.72 51.49 733.82 46.72 12.71 0.81 0.00 0.00 15.35 0.98 1570.59 57 PELMA EXTN. 0.00 0.00 485.30 63.30 275.85 35.98 1.40 0.18 0.00 0.00 4.12 0.54 766.67 58 PHUTAMURA BLOCK 0.00 0.00 843.21 91.82 71.75 7.81 3.35 0.37 0.00 0.00 0.00 0.00 918.32 59 PORDA BLOCK 5.04 0.60 154.67 18.52 619.61 74.19 46.58 5.58 0.00 0.00 9.25 1.11 835.13 60 RAI EAST BLOCK 8.62 0.60 538.38 37.58 815.76 56.94 29.52 2.06 17.80 1.24 22.64 1.58 1432.71 61 SARAPAL 4.84 0.42 345.67 29.97 754.47 65.42 30.60 2.65 2.61 0.23 15.14 1.31 1153.33 62 SARIYA BLOCK 42.19 5.92 104.56 14.68 505.15 70.93 60.28 8.46 0.00 0.00 0.00 0.00 712.17 63 SHERBAND BLOCK 3.78 0.42 436.93 48.67 418.12 46.58 38.88 4.33 0.00 0.00 0.00 0.00 897.71 64 SINGMOUJA JAMPALI 0.00 0.00 222.32 43.67 256.48 50.38 30.26 5.94 0.00 0.00 0.00 0.00 509.06 65 SITHRA‐KUREKELA (GSI) 28.64 0.26 6154.07 56.36 4500.79 41.22 213.73 1.96 0.00 0.00 21.80 0.20 10919.03 66 SOUTH OF BIJARI 24.08 0.65 383.27 10.31 3055.70 82.17 213.01 5.73 0.16 0.00 42.57 1.14 3718.78 67 SYANG BLOCK‐ (CAPTIVE) 0.00 0.00 843.17 84.30 149.69 14.97 0.00 0.00 0.00 0.00 7.31 0.73 1000.17 68 SYANG CENTRAL 0.00 0.00 801.18 80.34 195.93 19.65 0.11 0.01 0.00 0.00 0.00 0.00 997.22 69 SYANG EAST‐ A & B 13.25 0.60 1714.97 77.90 414.20 18.81 7.38 0.34 0.00 0.00 51.82 2.35 2201.63 70 SYANG NORTH 0.00 0.00 1295.19 84.46 237.47 15.48 0.90 0.06 0.00 0.00 0.00 0.00 1533.56 71 SYANG SOUTH 0.00 0.00 1643.99 84.39 299.93 15.40 4.10 0.21 0.00 0.00 0.00 0.00 1948.01 72 TARAIMAR BLOCK 34.58 3.16 17.84 1.63 933.32 85.35 103.66 9.48 0.00 0.00 4.07 0.37 1093.48 73 TERAM 6.89 1.06 51.62 7.92 547.58 84.04 45.50 6.98 0.00 0.00 0.00 0.00 651.58 74 TILAIPALLI BLOCK 0.00 0.00 294.30 18.38 1241.17 77.50 66.06 4.12 0.00 0.00 0.00 0.00 1601.53 75 UB (Rest Kusumghat) 18.70 1.64 635.92 55.70 461.97 40.47 25.07 2.20 0.00 0.00 0.00 0.00 1141.65 76 UB (Rest Singmouja jampali) 22.25 1.59 469.42 33.46 703.46 50.14 184.03 13.12 0.00 0.00 23.90 1.70 1403.06 77 UB‐1 0.00 0.00 283.28 48.87 280.71 48.43 15.68 2.71 0.00 0.00 0.00 0.00 579.67 78 WEST OF BASIN FATEPUR ‐ A 2.54 0.17 1296.16 85.90 208.73 13.83 1.46 0.10 0.00 0.00 0.00 0.00 1508.90 79 WEST OF BASIN FATEPUR‐ B 4.43 0.22 1376.87 68.80 598.77 29.92 1.08 0.05 0.00 0.00 20.09 1.00 2001.24 80 WEST OF BASIN FATEPUR‐C 0.00 0.00 919.04 70.76 367.45 28.29 3.06 0.24 0.00 0.00 9.18 0.71 1298.72 TOTAL 815.65 61319.28 50138.74 3927.85 1305.11 887.02 118393.64

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Fig – 7

Fig – 8

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Note: Value of classes shown in the Pie Chart in terms of total vegetation

Fig – 9

3.3.1 Vegetation cover Analysis

Vegetation cover is an association of trees and other vegetation type capable of producing timber and other forest produce. It is also defined as the percentage of soil which is covered by green vegetation. Leaf area index (LAI) is an alternative expression of the term vegetation cover which gives the area of leaves in m2 2 corresponding to an area of one m of ground. Primarily vegetation cover is classified into the following three sub-classes based on crown density as per modified FAO-1963 (Food & Agricultural Organisation of United Nations) norms: (a) dense forest (crown density more than 40%), (b) open/degraded forest (crown density between 10% to 40%), and (c) scrubs (crown density less than 10%). The plantation that has been carried out on wasteland along the roadside and on the overburden dumps / Backfilled areas is also included under vegetation cover as social forestry and plantation on over-burden dumps respectively. The percentage of vegetation cover shown in the analysis here include forest, scrubs and plantation.

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Analysis of the satellite data of the year 2014 indicates that vegetation cover in the Mand Raigarh Coalfield is 2188.50 km2 which is 63.51% of the total Mand Raigarh Coalfield area. Out of which, dense forest covers an area of 726.37 km2 (21.08%), open forest covers area of 1155.48 km2 (33.53%); Scrubs has covered 305.62 km2 (54.61%), Plantation under social forestry occupies 0.87 km2 (0.03%) and Plantation on OB dumps has an area of 0.16 km2 in 2014.

Comparing the results of 2014 with respect to analysis done in 2011, it reflects that overall vegetation cover in the Mand Raigarh Coalfield has marginally gone down by 0.08% (Refer Table 3.2). This change might be due to biotic interference, industrial activities in the area and degradation of natural vegetation over this period. However, it is important to note here that plantation in mining area is showing positive trend in comparison to the year 2011. (Refer Table 3.2).

3.3.2 Mining

Mining area includes the area of existing quarry, old quarries filled with water, advance quarry sites, Coal Stock/Dumps, Coal Faces, Barren Backfilled areas, Barren over-burden dumps and allied activities.

Mining area in Mand Raigarh Coalfield covers 14.23 km2 (0.41%) in the year 2014 which is almost double to what it was in the year 2011. The mining area in Mand Raigarh Coalfields comprising of Coal quarries which constitutes an area of about 10.04 km2 (0.29%), Advanced quarry site constitutes 0.11 km2, Quarry filled with water constitutes 0.39 km2 (0.01%), Coal face constitutes 0.07 km2, Coal dumps/stocks constitute 0.20 km2 (0.01%) and Barren over burden dumps constitutes 2.82 km2 (0.08%).

The analysis indicates that there is an overall increasing trend in mining activity with increase of about 6.94 km2 mining area as compared to year 2011.

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3.3.3 Agriculture

Land primarily used for farming and production of food, fibre and other commercial and horticultural crops falls under this category. It includes crop land and fallow land. Crop lands are those agricultural lands where standing crop occurs on the date of satellite imagery or land is used for agricultural purposes during any season of the year. Crops may be either kharif or Rabi. Fallow lands are also agricultural land which is taken up for cultivation but temporarily allowed to rest, un-cropped for one or more season. In this study, both crop land and fallow land has been combined in single class namely agricultural land.

Agriculture in Mand Raigarh Coalfield covers an area of 1087.11 km2 (31.55%), out of which Crop Land is 75.42 km2 (2.19%), and Fallow Land is 1011.69 km2 (29.36%) (Refer Table 3.2).

The analysis of satellite data of the year 2014 indicates that there is a slight decrease of about 0.23% in agriculture land use as compared to year 2011 in Mand Raigarh Coalfield area which might be due to increase in mining area and associated industrial activities.

3.3.4 Wasteland

Wasteland is a degraded and under-utilised class of land that has deteriorated on account of natural causes or due to lack of appropriate water and soil management. Wasteland can result from inherent/imposed constraints such as location, environment, chemical and physical properties of the soil or financial or other management constraints (NWDB, 1987).

Analysis of data reveals that waste land in the Mand Raigarh Coalfield occupies 96.41 km2 (2.80%) out of which Waste upland with or without scrubs occupies 88.54 km2 (2.58%), Fly Ash Ponds constitute 1.87 km2 (0.05%) and Sand bodies constituted 6.00 km2 (0.17%) in 2014.

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In comparison to year 2011, the wasteland class in Mand Raigarh Coalfield has reduced slightly by 0.03% which might be due to increase in mining area as well as settlement area under Mand Raigarh Coalfield.

3.3.5 Settlement/ Built-up land

All the man-made constructions covering the land surface are included under this category. Built-up land has been divided in to rural, urban and industrial classes based on availability of infrastructure facilities. In the present study, industrial settlement indicates only industrial complexes excluding residential facilities.

Settlements in Mand Raigarh Coalfield covers an area of 22.81 km2 (0.66%) out of the total coalfield area of 3445.77 km2. Analysis of the satellite data of the year 2014 indicated that settlement coming under the coalfield boundary of Mand Raigarh was distributed between Urban 1.01 km2 (0.02%), Rural 10.15 km2 ; (0.29%) and Industrial 11.65 km2 (0.34%) (Refer Table 3.2).

3.3.6 Surface Water bodies

Analysis of data reveals that water bodies in Mand Raigarh Coalfield occupy area of 36.71 km2 (1.07%).

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Chapter 4

Conclusion & Recommendations

4.1 Conclusion

In the present study, land use/vegetation cover map of Mand Raigarh coalfield is prepared based on LANDSAT 8 OLI Image of April 2014 in order to assess the impact of mining / industrial activities on vegetation cover and land use pattern in the year 2014 for effective natural resource management and its planning. The Land use/vegetation cover analysis will help to analyse and monitor the impact of mining and other industrial activities on land use pattern and its dynamics.

Study reveals that Mand Raigarh Coalfields covers an area of about 3445.77 km2. Vegetation cover constitutes 2188.50 km2 (63.51%) which indicates that there is a slight decrease of 0.08% in vegetation cover as compared to the year 2011. This decrease in vegetation cover in Mand Raigarh Coalfield may be due to increase in industrial activities in the region. Analysis of satellite data indicates that area under mining activities has increased from 7.29 km2 (0.21%) in the year 2011 to 14.23 km2 in 2014 which is 0.41% of the total coalfield area whereas agriculture and wasteland cover area of 1087.11 km2 (31.55%) and 96.41 km2 (2.80%) respectively under Mand Raigarh Coalfield. Settlements coming under the coalfield boundary cover area of 22.81 km2 which is 0.66% of the total coalfield area. Water bodies cover an area of 36.71 km2 (1.07%)

The detail data analysis is given under Table-3.2 & 3.3.

4.2 Recommendations

Keeping in view the sustainable development together with coal mining in the area, it is recommended that;

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a) Similar study should to be carried out regularly at interval of 3 years to monitor the land use dynamics in the coalfield for assessing the impact of coal mining and to take the remedial measures required, if any.

b) Efforts for afforestation should be given thrust in the coalfield on wasteland and mined out area to maintain the ecological balance in the region for sustainable development.

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Central Mine Planning & Design Institute Ltd. (A Subsidiary of Coal India Ltd.) Gondwana Place, Kanke Road, Ranchi 834031, Phone : (+91) 651 2230001, 2230002, 2230483, FAX (+91) 651 2231447, 2231851 Wesite : www.cmpdi.co.in, Email : [email protected]