mrcf report 2014

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

CMPDI

A Miniratna Company

MAND RAIGARH COALFIELD

Dharamjaygarh

Ghargoda

Tamnar

RAIGARH

FOR CLIENT USE ONLY

For client use only

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

Submitted to South Eastern Coalfields 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

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

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

(10) Executing Unit

Remote Sensing Cell Geomatics Division Central Mine Planning & Design Institute Ltd. Gondwana Place, Kanke Road, Ranchi

(11) User Agency South Eastern Coalfields 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 India 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

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Job No 561410027 Chapter -1 Page 1

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

Coal India 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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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 Raigarh district of Chhattisgarh 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 Kharsia (60 km)

Railway Stations on the Howrah-Nagpur section of South Eastern Railway. The

coalfield boundary is connected with Ib Valley Coalfield in the east and Korba

Coalfield in the west. The nearest airports for Raigarh are Bhilai, Raipur 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

(SoI) topo-sheet Nos. 64 j/14, 64 j/15, 64 j/16, 64 N/2, 64 N/3,64 N/4, 64 O/1, 64 N/6, 64 N/7, 64 N/8, 64 O/5,64 N/11, 64 N/12 and 64 0/9. 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 Mand River 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 Mahanadi 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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Job No 561410027 Chapter-2 Page 5

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 band

0.30 to 0.40 µm Transmitted through atmosphere. Detectable with 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 8.00

to to

5.0014.00

µm µm

Principal atmospheric windows in the thermal 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:

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.

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 workPlatform Sensor Spectral Bands in µm Radiometric

ResolutionSpatial

Resolution Temporal Resolution Country

LANDSAT 8

OLI

B2 B3 B4 B5

0.45 0.53 0.64 0.85

- - - -

0.51 0.59 0.67 0.88

Blue Green Red NIR

16-bit

(55,000-grey levels)

30 m 30 m 30 m 30 m

16 days

USA

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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Data Source Secondary Data Basic Data

LANDSAT 8 (OLI Image)

Surface Plan (Scale 1:50,000)

Pre-processing, geometric correction, rectification & georefrencing

Creation of Vector Database (Drainage, Road network Railway network)

Image Enhancement

Training set Identification

Signature Generation

Pre-Field Classification

Validation through Ground Truthing

Final Land Use/ Vegetation Cover Map

Integration of Thematic Information using GIS

Report Preparation

Training Set Refinement

Pass

Fail

Geocoded FCC Generation

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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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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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

Sl.# Vegetation\Land use classes as observed

in the field Built-up

land Vegetation

Cover Agriculture Wasteland Mining Area

Water Bodies

Total no. of observation points (Z)

% of observation

points

% of classification

accuracy % of omission

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 observation points (X) 14 18 20 26 8 5 90 - 88.59 -

% 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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incorporating information obtained from the satellite data and other different

sources.

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 Vegetation Cover

1.1 Dense Forest 1.2 Open Forest 1.3 Scrub 1.4 Plantation under Social Forestry 1.5 Plantation on OB Dumps 1.6 Plantation over Backfill

2 Mining Area

2.1 Coal Quarry 2.2 Advance Quarry Site 2.3 Barren OB Dump 2.4 Barren Backfilled Area 2.5 Coal Dump 2.6 Water Filled Quarry

3 Agricultural Land 3.1 Crop Land 3.2 Fallow Land

4 Wasteland 4.1 Waste upland with/without scrubs 4.2 Fly Ash Pond 4.3 Barren Rocky Land

5 Settlements 5.1 Urban 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

LAND USE / COVER CLASSES AREA STATISTICS Changes in Land Use / Cover Remarks 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 natural degradation. Open Forest 1159.39 33.65 1155.48 33.53 (-) 03.91 0.12

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

SECL in mining areas. Plantation on OB Dump 0.04 0.00 0.16 0.00 (+) 00.12 0.00 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

Expansion of existing mining projects and increase in production to meet the high coal demand.

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 Barren OB Dump 1.35 0.04 2.82 0.08 (+) 01.47 0.04 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

industrial & mining activities. Fallow Land 1019.38 29.58 1011.69 29.36 (-) 07.69 0.22 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

Decrease in wasteland area is due to increase in industrial & mining activities.

Fly-Ash Pond 1.34 0.04 1.87 0.05 (+) 00.53 0.02 Sand Body 6.00 0.17 6.00 0.17 00.00 0.00

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

Increase in settlement area is due to increase in industrial activities as well as other socio-economic reasons.

Rural 8.03 0.23 10.15 0.29 (+) 02.12 0.06 Industrial 11.56 0.34 11.65 0.34 (+) 00.09 0.00

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.

CMPDI

RSC-561410027 [ Chapter–3 Page 24]

Table 3.3

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

(Area in Hectare)

Area % Area % Area % Area % Area % Area % Area

1 AMGAON‐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 1764.442 AREA 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 1535.923 AREA 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 1613.504 BAISI BLOCK 3.17 0.53 47.97 8.01 514.58 85.95 32.96 5.51 0.00 0.00 0.00 0.00 598.685 BAROD BLOCK 0.00 0.00 148.25 40.01 37.94 10.24 28.49 7.69 153.55 41.44 2.32 0.63 370.546 BARPALI‐ KARMITIKRA (EAST) 0.00 0.00 125.87 32.21 209.32 53.57 11.36 2.91 0.00 0.00 44.19 11.31 390.747 BARPALI‐ KARMITIKRA (WEST) 0.00 0.00 262.28 58.67 152.71 34.16 8.10 1.81 0.00 0.00 23.99 5.36 447.088 BASIN 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 3.39 638.129 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 2398.9310 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 1004.9211 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 1121.8112 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 2.64 816.2113 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 1901.6814 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 2313.2715 BIJARI BLOCK 3.29 1.48 16.22 7.29 179.51 80.64 6.75 3.03 16.83 7.56 0.00 0.00 222.5916 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 1974.3117 CHHAL 3.78 0.53 247.50 34.79 143.78 20.21 127.60 17.94 166.96 23.47 21.74 3.06 711.3418 CHIMTAPANI BLOCK 0.00 0.00 513.25 62.48 298.78 36.37 9.16 1.11 0.00 0.00 0.32 0.04 821.5019 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 1288.1720 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 2499.5521 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 1.45 895.8222 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 0.25 921.3523 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 0.00 847.5324 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 0.57 975.6925 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 1.84 827.4426 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 1548.1127 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 2052.4128 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 1261.4229 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 2177.5130 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 2402.2831 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 1706.3132 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 1333.2633 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 2679.8934 FATEPUR 0.00 0.00 736.83 92.34 61.16 7.66 0.00 0.00 0.00 0.00 0.00 0.00 797.9935 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 1603.7636 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 1497.9637 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 6098.2238 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 2506.7339 GARE III 8.30 1.30 272.09 42.52 353.79 55.29 2.36 0.37 0.00 0.00 3.38 0.53 639.9240 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 0.62 871.1241 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 0.00 487.8342 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 3.59 714.8343 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 0.73 876.3144 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 4.01 839.8445 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 0.19 381.3346 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 0.00 406.0647 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 0.07 489.8748 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 2103.8249 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 1613.1250 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 1052.9651 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 2606.8352 KUSUMGHAT 0.00 0.00 74.39 15.57 378.83 79.28 24.62 5.15 0.00 0.00 0.00 0.00 477.8353 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 1737.1454 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 1474.2255 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 2659.8456 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 1570.5957 PELMA EXTN. 0.00 0.00 485.30 63.30 275.85 35.98 1.40 0.18 0.00 0.00 4.12 0.54 766.6758 PHUTAMURA BLOCK 0.00 0.00 843.21 91.82 71.75 7.81 3.35 0.37 0.00 0.00 0.00 0.00 918.3259 PORDA BLOCK 5.04 0.60 154.67 18.52 619.61 74.19 46.58 5.58 0.00 0.00 9.25 1.11 835.1360 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 1432.7161 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 1153.3362 SARIYA BLOCK 42.19 5.92 104.56 14.68 505.15 70.93 60.28 8.46 0.00 0.00 0.00 0.00 712.1763 SHERBAND BLOCK 3.78 0.42 436.93 48.67 418.12 46.58 38.88 4.33 0.00 0.00 0.00 0.00 897.7164 SINGMOUJA JAMPALI 0.00 0.00 222.32 43.67 256.48 50.38 30.26 5.94 0.00 0.00 0.00 0.00 509.0665 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 10919.0366 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 3718.7867 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 1000.1768 SYANG CENTRAL 0.00 0.00 801.18 80.34 195.93 19.65 0.11 0.01 0.00 0.00 0.00 0.00 997.2269 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 2201.6370 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 1533.5671 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 1948.0172 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 1093.4873 TERAM 6.89 1.06 51.62 7.92 547.58 84.04 45.50 6.98 0.00 0.00 0.00 0.00 651.5874 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 1601.5375 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 1141.6576 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 1403.0677 UB‐1 0.00 0.00 283.28 48.87 280.71 48.43 15.68 2.71 0.00 0.00 0.00 0.00 579.6778 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 1508.9079 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 2001.2480 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

Sl. Block NameLand Use / Cover Class Distribution

Waterbody TOTALMining AreaWaste LandSettlement Vegetation Cover Agricultural Land

CMPDI


Fig – 7

Fig – 8

CMPDI


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

corresponding to an area of one m2 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.

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

CMPDI


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.

CMPDI


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.

CMPDI


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%).

CMPDI


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;

CMPDI


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.

Central Mine Planning & Design Institute Ltd.

(A Subsidiary of Coal India Ltd.) Gondwana Place, Kanke Road, Ranchi 834031, Jharkhand

Phone : (+91) 651 2230001, 2230002, 2230483, FAX (+91) 651 2231447, 2231851 Wesite : www.cmpdi.co.in, Email : [email protected]

mrcf report 2014

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