Report on Land Use /Vegetation Cover Mapping of Singrauli Coalfield, NCL based on Remote Sensing Technique Year

Report on Land Use /Vegetation Cover Mapping of Singrauli Coalfield, NCL based on Remote Sensing Technique Year-2012-13 Submitted to COAL INDIA LIMITED September 2012

Document Control Sheet List of Figures List of Tables List of Plates Contents Restricted Page No. ii iii iii iii 1.0 Introduction 1-4 1.1 Project Reference 1.2 Project Background 1.3 Objectives 1.4 Location of the area and Accessibility 1.5 Physiography 2.0 Remote Sensing Concept & Methodology 5-17 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 2.6.7 Assessment of temporal changes 3.0 Land Use/ Vegetation Cover Monitoring 18-31 3.1 Introduction 3.2 Vegetation cover/ Land use Classification 3.3 Data Analysis & Change Detection 3.3.1 Vegetation Cover 3.3.2 Mining Area 3.3.3 Agricultural Land 3.3.4 Wasteland 3.3.5 Settlements 3.3.6 Water Bodies 3.3.7 Changes in Land Use/Vegetation Cover classes 4.0 Conclusion and Recommendations 33-34 4.1 Conclusion 4.2 Recommendations

Document Control Sheet (1) Job No. RSC/564607135 (2) Publication Date September 2012 (3) Number of Pages 34 (4) Number of Figures 12 (5) Number of Tables 9 (6) Number of Plates 2 (7) Title of Report Land Use/Vegetation Cover Mapping of Singrauli Coalfield, NCL. based on Remote Sensing Technique, Year 2012-13 (8) Aim of the Report To prepare Land Use/vegetation cover map on 1:50,000 scale for using Satellite Data of the year 2011 and assess the changes therein. (9) Executing Unit Remote Sensing Cell, Geomatics Division, Central Mine Planning & Design Institute Limited, Gondwana Place, Kanke Road, Ranchi 834031 (10) User Agency Coal India Ltd (CIL), Kolkata and Northern Coalfields Ltd (NCL), Singrauli (11) Authors Rajneesh Kumar, Sr Manager (RS) (12) Security Restriction Restricted Circulation, for client use only (13) No. of Copies CIL-3, NCL-3 (14) Distribution Statement Official ii

List of Figures 1.1 Map of India showing Location of Singrauli Coalfield. 2.1 Remote Sensing Radiation System 2.2 Electromagnetic Spectrum 2.3 Expanded diagram of the visible and infrared regions (upper) and microwave regions (lower) showing atmospheric windows. 2.4 Methodology for vegetation Cover mapping. 2.5 Geoid-Ellipsoid -Projection Relationship 3.1 Changes in vegetation cover Classes in Singrauli Coalfields during 2008 & 2011 3.2 Changes in Mining Areas in Singrauli Coalfields during 2008 & 2011 3.3 Changes in Agricultural Land in Singrauli Coalfields during 2008 & 2011 3.4 Changes in Waste Land in Singrauli Coalfields during 2008 & 2011 3.5 Changes in Built-up Land/Settlements in Singrauli Coalfields during 2008 & 2011 3.6 Changes in Waterbodies in Singrauli Coalfields during 2008 & 2011 3.7 Changes in Land Use/Vegetation Cover classes in Singrauli Coalfield during 2008 & 2011 List of Tables 2.1 Electromagnetic spectral regions 2.2 Characteristics of the satellite/sensor used in the present project work 3.1 Vegetation cover/land use classes identified in Singrauli Coalfield 3.2 Distribution of Vegetation Cover in Singrauli Coalfield During the year 2008 and 2011 3.3 Changes in Vegetation Cover in Singrauli Coalfield during the Year 2008 & 2011. 3.4 Changes in Mining Area in Singrauli Coalfield during the Year 2008 & 2011. 3.5 Changes in Agricultural Area in Singrauli Coalfield during the Year 2008 & 2011. 3.6 Changes in Wasteland in Singrauli Coalfield during the Year 2008 & 2011 3.7 Changes in Settlements in Singrauli Coalfield during the Year 2008 & 2011 iii

List of Plates List of maps/plates prepared on a scale of 1:50,000 are given below: 1. Plate No. HQREMA101101: FCC of Singrauli Coalfield based on IRS Resourcesat 2, LISS IV data of May 2011 2. Plate No. HQREMA101103- Land Use/Vegetation Cover Map of Singrauli Coalfield, NCL based on IRS Resourcesat 2, LISS IV data of May 2011 iv

Chapter 1 Introduction 1.1 Project Reference HOD (WBP/Env/FP),Coal India Ltd issued a work order to CMPDI vide letter No CIL/WBP/Env/2009/2428 dtd 29 th December 2009 for monitoring of the status of land reclamation in all major coalfields under CIL at an interval of every 3 years. Accordingly Land Use/Vegetation Cover Mapping of Moher sub basin, Singrauli coalfield of Northern Coalfields Ltd. Was carried out in the year 2008. Subsequently, the present study was carried out at interval of three years based on satellite data of the year 2011 to assess the regional impact of coal mining on land use/vegetation cover in Singrauli Coalfield.. Project Background Northern coalfield Ltd. is an ISO 14001:1996 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 assess the changes in vegetation cover in Singrauli coalfield in a span of last three years. While assessing the Land Use/ vegetation Cover in the coalfield, changes in different categories Vegetation Cover are also analysed to formulate the remedial measures, if any; required to be taken to maintain the ecological balance in the region. Job No 564607135 Chapter -1 Page 1

1.3 Objectives The objectives of the present study are: - to prepare land use/vegetation cover map of Singrauli Coalfields on 1:50,000 scale based on Resourcesat/ LISS IV satellite data - to assess the impact of coal mining on vegetation cover and analyse the changes in span of last three years. 1.4 Location of the Area & Accessibility The Singruali coalfield (Moher sub-basin) covers an area of about 300 sq km. it is bounded by Lat 24 0 7 and 24 0 12 N and Long 82 0 30 and 82 0 52 E. The major part of coalfield (about 220 sq km) lies in Singrauli district of Madhya Pradesh and a small part (about 80 km) lies in Sonbhadra district of Uttar Pradesh. Coalfield is connected by motorable road with Varanasi (220 km), Mirzapur (215 km), Rewa (206 km), Sidhi (100 km) and Satna (305 km). The nearest towns are Waidhan (25 km) and Renukoot (50 km). The nearest railway station is Singrauli, located on Chopan- Katni branch line which passes parallel to northern boundary of the coalfield. The nearest railway station for going to Delhi and Ranchi is Renukoot, which is on Garhwa-Chopan branch line. Nearest airstrip is at Miyorpur (80 km) approachable by road from Singrauli. There is also a helipad at Shaktinagar (20 km). 1.5 Physiography Singrauli coalfield presents a typical erosional landscape with plain and plateau topography. The general elevation above mean sea level varies from 280m on the plain to over 500 m on plateau in steps alternating with three escarpment faces which roughly correspond to the outcrop of the existing coal seams. The plain country in the south and east has a gentle slope towards Govind Ballabh Pant (GBP) Sagar reservoir. The important streams within this area are Baliya Job No 564607135 Chapter -1 Page 2

nala, Tippa Jahria nala, Bijul nala, Kachni and Mayar river. Bijul nala drains the northern part of the plateau and joins Son river at Ghatihata near Chopan, while southern part of the plateau is drained by number of streams which directly discharge into Govind Ballabh Pant Sagar. Soil cover is very thin on the plateau. Thick alluvial soil occurred in the valley which is loamy to sandy in nature. A map of India showing the location of Singrauli Coalfield is given in Fig1.1.. Job No 564607135 Chapter -1 Page 3

Fig 1.1 : Map of India Showing the Location of Singrauli Coalfields Job No 564607135 Chapter -1 Page 4

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 electromagnetic 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 electromagnetic 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. Job No 564607135 Chapter-2 Page 5

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). Job No 564607135 Chapter-2 Page 6

Job No 564607135 Chapter-2 Page 7 CMPDI

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 employed in remote sensing. atmosphere. Not 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 8.00 to to 5.00 µm 14.00 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 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. Job No 564607135 Chapter-2 Page 8

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 a 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. Job No 564607135 Chapter-2 Page 9

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, as follows, was used as primary data source for the study. IRS Resourcesat 2/ LISS IV; Band 2,3,4 Path 102, Row 055; Date of pass 23.05.2011. 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 IRS- Resourcesat 2 satellite/sensor that is used in the present study. Job No 564607135 Chapter-2 Page 10

Table 2.2 Characteristics of the satellite/sensor used in the present project work Platform Sensor Spectral Bands in µm Radiometric Resolution Spatial Resolution Temporal Resolution Country IRS- Resourc esat 2 LISS-IV B2 B3 B4 0.52-0.59 0.62-0.68 0.77-0.86 Green Red NIR 10-bit 5.8 m 5.8 m 5.8 m 5 days India NIR: Near Infra-Red 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. Job No 564607135 Chapter-2 Page 11

Basic Data Data Source Secondary Data IRS Resourcesat 2 (LISS IV) Surface Plan (Scale 1:50,000) Pre-processing, geometric correction, rectification & georefrencing Creation of Vector Database (Drainage, Road network Railway network) Image Enhancement Geocoded FCC Generation Training set Identification Signature Generation Pre-Field Classification Training Set Refinement Validation through Ground Truthing Fail Report Preparation Pass Final Land Use/ Vegetation Cover Map Integration of Thematic Information using GIS Fig 2.4 : Methodology for Land Use / Vegetation Cover Mapping Job No 564607135 Chapter-2 Page 12

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. Job No 564607135 Chapter-2 Page 13

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 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 Job No 564607135 Chapter-2 Page 14

In the present study, Polyconic projection along with Modified Everest 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 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 ERDAS Imagine 9.3 s/w. The enhanced and geocoded FCC (False colour composite) image of Singrauli Coalfield is shown in Plate No. 1 for the year 2011. 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 imageelements 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 Job No 564607135 Chapter-2 Page 15

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 [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 ERDAS Imagine 9.3 s/w. The classified image for the year 2011 for Singrauli Coalfield is shown in Drawing No. HQREMA11102. 2.6.5 Creation/overlay of vector database in GIS Plan showing leasehold areas of mining projects supplied by NCL 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. Job No 564607135 Chapter-2 Page 16

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 2011 was found to be 88.59%. 2.6.7 Assessment of temporal changes Change detection in Land Use/vegetation cover was carried out through GIS by analysing the Land Use/ vegetation Cover map of the year 2008 and 2011. Final Land Use/vegetation cover maps (on 1:50,000 scale) were printed using HP Design jet 4500 Colour Plotter. Job No 564607135 Chapter-2 Page 17

Chapter 3 Land Use/ Vegetation Cover Monitoring 3.1 Introduction The need for information on land use/ vegetation cover has gained importance due to the all-round concern on environmental impact of mining. The information on land use/cover inventory that includes 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 vegetation 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. Since production from the mines are increasing and hence the mining areas also keep on increasing, therefore it is has become very important to reclaim the areas where the mining operations have been completed to reclaim the surface of the earth to its original form along with the vegetation cover. Realising the need of monitoring of land use/ vegetation cover and land reclamation in Singrauli coalfield; CIL requested the services of CMPDI to prepare land use/vegetation cover map of all coalfields at an interval of 3 years, including Singrauli coalfield for assessing the impact of coal mining on land use pattern and vegetation cover using remote sensing data. The first report in this series was prepared in year 2008 to analyse the changes in land use/ vegetation cover over the 3 year period. The data which emerged out of the 2008 analysis was compared for temporal changes with the results of the study done earlier in 2005. Currently the findings of analysis of the data of year 2011 is now compared for temporal changes with the results of the analysis of 2008, for the changes in land use / vegetation cover during the 3 year interval. This will help in formulating the mitigative measures, if any required for environmental protection in the coal mining area. Job No 564607135 Chapter -3 Page 18

3.2 Land Use / Vegetation Cover Classification The array of information available on land use/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 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 Agency, 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 2011. Following land use/cover classes are identified in the Singrauli coalfield region (Table 3.1). Job No 564607135 Chapter -3 Page 19

Table 3.1 Land use / Vegetation Cover classes identified in Singrauli Coalfield - LEVEL I 1 Vegetation Cover LEVEL-II 3.1 Dense Forest 3.2 Open Forest 3.3 Plantation under Social Forestry 3.4 Plantation on OB Dumps 2 Scrubs 3.1 Scrubs 2 Mining Area 3 Agricultural Land 4 Wasteland 5 Settlements 5.1 Active Mining Area 5.2 Advance Quarry Site 5.3 Barren OB Dump 5.4 Barren Backfilled Area 5.5 Coal Dump 5.6 Water Filled Quarry 2.1 Crop Land 2.2 Fallow Land 4.1 Waste upland with/without scrubs 4.2 Fly Ash Pond 1.1 Urban 1.2 Rural 1.3 Industrial 6 Water Bodies 6.1 River/Streams /Reservoir 3.3Data Analysis & Change Detection Satellite data of the year 2011 was processed using ERDAS Imagine v.9.3 image processing s/w in order to interpret the various land use and vegetation cover classes present in the Singrauli coalfield. The analysis was carried out for entire coalfield covering about 284 sq. km. The area of each class was calculated and analysed using ERDAS Digital Image Processing s/w and ArcGIS s/w. Analysis of land use / vegetation cover pattern in Singrauli Coalfield and changes therein for the year 2008 and 2011 was carried out, details are and shown in table 3.2. Job No 564607135 Chapter -3 Page 20

TABLE 3.2 CMPDI STATUS OF VEGETATION COVER & LANDUSE PATTERN IN SINGRAULI COALFIELD DURING YEAR 2008 & 2011 Area in Sq km Year 2008 Year 2012 Change Land Use Classes % of % of Area % of Reasons Area Area total total total VEGETATION COVER Mine advance in dip Dense forest 23.48 8.28 21.45 7.57-2.03-0.71 side Open Forest 47.78 16.85 45.36 16.00-2.42-0.85 -do- Total Forest 71.26 25.13 66.81 23.57-4.45-1.56 Due to efforts of NCL Social Forestry 41.91 14.78 42.90 15.13 0.99 0.35 Plantation on OB Dump 28.94 10.21 29.84 10.53 0.90 0,32 Total Plantation 70.85 25.00 72.74 25.66 1.89 0.66 Total Vegetation Cover 142.11 50.13 139.55 49.23 2.56 0.90 Due to NCL efforts towards land reclamation Due to rapid pace of coal mining Scrubs 36.83 12.99 29.15 10.28 7.68 2.71 MINING AREA Coal Quarry 13.38 4.72 10.62 3.75-2.76 Due to backfilling -0.97 Barren Backfilled Area 12.71 4.48 21.27 7.50 8.56 3.02 Coal Dump 0.32 0.11 0.42 0.15 0.10 0.04 Water Filled Quarry 1.24 0.44 1.60 0.56 0.36 0.12 Advance Quarry Site 6.17 2.18 5.54 1.95-0.63-0.23 Barren OB Dump 6.13 2.16 9.49 3.35 3.36 1.19 Sub Total 39.95 14.09 48.94 17.26 8.99 3.17 AGRICULTURAL LAND Crop Land 8.86 3.13 8.58 3.03-0.28-0.10 Fallow Land 17.38 6.13 17.50 6.17 0.12 0.04 Sub Total 26.24 9.26 26.08 9.20 0.16 0.06 WASTELAND Wasteland 21.36 7.53 20.40 7.20-0.96-0.33 Fly-Ash Pond 0.29 0.10 0.06 0.02-0.23-0.08 Sub Total 21.65 7.64 20.46 7.22 1.19 0.42 SETTLEMENTS Urban 12.24 4.32 14.87 5.25 2.63 0.93 Rural 0.84 0.30 1.09 0.38 0.25 0.08 Industrial 2.38 0.84 2.32 0.82-0.06-0.02 Sub Total 15.46 5.45 18.28 6.45 2.82 1.00 WATER BODIES 1.24 0.44 1.91 0.67 0.67 0.23 TOTAL 283.48 100.00 283.48 100.00 0.00 0.00 Efforts of NCL towards Land reclamation Due to high technical reclamation Due to industrialization Due to Mining & Industrialization Due to industrialization Job No 564607135 Chapter -3 Page 21

3.3.1 Vegetation Cover Vegetation cover in the coalfield area has been found to be predominantly of five classes. Dense Forest Open Forest Plantation on Over Burden(OB) Dumps / Backfilled area, and Social Forestry Scrubs have been put into a separate class. There has been significant variation in the land use under the vegetation classes within the area as shown below in Table 3.3. TABLE 3.3 Changes in Vegetation Cover in Singrauli Coalfield during the year 2008 & 2011 Year 2008 Year 2011 Change Analysis Vegetation Cover Area (sq km) % of total Dense forest Forest having crown density of above 40% comes in this class. Dense forest over the area has decreased, basically due advance of the mines on their dip sides where there were forest areas such as Moher reserve forest etc. A total decrease in dense forest is estimated to be 2.03 sq km, i.e. 0.71% of the coalfield area. Open Forest Forest having crown density between 10% to 40% comes under this class. Open forest cover over the area has also reduced in the coalfield. There is some Area (sq km) % of total Area (sq km) % of total Dense forest 23.48 8.28 21.45 7.57-2.03-0.71 Open Forest 47.78 16.85 45.36 16.00-2.42-0.85 Total Forest 71.26 25.13 66.81 23.57-4.45-1.56 Social Forestry 41.91 14.78 42.90 15.13 0.99 0.35 Plantation on OB Dump 28.94 10.21 29.84 10.53 0.90 0.32 Total Plantation 70.85 25.00 69.74 24.60-1.11-0.40 Scrubs 36.83 12.99 29.15 10.28 7.68 2.71 Job No 564607135 Chapter-3 Page 22

area line east to Block B, where the open forest has reduced due to mining activities. Some of the reduction is also due to deforestation outside the mining areas. The total decrease observed in open forest is 2.42 sq km, i.e. 0.85% of the coalfield area.. Social Forestry Plantation which has been carried out on wastelands, along the roadsides and colonies on green belt come under this category. Analysis of data reveals that there is an increase of 0.99 sq km, which is 0.35% of the coalfield area. Plantation over OB Dump and backfilled area Analysis of the data reveals that NCL has carried out massive plantation on OB dumps as well as backfilled areas during the period for maintaining the ecological balance of the area. There is an increase of 0.90 sq km, ie 0.32% of the coalfield area in respect to the year 2008. Scrubs Scrubs are vegetation with crown density less than 10%.Scrubs in the coalfield has also decreased. This is because of advance of the mines in dip side and some scrubs being converted into settlements.. There has been decrease of 7.68 sq km, ie 2.71% of land with scrubs in the coalfield area. It is significant to note that the vegetation cover in Singrauli Coalfield has decreased by 2.56 sq km which is about 0.90% % of the coalfield area. This decrease is mainly due to advance of mine in Amlohri and Nigahi. Also significant depletion of forest is seen at Krishnashila where the mining activity is progressing rapidly. The variation in the vegetation classes which took place during year 2008 and 2011 within the area are shown in bar diagram in Figure 3.1. Figures represent area in sq km Job No 564607135 Chapter-3 Page 23

FIGURE - 3.1 CHANGES IN VEGETATION COVER IN SINGRAULI COALFIELD DURING 2008 & 2011 AREA (Sq Km) 60 50 40 30 20 10 0 47.78 45.36 41.91 42.9 36.83 28.94 29.15 29.84 23.48 21.45 2008 2011 YEAR Dense Forest Open Forest Scrubs Social Forestry Plantation on OB Dump/backfilled area 3.3.2 Mining Area The mining area was primarily been categorized as follows: Coal Quarry Advance Quarry Site, and Barren OB Dump Barren Backfilled Area Coal Dumps Water filled Quarry The change in land use pattern in the mining area is shown in Table-3.4. Analysis of the data reveals that the mining area which was 39.95 sq km in the year 2008 has increased to 48.94 sq km in the year 2011. The increase of 8.99 sq km, i.e. 3.17% of the coalfield area is primarily due to the increase in coal production. Job No 564607135 Chapter-3 Page 24

TABLE 3.4 Changes in Mining Area in Singrauli Coalfield during the year 2008 & 2011 Year 2008 Year 2011 Change Analysis Mining Area Area (sq km) % of total Area (sq km) % of total Area (sq km) % of total Coal Quarry 13.38 4.72 10.62 3.75-2.76-0.97 Barren Backfilled Area 12.71 4.48 21.27 7.50 8.56 3.02 Coal Dump 0.32 0.11 0.42 0.15 0.10 0.04 Water Filled Quarry 1.24 0.44 1.60 0.56 0.36 0.12 Advance Quarry Site 6.17 2.18 5.54 1.95-0.63-0.23 Barren OB Dump 6.13 2.16 9.49 3.35 3.36 1.19 Sub Total 39.95 14.09 48.94 17.26 8.99 3.17 The variation in the Mining areas which took place during year 2008 and 2011 within the coalfield area are shown in bar diagram in Figure 3.2. Job No 564607135 Chapter-3 Page 25

FIGURE - 3.2 CHANGES IN MINING AREAS IN SINGRAULI COALFIELD DURING 2008 & 2011 AREA (Sq Km) 25 20 15 10 5 0 21.27 13.38 12.71 10.62 9.49 6.17 6.13 5.54 0.32 0.42 2008 2011 YEAR Coal Quarry Barren Backfilled Area Coal Dump Adv Quarry Site Barren OB Dump 3.3.3 Agricultural Land Land primarily used for farming and production of food, fibre and other commercial and horticultural crops falls under this category. It includes crop land (irrigated and unirrigated) and fallow land (land used for cultivation, but temporarily allowed to rest) Total agricultural land which was 26.24 sq km in year 2008 has marginally decreased to 26.08 sq km in the year 2011. The reduction of 0.16 km (0.06%) in agricultural land in the coalfield is due to development of infrastructure and residential complexes for mining industry. The details are shown below in Table 3.5. Job No 564607135 Chapter-3 Page 26

TABLE 3.5 Changes in Agricultural Land in Singrauli Coalfield during the year 2008 & 2011 Year 2008 Year 2011 Change Analysis Agricultural Land Area (sq km) % of total Area (sq km) % of total Area (sq km) % of total Crop Land 8.86 3.13 8.58 3.03-0.28-0.10 Fallow Land 17.38 6.13 17.50 6.17 0.12 0.04 Sub Total 26.24 9.26 26.08 9.20 0.16 0.06 The variation in the Agricultural Land which took place during year 2008 and 2011 within the coalfield area are shown in bar diagram in Figure 3.3. FIGURE - 3.3 CHANGES IN AGRICULTURAL LAND IN SINGRAULI COALFIELD DURING 2008 & 2011 AREA (Sq Km) 20 18 16 14 12 10 8 6 4 2 0 17.38 17.5 8.86 8.58 2008 2011 YEAR Crop Land Fallow Land Job No 564607135 Chapter-3 Page 27

3.3.4 Wasteland Wasteland is degraded and unutilised class of land which is deteriorating 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 management constraints. There are two types of wastelands predominant within the coalfield area, viz waste upland and fly ash pond. There has been a slight reduction of 1.19 sq km, ie 0.42% of the coal field area. Some of the waste lands have been converted in to vegetated areas due to social forestry. Some wasteland has been converted to mining areas too. The land use pattern within the area for waste lands is shown below in Table 3.6. TABLE 3.6 Changes in Wastelands in Singrauli Coalfield during the year 2008 & 2011 Year 2008 Year 2011 Change Analysis Waste land Wasteland with/without Scrubs Area (sq km) % of total Area (sq km) % of total Area (sq km) % of total 21.36 7.53 20.40 7.20-0.96-0.33 Fly Ash Pond 0.29 0.10 0.06 0.02-0.23-0.08 Sub Total 21.65 7.64 20.46 7.22 1.19 0.42 The variation in the Waste Land which took place during year 2008 and 2011 within the coalfield area are shown in bar diagram in Figure 3.4 Job No 564607135 Chapter-3 Page 28

FIGURE - 3.4 CHANGES IN WASTE LAND IN SINGRAULI COALFIELD DURING 2008 & 2011 AREA (Sq Km) 25 20 15 10 5 0 21.36 20.4 0.29 0.06 2008 2011 YEAR Wasteland FlyAsh Pond 3.3.5 Settlements All the man-made constructions covering the land surface are included under this category. Built-up land has been further divided in to rural, urban and industrial classes. In the present study, industrial settlement indicates only industrial complexes excluding residential facilities. The details of the land use under this category are shown in Table 3.7 as follows: TABLE- 3.7. Job No 564607135 Chapter-3 Page 29

Changes in Settlements in Singrauli Coalfields during the year 2008 & 2011 Year 2008 Year 2011 Change Analysis Settlements Area (sq km) % of total Area (sq km) % of total Area (sq km) % of total Urban 12.24 4.32 14.87 5.25 2.63 0.93 Rural 0.84 0.30 1.09 0.38 0.25 0.08 Industrial 2.38 0.84 2.32 0.82-0.06-0.02 Sub Total 15.46 5.45 18.28 6.45 2.82 1.00 It is observed that the settlements within the coalfield have grown by 2.82 sq km, which is about 1.00% of the coalfield area. It is observed that the rural settlements have been also grown marginally by 1.09 sq km which is 0.38% of the coalfield area. The Urban settlement within the coalfield has grown by about 2.63 sq km, i.e. 0.93%. This increase is due to some population from rural areas shifting to urban areas to seek livelihood in the mining areas. It may be noted that the major industrial settlements, i.e. the thermal power plants in the area are out of the coalfield area. The figure above represents only the industrial structures which are within the coalfield area. The variation in the Built-up Land/Settlements which took place during year 2008 and 2011 within the coalfield area are shown in bar diagram in Figure 3.5 Job No 564607135 Chapter-3 Page 30

FIGURE - 3.5 CHANGES IN BUILT-UP LAND IN SINGRAULI COALFIELD DURING 2008 & 2011 AREA (Sq Km) 16 14 12 10 8 6 4 2 0 12.24 14.87 2.38 2.32 0.84 1.09 2008 2011 YEAR Urban Settlements Ryral Settlements Industrial Settlements 3.3.6 Water bodies It is the area of impounded water includes natural lakes, rivers/streams and man made canal, reservoirs, tanks etc. The water bodies in the study area have been found to be increased from 1.24 sq km in year 2008 (0.44%) to 1.91 sq km (0.67%) in 2011. The variation in area under various water bodies within the coalfield area is shown in Bar Diagram 3.6 FIGURE - 3.6 CHANGES IN WATERBODIES IN SINGRAULI COALFIELD DURING 2008 & 2011 2.5 AREA (Sq Km) 2 1.5 1 0.5 1.24 1.91 Waterbodies 0 2008 2011 YEAR Job No 564607135 Chapter-3 Page 31

3.3.7 Changes in Land Use/Vegetation Cover Classes The overall variation in various Land Use /Cover classes in Moher sub-basin of Singrauli Coalfield during the year 2008 and 2011 is shown in the Bar Chart below: 160 140 139.55 142.11 120 100 80 Area (Sq km) 60 40 20 72.74 70.85 48.94 39.95 26.08 26.24 20.46 21.65 18.28 15.46 1.91 1.24 Year 2008 2011 0 Fig 3.7 Overall Changes in Land Use/Cover Classes in Singrauli Coalfield in the Year 2008 & 2011 It can be seen from the chart above that there is a decrease in vegetation cover in the coalfield area mainly because of deforestation in dip side of the mines where the mines have advanced. However, the plantation carried out by NCL has increased in the 3 year period. It can also be seen that there is significant increase in Mining areas which is due to increase in coal production. Agricultural land has reduced marginally which may be due to some agricultural land in dip side of mining has been used for mining activities. Wasteland has also reduced marginally. This is also due to increase in mining activities. Settlements have also increase which is due to more population coming for livelihood to mining areas. Job No 564607135 Chapter-3 Page 32

Chapter 4 Conclusion & Recommendations 4.1 Conclusion In the present study, land use/vegetation cover map of Singrauli coalfield (Moher Sub-basin) is prepared based on IRS-Resourcesat 2/ LISS IV data of February 2011 in order to generate the database on vegetation cover and land use pattern to detect the changes in respect to the year 2008 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 in the area. Study reveals that vegetation cover has decreased by 2.56 sq km which is 0.90% of the coalfield area in span of last 3 years. The major factor for decrease in vegetation cover has been found to be loss of natural forests in the dip side where mines are advancing. The plantations carried out under social forestry and on OB dumps and backfilled areas have increased over this period. Social forestry has increased by 0.35% and plantation on OB dump and backfilled areas have increased by 0.32%. the overall increase in plantation is 0.90% in the coalfield. Scrubs have been kept as separate entity from the forests in line with the practice adopted by Forest Survey of India (FSI). The Scrubs have decreased by 2.71 % in the coalfield area because of the mine advancement. This shows that NCL s afforestation programme is progressing in an effective way. Study reveals that decrease in dense forest (- 2.03%) and open forests (-2.48%) is mostly due felling of forest to pave the way for mine advance in the dip side. Besides vegetation cover, other land use classes were also analysed and it was observed that in span of 3 years mining area has increased from 39.95 sq km to 48.95 sq km. This increase of 8.99 sq km (3.17%) in mining area is due to rapid increase in coal production. Job No 564607135 Chapter-3 Page 33

Further study reveals that agricultural land in study area has marginally reduced from 26.24 sq km to 26.08 sq km. This reduction of 0.16 sq km in agricultural land is mainly due to its conversion in mining area. The area of wasteland has also decreased very marginally from 21.65 sq km to 20.46 sq km during the last 3 years. This reduction of 1.19 sq km is mainly due to conversion of wasteland into mining and building the infrastructure. Area of settlement has increased from 15.46 sq km to 18.28 sq km. this increase of 2.82 sq km in settlement area has taken place mainly due to migration of rural population to urban mining settlements in the region The detail change analysis is given under Table-3.2. 4.2 Recommendations Keeping in view the sustainable development together with coal mining in the area, it is recommended that; a. To combat the effect of rapid mining and subsequent deforestation associated with it, NCL should make the afforestation programme more comprehensive to balance the ecological changes. b. Efforts should be made to protect the vegetated dumps. Fresh dumping should be avoided on the already stabilized and vegetated dumps. c. Plantation should be avoided in dip side of the mine. Job No 564607135 Chapter-3 Page 34

KAKRI BLOCK-B JAYANT NIGAHI AMLOHRI BINA

Central Mine Planning & Design Institute Limited (A Subsidiary of Coal India Ltd) Gondwana Place, Kanke Road, Ranchi - 834 031, Jharkhand, India Phone:(+91) 651 2231850/51/52/53 Fax:(+91) 651 2231447/2230826 Email: cmpdihq@cmpdi.co.in ; website : www.cmpdi.co.in