Report on Land use/vegetation cover Mapping of North and South Karanpura Coalfields based on Satellite Data of the Year 2015

Report on Land use/vegetation cover Mapping of North and South Karanpura Coalfields based on Satellite Data of the Year 2015 Submitted to Central Coalfields Ltd Ranchi Job No-561410027 [ Page i]

Report on Land use/vegetation cover Mapping of North and South Karanpura Coalfields based on Satellite Data of the Year 2015 Submitted to Central Coalfields Ltd Ranchi March-2016 Remote Sensing Cell Geomatics Division CMPDI(HQ), Ranchi Job No-561410027 [ Page i]

Document Control Sheet (1) Job No. 561410027 (2) Publication Date March 2016 (3) Number of Pages 32 (4) Number of Figures 7 (5) Number of Tables 5 (6) Number of Plates 2 (7) Title of Report Land use /Vegetation cover Mapping of North & South Karanpura Coalfield based on satellite data for the year 2015. (8) Aim of the Report To prepare Land use / vegetation cover map of North & South Karanpura Coalfield on 1:50000 scale based on IRS R-2 L4 MX satellite data for assessing the impact of coal mining on land use pattern and vegetation cover. (9) Executing Unit Remote Sensing Cell, Geomatics Division, (10) User Agency Central Coalfields Ltd. Central Mine Planning & Design Institute Limited, Gondwana Place, Kanke Road, Ranchi 834031. (11) Authors Hariharalal B, Manager (System) (12) Security Restriction Restricted Circulation (13) No. of Copies 6 (14) Distribution Statement Official A. K. Singh, Chief Manager (Remote Sensing) N. P. Singh, General Manager (Geomatics) Job No-561410027 [ Page ii]

Document Control Sheet List of Figures List of Tables List of Plates Contents Page No. ii iv iv iv 1.0 Introduction 1-5 1.1 Project Reference 1.2 Project Background 1.3 Objectives 1.4 Location and Accessibility 1.5 Physiography 2.0 Remote Sensing Concept & Methodology 6-19 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 Landuse / Cover Mapping 20-30 3.1 Introduction 3.2 Landuse / Cover Classification 3.3 Data Analysis & Change Detection 3.3.1 Settlements 3.3.2 Vegetation Cover 3.3.3 Agricultural Land 3.3.4 Mining Area 3.3.5 Wasteland 3.3.6 Water Bodies 4.0 Conclusion and Recommendations 31-32 4.1 Conclusion 4.2 Recommendations Job No-561410027 [ Page iii]

List of Figures 2.0 Location Map of North & South Karanpura 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 Land use / Cover mapping. 2.5 Geoid-Ellipsoid -Projection Relationship. 3.1 Area-wise percentage of different land use/cover in coalfield. List of Tables 2.1 Electromagnetic spectral regions. 2.2 Characteristics of the satellite/sensor used in the present project work. 2.3 Classification Accuracy Matrix. 3.1 Vegetation cover / Landuse classes identified in North & South Karanpura Coalfield. 3.2 Distribution of Landuse / Cover Patten in North & South Karanpura Coalfield in the year 2015 List of Plates List of maps/plates prepared on a scale of 1:50,000 are given below: 1. Plate No.1: HQ/REM/A0/15/001: IRS-R2 LISS-IV FCC of North & South Karanpura Coalfield. 2. Plate No.2: HQ/REM/A0/15/002: Land use / Vegetation Cover Map of North & South Karanpura Coalfield based on IRS R2/ LISS-IV data. List of Annexure Annexure 1. Block wise area of Land use/ Vegetation cover classes in North & South Karanpura Coalfield. Job No-561410027 [ Page iv]

Chapter 1 Introduction 1.1 Project Reference In view of the urgent need of creating the geo-environmental database of coalfields for monitoring the impact of coal mining on land use and vegetation cover, M/s Coal India Ltd. directed CMPDIL to take up the study through the techniques of Remote Sensing. Accordingly, a road map was submitted to Coal India Ltd. to implement the project of land use and vegetation cover monitoring of 28 major coalfields at regular interval of three years. A work no. CIL/WBP/Env/2009/2428 dated 29/12/2009 was issued by CIL initially for three years. After this period, a fresh work order was issued vide letter no. CIL/WBP/Env/2011 dated 12/10/2012 from Coal India Limited to CMPDIL for the period of 2012 to 2016-17 for land reclamation monitoring of the opencast projects as well as vegetation cover mapping of 28 major coalfields as per a defined plan for monitoring the impact of coal mining on Vegetation Cover. 1.2 Objectives The objectives of the present study are to prepare a regional land use and vegetation cover map of Karanpura Coalfields (North and South) on 1:50,000 scale based on IRS R2 LISS-IV satellite data of the year 2015, using digital image processing technique for accessing the impact of coal mining and associated industrial activities on the land use/ vegetation cover in the coalfield area with respect to the earlier study carried out in the year 2012. Job No-561410027 [ Page 1 of 32]

1.3 Location & Accessibility Karanpura Coalfield (KCF), situated about 60 km north-west of Ranchi and 20 km south-west of Hazaribagh, forms part of Ranchi, Hazaribagh and Palamau districts of Jharkhand State. The study area is bounded between North Latitudes 23 0 37 16 to 23 0 58 05 and East longitudes 84 0 45 00 to 85 0 28 44 and is covered by Survey of India (SoI) toposheet Nos. 73 A /9, 73 A /10, 73 A /13, 73 A /14, 73 E /1, 73 E /2, 73 E /5, 73 E /6 The location map and the incidence of study area on toposheets are shown in Figure 2.1. The area extends for about 38 km in north-south direction and 75 km in east-west direction encompassing an area of about 2280 sq. km. The Ashwa Pahar hill ranges divide the area into North Karanpura Coalfield (NKCF) and South Karanpura Coalfield (SKCF). NKCF is approachable from Ranchi on the southern side and Hazaribagh on the northern side. Khalari town is connected with Ranchi by all-weather metalled road, which in turn connects Tandwa, Barkagaon, Hazaribgh and Chatra towns. Though, the southern side of the Damodar river in the NKCF is approachable throughout the year, part of the northern portion remains cut off from Hazaribagh and Barkagaon during the monsoon season due to the absence of good road bridges on Garhi Nadi, Chundru Nala, Sadabahar Nala and other streams. The eastern and western parts of NKCF are connected with Tandwa by roads. SKCF, both on the northern and southern sides of Damodar river, is approachable from Ramgarh town located on the Hazaribagh-Ranchi National Highway No.33 and also from Ranchi via Patratu by an all weathered metalled road. Job No-561410027 [ Page 2 of 32]

KCF is also covered by Barkakana-Daltanganj branch of broad gauge railway line of the Eastern Railways connecting Gomoh and Dehri-on-Son. Mahauamilan, McCluskiganj, Khalari, Ray, Kole and Hendegir railway stations fall in NKCF, and Patraru, Bhurkunda, Barkakana and Argada railway stations fall in SKCF. 1.4 Drainage The Karanpura coalfields (both NKCF and SKCF) forms a part of the Damodar river basin. The general flow direction of the Damodar river is from west to east and is locally characterized by open and closed meanders. Some of the tributaries and sub-tributaries originate within the Karanpura coalfield and others originate in the crystalline rocks outside the study area. Garhi Nadi and Hararo Nadi are the two major tributaries of the Damodar river, flowing in the study area, which bring the discharge from various 2nd and 3rd order steams in the study area, and drain southwards through crystallines and sedimentary rocks of Gondwana Super Group. The smaller tributaries like Saphi Nadi, Chati Nala, Naikari Nadi etc. join the Damodar river from south and Nagarnadia Nala, Bandagarha Nala, Bolhariya Nala, Koti Nala, Patratu Nadi etc. join from the northern side. Apart from these, numerous streams/rivulets also join the Damodar river directly at different locations. The major part of the Karanpura coalfield is covered by Garhi Nadi and Haharo nadi watersheds. The major tributaries of Garhi Nadi are Daini, Chundru, Kuhubad, Satkundariya, Sadabahar Nala, Barki, Medhiya & Garua. The major tributaries of Haharo Nadi are Kukkurduba, Patra, Hendraj, Harhori, Lathorwa, Ghaghra, Damuhani, Pakwa, Badamahi & Rajhar. Job No-561410027 [ Page 3 of 32]

Mainly, the dendritic and parallel drainage patterns are prevalent in the area. The dendritic drainage is developed mainly in the moderate to deeply weathered pediplain over sedimentary and crystalline rocks indicating lack of structural control. The parallel type of drainage pattern is more common on the hill slopes of sedimentary and crystalline rocks. At places, joints/fractures also exhibit control on the stream pattern. Gulling is also common at places resulting in bad land topography. Job No-561410027 [ Page 4 of 32]

Fig. 1.1 : Location Map of Karanpura Coalfield Job No-561410027 [ Page 5 of 32]

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 Job No-561410027 [ Page 6 of 32]

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. 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 Job No-561410027 [ Page 7 of 32]

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-561410027 [ Page 8 of 32]

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 5.00 µm 14.00 µm Principal atmospheric windows in the thermal region. Images at these wavelengths are acquired by optical-mechanical scanners and special vediocon 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. 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-561410027 [ Page 9 of 32]

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) that 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 (airborne/satellite) in conjunction with traditional techniques harbours in an efficient, speedy and cost-effective method for natural resource management due to its inherited capabilities of being multispectral, repetitive and synoptic areal coverage. Generation of environmental 'Data Base' on land use, soil, forest, surface and subsurface water, topogra- Job No-561410027 [ Page 10 of 32]

phy and terrain characteristics, settlement and transport network, etc., and their monitoring in near real - time is very useful for environmental management planning; this is possible only with remote sensing data. 2.4 Data Source The following data are used in the present study: Primary Data Remote Sensing Satellite data viz. IRS R2 LISS-IV of January 2015 having 5 m. spatial resolution was used in the present study. The raw digital satellite data was obtained from NRSC, Hyderabad, on CD-ROM media. 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. For Karanpura Coalfield, Survey of India topo sheet no. 73 A /9, 73 A /10, 73 A /13, 73 A /14, 73 E /1, 73 E /2, 73 E /5, 73 E /6 as well as map showing location of coal blocks supplied by CCL were used in the study. 2.5 Characteristics of Satellite/Sensor The basic properties of a satellite s sensor system can be summarised as: 1. 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 Resourcesat satellite/sensor that was used in the present study. Job No-561410027 [ Page 11 of 32]

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 Rsourc esat (R2) LISS-IV B2 B3 B4 0.28-0.31 Green 0.25-0.38 Red 0.27-0.30 NIR 10-bit 5.8 m 5.8 m 5.8 m 5 days India NIR: Near Infra-Red 2.6 Data Processing The details of data processing carried out in the present study are shown in Figure 2.4. The processing methodology involves the following major steps: (a) (b) (c) (d) (e) (f) (g) Geometric correction, rectification and geo-referencing; Image enhancement; Training set selection; Signature generation and classification; Creation/overlay of vector database; Validation of classified image; Final thematic map preparation. Job No-561410027 [ Page 12 of 32]

Basic Data Data Source Secondary Data IRS R2 LISS-IV Topographical Maps (Scale 1:50,000) Pre-processing, geometric correction, rectification & georeferencing Creation of Vector Database (Drainage, Road Network, Coal block boundary) Image Enhancement Geo-coded FCC Generation Training set Identification Signature Generation Pre-Field Classification Training Set Refinement Validation through Ground Verification Fail Final Land Use/ Cover Map Pass Integration of Thematic Information on GIS Report Preparation Fig-2.4 Methodology of Land Use/Vegetation Cover Analysis Job No-561410027 [ Page 13 of 32]

2.6.1Geometric correction, rectification and geo-referencing 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 georeferencing 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-561410027 [ Page 14 of 32]

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 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 Job No-561410027 [ Page 15 of 32]

suited for small - scale mapping and larger area as well as for areas with North-South orientation (viz. India). Maps prepared using these projections are 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 v.14.0 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 was applied on the image data using Erdas IMAGINE s/w. The enhanced and geocoded FCC image of Karanpura Coalfield Coalfield is shown in Plate No. 1. 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 Job No-561410027 [ Page 16 of 32]

classification. Based on the variability of land use/cover condition and terrain characteristics and accessibility, around 150 points were selected to generate the training sets. 2.6.4 Signature generation and classification Image classification was carried out using the maximum likelihood 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 s/w. The classified image for the year 2015 for Karanpura Coalfield is shown in Plate No. 2. 2.6.5 Creation/overlay of vector database Plan showing coal block boundary are superimposed on the image as vector layer in the Arc GIS database. Road network, rail network and drainage network are also digitised on Arc GIS database and superimposed on the classified image. Job No-561410027 [ Page 17 of 32]

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 classification accuracy matrix is shown in Table 2.3. Classification accuracy in case of Plantation on OB Dump, Settlements and Barren OB Dump was 100%. Classification accuracy in case of Dense Forest and Water Bodies lie between 80% to 100%. In case of open forest and agriculture land, the classification accuracy varies from 80.0% to 90.0%. Classification accuracy for scrubs was 70% due to poor signature separability index. The overall classification accuracy in case of Karanpura Coalfield was 90%. 2.6.7 Final land use/vegetation cover map preparation Final land use/vegetation cover map (Plate - 2) was printed using HP Design jet 4500 Colour Plotter. The maps are prepared on 1:75,000 scale and enclosed as drawing No. HQ/REM/002 along with the report. A soft copy in.pdf format is also enclosed.. Job No-561410027 [ Page 18 of 32]

Table 2.3 : Classification Accuracy Matrix for Karanpura Coalfield in the Year 2015 Sl. No. Classes in the Satellite Data Class Total Obsrv. Points Land use classes as observed in the field C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 1 Urban Settlement C1 05 5 2 Dense Forest C2 10 8 1 1 3 Open Forest C3 10 1 8 1 4 Scrubs C4 10 1 1 7 1 5 Social Forestry C5 10 1 8 1 6 Agriculture Land C6 10 1 9 7 Barren OB C7 10 10 8 Plantation Area C8 10 10 9 Quarry Area C9 10 10 10 Water Bodies C10 10 10 Total no. of observation points 95 05 10 10 10 10 10 10 10 10 10 % of commission 00.0 20.0 20.0 30.0 20.0 10.0 0.0 0.0 0.0 0.0 % of omission 00.0 20.0 20.0 30.0 20.0 10.0 0.0 0.0 0.0 0.0 % of Classification Accuracy 100.0 80.0 80.0 70.0 80.0 90.0 100.0 100.0 100.0 100.0 Overall Accuracy (%) 90.00 Job No-561410027 [ Page 19 of 32]

Chapter 3 Land Use/ Cover Mapping 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. 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 resolution offers comprehensive and accurate information for mapping and monitoring of land use/cover pattern, dynamics of changing pattern and trends over a period of time.. By analysing the data of different cut-off dates, impact of coal mining on land use and vegetation cover can be determined. 3.2 Land Use/Cover Classification The array of information available on land use/cover requires to be arranged or grouped under a suitable framework in order to facilitate the creation of a land use/cover database. Further, to accommodate the changing land use/cover pattern, it becomes essential to develop a standardised classification system that is not only flexible in nomenclature and definition, Job No-561410027 [ Page 20 of 32]

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. Land use map was prepared on the basis of image interpretation carried out based on the satellite data for the year 2015 for North Karanpura & South Karanpura coalfields and following land use/cover classes are identified (Table 3.1). Table 3.1: Land use/cover classes identified in Karanpura Coalfield Level -I 1 Vegetation Cover 2 Agricultural Land 3 Wasteland 4 Mining 5 Settlement/ Built-Up Land Level -II 1.1 Dense Forest 1.2 Open Forest 1.3 Scrubs 1.4 Social Forestry 1.5 Plantation on Backfill 1.6 Plantation on OB Dumps 2.1 Crop Land 2.2 Fallow Land 3.1 Barren Rocky Land 3.2 Waste upland with/without scrubs 3.3 Fly Ash Pond 3.4 Sand body 4.1 Coal Quarry 4.2 Area under Backfill 4.3 Barren OB Dump 4.4 Quarry filled with Water 4.5 Coal Stock 4.6 Advance Quarry Site 5.1 Urban 5.2 Rural 5.3 Industrial 6 Water bodies 6.1 River/ Streams/ Reservoir/ Ponds Job No-561410027 [ Page 21 of 32]

Following maps are prepared on 1:75,000 scale: Plate No. 1 : Map No. HQ/REM/A0/15/001: FCC (IRS R2 LISS-IV data of Karanpura 2015) with all the block boundaries of Karanpura coalfield region. Plate No. 2 : Map No. HQ/REM/A0/15/002 - Land use/ Vegetation Cover Map of Karanpura Coalfield based on IRS R2-L4 MX data. 3.3 Data Analysis & Change Detection Satellite data of the year 2015 were processed using ERDAS IMAGINE 2014 image processing s/w in order to interpret the various land use/cover classes present in the study area of North Karanpura and South Karanpura covering 1224.00 and 194.87 sq. kms respectively. The area of each land use/cover class for Karanpura coalfield were calculated using ERDAS IMAGINE s/w and tabulated in Table 3.2A and Table 3.2B. Distribution of various land use classes are shown in the Pie Diagrams (Fig. 3.1,Fig. 3.2). 3.3.1 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. Total area of settlements in NKCF covers 28.96 km 2 (2.37%) which includes Urban (4.48 km 2 ; 0.37%), Rural (16.25 km 2 ; 1.33%) and Industrial (8.23 km 2 ; 0.67%) (Refer Table 3.2A). Total area of settlements in SKCF covers 9.20 km 2 (4.72%) which includes Urban (2.36 km 2 ; 1.21%), Rural (3.78 km 2 ; 1.94%) and Industrial (3.06 km 2 ; 1.57%) (Refer Table 3.2B). Job No-561410027 [ Page 22 of 32]

3.3.2 Vegetation Cover 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 m 2 corresponding to an area of one m 2 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 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 are in terms of total land use cover only. Details of area statistics of the vegetation cover in North Karanpura and South Karanpura area is given in Table 3.2A & Table 3.2B respectively. Analysis of data reveals that vegetation cover in NKCF occupies an area of 517.76 km 2 (42.30%). Out of which dense forest occupies 146.36 km 2 (11.96%), open forest cover occupies 249.70 km 2 (20.4%), scrub covers 107.22 km 2 (8.76%) of the area. Plantation under social forestry covers an area of 4.88 km 2 (0.40%) and plantation on OB dumps/ backfill has 9.06 km 2 (0.78%) of area under its coverage. Vegetation cover got reduced by an area of 1.80 km 2 during 2012-15, mainly due to the advancement of mining and other related activities in some of the big OCPs like Piparwar, Ashok, Amarapali etc, Analysis of data reveals that vegetation cover in SKCF occupies an area of 98.89 km 2 (50.75%). Out of which, dense forest occupies 2.68 km 2 (1.38%), open forest cover occupies 27.84 km 2 (14.29%), scrub covers 40.60 km 2 Job No-561410027 [ Page 23 of 32]

(20.83%) of the area. Plantation under social forestry covers an area of 16.86 km 2 (8.65%) and plantation on OB dumps/ backfill has 10.91 km 2 (5.59%) of area under its coverage. Vegetation cover got reduced by an area of 9.09 km 2 during 2012-15, mainly due to the advancement of mining and related activities, and some mix up with the agriculture areas on account of separability problems. 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. Analysis of data reveals that agriculture that covers in NKCF occupies an area of 638.96 km 2 (52.20%). Out of which crop land is 271.91 km 2 (22.21%) and fallow land covers an area of 367.05 km 2 (29.99%). Analysis of data reveals that agriculture land covers in SKCF occupies an area of 68.97 km 2 (35.39%). Out of which crop land is 26.16 km 2 (13.42%) and fallow land is 42.81 km 2 (21.97%). 3.3.4 Mining The mining area includes the area of existing quarry, area under backfilling, barren OB dumps, old quarries filled with water, advance quarry sites and coal stock/dumps. Job No-561410027 [ Page 24 of 32]

The mining activities covers an area of 12.93 km 2 (1.06%) in NKCF. Out of which 4.57 km 2 (0.37%) area is under coal quarry, 6.07 km 2 (0.49%) is under barren OB dump/ backfill and 0.40 km 2 (0.03%) comes under advance quarry site. The area increased by 6.80 km 2 on account of the increased mining activities in various OCPs and opening of Amarapali OCP during this period. The mining activities cover an area of 10.34 km 2 (5.31%) in the SKCF, Out of which 2.48 km 2 (1.27%) area is under coal quarry, and 5.51 km 2 (2.83%) area is under barren OB dump/ backfill. The total mining area increased by 8.80 km 2 on account of the increased mining activities in various OCPs and classification correction of old quarries and backfilled areas during this period. 3.3.5 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 the area of only wasteland in NKCF is 8.16 km 2 (0.67%) and in SKCF is 2.78 km 2 (1.43%) 3.3.5 Surface Water bodies Analysis of data reveals that surface water bodies in NKCF & SKCF covers an area of 17.23 km 2 (1.41%) and 4.69 Km 2 (2.41%) respectively. The surface water decreased only marginally due to the mining activities and more due to the climatic and natural drying up reasons. Job No-561410027 [ Page 25 of 32]

Sl. No. Table 3.2A Land use/ Vegetation cover pattern in the North Karanpura Land Use / Cover Classes Coalfield in the year 2015 January 2012 January 2015 IRS R2 / L4 MX IRS R2 / L4 MX CMPDI (Based on IRS R2 / L4 MX Data) Change in area Level - I Level - II Km 2 % Km 2 % Km 2 Possible Reasons for Change Dense Forest 146.80 11.98 146.36 11.96-0.44 Natural degradation and 1 Forest Area Open Forest 249.77 20.39 249.70 20.40-0.07 mine opening in Amarapli Sub Total 396.57 32.37 396.06 32.36-0.51 OC. 2 Scrubs Scrubs 107.16 8.75 107.22 8.76 0.06 3 4 Plantation Area Agricultural Land 5 Wasteland 6 Mining Area 7 Settlements Social Forestry 3.18 0.26 4.88 0.40 1.70 Trees cut in dip sides of Plantation on OB Piparwar for mining & 12.65 1.03 9.60 0.78 Dump/ Backfill -3.05 other infrastructural setups Sub Total 15.83 1.29 14.48 1.18-1.35 Total Vegetation area (1+2+3) 519.56 42.41 517.76 42.30-1.80 Crop Land 273.38 22.32 271.91 22.21 Fallow Land 365.86 29.86 367.05 29.99-1.47 1.19 Due to advancement of mining in Ashok, Piparwar, Amarapali etc. Sub Total 639.24 52.18 638.96 52.20-0.28 Waste up land Land used for mining and with / without 4.67 0.38 4.83 0.39 other activities, development Scrubs 0.16 of roads etc. Rocky Land 0.00 0.00 0.53 0.04 0.53 Sand Body 5.28 0.43 2.80 0.23-2.48 Sub Total 9.95 0.81 8.16 0.66-1.79 Coal Quarry 4.17 0.34 4.57 0.37 0.40 Advancement of mining Backfilling 0.00 0.00 3.83 0.31 3.83 activities in big OC mines Barren OB Dump 1.86 0.15 2.24 0.18 0.38 of NK area and resultant Water filled Quarry 0.00 0.00 1.22 0.10 1.22 OB/backfill area. Coal Dump 0.00 0.00 0.67 0.05 0.67 Advance Quarry 0.10 0.01 0.40 0.03 Site 0.30 Sub Total 6.13 0.50 12.93 1.06 6.80 Urban 4.86 0.40 4.48 0.37-0.38 Shifting of settlements for mining and allied activities Rural 16.74 1.37 16.25 1.33-0.49 Industrial 10.63 0.87 8.23 0.67-2.40 Sub Total 32.23 2.64 28.96 2.37-3.27 8 Water Body River, Ponds 17.87 1.46 17.23 1.41-0.64 TOTAL 1224.00 100.00 1224.00 100.00 Natural reasons, drying up Job No-561410027 [ Page 26 of 32]

Sl. No. Table 3.2B Land use/ Vegetation cover pattern in the South Karanpura Land Use / Cover Classes Coalfield in the year 2015 January 2012 January 2015 IRS R2 / L4 MX IRS R2 / L4 MX (Based on IRS R2 / L4 MX Data) Change in area Level - I Level - II Km 2 % Km 2 % Km 2 Possible Reasons for Change Dense Forest 2.70 1.39 2.68 2.70-0.02 Natural degradation and 1 Forest Area Open Forest 28.01 14.38 27.84 28.01-0.17 becoming scrubs. Sub Total 30.71 15.77 30.52 30.71-0.19 2 Scrubs Scrubs 39.86 20.45 40.60 39.86 0.74 Social Forestry 18.50 9.49 16.86 18.50-1.64 Mining & other infrastructural 3 setups, some agri. ar- Plantation Plantation on OB 18.36 9.42 10.91 18.36-7.45 Area Dump/ Backfill ea mix-up Sub Total 36.86 18.91 27.77 36.86-9.09 Total Vegetation area (1+2+3) 107.43 55.13 98.89 107.43-8.54 4 Agricultural Land Crop Land 27.95 14.34 26.16 27.95-1.79 Due to human interference and other activities etc. Fallow Land 36.94 18.96 42.81 36.94 5.87 Sub Total 64.89 33.30 68.97 64.89 4.08 Waste up land with Land used for mining and 0.00 0.00 1.74 0.00 1.74 / without Scrubs other activities, development 5 Wasteland Rocky Land 0.00 0.00 0.00 0.00 0 of roads etc. Sand Body 1.47 0.75 1.04 1.47-0.43 Sub Total 1.47 0.75 2.78 1.47 1.31 Coal Quarry 1.54 0.79 2.48 1.54 0.94 Advancement of mining activities Backfilling 0.00 0.00 1.69 0.00 1.69 in OC mines of SK Barren OB Dump 0.00 0.00 3.82 0.00 3.82 area and resultant 6 Mining Water filled Quarry 0.00 0.00 2.03 0.00 2.03 OB/backfill area. Area Coal Dump 0.00 0.00 0.20 0.00 0.2 Advance Quarry Site 0.00 0.00 0.12 0.00 0.12 Sub Total 1.54 0.79 10.34 1.54 8.8 Urban 1.63 0.84 2.36 1.63 0.73 Shifting of settlements for 7 Settlements Rural 4.40 2.26 3.78 4.40-0.62 mining and allied activities Industrial 6.67 3.42 3.06 6.67-3.61 Sub Total 12.7 6.52 9.20 12.7-3.5 8 Water Body River, Ponds 6.84 3.51 4.69 6.84-2.15 Natural reasons, drying up TOTAL 194.87 100.00 194.87 100.00 Job No-561410027 [ Page 27 of 32]

Plate 1 : Geocoded FCC (Band 3, 2, 1) of Karanpura CF based on IRS R2- L4FMX Satellite Data of Year 2015 Job No-561410027 [ Page 28 of 32]

Plate 2 : Land Use/ vegetation Cover Map of Karanpura CF based on IRS R2 L4FMX Satellite Data of Year 2015 Job No-561410027 [ Page 29 of 32]

Land Use/ Vegetation Cover in NK Coalfield in 2015 1.06 1.41 0.67 2.37 Vegetation Cover Agri. Land 42.3 Waste Land 52.2 Mining Area Water Body Settlements Fig. 3.1A Land Use/ Vegetation Cover in SK Coalfield in 2015 Vegetation Cover 1.43 5.31 2.41 4.72 Agri. Land 35.39 50.75 Waste Land Mining Area Water Body Settlements Fig. 3.1B Job No-561410027 [ Page 30 of 32]

4.1 Conclusion Chapter 4 Conclusion & Recommendations In the present study, land use/ vegetation cover mapping has been carried out based on IRS-R2 L4FMX satellite data of January, 2015 in order to generate the database on land use/vegetation cover in Karanpura Coalfield for monitoring the impact of coal mining on land environment. The land use/cover data will help in assessing the land restoration status as well as for formulating the mitigation measures required, if any. Study reveals that the total area of settlements which includes urban, rural and industrial settlements in the NKCF covers an area of 28.96 km 2 (2.37%). Vegetation cover, which includes dense forests, open forests, scrubs, avenue plantation & plantation on over-burden dumps covers an area of 517.76 km 2 (42.30%). The analysis further indicates that total agricultural land which includes both crop and fallow land covers an area of 638.96 km 2 (52.20%). The mining area which includes coal quarry, advance quarry site, barren OB dump and coal dump covers 12.93 km 2 (1.06%). Surface water bodies covered an area of 17.23 km 2 (1.41%). As the miming activities are progressing at a faster rate, the process of reclamation and restoration of the mined out areas need to be enhanced for geo-environmental protection and social benefits. Study reveals that the total area of settlements in the SKCF covers 9.20 km 2 (4.72%). Vegetation cover which includes dense forests open forests, scrubs, avenue plantation & plantation on over-burden dumps covers 98.89 km 2 (50.75%). The analysis further indicates that total agricultural land which includes both crop and fallow land covers 68.97 km 2 (35.39%). Job No-561410027 [ Page 31 of 32]

The mining area which includes coal quarry, advance quarry site, barren OB dump and coal dump covers 10.34 km 2 (5.31%). Surface water bodies covered 4.69 km 2 (2.41%) area. 4.2 Recommendations Keeping in view the sustainable development together with coal mining in the area, it is recommended that similar study should be carried out regularly at an interval of three years to assess the impact of coal mining on land use pattern and vegetation cover in the coalfield to formulate the remedial measures, if any, required for mitigating the adverse impact of coal mining on land environment. Such regional study will also be helpful in assessing the environmental degradation /upgradation carried out by different industries operating in the coalfield area. Job No-561410027 [ Page 32 of 32]

Annexure-A BLOCKWISE STATUS OF LAND USE/ VEGETATION COVER PATTERN IN NK COALFIELDS IN THE YEAR 2015 (Area in Sq. Km) Dense Open Scrub Water Crop Fallow Plantat Coal OB Adv. Social Plantai Backfil Water Coal Waste Ind. Rural Urban Sand Total BLOCK NAME Forest Forest s body Land Land ion Quarry Dump Quarry Forestry on on l filled Quarry Dump Land Settle ments Settle ments Settle ments Body 1 MANKI 0.00 0.00 1.43 0.00 0.14 1.06 0.00 0.00 0.00 0.00 0.26 0.00 0.00 0.00 0.00 0.24 0.02 0.00 0.13 0.04 3.32 2 DEONAD - III 0.00 4.29 1.35 0.31 1.28 3.76 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.38 11.37 3 KARKAT A 0.00 1.60 1.11 0.20 0.18 1.53 0.00 0.00 0.00 0.00 1.05 0.19 0.02 0.01 0.00 0.01 0.16 0.03 0.05 0.00 6.13 4 ROHINI 0.00 0.19 0.81 0.09 0.21 1.01 0.01 0.09 0.09 0.00 1.23 0.65 0.65 0.20 0.02 0.10 0.02 0.00 0.09 0.02 5.49 5 CHURI 0.00 0.18 3.02 0.30 0.45 2.09 0.00 0.00 0.00 0.00 0.15 0.00 0.00 0.00 0.00 0.00 0.03 0.01 0.03 0.06 6.32 6 DEONAD - II 0.00 24.07 3.50 0.18 2.74 6.88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.06 0.00 0.02 37.47 7 RAY 0.00 1.07 0.24 0.21 0.07 0.39 0.00 0.00 0.00 0.00 0.13 0.00 0.00 0.00 0.00 0.03 0.03 0.00 0.01 0.00 2.19 8 MAHUAMILAN 0.00 3.80 0.61 0.01 0.20 0.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.09 9 BENT I 0.00 0.02 4.71 0.17 0.58 2.91 0.00 0.00 0.00 0.00 0.15 0.00 0.00 0.00 0.00 0.34 0.09 0.13 0.00 0.10 9.20 10 PURNADIH 0.00 0.98 3.00 0.17 0.35 2.89 0.00 0.35 0.50 0.05 0.00 0.01 0.00 0.21 0.07 0.35 0.17 0.07 0.00 0.24 9.41 11 BACHARA 0.00 6.81 1.16 0.66 0.17 1.59 0.00 0.00 0.00 0.00 0.32 0.00 0.00 0.00 0.00 0.27 0.01 0.15 0.28 0.06 11.47 12 BUNDU 0.30 4.96 0.07 0.00 0.22 0.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.92 13 MANGARDAHA 0.00 0.88 0.28 0.43 0.11 0.57 0.06 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.02 0.05 0.06 0.05 0.04 0.00 2.60 14 PIPARWAR 0.00 0.00 0.38 0.02 0.02 0.23 0.16 1.53 0.00 0.04 0.58 1.47 1.30 0.28 0.17 0.51 0.02 0.08 0.00 0.00 6.77 15 ASHOK KARKAT A 0.00 15.37 3.00 0.88 8.11 17.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.45 0.20 0.00 0.50 45.76 16 CHAKLA 0.00 4.14 0.66 0.02 1.76 2.23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.83 17 ASHOK 0.00 4.00 0.62 0.04 0.50 3.36 0.00 1.12 0.18 0.03 0.08 0.63 0.79 0.04 0.02 0.44 0.10 0.05 0.00 0.01 12.00 18 DEONAD - I 0.00 22.95 3.12 0.36 4.81 11.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.05 42.39 19 CHIT ARPUR 0.00 2.13 0.69 0.23 2.26 1.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00 7.11 20 CHANO RIKBA-A 0.01 0.01 0.34 0.00 1.87 0.61 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.00 0.01 0.00 0.00 2.93 21 CHANO RIKBA-B 0.04 0.11 2.17 0.00 7.83 7.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.00 0.07 0.00 0.00 17.82 22 ROHNE BLOCK 0.34 2.51 0.95 0.04 2.15 2.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 8.10 23 PINDARKOM 0.00 0.41 0.07 0.00 0.04 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.54 24 INDRAT OLI 0.57 5.58 3.15 0.00 3.26 4.85 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.17 0.00 0.00 0.00 0.00 17.59 25 T IT ARIAKHAR 0.00 0.00 0.14 0.01 0.44 0.34 0.00 0.26 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 1.31 26 RAUT PARA 0.08 2.46 1.97 0.06 2.90 2.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.55 0.00 0.00 0.00 0.00 10.31 27 GANESHPUR 0.00 0.79 0.65 0.01 0.31 0.61 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.37 28 SHEREGARHA 0.00 0.84 0.25 0.02 0.38 1.26 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.75 29 BABUPARA 2.25 1.63 1.15 0.01 2.20 1.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.16 0.00 0.00 0.00 0.00 8.52 30 MAGADH 0.00 8.11 1.21 0.15 3.78 8.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.29 0.00 0.00 22.44

31 BADAM 0.11 0.07 0.30 0.22 2.37 1.32 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 4.50 32 GONDALPARA 0.78 2.18 0.37 0.08 2.93 0.77 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.27 0.00 0.06 0.00 0.00 7.43 33 MAGADH NORTH 0.00 0.05 0.24 0.07 0.45 2.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 0.00 0.00 3.33 34 SARADHU BLOCK 0.00 2.56 0.36 0.08 0.92 1.54 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 5.48 35 KOYAD-KISANPUR 0.00 1.98 0.64 0.08 1.54 5.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.00 9.92 36 CHATTI BARIATU 0.00 0.00 0.17 0.01 0.85 1.52 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.54 37 MANAT U 0.00 0.66 0.46 0.05 1.27 1.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.14 38 KOYAD 0.00 0.35 0.33 0.08 1.70 4.93 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.00 0.01 7.46 39 CHATTI BARIATU 0.00 0.30 0.56 0.31 2.59 3.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.00 0.00 7.33 40 DUMARI 0.09 0.14 0.16 0.07 1.37 1.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.55 41 KERNDARI 'A' 0.02 0.01 0.34 0.14 4.87 1.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 7.10 42 KERENDARI 'B' 0.00 0.05 0.61 0.20 8.19 2.93 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.19 0.00 0.00 12.17 43 KERENDARI "C" 0.00 0.33 0.41 0.14 7.20 3.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.32 0.00 0.00 11.57 44 PAKRI-BARWADIH 0.03 2.28 2.13 0.48 18.11 9.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.33 0.00 0.00 32.89 45 DUMRI BLOCK 7.26 1.83 0.63 0.27 3.10 3.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 16.18 46 BRINDA BLOCK 0.00 1.12 0.96 0.32 2.28 4.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 9.08 47 SISAI BLOCK 0.04 0.04 0.38 0.14 1.10 1.79 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.50 48 KASIADIH 0.00 2.01 0.33 0.05 2.04 3.67 0.00 0.00 0.20 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.01 8.36 49 AMRAPALI 0.00 2.58 1.22 0.43 1.37 7.36 0.00 0.92 0.48 0.12 0.00 0.00 0.00 0.25 0.15 0.00 0.00 0.50 0.00 0.16 15.54 50 DHADHU NORTH 0.01 3.14 0.61 0.03 2.46 4.78 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 11.04 51 DHADHU EAST 0.00 14.13 1.14 0.09 4.74 6.98 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 27.10 52 PACHARA 0.00 2.69 0.90 0.21 1.01 3.51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.24 8.56 53 PACHARA SOUTH 0.00 0.02 0.34 0.11 0.25 1.69 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.02 2.43 54 TANDWA SOUTH 2.39 2.97 2.38 0.20 3.89 22.09 0.00 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.42 0.00 0.00 34.42 55 MOIT RA 0.00 0.01 0.23 0.07 3.39 0.92 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.05 0.00 0.00 4.69 56 K D HESALONG 0.00 0.00 0.07 0.06 0.00 0.09 0.00 0.02 0.18 0.00 0.00 0.95 0.59 0.08 0.19 0.23 0.16 0.00 0.00 0.02 2.64 57 DAKARA 0.00 0.00 0.86 0.00 0.01 0.45 0.03 0.29 0.61 0.04 0.04 0.26 0.40 0.11 0.04 0.25 0.09 0.01 0.16 0.00 3.67 TOTAL 14.34 161.41 58.90 8.57 129.29 193.47 0.26 4.57 2.24 0.38 4.13 4.16 3.76 1.22 0.68 4.52 1.50 3.95 0.79 2.02 600.15

BLOCKWISE STATUS OF LAND USE/ VEGETATION COVER PATTERN IN SK COALFIELDS IN THE YEAR 2015 (Area in Sq. Km)