Department of Mining Engineering Indian School of Mines Dhanbad

Monitoring and assessment of land cover Monitoring and assessment of land cover changes induced by coal mining in jharia coal changes induced by coal mining in jharia coal

field field Using liss iii-mss dataUsing liss iii-mss data

Dr. Dheeraj KumarDr. Dheeraj KumarB.Tech, M.Tech, Ph.D.(IIT KGP)B.Tech, M.Tech, Ph.D.(IIT KGP)

www.dkumar.orgdheeraj@dkumar.orgdheeraj@dkumar.org

Mukul Supakar, Research ScholarMukul Supakar, Research ScholarDheeraj Kumar, Associate ProfessorDheeraj Kumar, Associate Professor

Prof. P. P. BahugunaProf. P. P. Bahuguna

DEFINITION OF PROBLEMProblems that attracted us to choose this topic

Effect of mining on vegetation Socio-economic effect due to mining activity Global warming Exploitation of coking coal

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OBJECTIVE Assessment of temporal and spatial

land cover (vegetation) change distribution using remotely sensed imagery;

To quantify land-cover changes in terms of percentage of area affected and rates of change

To understand the impact of coal mining on vegetation.

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Location of Study Area

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Jharia Coal Jharia Coal Filed(JCF)Filed(JCF)))

Latitude 23-40-Latitude 23-40-00 and 23-50-00 00 and 23-50-00

NN

Longitude 86-10-Longitude 86-10-00 and 86-30-00E00 and 86-30-00E

[source: www.google.com]

5[source: SOI toposheet]

6[source: www.google.com]

MATERIAL USED1. Linear Imaging Self-scanning Sensor

(LISS) III- Multi Spectral Scanner (MSS) image taken in 1997, 2001

2. LISS-III MSS Panchromatic (PAN) merged image taken in 2004

3. Survey of India topo-sheet (1:50000) of JCF for reference data(73l/4, 73I/1, a(73l/4, 73I/1, 73I/2, 73I/573I/2, 73I/5)

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Flow diagram of methodology

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a) Build- up landb) Barren landc) Mining aread) Water bodiese) Forest area (vegetation)f) Minor vegetation (agriculture and scrubland) 

IDENTIFICATION OF LAND-COVER CLASS FROM SATELLITE IMAGERY

On the FCC of LISS-3 MSS band 4, 3 and 2 (RGB)

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IMAGE DIFFERENCINGImage differencing was performed by subtracting the reflectance of the red band of the two imageries, [red reflectance of 2nd imagery (2001) - red reflectance of 1st imagery (1997)]

Some dark spot in upper north west corner of the figure is seen showing that there is least change in vegetation as mining activities is minimum and some dense forest is present in that region

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SUPERVISED LAND-COVER CLASSIFICATION

Using maximum likelihood classifier

Identification of class from satellite imagery

Photo interpretation from Survey of India (SOI) topo-sheet of Jharia Coal Fields (JCF)

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Supervised land-cover classification

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Land-cover area from supervised classification

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Classification accuracy of LISS-III (1997)

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M

Descriptive modeling of land-cover change process

Markov chain, the land-cover distribution at t2 (2001) calculated from the initial land-cover at t1 (1997) by means of transition matrix. The Markov chain is expressed as:Vt2 = M* V t1

M is a transition matrix for time interval t= t2-t1 (2001-1997=4)

  11 1

1

.............. .. ...............

n

m mn

P p

M

p p

1

qij

ij i ijji

np wheren n

n

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1

1...

t

c

V

cm

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A positive variation of a land-cover proportion shows an increase in area for the land-cover class from the first to the second date

Results

The overall rate of land-cover change for the two time intervals (1997-2001 and 2001-2004) were 29.15% , and 23.82% respectively.

Total land-cover area that remains unchanged is 54.95% (annually) only.

The main driving force for the land-cover change in this region is mining, decrease in rainfall in recent years and growing pressure of population.

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Observed land-cover proportion for 1997, 2001, and 2004 in percentage

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annual rates of land-cover changes in percent for the period of 1997-2004

The main reasons for decrease in vegetation

conversion of vegetation into built-up area, and the probability of this conversion is about 16.02%

probability of conversion of vegetation into mining areas is 8.87%

probability of conversion of vegetation into barren land is also about 13.12%.

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Evolution of land-cover proportion projected (p) by Markov model based on observed (o) land-cover proportions in the recent past.

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From 1997 to 2012, this model predicted that the natural vegetation could drop from 43.70% to 29.11% of the study area,

agricultural land would decrease from 14.1% to 10.47%,

barren land would increase from 16.5% to 33.15% and

mining areas would increase from 4.6% to 12.36%.

as the mining areas increases, built-up area would increase from 17.98% to 22.99%.

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How is the land-cover change process likely to progress in near future?

Validation of results

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Land-cover proportions projection (P2004) by extrapolation from the 1997-2001 transition matrix, and the observed (O2004)land-cover proportion

land-cover proportions projection (P2008) by extrapolation from the 1997-2001 transition matrix, and the observed (O2008) land-cover proportion.

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Where are the change and no change areas located?

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conclusionsThe analysis of land-cover change detection from

temporal series of medium-resolution satellite data such as LISS-III MSS data, proved to be very helpful, if the land-cover change detection is quantified in terms of percentage of area affected and the rate of change

The projection of the future land-cover pattern on the basis of Markov chain shows a continuous trend of increase in mining area, barren land, and built-up area where as vegetation is in continuous decreasing trend.

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