13

th Esri India User Conference 2012

Page 1 of 9

LULC AND CLIMATE CHANGE IMPACT ON CARBON STORGE AND SEQUESTRATION A CASE STUDY OF RANTHAMBHORE TIGER RESERVE, RAJASTHAN

Dr Kuldeep Pareta1

1 Senior Project Manager (RS/GIS & NRM), Spatial Decisions

B-30 Kailash Colony, New Delhi 110048 (INDIA)

Abstract: The accumulation of CO2 in the atmosphere due to fossil fuel use, deforestation and other anthropogenic sources is changing the global climate. Carbon emissions from tropical deforestation have long been recognized as a key component of the global carbon budget, and more recently of our global climate system. The Intergovernmental Panel on Climate Change (IPCC) estimated that 1.86 billion tons of carbon is released annually due to LULC & climate change, of which the major part is tropical deforestation, and it is a major source of carbon emissions and an active contributor to global warming. Agriculture expansion due to increasing population has been regarded as one of the primary causes of LULC change / deforestation and a major source of carbon emissions form terrestrial-ecosystems. The main objective of this study is to investigate the possible effects of LULC change, climate variability, and increasing CO2 concentration on the carbon storage potential of the Ranthambhore Tiger Reserve. With this purpose in mind some recent experiences were critically reviewed and some new ideas have been considered. Author has used ESRI ArcGIS-10x based InVEST-2.4.2 carbon model, and NLLUF-KP10 model for this study. The estimated carbon stock in Ranthambhore Tiger Reserve is 34.15, 32.35, 30.66, 28.78, and 27.52 M.Kg. / Ha for year 1975, 1990, 2000, 2006, and 2012 respectively.

Keyword: Landuse, Climate Change, Biomass Estimation, Carbon Stock, and Remote Sensing

About the Author:

Dr Kuldeep Pareta, Ph. D Geomorphology Author has obtained M.Sc. degree in Geography from Dr Hari Singh Gour University (Central University), Sagar - Madhya Pradesh in 2001, subsequently Ph.D. in Geomorphology & Hydro-Geology from same university in 2005. Presently, he is working as Senior Project Manager (RS/GIS & Natural Resource Management) in Spatial Decisions, New Delhi 110 048 (INDIA), and has over 11 years of research and development experience in the field of National Resource Management, Geomorphology, Hydro-Geology, Watershed Modelling, and National Disaster Management. He has published over 33 research papers in various National and International Journals, and two International books. He was conferred Prof. S.M. Ali Memorial Gold Medal in 2001 and MP Young Scientist award in the year 2004. He is a member of Indian Society of Remote Sensing, Madhya Bharti Journal, International Journal of Scientific and Engineering Research, International Association for Environmental Hydrology, International Association of Geomorphologists and has visited Vietnam for his research works.

E mail ID: kuldeep.p@spatialdecisions.in

kpareta13@gmail.com

Contact No: +91-9871924338

13

th Esri India User Conference 2012

Page 2 of 9

1. Introduction: The state of terrestrial ecosystems at the present day is a product of past climate and anthropogenic land cover change (ALCC; Pongratz et al., 2009; Strassmann et al., 2008). Many processes in the terrestrial biosphere operate on centennial to millennial timescales including the growth and development of forests and the dynamics of soil organic matter decomposition (Trumbore, 2000; Canadell et al., 2007). Although there is evidence that even old trees continue to accumulate carbon at a steady rate (Luyssaert et al., 2008), forests tend to be most productive during their early stages of growth; as a forest ecosystem matures, the rate at which it sequesters carbon tends to decrease (Albani et al., 2006) and net ecosystem production approaches zero (Canadell et al., 2007). Thus, the potential for carbon to be stored in terrestrial ecosystems in the future depends strongly on past ecosystem history and the trajectory of ecosystems at present (Magnani et al., 2007).

2. Study Area: The study area lies in the Sawai Madhopur district of western Rajasthan as shown in the Fig. 1. Ranthambhore National Park lies between 76.32 to 76.68 E longitudes and 25.80 to 26.21 N latitudes and at the junction of the Aravalis and the Vindhyanchal mountain ranges. The Ranthambhore tiger reserves spread over 1394.5 Sq Km of which core area 400 Sq Km (i.e. Ranthambhore national park and immediate contiguous areas) is main area for tiger and wildlife herbivore population. The reserve is situated in south eastern part of Rajasthan in Sawai Madhopur and Karauli districts. The park is located on the Western bank of mighty river Chambal. The other river Banas divides the reserve into two parts, the north eastern part being the Keladevi sanctuary. It is a unique example of natural and historical richness in arid and denuded tract of Sawai Madhopur District of eastern Rajasthan.

Being a typical representative of dry-deciduous dhok (Anogeissus pendula, Edgew) forest; the vegetation of Ranthambhore is considered as Tropical Dry Deciduous Forest, and Tropical Thorn Forest (Champion and Seth, 1968). Ranthambhore supports six large wild mammalian herbivores; five ungulates, viz. chital (Axis axis, Erxleben); sambar (Cervus unicolor, Kerr); nilgai (Boselaphus tragocamelus, Pallas); chinkara (Gazella bennetti, Sykes); wild pig (Sus scrofa, L.); and a primate viz. common langur (Presbytis entellus, Dufresne). Blackbuck (Antilope cervicapra, L.) is reported from certain fringe areas. Apart from the tiger, the other major carnivores are leopard (Panthera pardus, L.) and striped hyaena (Hyaena hyaena, L.).

3. Data Used & Sources: Table 1

Data Used and its Sources

S.No. Data Layer Sources

1. Base Map Survey of India toposheets (1:50,000 and 1:250,000 Scale) Various satellite imagery

2. LULC Map and NDVI

2012: ResourceSAT-2 LISS-III satellite imagery (23.5 m) 2006: LANDSAT-7 ETM

+ satellite imagery (30 m)

2000: LANDSAT-5 TM satellite imagery (60 m) 1990: LANDSAT-3 MSS satellite imagery (79x82 m) 1975: LANDSAT-1 RBV satellite imagery (79x82 m)

3. Elevation Map Survey of India toposheets IRS-P5 CartoSAT-1 DEM (30 m) & ASTER - DEM (30 m)

4. Soil Map National Bureau of Soil Survey (NBSS) Soil map updated through multi-spectral satellite imagery

5. Geological Map Geological Survey of India (GSI) Geological map updated through multi-spectral imagery

6. Village Cadastral Map Survey and Land Records Department Updated through various satellite imagery

7. Demography Census of India - 2001 & 2011

8. Climate India Meteorological Department (IMD) - 2001 to 2011

13

th Esri India User Conference 2012

Page 3 of 9

Figure 1: Location Map of the Study Area

4. Climate: The climate of the study area can be classified as semi-humid. It is characterized by very hot summers and very cold winters with fairly good rainfall during south-west monsoon period. The rainfall over the Sawai Madhopur is scanty (annual average is 877.8 mm) and is concentrated in four months only, i.e., during south-west monsoon in June to September (Table 2). The most of the rainfall is received approx. 89.54% during the monsoon months. The study area is prone to mild and normal type of droughts. The Ranthambhore tiger reserve area is hot and arid with large variations in temperature. The average temperature in summers ranges from 25 to 43 degree Celsius while winters temperatures are 4 to 20 degree Celsius (Table 2). The relative humidity is generally low in most part of the year; it becomes as low as 10 to 15 percent during summer months. However during the rainy days the relative humidity goes over 60%. Meteorological Plot of Sawai Madhopur station is shown in Fig. 2.

Table 2 Climatic Data for Sawai Madhopur Station

Items Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual

Rai

nfa

ll

(in

mm

)

2001 0.0 0.0 0.0 21.0 121.0 125.0 432.0 223.0 8.0 48.0 0.0 0.0 978.0

2002 7.0 35.0 0.0 3.0 8.0 108.0 12.0 112.0 47.0 0.0 0.0 2.0 334.0

2003 4.0 48.0 0.0 0.0 0.0 77.0 272.0 350.0 136.0 0.0 0.0 1.0 888.0

2004 4.0 0.0 0.0 0.0 72.0 53.0 54.0 689.0 71.0 42.0 0.0 0.0 985.0

2005 0.0 0.0 1.0 41.0 0.0 220.0 578.0 74.0 107.0 0.0 0.0 0.0 1,021.0

2006 0.0 0.0 8.0 0.0 25.0 105.0 387.0 210.0 118.0 8.0 0.0 0.0 861.0

2007 0.0 50.0 21.0 11.0 8.0 240.0 362.0 290.0 139.0 0.0 0.0 0.0 1,121.0

2008 0.0 0.0 0.0 0.0 33.0 251.0 394.0 187.0 75.0 16.0 6.0 0.0 962.0

2009 0.0 0.0 3.0 0.0 21.0 25.0 119.0 108.0 35.0 86.0 29.0 0.0 426.0

2010 4.0 8.0 0.0 0.0 5.0 3.0 166.0 407.0 342.0 0.0 184.0 6.0 1,125.0

2011 0.0 16.0 0.0 0.0 4.0 308.0 245.0 310.0 72.0 0.0 0.0 0.0 955.0

Tem

p.

(0 C)

Mean 16.1 19.1 25.1 30.0 34.5 34.5 29.7 28.0 28.0 25.6 20.2 17.2 25.7

Min 7.8 9.8 16.0 21.2 26.7 28.6 25.8 24.7 23.5 18.3 11.0 8.3 18.5

Max 24.5 28.3 34.2 38.7 42.4 40.4 33.7 31.2 32.5 32.9 29.3 26.1 32.9

13

th Esri India User Conference 2012

Page 4 of 9

Figure 2: Meteorological Plot of Sawai Madhopur Station

5. Land Use / Land Cover Mapping: LULC is a very important aspect of central strategies for managing natural resources and monitoring carbon storge & sequestration. The LULC pattern of any region is an outcome of various physic-cultural factors and their utilization by man in time and space. Hence, it is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to be able to simulate changes (Prakasam 2010). Land use and land cover are two separate terminologies which are often used interchangeably (Dimyati et al. 1996). The land use describes the use to which the land is put, whilst land-cover describes the surface cover characteristics (Mc Cloy 1995).

In this study, author has used Landsat-1 RBV (79x82m), Landsat-3 MSS (79x82m), Landsat-5 TM (60m), Landsat-7 ETM+ (30m),

and ResourceSAT-2 LISS-III (23.5m) satellite imagery for year 1975, 1990, 2000, 2006, and 2012 respectively (Fig. 3). Image Classification toolbar in ArcGIS-10 software has used to generate different raster based land use - land cover layers (Fig. 4). ArcGIS-10 Image Classification Toolbar based supervised classification is the essential tool for extracting quantitative information from remotely sensed image data. LULC of the study area comprise mostly of forest land average approx. 65% and agricultural land average approx. 17%. This initial observation is confirmed by the statistics shown in Table 3.

Figure 3: Multi-Spectral Satellite Imageries (1975 to 2012)

Table 3 Land Use - Land Cover Statistics of the Study Area

Land Use - Land Cover Classes

1975 1990 2000 2006 2012

Sq. Kms % Sq. Kms % Sq. Kms % Sq. Kms % Sq. Kms %

Settlement 2.13 0.27 2.31 0.30 2.57 0.33 2.86 0.37 4.47 0.57

Crop Land 22.89 2.93 40.54 5.18 53.27 6.81 60.44 7.73 85.31 10.91

Fallow Land 51.77 6.62 89.94 11.50 90.52 11.57 104.04 13.30 60.49 7.73

Barren Land 33.51 4.28 44.03 5.63 45.57 5.83 63.03 8.06 71.58 9.15

Open Forest 232.33 29.70 247.94 31.70 268.50 34.32 254.35 32.52 271.84 34.75

Dense Forest 335.46 42.89 281.54 35.99 241.53 30.88 213.40 27.28 190.80 24.39

Ravines 73.67 9.42 50.20 6.42 56.26 7.19 60.18 7.69 73.69 9.42

River Bed 11.65 1.49 14.05 1.80 15.96 2.04 12.22 1.56 12.30 1.57

River 9.05 1.16 6.82 0.87 4.17 0.53 7.61 0.97 7.61 0.97

Water Bodies 9.77 1.25 4.86 0.62 3.88 0.50 4.10 0.52 4.14 0.53

Total 782.23 100.00 782.23 100.00 782.23 100.00 782.23 100.00 782.23 100.00

6. Land Use / Land Cover Change Pattern Analysis: Post-Classification approach is used in the study to detect the changes between two dates. In this approach Images belonging to different dates are classified and labelled individually. Later, the classification results are compared directly and the area of changes is extracted (Singh, 1989; Jensen, 1996; Yuan et al., 1999). Individual classification of two date’s image minimizes the problem of normalizing for atmospheric and sensor differences between two dates (Singh, 1989). To find out the change categories, Image Analyst extension of ArcGIS-10 software was used. Images of two years were input and as a result the output image contained the change categories. The summery of land use land cover change pattern analysis of ranthambhore tiger reserve is shown in Table 4, Table 5, Table 6, and Table 7.

13

th Esri India User Conference 2012

Page 5 of 9

Figure 4: Land Use Land Cover Maps (1975 to 2012)

Table 4 Land Use Land Cover Change Matrix (Year 1975 to 1990)

LULC Categories

Sett

lem

en

t

Cro

p

Lan

d

Fallo

w

Lan

d

Bar

ren

Lan

d

Op

en

Fore

st

De

nse

Fore

st

Rav

ine

s

Riv

er

Be

d

Riv

er

Wat

er

Bo

dy Area for

1975 (Kms

2)

Settlement 2.13 2.13

Crop Land 0.03 5.99 11.05 2.51 0.72 1.51 0.91 0.17 0.00 22.89

Fallow Land 0.12 9.53 23.25 5.24 8.29 3.68 0.70 0.82 0.14 51.77

Barren Land 0.03 4.48 9.74 9.55 3.76 2.63 2.57 0.47 0.26 0.01 33.51

Open Forest 16.78 30.43 15.11 159.98 0.44 9.06 0.03 0.05 0.45 232.34

Dense Forest 0.50 1.51 2.07 57.99 267.18 5.76 0.03 0.08 0.35 335.46

Ravines 2.84 13.15 8.31 15.56 2.48 29.72 0.87 0.59 0.15 73.67

River Bed 0.30 0.30 0.10 0.02 0.04 0.40 8.27 2.22 11.65

River 0.10 0.41 0.24 0.36 0.60 0.54 3.34 3.45 9.05

Water Body 0.00 0.10 0.90 1.28 3.00 0.54 0.04 0.03 3.88 9.77

Area for 1990 (Kms2) 2.31 40.54 89.94 44.03 247.9 281.55 50.20 14.05 6.8 4.86 782.23

Table 5 Land Use Land Cover Change Matrix (Year 1990 to 2000)

LULC Categories

Sett

lem

en

t

Cro

p

Lan

d

Fallo

w

Lan

d

Bar

ren

Lan

d

Op

en

Fore

st

De

nse

Fore

st

Rav

ine

s

Riv

er

Be

d

Riv

er

Wat

er

Bo

dy Area for

1990 (Kms

2)

Settlement 2.31 2.31

Crop Land 0.12 15.79 15.74 2.27 2.23 0.04 3.73 0.60 0.02 40.54

Fallow Land 0.07 19.09 47.77 7.54 2.22 0.43 12.63 0.05 0.11 0.02 89.94

Barren Land 0.06 5.38 7.86 19.28 5.82 0.36 5.09 0.20 44.03

Open Forest 10.09 15.73 7.31 198.28 8.51 7.61 0.01 0.16 0.27 247.95

Dense Forest 0.92 0.75 0.90 45.66 231.19 1.85 0.22 0.06 281.54

Ravines 1.43 2.41 8.23 13.53 0.27 24.08 0.21 0.04 50.20

River Bed 0.47 0.10 0.01 0.01 1.11 11.36 0.98 14.04

River 0.10 0.05 0.01 0.15 0.18 0.17 3.53 2.61 0.00 6.82

Water Body 0.00 0.11 0.03 0.62 0.54 0.03 3.53 4.86

Area for 2000 (Kms2) 2.57 53.27 90.52 45.57 268.50 241.54 56.26 15.96 4.17 3.88 782.23

13

th Esri India User Conference 2012

Page 6 of 9

Table 6 Land Use Land Cover Change Matrix (Year 2000 to 2006)

LULC Categories

Sett

lem

en

t

Cro

p

Lan

d

Fallo

w

Lan

d

Bar

ren

Lan

d

Op

en

Fore

st

De

nse

Fore

st

Rav

ine

s

Riv

er

Be

d

Riv

er

Wat

er

Bo

dy Area for

2000 (Kms

2)

Settlement 2.57 2.57

Crop Land 0.07 21.72 23.19 5.26 0.12 0.16 2.23 0.38 0.08 0.07 53.27

Fallow Land 0.18 23.59 50.09 10.15 2.89 0.21 3.21 0.00 0.12 0.08 90.52

Barren Land 0.01 2.15 6.90 28.81 1.55 0.13 5.99 0.02 0.01 0.02 45.57

Open Forest 6.92 13.91 9.01 208.1 17.09 12.90 0.19 0.34 268.50

Dense Forest 0.87 2.09 1.27 39.53 195.0 2.22 0.36 0.18 241.53

Ravines 0.03 4.88 7.73 8.00 1.34 0.56 32.80 0.68 0.24 0.00 56.26

River Bed 0.30 0.00 0.35 0.51 0.73 10.6 3.39 0.01 15.96

River 0.01 0.06 0.04 0.07 0.19 0.08 0.48 3.22 0.01 4.17

Water Body 0.00 0.07 0.14 0.20 0.05 0.02 0.00 3.39 3.88

Area for 2006 (Kms2) 2.86 60.44 104.0 63.03 254.3 213.4 60.18 12.2 7.61 4.10 782.23

Table 7 Land Use Land Cover Change Matrix (Year 2006 to 2012)

LULC Categories

Sett

lem

en

t

Cro

p

Lan

d

Fallo

w

Lan

d

Bar

ren

Lan

d

Op

en

Fore

st

De

nse

Fore

st

Rav

ine

s

Riv

er

Be

d

Riv

er

Wat

er

Bo

dy Area for

2006 (Kms

2)

Settlement 2.86 2.86

Crop Land 0.53 41.05 2.78 11.22 2.96 0.01 1.90 60.44

Fallow Land 0.31 38.57 54.81 6.02 2.66 0.04 1.31 0.32 104.04

Barren Land 0.22 3.53 1.62 47.40 4.92 0.07 4.85 0.42 63.03

Open Forest 0.04 0.51 0.35 3.89 223.77 5.90 19.79 0.10 254.35

Dense Forest 0.20 0.05 0.01 0.03 27.92 184.68 0.50 213.40

Ravines 0.24 0.96 0.81 2.96 9.16 0.09 45.34 0.58 0.04 60.17

River Bed 0.07 0.64 0.12 0.05 0.45 0.01 10.88 12.22

River 0.00 7.61 7.61

Water Body 0.00 0.00 4.10 4.10

Area for 2012 (Kms2) 4.47 85.31 60.49 71.58 271.84 190.80 73.69 12.30 7.61 4.14 782.23

7. Overview of Forest Carbon Stock Measurements: The main carbon pools in tropical forest ecosystems are the living biomass of trees and understory vegetation and the dead mass of litter, woody debris and soil organic matter. The carbon stored in the aboveground living biomass of trees is typically the largest pool. Estimating aboveground forest biomass carbon is the most critical step in quantifying carbon stocks and fluxes from tropical forests. Measurement protocols for other carbon pools are described elsewhere (e.g. Post et al 1999, Brown and Masera 2003, Pearson et al 2005, IPCC 2006).

The dry biomass can be converted to carbon content by taking half of the biomass weight (carbon content ≈50% of biomass; Westlake 1966). Carbon is lost to the atmosphere as CO2. To convert carbon in biomass to CO2, the tonnes of carbon are multiplied by the ratio of the molecular weight of carbon dioxide to the atomic weight of carbon (44/12). Estimating the biomass density of forest components is, therefore, the first step in forest carbon accounting.

7.1 The InVEST 2.4.2 Carbon Model: Carbon storage on a land parcel largely depends on the sizes of four carbon “pools:” aboveground biomass, belowground biomass, soil, and dead organic matter. The InVEST Carbon Storage and Sequestration model aggregates the amount of carbon stored in these pools according to the land use maps. For each LULC type, the model requires an estimate of the amount of carbon in at least one of the four fundamental pools, which has described in Table 8.

13

th Esri India User Conference 2012

Page 7 of 9

Table 8 Carbon Stored in Above and Below-Ground Biomass Differs Strongly among LULC Classes

LULC Code

LULC Classes

Amount of Carbon Stored in (in Mg ha-1

)*

Aboveground Biomass

Belowground Biomass

Soil Organic Dead Organic Matter

1 Dense Forest 140 70 35 12

2 Sparse Forest 65 40 25 6

3 Crop Land 23 35 30 5

4 Fallow Land 15 35 30 4

5 Scrub Land 30 30 30 13

6 Open Land 5 5 15 2

7 Urban Settlement 4 5 15 1

8 Other Land 0 0 0 0 *Note: The unit for all carbon pools is Mg of elemental carbon ha-1. This means that if your data source has information on Mg of CO2 stored ha-1, you need to convert those numbers to elemental carbon by multiplying Mg of CO2 stored ha

-1 by 0.2727.

The model runs on a gridded map of cells called raster format in GIS. Each cell in the raster is assigned a land use land cover (LULC) type such as forest, pasture, or agricultural land, etc. After running the model in raster format, results has been summarized and Shown in Table 9.

Table 9 Year wise Carbon Stored in Ranthambhore Tiger Reserve

Year 1975 1990 2000 2001 2012

Forest Area (Sq Kms) 567.79 529.48 510.03 467.75 462.64

Carbon Stored (Million Kg / Ha.) 34.15 32.35 30.66 28.78 27.52

Over the past century, human activities like burning of fossil fuels, deforestation and urbanization have resulted in high concentration of CO2 and other greenhouse gases in the atmosphere. CO2 is one of the most common greenhouse gases resulting global warming which in turn brings about rise in sea level, draught, deforestation, etc. The forest area in Ranthambhore tiger reserve has decreased from 1975 to 2012. Changes in forest area have resulting from human activities at large scale (tourism), and deforestation due to climate change.

7.2 The NLLUF-KP10 Model: NLLUF Model is the combinations of vegetation indexes (NDVI and LAI), LULC and Field data; which has developed by Pareta in 2010. The relation between remotely sensed measurements and vegetation parameters is captured by various vegetation indices. A vegetation index is defined as a mathematical combination of bands that indicates the presence of green vegetation (Lillesand and Kiefer, 1999).

Table 10 Model Parameter, Statistics, and Carbon Stock

S.No. Model Parameter Formula Result

1. NDVI Carbon = 107.2 * e (NDVI * 0.0194), (0.67 to -0.81) 35.97

2. SAVI SAVI = (1+L) * (NIR - R) / (NIR + R + L), Where L=0.5 162.33

3. LAI LAI = -ln (SAVI + 0.371) / 0.48 10.60

4. LULC based Carbon Stock (M. Kg / Ha.) Dense Forest 257

27.52

Sparse Forest 136

Crop Land 93

Fallow Land 84

Scrub Land 103

Open Land 27

Settlement 25

Other Land 0

5. Hybrid Field Data (HFD) ((DBH) 2

/ 12.57)m2 * H1m, DBH=12.0cm, and H=10.9m 19.34

Total Carbon Stock (Million Kg / Ha.) {NDVI + LAI(SAVI) + LULC + HFD}/4 23.35

13

th Esri India User Conference 2012

Page 8 of 9

Vegetation indices are routinely used to determine green biomass, green leaf area, LAI, stand biomass, percent ground cover, and amount of photosynthetically active vegetation (Baret and Guyot, 1991). Near infrared / red spectral band ratios such as the soil adjusted vegetation index (SAVI), and normalized difference vegetation index (NDVI), are widely used to estimate the LAI (Bulcock et al, 2010). The tree height (H) and diameter at breast height (DBH) were measured individually within the sample plots. The measurements of individual trees were summed as total volume for the plot. The measured data were extrapolated to find the growing stock per ha in each site studied. The model parameter, formula, and statistics are summarized in Table 10.

Author has estimate the carbon stock in the Ranthambhore tiger reserve for year 2012, which is 23.35 million kg per hectare by using NLLUF-KP10 model, and 27.52 million kg per hectare using InVEST model.

8. Conclusion: Analysis of the carbon sequestration potential of Ranthambhore National Reserve can have great implications in assessing its input on mitigating Climate Change. This requires analysing the carbon sequestered by the forest over a period of time. Monitoring changes and time series analysis is quite difficult with traditional method of surveying. Remote sensing and GIS analytical techniques can be applied for forest cover change detection and change in biomass. Through multi-temporal satellite images, it is not only possible to monitor the changes in coverage over the years but also provide estimates of carbon stocks built-up or lost. Remote sensing provides the synoptic view of study area with coverage on different timelines used for detection of change. Remotely sensed satellite data is a technology to calculate the biomass and ultimately carbon sequestration value of plants in a larger area in quick turnaround time and is cost effective.

Acknowledgement: I am profoundly thankful to my Guru Ji Prof. J. L. Jain, who with his unique research competence, selfless devotion, thoughtful guidance, inspirational thoughts, wonderful patience and above all parent like direction, behaviour and affection motivated me to pursue this work. I am also thankful to Kapil Chaudhery, Director Spatial Decisions for providing the necessary facilities to carry out this work.

References:

1. Albani M., Medvigy, D., Hurtt G.C., Moorcroft P.R., 2006. The Contributions of Land-Use Change, CO2 Fertilization, and Climate Variability of the Eastern US Carbon Sink. Global Change Biology. Vol. 12, pp. 2370-2390.

2. Baret, F., and Guyot, G., 1991. Potentials and Limits of Vegetation Indices for LAI and APAR Assessment. Remote Sens. Environ. Vol. 35, pp. 161-173.

3. Brown, S., Iverson L.R., Prasad A. and Liu D., 1993. Geographic Distribution of Carbon in Biomass and Soils of Tropical Asian Forests. Geo-Carto Int. Vol. 8, pp. 45-59.

4. Bulcock, H.H., and Jewitt, G.P.W., 2010. Spatial Mapping of Leaf Area Index using Hyper-Spectral Remote Sensing for Hydrological Applications with a Particular Focus on Canopy Interception. European Geosciences Union, Hydrol. Earth Syst. Sci. Vol. 14, pp. 383-392.

5. Canadell, J.G., Pataki, D.E., Gifford, R., 2007, Saturation of the Terrestrial Carbon Sink. In: Terrestrial Ecosystems in a Changing World (eds Canadell JG, Pataki DE, Pitelka LF), pp. 59 78. Springer, Heidelberg.

6. Champion, H.G., and Seth, S.K., 1968. A Revised Survey of Forest Types of India. Government of India, Delhi.

7. Dimyati, M., Mizuno, K., Kitamura, T., 1996. An Analysis of Land Use / Cover Change using the Combination of MSS Landsat and Land Use Map: A Case Study in Yogyakarta, Indonesia. International Journal of Remote Sensing. Vol. 17(5), pp. 931-944.

8. IPCC, 2006. Guidelines for National Greenhouse Gas Inventories. Vol. 4

9. Jensen, J.R., 1996. Introduction Digital Image Processing. Prentice Hall, New York

10. Lillesand, T.M., and Kiefer, R.W., 1999. Remote Sensing and Image Interpretation. John Wiley & Sons Inc., New Jersey.

11. Luyssaert, S., Sebastiaan, L., Schulze, E.D., Börner, A., Knohl, A., Hessenmo¨ller, D., Law, B.E., Ciais, P., and Grace, J., 2008. Old-Growth Forests as Global Carbon Sinks. Nature. Vol. 455, pp. 213-215.

12. Magnani, F., Mencuccini, M., Borghetti, M., 2007. The Human Footprint in the Carbon Cycle of Temperate and Boreal Forests. Nature. Vol. 447, pp. 848-850.

13

th Esri India User Conference 2012

Page 9 of 9

13. McCloy Keith R., 1995. Resource Management Information System: Process and Practice. London: Taylor and Francis.

14. Pareta, K., and Pareta, U., 2011. Forest Carbon Management using Satellite Remote Sensing Techniques: A Case Study of Sagar District (M. P.). International Scientific Research Journal. Vol. 3 (4), pp. 335-348.

15. Pearson, T., Walker, S., and Brown, S., 2005. Sourcebook for Land Use, Land-Use Change and Forestry Projects Winrock International and the BioCarbon Fund of the World Bank. pp 57.

16. Pongratz, J., Reick, C., Raddatz, T., Claussen, M., 2009. Effects of Anthropogenic Land Cover Change on the Carbon Cycle of the Last Millennium. Global Biogeochemical Cycles. Vol. 23, pp. 413-417.

17. Post, W.M., Izaurralde, R.C., Mann, L.K., and Bliss, N., 1999. Monitoring and Verification of Soil Organic Carbon Sequestration Proc. Symp. Carbon Sequestration in Soils Science, Monitoring and Beyond (December) ed N J Rosenberg, R C Izaurralde and E L Malone (Columbus, OH: Batelle Press), pp. 41.

18. Prakasam, C., 2010. Land Use and land Cover Change Detection through Remote Sensing Approach: A Case Study of Kodaikanal Taluk, Tamil Nadu. International Journal of Geomatics and Geosciences. Vol. 1(2), pp. 46-55.

19. Singh, A., 1989. Digital Change Detection Techniques using Remotely Sensed Data. International Journal of Remote Sensing. Vol.10, pp.989-1003.

20. Strassmann, K.M., Joos, F., Fischer, G., 2008. Simulating Effects of Land Use Changes on Carbon Fuxes: Past Contributions to Atmospheric CO2 Increases and Future Commitments due to Losses of Terrestrial Sink Capacity. Tellus. Vol. 60B, pp. 583-603.

21. Trumbore, S., 2000. Age of Soil Organic Matter and Soil Respiration: Radiocarbon Constraints on Belowground C Dynamics. Belowground Processes and Global Change. Vol. 10, pp. 399-411.

22. Westlake, D.F., 1966. The Biomass and Productivity of Glyceria Maxima: I. Seasonal Changes in Biomass. J. Ecol. Vol. 54, pp.745-53.

23. Yuan, D., Elvidge, C.D., Lunetta, R.S., 1998. Survey of Multispectral Methods for Land Cover Change Analysis. In Remote Sensing Change Detection: Environmental Monitoring Methods and Applications, edited by R.S. Lunetta and C.D. Elvidge (Chelsea, MI: Ann Arbor Press). pp. 21-39.