Indian National
Carbon Project –SDG goal 13
C S Jha & team"...the "environment" is where we
live; and "development" is what weall do in attempting to improve ourlot within that abode. The two are
inseparable.“ – Our common future

GEOSS-AP, 2017

National Remote Sensing Centre
Indian Space Research Organisation
Hyderabad , India

United Nations
Bruntland
report - 1987
In the middle of the 20th century, we saw our planet from space for the
first time. Historians may eventually find that this vision had a greater
impact on thought than did the Copernican revolution of the 16th
century, which upset the human self-image by revealing that the Earth is
not the centre of the universe. From space, we see a small and fragile
ball dominated not by human activity and edifice but by a pattern ofclouds, oceans, greenery, and soils. Humanity's inability to fit its
activities into that pattern is changing planetary systems, fundamentally.
Many such changes are accompanied by life-threatening hazards. This
new reality, from which there is no escape, must be recognized - andmanaged.

Top Fossil Fuel Emitters (Absolute)
Top four emitters in 2012 covered 58% of global emissions
China (27%), United States (14%), EU28 (10%), India (6%)

With leap year adjustment in 2012 growth rates are: China 5.6%, USA -4.0%, EU -1.6%, India 7.4%.
Source: CDIAC Data; Le Quéré et al 2013; Global Carbon Project 2013

Global Carbon Cycle (GtC/yr)

Source: Le Quéré et al 2013; CDIAC Data; NOAA/ESRL Data; Global Carbon Project 2013 Annual automated forest loss alert system
 Stubble burning in Indo-Gangetic plains Satellite monitoring of Pollutants
 SAR and LiDAR based forest biomass
 Biodiversity mapping at 1:10,000

Energy andEnvironment

INDC – 2.5 b tons of
CO2 – COP

NRSC FACILITIESOff Campus Technical Facilities
o Satellite Data Reception, Larsemann Hills, Antarticao Hangar Facilities for Aircraft at Begumpet Airporto Field Observation Laboratories for Carbon & Water Fluxes at Betul (MP),Sunderbans (WB), ….

Our Activities• Global• Realtime, OTS• Archive• Aerial• Satellite• Thematic Maps
• Geophysical Prod
• Information
Extraction

Data
Acquisition

Processing,
Dissemination& Archival

Thematic
Products

Web-GIS
Platform&
Services• BHUVAN• WRIS• NDEM-DSC

Presentation Outline• Background – SDG and climate change• Applications of RS by NRSC (ISRO) in– National Biodiversity Characterization– Vegetation and soil Carbon – pools & fluxes– Indian National Communications to the UNFCCC– Forest and Agriculture Fires inventory & Alert– National forest cover loss assessment• Challenges and way forward

India – Vegetation Types

Biodiversity Characterisation

 Primary Database on Vegetation
Reddy, C.S., Jha, C.S., Diwakar, P.G. & Dadhwal, V.K. 2015. Nationwide classificationof forest types of India using remote sensing and GIS (Environmental Monitoringand Assessment DOI: 10.1007/s10661-015-4990-8).

Vegetation type and land cover map

Biodiversity CharacterisationClass
Tropical wet evergreen forest

Area (km2)47192% of Area
1.44

Tropical dry evergreen forest

378

0.01

Subtropical broad leaved hill forest

20623

0.63

Subtropical dry evergreen forest

354

0.01

Montane Wet Temperate forest

7457

0.23

Tropical Semi evergreen forest

48295

1.47

Tropical moist deciduous forest

20,7,649

6.32

Tropical dry deciduous forest

21,7,713

6.62

Tropical thorn forest
Subtropical Pine forest
Himalayan Moist Temperate forest

8166
22880
27510

0.25
0.70
0.84

Himalayan Dry Temperate forest

7634

0.23

Sub Alpine forest
Littoral and swamp forests

5774
3940

0.18
0.12

19614
4415
553
4278
10400
853
21236481494
5592
17,49,076
95586
267837
84801
62314
62521
32,87,263

0.60
0.13
0.02
0.13
0.32
0.03
6.46
2.48
0.17
53.21
2.91
8.15
2.58
1.90
1.90
100.00Tropical moist scrub
Subtropical scrub
Temperate scrub
Moist Alpine scrub
Dry Alpine scrub
Mangrove scrub
Tropical dry scrub
Grasslands
Savanna
Agriculture
Plantations/Orchards
Barren land
Water bodies
Snow
Settlements
Grand total

Reddy, C.S., Jha, C.S., Diwakar, P.G. & Dadhwal, V.K. 2015. Nationwide classification of forest types of India using remotesensing and GIS (Environmental Monitoring and Assessment DOI: 10.1007/s10661-015-4990-8).

Biological Richness

Biologically rich areas delineated using geospatial techniques……..Provides baseline forconservation prioritization and monitoringInventorisation and Monitoring of Biosphere Reserves
Agasthyamalai

Nilgiri

Biosphere monitoring under MAB is criticalfor verifying conservation efficacy.
Long term satellite records help to clarify
issues better.

Similipal

KachchhStudy requested by MoEnF aims at create NR
database for analysing Land use / land coverin four major biospheres of the country viz.,
Simlipal, Nilgiri, Rann and Agasthymalai.
Assess temporal changes in land use dynamics(at 5 year interval, starting from date of
notification) using IRS Dataset.
Datasets correspond to 1990,1995,2000,2005
and 2010 periods in tandem with current
phytosociology.

National Carbon Project (NCP)
ISRO’s Geosphere Biosphere Programme (ISRO-GBP)•

NCP was initiated as a major integrated project under ISRO-GBP in 2007 with major
goals on• Assessment of Carbon Pools, Fluxes and Net Carbon balance for terrestrial
biosphere in India• To establish a observational network and remote sensing-based spatial
databases for modeling and periodic assessment of carbon balance• To provide support to national activity with respect to carbon balance under
National Communication to UNFCCC•

Achieved through implementation of 3 sub-projects, namely––––

VCP : Vegetation Carbon Pool – National integrated sampling
SCP : Soil Carbon Pool – National Integrated sampling
SVF : Soil & Vegetation - Atmosphere Fluxes – Flux Towers, RS-based modeling
Atmospheric Carbon Observations & Hydro-geochemistryIGBP-NCP Terrestrial Carbon Cycle
Component Studies• Soil-Vegetation-Atmosphere fluxes (SVAF)
 Flux towers, Forests & Agriculture
eddy flux based observation network to monitor net carbon exchange in forests &agriculture
Upscaling flux measurements to regional scale using remote sensing• Vegetation Carbon Pools (VCP) Phytomass
 Long term forest cover change
National, spatially explicit Tier-III based forest carbon pool and change assessment overIndia through conjunctive use of remote sensing, and field data (with revisit
measurement)• Soil Carbon Pools (SCP) Soil Carbon Pools dataset
National spatial estimates of organic and inorganic soil carbon stocks

Soil-Vegetation-Atmosphere Fluxes (SVAF)
Flux towers - Objectives• Establish eddy flux based observation network to monitor net
carbon exchange• Measurement and modeling of soil respiration / emission fluxes• Up-scaling of carbon assimilation fluxes to regional scale using
satellite remote sensing• Modeling C-fluxes in forest ecosystem using forest cover change,
forest growth models and inventory data
• Evaluation of spatial terrestrial carbon model for long-term
simulation of carbon cycle over India

Sub Project: Soil-Vegetation-Atmosphere fluxes (SVAF)
Forest & Agriculture Ecosystems
Diurnal Average variation of CO2 Flux – Betul, M.P
10
CO2 Flux (µmol m-2 s-1)

Objectives• To establish eddy flux tower based observation
network for monitoring net carbon exchange.
• Up-scaling flux measurements to regional scaleusing remote sensing

0

-10
-20
Jan

-30

0

Feb

12

0

Mar

12

0

Apr

12

0

12

May
0

12

Jun
0

147

12

Sep
0

12

Oct
0

12

Nov
0

12

Dec
0

12

December

2012.87 g CO2 m-2(or)
548.46 g C m-2.

-83
-218

-224

-302
-563

Feb

Aug
0

Net Annual Production

216

3
Jan

12

Loss
185

95

4

Jul
0

Time of the day

Net CO2 Sequestered (g CO2 m-2)
Gain

12

Mar

Apr May

Jun

Jul

Aug

-383

-431
-451

Sep

Oct

Nov

Dec

5

Diurnal Average variation of CO2 Flux – Sundarbans, W.B

1

7

9

8

Being Established
Operational

2

6

1.
2.
3.
4.
5.
6.
7.
8.
9.

Betul (Teak Forests), Madhya Pradesh
Sundarban (Mangroves), West Bengal
Barkot (Sal), Uttarakhand
Haldwani (mixed plantation), Uttarakhand
Sahranpur (rice crop), Uttar Pradesh
Maruteru (rice crop), Andhra Pradesh
Jawalgera (crop), Kanrnataka
Kanha (Sal Forests), Madhya Pradesh
Bandipura (deciduous forests) , Karnataka

CO2 Flux (µmol m-2 s-1)

5
0
-5
-10

Jan

-15

0

Feb

12

0

12

Mar
0

Apr

12

0

12

May
0

12

Jun
0

12

Jul
0

12

Aug
0

12

Sep
0

12

Oct
0

12

Nov
0

12

Dec
0

12

Time of Day
Net CO2 Sequestered (g CO2 m-2)
Gain

Loss

Net Annual Production

-14
-66

-50
-82

-97

-77
-108

-103

-110

-109

Aug

Sep

Oct

-99
-91

-200

Jan

Feb

Mar

Apr May

Jun

Jul

Nov

Dec

1005.32 g CO2 m-2(or)
273.93 g C m-2.

TG GPP (g C m-2d-1)

300

Upscaling of
tower based
fluxes toregional level
using remotesensing

250
200
150

100

y = 0.9891x + 9.3139
R² = 0.9014

50
0

0

Betul, Teak Forests, MP

Sundarbans, WB

Kanha flux tower

100

200

Observed GPP (g C m-2d-1)

300

TemperatureGreenness
ModelNational Carbon Project – Soil-vegetation-Atmosphere Flux Studies

Flux Tower Measurement: Status
Flux Towers Status

Implementation Steps
Site Selection
MOU / Forest Dept
AAI/DGCA Approval
Site Infrastructure
Tower Establishment
Instrument installation
Operational with field data

4
3
7

6

1

Flux Tower Status
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.

Betul , teak forests
Sundarbans, mangroves
Haldwani, mixed plantation
Barkot, sal forests
Maruteru, rice cropKanha, Sal Forests
Rice Crop, Meerut
Gumngaon (NEERI)
Mahabubnagar (By IITM)
Grasslands, Madurai
Rice crop, CRRI, Cuttack
Rubber Plantation, RRI, Kottayam

2
11

8

9

5

ISRO-GBP Studies
Other Studies

1210

Sensors on Betul Flux Tower
CO2, H2O Flux

Sap Flow

Fast Response – IRGA & Ultrasonic Anemometer
Frequency of Measurement: 10 Hz

Wind Speed & Dir.

National Carbon Project – Soil-vegetation-Atmosphere Flux Studies

RH & Air Temp.

Sap Flow Meter (HRM Sensor)
Frequency of Measurement: 15 min

Incoming Sol. Rad.

Phenology Camera

Data Logger

Phenocam (CC5MPX)
Frequency of Measurement: 30 min

Data Logger – Campbell Sci. CR3000

Soil Moist.

Soil Temp.

Automated Soil CO2

Delta-T PR2 - 6

Komoline KDS-037

LI-8100A

CGR3 Pyygeometer (LW)

CMP6
Pyranometer (SW)RM YOUNG - 5103

Rotoronic Hygroclips S3

CGR03, CMP6

Sunderban Carbon Flux Tower Location

National Carbon Project – Soil-vegetation-Atmosphere Flux Studies

India

Sunderban Biosphere Reserve

Tower Location

Bonnie Camp

Jetty

CreeksCanopy Walkway

Creek-2

Tower Location

Creek-1

Carbon Flux Tower Location

Seasonal Diurnal Average variation of CO2 Flux – Betul, M.P
CO2 Flux (µmol m-2 s-1)

10
0

-10
-20
Jan

-30

0

Feb

12

0

Mar

12

0

Apr

12

0

12

May
0

12

Jun
0

147

95

Aug
0

12

Sep
0

12

Oct
0

12

Nov
0

12

Dec
0

12

December

Net Annual Production

216

2012.87 g CO2 m-2(or)
548.46 g C m-2.

-83
-218

-224

-302
-563

Feb

12

Loss
185

Jan

Jul
0

Time of the day

Net CO2 Sequestered (g CO2 m-2)
Gain

12

Mar

Apr May

Jun

Jul

Aug

-383

-431
-451

Sep

Oct

Nov

Dec

Seasonal Diurnal Average variation of CO2 Flux – Sundarbans, W.B.
CO2 Flux (µmol m-2 s-1)

5
0
-5
-10
Jan

-15

0

Feb

12

0

12

Mar
0

Apr

12

0

12

May
0

12

Jun
0

12

Jul
0

12

Aug
0

12

Time of Day
Net CO2 Sequestered (g CO2 m-2)
Gain

Loss

Net Annual Production

-14
-66

-50
-82

-97

-77
-108

-103

-110

-109

Aug

Sep

Oct

-99
-91

-200

Jan

Feb

Mar

Apr May

Jun

Jul

Nov

Dec

1005.32 g CO2 m-2(or)
273.93 g C m-2.

Sep
0

12

Oct
0

12

Nov
0

12

Dec
0

12

Nov2011
Dec2011
Jan2012
Feb2012
Mar2012
Apr2012
May2012
Jun2012
Jul2012
Aug2012
Sep2012
Oct2012
Nov2012
Dec2012
Jan2013
Feb2013
Mar2013
Apr2013
May2013
Jun2013
Jul2013
Aug2013
Sep2013
Oct2013
Nov2013
Dec2013
Jan2014
Feb2014
Mar2014
Apr2014
May2014
Jun2014
Jul2014
Aug2014
Sep2014
Oct2014
Nov2014
Dec2014
Jan2015
Feb2015
Mar2015
Apr2015
May2015

Heat Flux (Wm-2)

National Carbon Project – Soil-vegetation-Atmosphere Flux Studies

Latent Heat & Sensible Heat Flux- Betul

700

600

500

400

300

200

100

0

-100

Latent Heat Flux
Sensible Heat Flux

National Carbon Project – Vegetation Carbon Pools Assessment

Approaches for Biomass Estimation
Approaches for biomass estimation:
-Field measurement.

- Remote sensing

-Geographic information System

Most common used remote sensing approaches are-

- Direct Remote Sensing (DR) Approach- Stratify & Multiply (SM) Approach- Combine & Assign ( CA) Approach

Sub Project: Vegetation Carbon Pools
Objectives• Spatial distribution of phytomass carbon in Indian forests• Longterm forest cover change• Assessment of trees outside forests
Yellapur, 2014

Terrestrial Laser Scanner Studies

VCP-Phase-1

VCP-Phase-2
SingleTree
Extraction

AGB Estimation using L-Band SAR

AGB Estimation using GLAS (LiDAR)

Importance Ra
Spatial estimation of Biomass using a Random Variable
ForestNon parametric approach, resistant to noise and outliers,
very large number of variables possible, Can use
categorical and continuous variables
Model based validation

Spatial Data Used:• Phenology MODIS•

Bio-Climate Variables WORDCLIM•

L-band Microwave ALOS
-HV, HH• Forest Height GLAS•

Topography SRTM
-Elevation, Slope, Aspect

HV
bio19
bio12
mean
bio4
medianbio18
elevation
bio17
bio15
bio7
bio13
bio9
bio2
bio3
bio16
bio8
bio6
base
min
slope
heightbio14
mid_seas
bio1
bio5
peak
bio11
HH
max
10

12

14 16 18
%IncMSE
Rattle 2015-Sep-

Random forest variable
importance

As expected, HV is of high importance but GLOBAL forest height is insignificant

National Biomass Map – SAR, Phenology, Met & plot data

Knn estimate with a RF distance measure500

Predicted vs. Observed
Random Forest Modelindia.csv

300
200
100
0

Predicted
Predicted

400

Linear Fit to Points
Predicted=Observed

Pseudo R-square=0.806
0

100

200

300

400

Observed

AWBM
Rattle 2015-Sep-25 14:06:10 suraj

500

600

Error budget

MeasureDBH ofone treeAllometricModel
Sum overtrees

AGB ofone treeQualityof data
Qualityof modelSize of plot

AGB ofone plot

Average overplots

Representativenessof plots

Type 1 Single tree, averages out across plots
Type 2 Choice of allometric modelType 3 Minimized by large sized multi plot census
Type 4

AGB of forest

Chave, J., Condit, R., Aguilar, S., Hernandez, A., Lao, S., Perez, R., 2004. Error propagation and scaling for tropical forest biomass estimates. Philosophical Transactions of theRoyal Society of London. Series B: Biological Sciences 359, 409–420.

NRSC Large Plot Network

13

22

24

15

Odisha Forest Department (5 sites + distributed plots)

Lidar Remote sensing of forest
biomass

Returns colored by height above ground

ALTM (2005 )Growth analysis incomparision with2005 aerial LiDAR,
very high point
density in 2013

Target on ground

Uppangala, Karnataka

Intensity image

Target from air

15, 1 ha plot
Biomass range (t/ha) 131 to 687 (443 ±Biomass modelling using
Distribution metrics

Large Plot Experiment Design
1. At least 15 1-ha permanent sampling plots per site. Plotsestablished and monitored according the best tropicalforestry standards (Rainfor or CTFS protocols) including
location and height

2. Aerial LiDAR scanning (ALS) over 100 sq km, with better
than 10 pts per sq. m, repeat measurement every 5 years
3. Terrestrial LiDAR scanning (TLS) at (at least) two of thepermanent plots, ideally all
4. Weather/soil moisture measurements
5. Wood specific gravity and seasonal biophysicalparameters

Example tree map for 1 ha permanent plot
Tree wise DBH

0

20

40

Y-axis

60

80

100

Terminalia paniculata
Tectona grandis
Lagerstroemia microcarpa
Dalbergia latifolia
Meyna laxifolia
Xylia xylocarpa
Misc

0

20

40

60
X-axis80100

Biomass Estimation using Aerial LiDAR

Uppangala CHM, 7 sq km

Selected tree measurements shown on CHM(circles are proportional to field measured DBH)

Biomass Estimation using Aerial LiDAR

Betul CHM, 100 sq km

35

40

15

25

30

35

Median Height (meters)

40

2.0

35 12 40 14

10

100

140

RSErel=15.29%
RSErel=14.11%

60

10 20 30 40 50
Field AGB (Tons/ha)

Field AGB (Tons/ha)

700
500
300
100

Field AGB (Tons/ha)

RSErel=17.93%

20

10
30

Betul (Power Model)
AGB(100)(100)
Basal Area

Uppangala (Power Model)
AGB (100)15825(meters)
MedianMedian
Height Height(meters)

Median Height (meters)

10

6 20

Fi

4.0

2

10

15

6 20

825

10
30

35 12 40 14

MedianMedian
Height Height(meters)(meters)a single LiDAR derived metric, the median canopy height is useful to describe the variationin above ground biomass at the hectare scale in two very different forest systems.
A similar relationship has been described in other continents

18

30

14

25

8 10

20

Field AGB (Tons/ha)

15

Fi

Fiel

4.5

Fiel

10

Biomass Model with ALS derived H50

TLS SCANNING• Instruments Required:
1. Terrestrial LiDAR Scanner and Camera

2. Reflectors
3. Tripods for both Scanner and Reflectors

4. Car Battery

TLS Tripod

Reflector Tripod

TLS

Reflector

Scan design for Evergreen Forest

Scan Design for Dry Deciduous Forest

Scan Configuration

Bomanali Point CloudTransectSingle Tree
• Extracted manually from thefull point cloud.
• Higher order branches werealso removed .

Areas to work on
1. Capacity to handle extremely huge datasets.
2. Accurate Single tree detection.
3. Very precise Filtering of redundant points.
4. For further exploration:– Detection and filtering of points corresponding
to leaves.

Future Plans
Expansion of network and new instrumentation••

Addition of network to cover major eco-regions in India with similar
methodology (add ten more locations by 2020)
Development of alternate assessment approach using drone LiDAR and highresolution satellite imagery
Development of new techniques••

TLS based improvement to Allometry
Development of tree based approaches for estimation of AGB in place of theexisting area based approach
Collaborations••

Collaborations for upscaling using RS – JAXA, GEDI, BIOMASS
Readiness for NISAR CalVal and productSouth Asia (SAARC countries):
Long Term Forest Cover Change Over Eight Decades

Contributors:
C. Sudhakar Reddy, K.V. Satish, S. V. Pasha, K.R.L. Saranya,
C.S. Jha, R.S. Reddy, P.G. Diwakar & V.K. Dadhwal

Forestry & Ecology Group, Remote Sensing Applications Area,
National Remote Sensing Centre , Hyderabad

Background
Deforestation has impacts on carbon stocks, biodiversity,
ecosystem services and livelihoods.

 The scarcity of data on deforestation limits understandingof the carbon cycle, biodiversity loss and climate change(IPCC, 2005; CBD, 1992).
 A long term forest change dataset is useful to model net
Carbon emissions.
 Remotely sensed data contributes for specifying thelocation of deforestation and the extent of the deforested
area.
Objectives

 To prepare forest cover map of South Asian countries
 Geospatial analysis of long term forest cover changes

Datasets Used
Sl.

1

Maps/
RS data

PeriodTopographical 1920maps
1940

Scale/
Sri
India Nepal Bangladesh Bhutan
Afghanistan Pakistan Total
Resolution*Lanka

1:250,000

251

17

19

6

NA

NA

10

303

2 Landsat MSS

1975

80m

356

11

14

4

8

14

16

423

3 Landsat MSS

1985

80m

452

9

16

2

5

5

8

487

4

1990

30m

-

5

-

-

-

4

4

13

1995

72.5m

470

7

17

8

5

3

5

514

2005

56m/23.5m

64

12

9

4

5

14

15

123

2014

30m

-

12

13

4

12

12

15

58

Resourcesat-2
2013/14
AWiFS

56m

64

2

-

-

-

-

-

66

1657

75

88

28

35

51

59

1997

Landsat TM

IRS 1A /1B
LISS-IIRS P6
6 AWiFS/ LISSIII5

7 Landsat 8 OLI
8•Resolution for satellite datasets(Topographical maps: http://www.lib.utexas.edu/maps/ams/; Landsat data: NASA; IRS data : NRSC,ISRO)

South Asia : Forest Cover (2013/2014)

Total Geographical Area: 51,35,263 km2
Forest cover: 14.7% of geographical area

NRSC/ISRO

South Asia : 5 km gridded Fractional Forest Cover map(2013/2014)

NRSC/ISRO

South Asia : Forest Cover (Area in km2)
Country

India

Total
Geographical Forest 1930 Forest 1975 Forest 2013/14
Area3287253
8,69,012
6,53,220
6,25,565

Bangladesh

14757023,14016,56814,086

Bhutan38394

26,896

26,26926,207

Pakistan
Sub total

79609542,69,312

36,379
9,55,427

20,479
7,16,536

20,306
6,86,164

Sri Lanka
Afghanistan
Sub total
Total

65610
652860
7,18,470
49,87,782

No Data
No DataNA
9,55,427

23217
6739
29,956
7,46,492

21936
6646
28,582
7,14,746

The percentage of forest loss in last 8 decades: 44.2% in Pakistan, 39.1% in Bangladesh,

28% in India, 5.5% in Sri Lanka, 2.6% in Bhutan and 1.4% in Afghanistan.

Country wise forest cover 2013/14 (Area in %)68.3% of forest area / TGA

70.0

60.0
50.0
33.4

40.0

27.6
30.0

19.0

20.0

9.5
2.6

10.0

1.0

0.0

India

Bangladesh

BhutanNepal

Sri Lanka

Pakistan

Afghanistan• South Asia represents 14.7% of geographical area under forest cover in 2013/14.

• The highest percentage of forest cover found in Bhutan, followed by Sri Lanka.
1/12/201753

South Asia:
5 km gridded Fractional Forest Cover (1930, 1975 & 2014)
Pakistan
1930

1975

2014

1975

Sri Lanka

2014

1975

Afghanistan 2014

Long term forest cover change
in India, Bangladesh, Bhutan and Sri Lanka

1930-2014

1975-2014

Reddy, C.S., Jha, C.S., Dadhwal, V.K., Harikrishna, P., Pasha, S.V., Satish, K.V. et al. 2016. Biodiversity and Conservation 25: 93–116.
Reddy, C.S., Jha, C.S., Diwakar, P.G. & Dadhwal, V.K. 2015. Environmental Monitoring and Assessment 187:1-30.
Reddy, C.S., Pasha, S.V., Jha, C.S., Diwakar, P.G. & Dadhwal, V.K. 2016. Global and Planetary Change 139: 173–182.
Reddy, C.S., Rakesh, F., Jha, C.S., Athira, K., Diwakar, P.G. Dadhwal, V.K. et al. 2016. Global and Planetary Change 143: 50-65.

Forest cover change in Bangladesh
PeriodArea (km2)% of Total
Geographical
Area

1930
1975
1985
1995
2005
2014

23140
16568
16275
15852
14957
14086

15.7
11.2
11.0
10.7
10.1
9.5

Net rate of

Gross rate ofRate ofDeforestation

Deforestation

Afforestation

1930-1975

0.74

0.74

0.00

1975-1985

0.47

0.48

0.17

1985-1995

0.26

0.37

0.10

1995-2005

0.53

0.56

0.03

2005-2014

0.75

0.77

0.02

PeriodFormula to calculate rate offorest cover change

0.90

Rate of forest cover change

0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00

1930-1975

1975-1985

Net Rate of Deforestation

1985-1995

Gross Rate of Deforestation

1995-2005

2005-2014

Net Rate of Afforestation

Highlights of the study The highest annual rate of deforestation found in Pakistan, Bangladesh and India
between 1930-1975.
The highest annual rate of deforestation found in Bangladesh (0.43), followed by
Sri Lanka (0.15) from 1975-2014.

 Bhutan and Afghanistan shows low annual rate of deforestation among South
Asian countries.


Overall annual rate of deforestation for South Asia was estimated as 0.60 during
1930-1975 and 0.12 in 1975-2013/14.

National Carbon Project – Vegetation Carbon Pools Assessment

India: Forest cover

Period
1930
1975
1985
1995
2005
2013

Area (km2)
869,012653,220
638,460
630,795
627,233
625,565

Forest cover change% of TGA
26.4
19.9
19.4
19.2
19.1
19.0

Comparison of forest cover as per FAO, FSI & NRSC

Reddy, C.S., Jha, C.S., Dadhwal, V.K., Hari Krishna, P., Pasha, S.V, Satish, K.V., Dutta, K., Saranya, K.R.L., Rakesh, F., Rajashekar, G. & Diwakar, P.G.
2015. Quantification and monitoring of deforestation in India over eight decades (1930-2013) (Biodiversity and Conservation).

Historical trend in phytomass carbon

7000.00

100.0090.00

6000.00

Carbon (Tg)

70.00

60.00

4000.00

50.00
3000.00

40.00

Forest area (Mha)

80.00
5000.00

30.00

2000.00

20.00
1000.00

10.00

0.00

0.00
1930

1975

1985
Forest area

1995

2005

2013

CarbonC. Sudhakar Reddy, F. Rakesh, C.S. Jha, K. Athira, Sonali Singh, V.V.L. Padma Alekhya, G. Rajashekar, P.G. Diwakar & V.K. Dadhwal Indian forest phytomass carbon stocks andfluxes during 1930-2013 – Geospatial assessment. Accepted in Global and Planetary change.

Sub Project: Soil Carbon Pools
Objectives• Spatial assessment of organic and inorganic soil carbon stocks

Indian National Communication to UNFCCC
SNC, BU-Report – 2014 & TNC

Remote Sensing based Inputs to Land use, land-use Change and Forestry (LULUCF)

Results
2005

177 TC sequestered2010

Importance

Forest fires effect ecology, biodiversity, climate, environment,
human health and economy.

Significant source of carbon to the atmosphere.

Emission of trace gases and aerosol particles.

Direct contributors to climate change.

Relevance> 50% of Indian forests are prone to fire, ~6-9% experience
intense fire episodes~ Rs 500 crores is spent annually in India on fire fighting
Information on location, extent, intensity and rate of spread –critical

Near real time information necessary for effective fire management

RS sensors have the potential to identify active fires and burn scars



Identified as a continued effort as part of National Meet, Sep 2015

Daily monitoring of Forest Fires in India

NRSC, ISRO, 2014

Active Fire Monitoring - 2015

Khurpa Tal, Naini Tal

Uttarakhand burned area analysis using RISAT-1 MRS SAR data

Agriculture Residue (Stubble) Burning
Background• The Rice-wheat cropping system (RWS) is widelypracticed in the Indus (Punjab and Haryana) and
Gangetic Plains (UP, Bihar & WB), which accounts
for nearly 12 million hectares.
• Common practice to clear remaining straw andstubble after harvest to prepare the field for next
cropping cycle• Very few localized studies exist• Uncertainty in estimates of total area and amountof residue (biomass) burnt.

MODIS based active fire detections during Sep-Nov 2014
Based on Overpasses at ~1030 and ~1430 daily
District Reference
PunjabSl.No District
Sl.No District
Sl.No District
Sl.No District
1 Amritsar
7 Fazika
13 Ludhiana
19 Patiala
2 Barnala
8 Firozpur
14 Mansa
20 Rupnagar
3 Bathinda
9 Gurdaspur 15 Moga
21 Sangrur
4 Chandigarh
10 Hoshiarpur 16 Muktsar
22 Sas Nagar (Mohali)
5 Faridkot
11 Jalandhar
17 Nawan Shehar 23 Tarn Taran
6 Fatehgarh Sahib 12 Kapurthala 18 Pathankot

HaryanaSl.No District
1 Ambala
2 Bhiwani
3 Faridabad
4 Fatehabad
5 Gurgaon

Kiran Chand et al., Current Science, 2015

Sl.No District
Sl.No District
11 Kurukshetra
16 Rohtak
12 Mahendragarh 17 Sirsa
13 Panchkula
18 Sonepat
14 Panipat
19 Yamuna Nagar
15 Rewari

15-Nov-14
12-Nov-14
09-Nov-14
06-Nov-14
03-Nov-14
31-Oct-14
28-Oct-14
25-Oct-14
22-Oct-14
19-Oct-14
16-Oct-14
13-Oct-14
10-Oct-14
07-Oct-14
04-Oct-14
01-Oct-14
28-Sep-14
25-Sep-14
22-Sep-14
19-Sep-14
16-Sep-14
13-Sep-14
10-Sep-14
07-Sep-14
04-Sep-14
01-Sep-14

Active Fire Detections

40000

20000

0

2500

2000

1500

1000

500

15-Nov-14
12-Nov-14
09-Nov-14
06-Nov-14
03-Nov-14
31-Oct-14
28-Oct-14
25-Oct-14
22-Oct-14
19-Oct-14
16-Oct-14
13-Oct-14
10-Oct-14
07-Oct-14
04-Oct-14
01-Oct-14
28-Sep-14
25-Sep-14
22-Sep-14
19-Sep-14
16-Sep-14
13-Sep-14
10-Sep-14
07-Sep-14
04-Sep-14
01-Sep-140Total fire detections = 15222

Sl.No District
6 Hisar
7 Jhajjar
8 Jind9 Kaithal
10 Karnal

Fire Radiative Power (MW)MODIS based number of fire occurrence years during 2004-2014 (Sep-Nov)

District Reference
PunjabSl.No District
Sl.No District
Sl.No District
Sl.No District
1 Amritsar
7 Fazika
13 Ludhiana
19 Patiala
2 Barnala
8 Firozpur
14 Mansa
20 Rupnagar
3 Bathinda
9 Gurdaspur 15 Moga
21 Sangrur
4 Chandigarh
10 Hoshiarpur 16 Muktsar
22 Sas Nagar (Mohali)
5 Faridkot
11 Jalandhar
17 Nawan Shehar 23 Tarn Taran
6 Fatehgarh Sahib 12 Kapurthala 18 Pathankot

HaryanaSl.No District
1 Ambala
2 Bhiwani
3 Faridabad
4 Fatehabad
5 Gurgaon

Sl.No District
6 Hisar
7 Jhajjar
8 Jind9 Kaithal
10 Karnal

Sl.No District
Sl.No District
11 Kurukshetra
16 Rohtak
12 Mahendragarh 17 Sirsa
13 Panchkula
18 Sonepat
14 Panipat
19 Yamuna Nagar
15 Rewari

Automated Forest Loss Alerts

Historical trend in India’s forest area
Annual Rate of Forest Cover Change
0.9
0.8
0.7

0.6
0.5
0.4
0.3
0.2
0.1
0
1930-1975

1975-1985

1985-1995
Gross Rate

Reddy, C.S., Jha, C., Dadhwal, V., Krishna, P.H., Pasha, S.V., Satish, K.,
Dutta, K., Saranya, K., Rakesh, F., Rajashekar, G., others, 2016.
Quantification and monitoring of deforestation in India over eight
decades (1930-2013). Biodiversity and Conservation 25, 93–116.

1995-2005

2005-2013

Net Rate

 Intended Nationally Determined Contribution(INDC): additional carbon sink of 2.5 to 3.0
billion tonnes of CO2 by 2030

Automated Forest CoverChange Alert System
Forestry and Ecology Group

National Forest Cover Monitoring
Current Scenario National Forest Cover Mapping – Forest Survey of India (FSI)Biennial (2 years)
LISS-3 (24m data)
Large scale Mapping (1:50000)
Four Density Classes with Area Estimates

× No spatial Map of Forest Cover Change.

Requirement
 Monitoring at annual to sub-annual scales for detectionof forest cover change.
 Automated Method.
 Detect Fine changes.
 Not sensitive to Phenological changes.
 Not Sensitive to transient events (cloud, snow etc.,).
 Low Commission error (False alerts).

Fine change

Cloud and Snow

Phenological changes

Automated forest cover loss locations using IRS AWiFS
30

WEST BENGAL

30

UTTARANCHAL37

UTTAR PRADESH

22

108

70
68

TRIPURA

199

435
433

TELANGANATAMIL NADU

14

RAJASTHAN

0
2

43
80
57

PUNJAB

202
178

ORISSANAGALANDMIZORAMMEGHALAYAMANIPUR

28
41
200

23

MAHARASHTRA
95

MADHYA PRADESH
0

KARNATAKA

60
40
16
31

HARYANAGOA

140

CHHATTISGARH
28
20

BIHAR

250

108
129
109
104
118
77

ASSAM
ARUNACHAL PRADESH
Andhra Pradesh
0

232

96

0
2
1
5

GUJARAT233

44

JHARKHAND
HIMACHAL PRADESH

294

159

40
59

KERALA

Total Locations
 2013-14 = 2608
 2014-15 = 2691

331

173
3
2

100

Loss Locations 2014-15

200

300

400

Loss Locations 2013-14

500Comparision of AWiFS loss locations with Very High resolution satellite imagery

2010

2014

30 October 2014

22 November 2014

2011

2014

22 November 201406 December 2014

Way forward• Monitoring of Indian vegetation• Efforts towards reducing uncertainty in AGB• Advances in SAR, LiDAR for tree height,
volume, LAI etc.
• National fire burnt area assessment• Fire Danger Rating and emission scenarios• Developing methods for tier-3 based inputs to
NATCOMAcknowledgements :ISRO Projects Teams
andCollaborators