TS3-2-SubhashAshutosh

8/8/2019 TS3-2-SubhashAshutosh

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Subhash AshutoshJoint DirectorForest Survey of India

[email protected]

Application of Geomatics in PreparationApplication of Geomatics in Preparationof PDD for A/R Projects under CDMof PDD for A/R Projects under CDM


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AFORESTATION & REFORESTATION PROJECTSAFORESTATION & REFORESTATION PROJECTS

UNDER CDMUNDER CDM


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describes the project

illustrates how the methodology will be applied estimates the greenhouse gases and

environmental and socio-economic impacts of the

project

gives baseline information, and

presents a measurement & monitoring plan

Project Design Document (PDD) of LULUCF

Projects under CDM


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Important Requirements & Concepts in the PDD

Defining Project Boundary

Baseline Scenario

Additionality

Leakage Permanence

Measurement & Monitoring Plan


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project boundary

analyzing eligibility of land

baseline scenario

measurement and monitoring plan

Application of Geomatics in Preparation of PDD

A spatial database in GIS along with the timeseries of satellite data would facilitate otherconcepts like additionality and project

management in general


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Boundary of the Project Areas

The project boundary should geographically delineate allsequestrations and emissions that are significant, can be

attributed to the project and are under the control of the

project participants.


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Project can vary in size

10s ha 1000s ha

Project can be one contiguous block or many small

blocks of land spread over a wide area

One or many landowners

Define Project Boundary

Spatial Database in GIS with attributes attachedto each polygon


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handheld / DGPS

small patches < 0.4ha;

handheld GPS is not effective

cost of DGPS starts from Rs 4.50 lakh

cost of handheld GPS starts from Rs 7000/-

WGS,84 datum

UTM Projection system

compatible to OSM of SOI

Use of GPS in Boundary Survey


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Accurate registration of the boundary with the satellitedata is necessary

Projection transformation to bring the boundary andsatellite data to the same projection system

datum

spheroid

projection system

Geo-referencing the image using the locally picked upGCPs (with the help of GPS)

Developing a local projection system with the help of

DGPS

Use of GPS in Boundary Survey contd.


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Project Area One block Project Area Many parcels of land


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Analysing Eligibility of Lands w.r.t December 1989Rule (& Definition of Forest)

no forest to be present within the project boundariesbetween 31 December 1989 and the start of the projectactivity

Documentary proof of forest absence to be provided


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Designated National Authorities, have the role of decidingfor their country where to lay the thresholds from a rangedetermined at COP9, namely:

Minimum tree crown cover value between 10 and 30 %Minimum land area value between 0.05 and 1 hectare;

Minimum tree height value between 2 and 5 metres.

The definition of forest accepted by India is

Forest is a minimum area of land of 0.05 ha with treecrown cover (or equivalent stocking level) of more than30% with trees with the potential to reach a minimum

height of 5 meters at maturity in-situ.

Definition of Forest


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Eligibility of Lands

31st December 1989 Rule

no forest to be present within the project boundaries between

31 December 1989 and the start of the project activity

(The tool requires proof that the area is not currently forest, that it wasnot forest on 31 December 1989, and that at no intermediate time wasit forested and subsequently deforested)

Satellite data set of the start, current and intervening periods

status of forest as per the definition

extent

canopy density


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Analysis of Eligibility of Land Parcels by FSI in the HPMid-Himalayan Region Afforestation Bio-Carbon Project

The interventions proposed under the BC project makes the

villagers a strategic seller of carbon credits under the CDM

Afforestation on 12000 Ha involving 600 Gram Panchayats

Parcel of lands are spread over the nine districts of the state more

than 1200 polygons

Focus is only on afforestation for which the farmers will receive cashincentive (by being a potential seller of carbon credit) on three types

of lands;(i) non-arable agriculture waste land,

(ii) degraded forest land,

(iii) degraded common property land

Project Features


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FSIs Assistance in the Project

impart training in delineating boundaries of the proposed plantation

sites using GPS

provide forest cover maps for baseline scenario

analyse eligibility of the identified lands with respect to 31st

December, 1989 Rule and CDM definition of forest

using co-registered two date satellite data set

Generate maps of each afforestation site

facilitate in creating a GIS database for the project

suggest inventory design for monitoring of the plantations as per the

CDM guidelines


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Methodology

GPS survey of all the land parcels was done to generateboundaries and measure area

Boundaries of the patches from GPS provided vectorcoverage in GIS

Satellite images of the two dates were co-registered using

image-to-image rectification approach (Autosync software) Satellite images of the two dates i.e prior to December 1989

and the current one were classified using hybrid

classification approach to give forest cover in three canopydensity classes; < 10%, 10-30% and >30%

Vector coverage of the patches of lands was overlaid on theclassification to analyze each patch for canopy densitybefore 31st December 1989 and currently


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1989LANDSAT TM

2004

IRS P6 LISS III


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LANDSAT TM 1990 LISS III 2004


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Eligibility of Polygons of Banoli-Khas


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In the first phase of the project, eligibility of 97 parcels oflands has been examined

the area of parcels of land range between 1.8 ha to 52 ha

93 parcels of lands were found to confirm to the eligibility

criteria

documentary evidence (maps) showing eligibility of eachparcel of land was generated

Outcome


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Problems Faced in this Approach

Shift in the GPS (generated) boundaries and the

Satellite Image The shift is more pronounced in hilly terrain (if ortho-

rectified image is not being used)

Traversing boundaries of the larger land parcels (>5ha) with GPS in hilly terrain is difficult and timeconsuming exercise

At places in the shadows, GPS signal is weak orabsent


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Modified Approach

instead of traversing the boundaries of the identified parcels oflands, only GPS observation of the co-ordinates of a central point ofthe parcel is taken

details of land cover and features around the identified parcel of landis recorded on a form

point locations of the land parcels are downloaded to give a point

vector coverage

point coverage showing central points of the parcels are thenoverlaid on the remote sensing data

details recorded on the field forms are then also used to delineate

the identified parcels of the lands on the remote sensing data by on-screen digitization of the polygons

multi spectral images (FCCs) would be more helpful in accuratelydelineating the identified polygons because of enhanced

interpretability of the features seen in tonal variations


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an alternative to the multi spectral image could be fused image of

panchromatic image (high resolution) with the multi spectral image(somewhat coarser resolution)

this method saves cost and time significantly

it is also more objective in approach because, before delineating the

polygons of the land parcels, the applicability for eligibility of lands

under the 31st December 1989 Rule is also examined

once the land parcels have been delineated on the satellite image,suitable maps of the same on large scale say 1: 10,000 or larger

showing high resolution satellite image in the background can begenerated to facilitate demarcation of the lands on the ground


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Fused Image of IRS P6 LISS-III and PAN Data


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M f th L d P l F d i f IRS P6


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Map of the Land Parcel on Fused image of IRS P6Liss-III and PAN data

1: 5,000 Scale

Satellite Data Options


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PAN

5.8m

LISS III

23.5m

LANDSAT TM30m

IKONOS

1m

Satellite Data Options


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IKONOS PAN 1:5000


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LISS IV Image

Scale 1: 10000

Scale 1: 50000


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Baseline Scenario

represents GHG emissions that would occur in theabsence of the proposed project activity


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Baseline Scenario (contd.)

Annex 3 of the Project Design Document

land-use determination

baseline carbon stocks (estimate through measurements)

LEGEND

Landuse / Landcover Map based on Classification of Satellite Data

Baseline Scenario: steps


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Baseline Scenario: steps

Land use land cover map of the project area

Stratify the project area

Soil

Climate

previous land use

existing vegetation

tree species to be planted

year to be planted

anthropogenic influence, etc.

Determine baseline scenario (focus on possible encroachment)

Determine baseline carbon stock changes

Sites with trees: yield data, allometric equations


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Measurement & Monitoring Plan

To estimate the carbon stock on the land periodically

Stratified sampling approach

Cost effective method

Conservative estimates

Accurate and precise within a limit

Steps


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Define Project Boundary

Stratify project area

Decide which carbon pools to

measure

Develop sampling design plot type,

shape, size, number, and layout

Determine measurement

frequency

Steps


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Stratification based sampling method is key to baselinescenario and measurement & monitoring plan

Satellite data based mapping provides the practicable

and cost effective way of doing this

Area and objectives specific approach for stratificationusing remote sensing

St tif th P j t A


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Stratify the Project Area

The stratification should be carried out using criteriathat are directly related to the variables to be

measured and monitored for example, the carbon pools in trees

there is a trade-off between the number of strata andsampling intensity.

the purpose of stratification is to partition natural

variation in the system and so reduce monitoringcosts.


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Potential stratification options

Land use (for example, forest, plantation, agroforestry,grassland, cropland, irrigated cropland);

Vegetation species (Forest type); Slope (for example, steep, flat); Drainage (for example, flooded, dry); Age of vegetation;

Proximity to settlement. Typically, a project might have between one and six

strata.


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Remote Sensing & GIS Based Approach forStratification of Forests for Growing Stock

Assessment

: A Case Study of Kolasib Division (Mizoram)

Methodology


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DRAINAGE / ROAD /

NETWORK /

SETTLEMENT

SOI

TOPOSHEETS

VEGETATION MAPS /

THEMATIC MAPS /

ATTRIBUTE

CONTOURS

BUFFER

SATELLITE

DATA

DEM

SPATIAL DATA

IN GIS

SLOPE MAP

ASPECT MAP

ALTITUDE

MANAGEMENT MAPSField Inventory

Stratification of forest

areas by overlay and

criteria application

Marking of random sample

points as per the

inventory design

Generation of maps

for field inventory

Forest Type map Forest Cover map

Radiometric

and Geometric

Correction

CLASSIFICATION

(Hybrid Approach)

NDVI

Transformation

ADMINISTRATIVE

MAPS

Ground Truth

BOUNDARIES

STATE, DISTRICT

DIVISION, RANGE,

BEAT, BLOCK, RF, PF, PA


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Assessment of Growing Stock using Remote Sensing & GIS: A Case Study of Kolasib Division

Mizoram stateIndia

STUDY AREA


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Kolasib Forest Division

Mizoram

Mizoram stateIndia


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Forest Cover

Forest type

Slope

Altitude

Aspect

Spatial Layers taken into account for Stratification

Division boundary


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Kolasib Forest Division

Division Boundary

Legend

Division boundary

Range boundary


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Range Boundary

Legend

Range boundary

S l


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Settlements

Settlements

Range Boundary

Settlements

Legend

R d


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Roads

Division Boundary

Unmetalled Road

Metalled Road

Legend

Reserved forest area


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Division Boundary

Legend

Reserved Forest

Innerline

reservedfores

t

Tut-Langkaihprotectedreserved forest

Contours - 100m interval


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Contours 100m interval

Division Boundary

Legend

Contour

Digital Elevation Model


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LegendLow

High

Perspective View of Kolasib Forest Division


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(FCC Draped on Digital Elevation Model)

Altitude


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Altitude

Legend

Slope


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Slope

Legend


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Forest Strata


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Legend

Sampling Design


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Stratified random sampling method with probability proportionto area of each class was adopted for the pilot forest inventory

145 stratified random points were generated

The sampling intensity was kept 0.009 % of the total geographical

area of the study area.

Among these 145 points, 89 points belongs to reserved forest area,

Sampling intensity for reserved forest area is 0.012%.

Estimation of Growing Stock:

Tree - Volume equation (FSI and FRI)

Bamboo Regression equation (FSI)

Distribution of Sample plots


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2341559180752Total

102398153Non Forest

148117Road side Teak Plantation

251401957Open Pure Bamboo

291422254Dense Pure Bamboo

421543295Open Mixed Bamboo

3632028186Dense Mixed Bamboo

392393078Open Misc. Forest

3931730122Dense Misc. Forest

Total No of

Plots inKolasib Forest

DivisionGeographic

area(Sq.Km.)

Plots inReserved

ForestGeographic

area(Sq.Km.)

Strata


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Stratified Random Points

Results


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Area Distribution

84.671320.001559.00Total

62.1945.3973.00VAIRENGTE

86.29106.99124.00SAIPUM

94.72242.48256.00N-HLIMEN

75.30115.95154.00KOLASIB

87.73250.89286.00KAWNPUI

93.65234.12250.00BUKPUI

67.7637.9456.00BUHCHANG

69.3665.8995.00BILKHAWTHLIR

83.14220.31265.00BAIRABI

Percentage(%)

Forest cover(Sq.km.)

GeographicArea(Sq.km.)Range name

Growing Stock in Kolasib Forest Division Entire Division


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Tree Data

Stem/ha (nos.) = 139.14

Total Stem (nos.) = 22111529Volume per ha (cu m) = 32.58Total Volume (cu m) = 5360781

Bamboo Data (Clump Forming)

Culm /ha (Nos.) = 67.26Total Culm (Nos.) = 11400187Weight/ha (Tonnes ) = 0.425Total Weight (Tonnes) = 66237

Bamboo Data (Non Clump Forming)

Culm /ha (Nos.) = 4891.18Total Culm (Nos.) = 765663875Weight/ha (Tonnes ) = 15.13

Total Weight (Tonnes) = 2472220

Growing Stock in Kolasib Forest Division Entire Division

Growing Stock in Kolasib Forest Division Reserved Forest


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Tree Data

Stem/ha (nos.) = 141.83Total Stem (nos.) = 8748312

Volume per ha (cu m) = 34.39Total Volume (cu m) = 2499581

Bamboo Data (Clump Forming)

Culm /ha (Nos.) = 73.13Total Culm (Nos.) = 6073775Weight/ha (Tonnes ) = 0.425

Total Weight (Tonnes) = 31665

Bamboo Data (Non Clump Forming)

Culm /ha (Nos.) = 4911.25Total Culm (Nos.) = 325887851Weight/ha (Tonnes ) = 15.86Total Weight (Tonnes) = 1164191

Strata wise Error


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6.23Total

3.15Non Forest

8.59Road side teak Plantation

2.40Open Pure Bamboo

7.45Dense Pure Bamboo

9.15Open Mixed Bamboo

6.96Dense Mixed Bamboo

5.10Open Misc. Forest

6.57Dense Misc. Forest

Standard Error (%)Forest classes

Strata wise Error

Summing up


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Summing up

application of Geomatics in preparation of PDD is anecessity, rather than an option

Geomatics based methodologies in survey, mappingand natural resource assessment are well established

specific methodologies with respect to PDD forLULUCF projects under CDM need to be standardised

capacity building needs priority and thrust


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Thank youThank you

TS3-2-SubhashAshutosh

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