Challenges in biomass and carbon assessment in Himalayas

PRADEEP KUMARCHIEF CONSERVATOR OF FORESTS

FORESTS, ENVIRONMENT AND WILDLIFE MANAGEMENT DEPARTMENT

SIKKIM

9 million sqkm of the Earth’s surface, 23 %, In India 9 states 63%

Optical

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VEGETATION

SL_NO DIST

Com-No.

Site-ID

Plot_ID

Tree_ID

Local_Name

Botanical Name

Volume equation (by FSI)

CBH(cm)

Dia(cm) Volume SP_gravity Tree Biomass

0.22335 0.51 0.11391

1

EgangRgang

B

1 Bhusuk 1 1 1 Gobrey

Echinocarpus desycarpus

V/D2=0.25564/D2-0.030418/D+0.0012897 350

111.41 3.62564   0.00000

2

EgangRgang

B

1 Bhusuk 1 1 2 Tarsing

Belischmiedia sikkimensis

V=0.51191-1.78643*√D+11.19974*D2 200 63.66 2.59721 0.45 1.16874

3

EgangRgang

B

1 Bhusuk 1 1 3

Titey Chanp

Michelia cathcartii Hk.f.& T.

V/D2*H=0.00667/D2*H+0.32949 190 60.48 0.11489   0.00000

4

EgangRgang

B

1 Bhusuk 1 1 4 Kawlo

Machilus grammineana

V=0.12652-0.018037*D+0.000956*D2 210 66.85 0.23543 0.51 0.12007

5

EgangRgang

B

1 Bhusuk 1 1 5 Gobrey

Echinocarpus desycarpus

V/D2=0.25564/D2-0.030418/D+0.0012897 215 68.44 0.11650   0.00000

6

EgangRgang

B

1 Bhusuk 1 1 6 Kawlo

Machilus grammineana

V=0.12652-0.018037*D+0.000956*D2 180 57.30 0.05897 0.70 0.04122

0.02467 0.48 0.01189

But foreshortening, layover and shadowing limit the application

LIDAR

As quoted by the companyWeighing less than 10kg, LiDAR platform

called the “Phoenix AL-2” combines the latest UAV, LiDAR and GNSS technology.

Could prove to be a cost effective, accurate and safe micro-mapping solution.

“As far as the laws of mathematics refer to reality, they are not certain;

and as far as they are certain, they do not refer to reality.” ― Albert Einstein

NEED TO UNDERSTAND WHAT IS GOING TO HAPPEN RATHER THAN JUMPSTARTING TO ADAPTATION

Model Current and Future Climate

Current Species Distribution

Develop algorithms

Model Future Distribution

Understand what is going to happen

ADAPTATION

ADAPTATION BASED ON SCIENCE, NOT ON PERCEPTIONS

In India >90 species of Rhodo. 36 ~40 species in Sikkim. State tree of Sikkim R. niveum.

MODELING PROCEDURE‘Mechanistically’ or ‘Correlatively’Maxent is a maximum entropy based

machine learning program that estimates the probability distribution for a species’ occurrence by finding the probability distribution of maximum entropy based on environmental constraints distribution .

MODELING PROCEDUREAll the bioclimatic layers in file format ASCII

were used with resolution of 30ARC seconds. 70% were used in calibrating the model and

remaining 30% were used for testing the model.

112+63 locations

Bioclimatic variables BIO1 = Annual Mean

TemperatureBIO5 = Max Temperature of

Warmest Month

BIO13 = Precipitation of Wettest Month

BIO15 = Precipitation Seasonality (Coefficient of

Variation)

BIO6 = Min Temperature of Coldest Month

Test Statistics

For threshold independent assessment ROC analysis, which characterizes performance of model at all possible thresholds by a single number AUC was used.

The ROC describes the relationship between (sensitivity) and the (1 – specificity).

CLIMATE DATASETWorldClim database developed by Hijmans

et el.Data resolution 30 seconds (0.93 km x

0.93km = 0.86 km2 at equatorStatistically downscaled datasets obtained

from International Centre for Tropical Agriculture 2010 originally downloaded from the IPCC data portal and re-processed using a spline interpolation algorithm of the anomalies

CLIMATE DATASET contd.The future climate change scenario pertained

to HadClim Emission scenario SRES-A1B (corresponding to A1: Maximum energy requirements -emissions differentiated dependent on fuel sources. B: Balance across sources).

Altitude not used in the modelling

Representation of the Maxent model for current distribution of Rhododendron

Projection of the Maxent model for Rhododendron onto the environmental variables for future climate

AUC Analysis through ROC Curve

“Life must be lived forward, But understood backward”

-Kierkegaard

PAST CLIMATE RECONSTRUCION

Reconstructed late summer temperature (July-September) from Abies densa of Eastern Himalaya

Some marked cool and warm period in this reconstructed seriesCool PeriodA.D. 1781-1792A.D. 1881-1810 (-0.31oC)A.D. 1827-1836A.D. 1850-1859A.D. 1893-1902A.D. 1929-1938A.D. 1970-1978

Warm PeriodA.D. 1813-1822A.D. 1938-1846A.D. 1905-1914A.D. 1960-1969A.D. 1978-1987 (+0.25oC)

Markedly cool late summerA.D. 1782-1786, A.D. 1830-1831A.D. 1899, A.D. 1933, A.D. 1975

Much warmer summersA.D. 1777-1779, A.D. 1817,A.D. 1843, A.D. 1904-1906, A.D. 1926-1927, A.D. 1980-1982

Bhattacharyya, A., Chaudhary, V., 2003.

Abies densa Forest in and around Zema

Sample collected during 2008: 73 cores from 39 trees

Preliminary result: Chronology extending from AD 1628-2007 (need further correction of the samples)

Abies densa chronology from Zema. Sikkim (AD 1628-2007)

0

0.5

1

1.5

2

2.5

1600 1650 1700 1750 1800 1850 1900 1950 2000

QUANTITATIVE CLIMATE RECONSTRUCTION BASED ON POLLEN DATA

Contemporary climate data

Calibration dataset

Transfer Function

Modern Pollen

Fossil Pollen

Pollen Diagram Reconstruction

Interpolated climate dataset at each surface pollen site.

Correspondence Analysis (CA)

Detrended Correspondence Analysis (DCA)

Principal Component Analysis (PCA)

Redundancy Analysis (RDA)

Canonical Correspondence Analysis (CCA)

Weighted Averaging Partial Least Square (WA-PLS)

Principal Component Regression,

Correspondence Analysis Regression

Modern Analog Technique