Challenges in biomass and carbon assessment in Himalayas
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Transcript of Challenges in biomass and carbon assessment in Himalayas
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 RegressionModern Analog Technique