(1) The study estimated the above-ground biomass of Milanchowk Community Forest in Nepal using field inventory data and satellite imagery. (2) A regression model was developed between above-ground biomass measured in 20 sample plots and vegetation indices derived from a Sentinel 2 image. (3) The maximum, minimum and average biomass estimates from the sample plots were 2164 kg, 422 kg and 1263 kg respectively. The regression model was validated and found to accurately estimate biomass across 10 additional plots.

1. Forest Biomass Estimation using GIS
A Group Presentation on
GIS Application in Natural Resources FSE (701)
Group: Syntax
S.N. Name Class Roll no. S.N. Name Class Roll no.
1. Binita K.C. 5 5. Rabin Thapa 25
2. Kusum Saru 15 6. Santosh Ghimire 34
3. Niranjan Paudel 19 7. Susmita Khanal 39
4. Prabesh Adhikari 22 8. Yamuna Paudel 43
Submitted to: Assistant Professor Jeetendra Gautam

2. OUTLINE OF THE PRESENTATION
INTRODUCTION
OBJECTIVES
METHODOLOGY
FINDINGS AND RESULT
CONCLUSION
REFERENCES
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3. INTRODUCTION
Biomass is defined as mass of live or dead organic matter that includes the above-
ground and below-ground living biomass, dead mass, and litter (GTOS, 2009).
The largest carbon pool are typically stored in the above-ground living biomass. Thus
estimating above-ground forest biomass (AGB) is the most important step in
quantifying carbon stocks from forests (Gibbs et al., 2007).
The most accurate ways of calculating biomass data is based on field measurement
through destructive sampling or allometric equations method (Englhart et al., 2011).
However, these methods are often cost and time consuming, labor intensive, have
limited spatial distribution, and impractical especially in remote areas (Englhart et al.,
2011).
Remote sensing techniques have the capacity to overcome the limitations of
conventional field measurement methods and have now become the primary source in
estimating AGB (Lu, 2006).
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4. INTRODUCTION
These methods have been explored in terms of biomass and its changes to increase our
understanding of the role of forests in the carbon cycle for greenhouse gas inventories
and terrestrial carbon accounting (Muukkonen & Heiskanen, 2007).
Although much research has explored biomass estimation using remote sensing
technology methods to select suitable variables from remote sensing data and develop
estimation models suitable for specific studies are still poorly understood.
It is crucial to summarize the current status of remote sensing-based biomass
estimation techniques and discuss potential solutions to improve biomass estimation
performance.
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5. OBJECTIVES
General Objective:
To estimate the biomass of forest using GIS.
Specific Objective:
To calculate above ground biomass of Milanchowk CF from field inventory.
To establish a regression model between aboveground biomass and Sentinel 2
image.
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6. METHODOLOGY
Study Area:
The study was carried out in the
Milanchowk Community Forest, located
in Chitlang, Makawapur District.
It is extended from 27.34'52" N to
27.40'59"N and 85.01'21"E to 85.12'20"E
The average annual rainfall has been
recorded as 1863mm/year.
The dominant species of the study area
are Pinus and Quercus species.
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7. METHODOLOGY
Methodology Steps:
1) Download Sentinel 2 imaginary from USGS Earth Explorer.
2) Add Sentinel 2 imaginary data in Arc GIS 10.8 and calculate NDVI value.
3) Add boundary and sample plot point of CF.
4) Calculate Red band, Green bend, Blue band, NIR and NDVI value of
sample point.
5) Develop equation (Biomass vs Red, Green, Blue, NIR, NDVI Value) from
R-Studio programming software.
6) Validation Analysis using Paired t-test.
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8. METHODOLOGY
Data Collection:
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S.N. Data collection Activities
1. Primary data
collection
 The systematic sampling method with 10m * 10m sample plot
is used for field measurement.
 The biomass is estimated by using the equation: AGB =
0.0509 * pD2H (Where, AGB = above-ground tree biomass
[kg]; p = wood specific gravity [g/cm3; D = tree diameter at
breast height [cm]; and H = tree height [m])
 Sentinel 2 image is used to calculated NDVI Value.
2. Secondary data
collection
 Books
 Journals
 Article
 Reports

9. FINDINGS AND RESULT
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Sample plot Biomass (kg)
1 1602
2 1429
3 1490
4 948
5 2164
6 1956
7 1253
8 572
9 1479
10 1080
11 1094
12 653
13 723
14 1814
15 1118
16 1113
17 845
18 422
19 1669
20 1847
 Out of 30 sample plots, biomass estimation is done
from 20 sample plots which are used to generate the
biomass equation.
 The maximum, minimum and average biomass
estimates from 20 sample plot of Milanchowk C.F.
are 2164 kg, 422 kg and 1263 kg respectively.
Biomass estimation

10. FINDINGS AND RESULT
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NDVI Value:
The high, low and average
NDVI value of Milanchowk
C.F. are 0.38033, 0.23325 and
0.322 respectively.

11. FINDINGS AND RESULT
S.N. Red Green Blue NIR NDVI
1. 1599 1732 1823 2754 0.265334
2. 1591 1729 1829 3162 0.330528
3. 1510 1685 1796 2699 0.28249
4. 1521 1766 1807 3084 0.339414
5. 1575 1820 1837 3401 0.366961
6. 1569 1768 1824 3128 0.331914
7. 1756 1770 1861 3031 0.266346
8. 1509 1734 1803 2843 0.306526
9. 1512 1716 1806 2804 0.299351
10. 1519 1694 1796 2938 0.318376
11. 1537 1731 1811 3175 0.347623
12. 1579 1729 1813 3006 0.311232
13 1595 1724 1816 2997 0.305314
14. 1575 1715 1811 3292 0.352784
15. 1547 1742 1812 3218 0.350682
16. 1561 1739 1820 3106 0.331048
17. 1559 1741 1817 3083 0.328307
18. 1544 1756 1802 3197 0.348661
19. 1558 1727 1815 3182 0.342616
20. 1562 1746 1817 3057 0.323663
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The Red band value, Green band
value, Blue band value, NIR value
and NDVI values are tabulated as:

12. FINDINGS AND RESULT
Equation formulation:
To develop the biomass equation, five independent variables are used i.e.
(Biomass ~ Red band, Green band, Blue band, NIR, NDVI value).
Linear regression equation is used to fit dependent variable with independent
variables.
The fit regression equation derived from biomass modelling is:
Biomass ~ A + B * Red + C * Green + D * Blue + E * NIR + G * NDVI
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13. FINDINGS AND RESULT
The resultant data obtained from R- programme are:
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Constant Value
A -7.153034e+03
B -5.855211e+01
C -8.727644e+00
D 4.992052e+01
E 2.665179e+01
G -1.767242e+05

14. FINDINGS AND RESULT
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Equation Validation Analysis:
 Ten sample plots are used for equation
validation analysis.
 The resultant p value obtained from paired t–
test is p=0.08756 > 0.05, which indicates
above biomass equation is valid.

15. CONCLUSION
The maximum, minimum and average biomass are estimated as 2164 kg, 422 kg and
1263 kg respectively.
The high, low and average NDVI value are 0.38033, 0.23325 and 0.322 respectively.
The best fitted regression equation derived from biomass modelling is Biomass ~ A +
B * Red + C * Green + D * Blue + E * NIR + G * NDVI .
The biomass equation developed from this study can be used by the relevant
institutions, organizations and academics for research purposes as well as forest
management authorities.
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16. REFERENCES
Englhart, S., Keuck, V. & Siegert, F. (2011). Aboveground biomass retrieval in tropical
forest. Remote Sensing of Environment 115, pp. 1260-1271.
Gibbs, H.K., Brown, S., Niles, J.O. & Foley, J.A. (2007). Monitoring and estimating
tropical forest carbon stocks: making REDD a reality. Environmental Research Letters 2
(4), 13pp.
GTOS (Global Terrestrial Observing System). (2009). Assessment of the status of the
development of the standards for the terrestrial essential climate variables: Biomass.
Report FAO.
Lu, D.S. (2006). The potential and challenge of remote sensing-based biomass
estimation. International Journal of Remote Sensing, 27 (7), pp. 1297-1328.
Muukkonen, P. & Heiskanen, J. (2007). Biomass estimation over a large area based on
standwise forest inventory data and ASTER and MODIS satellite data: A possibility to
verify carbon inventories. Remote Sensing of Environment, 107(4), 617-624.
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