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Forest Biomass Estimation using GIS data.pptx
1. Forest Biomass Estimation using Sentinel-2 Satellite Image
An Assignment Presentation of FSE 701 Geospatial Technology and Application in NRM
Submitted to Submitted by
Jeetendra Gautam Dipendra Koirala
Assistant Professor Pramila Paudel
Agriculture and Forestry University, FOF Shishir Lamsal
Hetauda Bidhata Lammichhane
2. Introduction
• Forest biomass is any plant matter or tree material produced by forest growth that can be converted to
an energy source (Raunikar et al., 2010).
• The two primary methods for estimating forest biomass are conventional field-based techniques and
remote sensing methodologies.
• The majority of RS studies make use of optical (Landsat, Sentinel 2A, and LiDAR), synthetic aperture
radar (SAR, Sentinel 1), or a combination of datasets for modeling and estimating AGB.
• Sentinel-2 is a polar-orbiting sensor comprised of two satellites, each of which carries an MSI with a
290 km wide swath and a multipurpose design with 13 spectral bands spanning from visible and near-
infrared (NIR) wavelengths to shortwave infrared wavelengths at fine (10, 20m) and coarse
(60m)spatial resolution .
3. General Outlines of the work
n s
Extract multi value
Export table to excel
Creation of regression
equations
Choice of regression equation
Verification of regression
equation
Add X-Y data
Study Area
Field visit
Sentinel-2
image
NDVI
Volume calculation
Create random points
25. Result and Discussions Contd.
Calculation of Actual Volume and Predicted Volume
B2(Blue) B3(Green) B4(Red) B8(NearInfrared) NDVI
Actual
Volume
Reg.equ.2 Reg.equ.3 Reg.equ.4 Reg.equ.5
1094 1315 1315 2715 0.347394541 3.25 17.84015 15.99847619 19.07473226 17.89459959
1063 1362 1200 4387 0.570431359 2.24 -11.42929 -6.94877463 -10.6417036 -10.43661102
1111 1408 1238 3920 0.51996898 1.65 5.19694 4.381155527 5.062327615 4.924862426
26. Result and Discussions Contd.
Line Fit Plot
-15
-10
-5
0
5
10
15
20
25
1 2 3
Volume
Line Fit Plot
Actual Volume
Reg. equ. 2
Reg. equ. 3
Reg. equ. 4
Reg. equ. 5
27. Result and Discussions Contd.
Calculation of RMSE Value
Based on a rule of thumb, it can be said that RMSE values between 0.2 and
0.5 shows that the model can relatively predict the data accurately.
Predicted vol. Predicted vol. Predicted vol. Predicted vol.
Reg. equ. 2
Square of
Diff. equ. 2
Reg. equ. 3
Square of
Diff. equ. 3
Reg. equ. 4
Square of
Diff. equ. 4
Reg. equ. 5
Square of
Diff. equ. 5
3.25 17.84015 212.872477 15.99847619 162.5236451 19.07473226 250.422151 17.89459959 214.4642971
2.24 -11.42929 186.8494891 -6.94877463 84.43357919 -10.6417036 165.9382876 -10.43661102 160.6964669
1.65 5.19694 12.58078336 4.381155527 7.459210515 5.062327615 11.64397975 4.924862426 10.72472391
11.72323547
RMSE 9.208988993 11.9443769 11.34145035
Actual
Volume
RMSE values for different regression equations are not close to 0.2
- 0.5, so it means that predicted values are not close to Observed
Value
28. References
Raunikar, R., Buongiorno, J., Turner, J. A., and Zhu, S. (2010). Global
Outlook for wood and Forests with the Bioenergy Demand Implied by
Scenarios of the Intergovernmental Panel on Climate Change. For.
Pol. Econ. 12, 48–56. doi:10.1016/j.forpol.2009.09.013