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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
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 .
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
Result and Discussions
At first, study area is selected using Google earth.
Result and Discussions Contd.
Now, Study area is added on GIS through kml to layer tool.
Result and Discussions Contd.
Display on the GIS.
Result and Discussions Contd.
Create random points.
Result and Discussions Contd.
Now, download Sentinel-2 Satellite Image from copemicus.
Source: https://scihub.copernicus.eu/
Result and Discussions Contd.
Adding the Sentimal-2 data with 10m resolution.
Result and Discussions Contd.
Now, Random points centred on 10*10 pixel size.
Result and Discussions Contd.
Extract Multi Values to Points.
Result and Discussions Contd.
Extract it to excel file.
Result and Discussions Contd.
NDVI= (NIR - R) / (NIR + R)
Plot no. B2 B3 B4 B8 NDVI
1 1278 1593 1553 3083 0.330025884
2 1228 1484 1480 2882 0.321412196
3 1106 1462 1266 3890 0.508921645
4 1126 1366 1399 2760 0.327242126
5 1138 1514 1319 3752 0.479787024
6 1213 1533 1457 3274 0.384062566
7 1118 1448 1272 3910 0.509069857
8 1094 1315 1315 2715 0.347394541
9 1063 1362 1200 4387 0.570431359
10 1111 1408 1238 3920 0.51996898
B2 Blue
B3 Green
B4 Red
B8 Near Infrared
Result and Discussions Contd.
Field visits.
Result and Discussions Contd.
Volume calculation.
Plot S.N. Species Diameter(cm) Height(m) Quality Volume(m*3)
1 1 Sal 38 17 1 1.23
2 1 Siris 55 24 2 3.09
2 2 Sal 70 24 1 5.36
2 3 Gutel 30 9 4 0.42
3 1 Terai other species 45 11 3 1.11
3 2 Terai other species 30 11 4 0.54
4 1 Chilaune 65 22 1 3.33
4 2 Sal 55 23 1 3.25
5 1 Terai other species 37 16 4 1.12
5 2 Gutel 50 18 1 2.00
6 1 Siris 45 19 1 1.73
7 1 Terai other species 35 12 2 0.77
7 2 Terai other species 35 13 3 0.83
8 1 Sal 54 24 1 3.25
9 1 Gutel 48 22 1 2.24
10 1 Terai other species 45 11 3 1.11
10 2 Terai other species 30 11 4 0.54
Result and Discussions Contd.
Plot-wise details.
Plot no. B2 B3 B4 B8 NDVI
Plot Wise
Volume
1 1278 1593 1553 3083 0.330025884
1.23
2 1228 1484 1480 2882 0.321412196 8.87
3 1106 1462 1266 3890 0.508921645 1.65
4 1126 1366 1399 2760 0.327242126 6.58
5 1138 1514 1319 3752 0.479787024 3.12
6 1213 1533 1457 3274 0.384062566 1.73
7 1118 1448 1272 3910 0.509069857 1.61
8 1094 1315 1315 2715 0.347394541 3.25
9 1063 1362 1200 4387 0.570431359 2.24
10 1111 1408 1238 3920 0.51996898 1.65
Result and Discussions Contd.
Unlocking Data analysis for Regression analysis in Excel
Result and Discussions Contd.
Unlocking Data analysis for Regression analysis in Excel
Result and Discussions Contd.
Performing Regression analysis
Result and Discussions Contd.
Regression analysis.
Regression Equation 1.
Vol. = 0.1719*B2 + 0.04991*B3 – 0.21229*B4 – 0.03136*B8 + 6.995*NDVI + 125.998
Result and Discussions Contd.
Regression Analysis.
Regression Equation 2.
Vol. = 0.1712*B2 + 0.0503*B3 – 0.2138*B4 – 0.0304*B8 + 128.248
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.998369242
R Square 0.996741143
Adjusted R Square 0.99022343
Standard Error 0.295964898
Observations 7
ANOVA
df SS MS F Significance F
Regression 4 53.58306 13.39576604 152.928 0.006507093
Residual 2 0.17519 0.087595221
Total 6 53.75825
CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 128.2478992 7.309123 17.54627768 0.003232 96.79928184 159.6965166 96.79928184 159.6965166
B2 0.17120821 0.013125 13.04473794 0.005825 0.114737204 0.227679216 0.114737204 0.227679216
B3 0.050285 0.00835 6.021909594 0.026485 0.014356381 0.086213618 0.014356381 0.086213618
B4 -0.213810975 0.014141 -15.11985995 0.004346 -0.274655081 -0.152966868 -0.274655081 -0.152966868
B8 -0.030453828 0.002012 -15.13731232 0.004336 -0.03911007 -0.021797585 -0.03911007 -0.021797585
Result and Discussions Contd.
Regression Analysis.
Regression Equation 3.
Vol. = 0.1437*B2 + 0.0585*B3 – 0.2562*B4 – 227.3524*NDVI + 197.645
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.991445709
R Square 0.982964593
Adjusted R Square 0.94889378
Standard Error 0.676680771
Observations 7
ANOVA
df SS MS F Significance F
Regression 4 52.84246088 13.21061522 28.85063 0.033780608
Residual 2 0.915793733 0.457896866
Total 6 53.75825461
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 197.6459119 27.25429859 7.251917025 0.018489 80.38012972 314.9116942 80.38012972 314.9116942
B2 0.143751533 0.028310153 5.077737803 0.036665 0.021942777 0.26556029 0.021942777 0.26556029
B4 -0.256217912 0.038983204 -6.57252062 0.022375 -0.423949101 -0.088486723 -0.423949101 -0.088486723
B3 0.058552037 0.02047401 2.859822675 0.103613 -0.029540516 0.14664459 -0.029540516 0.14664459
NDVI -227.352414 34.99116309 -6.497423746 0.022878 -377.9072373 -76.79759057 -377.9072373 -76.79759057
Result and Discussions Contd.
Regression Analysis.
Regression Equation 4.
Vol. = 8.154*10^(-5)*B2^2 + 0.0471*B3 – 0.2947*B4 – 294.9011*NDVI^2 + 282.6602
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.998749374
R Square 0.997500312
Adjusted R Square 0.992500936
Standard Error 0.259209246
Observations 7
ANOVA
df SS MS F Significance F
Regression 4 53.62387575 13.40596894 199.525 0.004993128
Residual 2 0.134378866 0.067189433
Total 6 53.75825461
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 282.6602839 13.67407892 20.67124854 0.002332 223.825471 341.4950969 223.825471 341.4950969
(B2)^2 8.15426E-05 5.22112E-06 15.61784536 0.004075 5.90779E-05 0.000104007 5.90779E-05 0.000104007
B3 0.047107463 0.006917095 6.810295846 0.020888 0.017345605 0.07686932 0.017345605 0.07686932
B4 -0.294703747 0.016695773 -17.65139916 0.003194 -0.366539858 -0.222867635 -0.366539858 -0.222867635
NDVI^2 -294.9011458 16.67518329 -17.68503175 0.003182 -366.6486687 -223.1536229 -366.6486687 -223.1536229
Result and Discussions Contd.
Regression Analysis.
Regression Equation 5.
Vol. = 0.18386*B2 + 0.04096*B3 – 0.2749*B4 – 274.40*NDVI^2 + 157.52
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
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
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
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
THANK
YOU

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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
  • 4. Result and Discussions At first, study area is selected using Google earth.
  • 5. Result and Discussions Contd. Now, Study area is added on GIS through kml to layer tool.
  • 6. Result and Discussions Contd. Display on the GIS.
  • 7. Result and Discussions Contd. Create random points.
  • 8. Result and Discussions Contd. Now, download Sentinel-2 Satellite Image from copemicus. Source: https://scihub.copernicus.eu/
  • 9. Result and Discussions Contd. Adding the Sentimal-2 data with 10m resolution.
  • 10. Result and Discussions Contd. Now, Random points centred on 10*10 pixel size.
  • 11. Result and Discussions Contd. Extract Multi Values to Points.
  • 12. Result and Discussions Contd. Extract it to excel file.
  • 13. Result and Discussions Contd. NDVI= (NIR - R) / (NIR + R) Plot no. B2 B3 B4 B8 NDVI 1 1278 1593 1553 3083 0.330025884 2 1228 1484 1480 2882 0.321412196 3 1106 1462 1266 3890 0.508921645 4 1126 1366 1399 2760 0.327242126 5 1138 1514 1319 3752 0.479787024 6 1213 1533 1457 3274 0.384062566 7 1118 1448 1272 3910 0.509069857 8 1094 1315 1315 2715 0.347394541 9 1063 1362 1200 4387 0.570431359 10 1111 1408 1238 3920 0.51996898 B2 Blue B3 Green B4 Red B8 Near Infrared
  • 14. Result and Discussions Contd. Field visits.
  • 15. Result and Discussions Contd. Volume calculation. Plot S.N. Species Diameter(cm) Height(m) Quality Volume(m*3) 1 1 Sal 38 17 1 1.23 2 1 Siris 55 24 2 3.09 2 2 Sal 70 24 1 5.36 2 3 Gutel 30 9 4 0.42 3 1 Terai other species 45 11 3 1.11 3 2 Terai other species 30 11 4 0.54 4 1 Chilaune 65 22 1 3.33 4 2 Sal 55 23 1 3.25 5 1 Terai other species 37 16 4 1.12 5 2 Gutel 50 18 1 2.00 6 1 Siris 45 19 1 1.73 7 1 Terai other species 35 12 2 0.77 7 2 Terai other species 35 13 3 0.83 8 1 Sal 54 24 1 3.25 9 1 Gutel 48 22 1 2.24 10 1 Terai other species 45 11 3 1.11 10 2 Terai other species 30 11 4 0.54
  • 16. Result and Discussions Contd. Plot-wise details. Plot no. B2 B3 B4 B8 NDVI Plot Wise Volume 1 1278 1593 1553 3083 0.330025884 1.23 2 1228 1484 1480 2882 0.321412196 8.87 3 1106 1462 1266 3890 0.508921645 1.65 4 1126 1366 1399 2760 0.327242126 6.58 5 1138 1514 1319 3752 0.479787024 3.12 6 1213 1533 1457 3274 0.384062566 1.73 7 1118 1448 1272 3910 0.509069857 1.61 8 1094 1315 1315 2715 0.347394541 3.25 9 1063 1362 1200 4387 0.570431359 2.24 10 1111 1408 1238 3920 0.51996898 1.65
  • 17. Result and Discussions Contd. Unlocking Data analysis for Regression analysis in Excel
  • 18. Result and Discussions Contd. Unlocking Data analysis for Regression analysis in Excel
  • 19. Result and Discussions Contd. Performing Regression analysis
  • 20. Result and Discussions Contd. Regression analysis. Regression Equation 1. Vol. = 0.1719*B2 + 0.04991*B3 – 0.21229*B4 – 0.03136*B8 + 6.995*NDVI + 125.998
  • 21. Result and Discussions Contd. Regression Analysis. Regression Equation 2. Vol. = 0.1712*B2 + 0.0503*B3 – 0.2138*B4 – 0.0304*B8 + 128.248 SUMMARY OUTPUT Regression Statistics Multiple R 0.998369242 R Square 0.996741143 Adjusted R Square 0.99022343 Standard Error 0.295964898 Observations 7 ANOVA df SS MS F Significance F Regression 4 53.58306 13.39576604 152.928 0.006507093 Residual 2 0.17519 0.087595221 Total 6 53.75825 CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 128.2478992 7.309123 17.54627768 0.003232 96.79928184 159.6965166 96.79928184 159.6965166 B2 0.17120821 0.013125 13.04473794 0.005825 0.114737204 0.227679216 0.114737204 0.227679216 B3 0.050285 0.00835 6.021909594 0.026485 0.014356381 0.086213618 0.014356381 0.086213618 B4 -0.213810975 0.014141 -15.11985995 0.004346 -0.274655081 -0.152966868 -0.274655081 -0.152966868 B8 -0.030453828 0.002012 -15.13731232 0.004336 -0.03911007 -0.021797585 -0.03911007 -0.021797585
  • 22. Result and Discussions Contd. Regression Analysis. Regression Equation 3. Vol. = 0.1437*B2 + 0.0585*B3 – 0.2562*B4 – 227.3524*NDVI + 197.645 SUMMARY OUTPUT Regression Statistics Multiple R 0.991445709 R Square 0.982964593 Adjusted R Square 0.94889378 Standard Error 0.676680771 Observations 7 ANOVA df SS MS F Significance F Regression 4 52.84246088 13.21061522 28.85063 0.033780608 Residual 2 0.915793733 0.457896866 Total 6 53.75825461 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 197.6459119 27.25429859 7.251917025 0.018489 80.38012972 314.9116942 80.38012972 314.9116942 B2 0.143751533 0.028310153 5.077737803 0.036665 0.021942777 0.26556029 0.021942777 0.26556029 B4 -0.256217912 0.038983204 -6.57252062 0.022375 -0.423949101 -0.088486723 -0.423949101 -0.088486723 B3 0.058552037 0.02047401 2.859822675 0.103613 -0.029540516 0.14664459 -0.029540516 0.14664459 NDVI -227.352414 34.99116309 -6.497423746 0.022878 -377.9072373 -76.79759057 -377.9072373 -76.79759057
  • 23. Result and Discussions Contd. Regression Analysis. Regression Equation 4. Vol. = 8.154*10^(-5)*B2^2 + 0.0471*B3 – 0.2947*B4 – 294.9011*NDVI^2 + 282.6602 SUMMARY OUTPUT Regression Statistics Multiple R 0.998749374 R Square 0.997500312 Adjusted R Square 0.992500936 Standard Error 0.259209246 Observations 7 ANOVA df SS MS F Significance F Regression 4 53.62387575 13.40596894 199.525 0.004993128 Residual 2 0.134378866 0.067189433 Total 6 53.75825461 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 282.6602839 13.67407892 20.67124854 0.002332 223.825471 341.4950969 223.825471 341.4950969 (B2)^2 8.15426E-05 5.22112E-06 15.61784536 0.004075 5.90779E-05 0.000104007 5.90779E-05 0.000104007 B3 0.047107463 0.006917095 6.810295846 0.020888 0.017345605 0.07686932 0.017345605 0.07686932 B4 -0.294703747 0.016695773 -17.65139916 0.003194 -0.366539858 -0.222867635 -0.366539858 -0.222867635 NDVI^2 -294.9011458 16.67518329 -17.68503175 0.003182 -366.6486687 -223.1536229 -366.6486687 -223.1536229
  • 24. Result and Discussions Contd. Regression Analysis. Regression Equation 5. Vol. = 0.18386*B2 + 0.04096*B3 – 0.2749*B4 – 274.40*NDVI^2 + 157.52
  • 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