Forest Biomass Estimation using GIS.pptx

Forest Biomass Estimation using GIS.pptx

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  Forest Biomass Estimationusing 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
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  OUTLINE OF THEPRESENTATION INTRODUCTION OBJECTIVES METHODOLOGY FINDINGS AND RESULT CONCLUSION REFERENCES 1 3/21/2022
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  INTRODUCTION Biomass is definedas 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). 2 3/21/2022
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  INTRODUCTION These methods havebeen 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. 3 3/21/2022
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  OBJECTIVES General Objective: To estimatethe 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. 4 3/21/2022
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  METHODOLOGY Study Area: The studywas 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. 5 3/21/2022
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  METHODOLOGY Methodology Steps: 1) DownloadSentinel 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. 6 3/21/2022
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  METHODOLOGY Data Collection: 7 3/21/2022 S.N. Datacollection 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
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  FINDINGS AND RESULT 8 3/21/2022 Sampleplot 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
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  FINDINGS AND RESULT 9 3/21/2022 NDVIValue: The high, low and average NDVI value of Milanchowk C.F. are 0.38033, 0.23325 and 0.322 respectively.
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  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 1 0 3/21/2022 The Red band value, Green band value, Blue band value, NIR value and NDVI values are tabulated as:
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  FINDINGS AND RESULT Equationformulation: 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 11 3/21/2022
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  FINDINGS AND RESULT Theresultant data obtained from R- programme are: 12 3/21/2022 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
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  FINDINGS AND RESULT 13 3/21/2022 EquationValidation 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.
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  CONCLUSION The maximum, minimumand 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. 14 3/21/2022
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  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. 15 3/21/2022