Today the carbon content in the atmosphere is predominantly increasing
due to greenhouse gas emission and deforestation. Forest plays a key role in
absorbing carbon dioxide from atmosphere by process of sequestration through
photosynthesis and stores in form of wood biomass which contains nearly 70% - 80%
of global carbon. Different forms of biomass in the environment include agricultural
products, wood, renewable energy and solid waste. Therefore, it is essential to
estimate the biomass content in the environment. In olden days, biomass is
estimated by forest inventory techniques which consume lot of time and cost.
The spatial distribution of biomass cannot be obtained by traditional inventory
forest techniques so the application of remote sensing in biomass assessment is
introduced to solve the problem. Overall accuracy of classified map indicates that
land features of Surat Thani on map show an accuracy of 91.13% with different
land features on ground. Both optical (LANDSAT-8) and synthetic aperture radar
(ALOS-2) remote sensing data are used for above ground biomass (AGB)
assessment. Biomass that stores in branch and stem of tree is called as above
ground biomass. Twenty ground sample plots of 30 m × 30 m utilized for biomass
calculation from allometric equations. Optical remote sensing calculates the
biomass based on the spectral indices of Soil Adjusted Vegetation Index (SAVI)
and Ratio Vegetation Index (RVI) by regression analysis (R2 = 0.813).
Synthetic aperture radar (SAR) is an emerging technique that uses high
frequency wavelengths for biomass estimation. HV backscattering of ALOS-2 shows
good relation (R2 = 0.74) with field calculated biomass compared to
HH (R2 = 0.43) utilizes for biomass model generation by linear
regression analysis. Combination of both optical spectral indices (SAVI, RVI)
and HV (ALOS-2) SAR backscattering increases the plantation biomass accuracy to
(R2 = 0.859) compared to optical (R2 = 0.788) and SAR (R
References
[1]
Carreiras, J.M.B., Vasconcelos, M.J. and Lucas, R.M. (2012) Understanding the Relationship between Aboveground Biomass and ALOS PALSAR Data in the Forests of Guinea-Bissau (West Africa). Remote Sensing of Environment, 121, 426-442. http://dx.doi.org/10.1016/j.rse.2012.02.012
[2]
Rosenqvist, A., Milne, A., Lucas, R., Imhoff, M. and Dobs, C. (2003) A Review of Remote Sensing Technology in Support of Kyoto Protocol. Environmental Science & Policy, 6, 441-455.
http://dx.doi.org/10.1016/S1462-9011(03)00070-4
[3]
Ji, L., Wylie, B.K., Nossov, D.R., Peterson, B., Waldrop, M.P., McFarland, J.W., Rover, J. and Hollingsworth, T.N. (2012) Estimating Aboveground Biomass in Interior Alaska with Landsat Data and Field Measurements. International Journal of Applied Earth Observation and Geoinformation, 18, 451-461. http://dx.doi.org/10.1016/j.jag.2012.03.019
[4]
Wang, X.Q., Pangb, Y., Zhang, Z.J. and Yuan, Y. (2014) Forest Aboveground Biomass Estimation Using SPOT-5 Spectral Indices and Textural Derivatives. IEEE Transactions on Geoscience and Remote Sensing, 2830-2832.
[5]
Yin, G.D., Zhang, Y., Sun, Y., Wang, T., Zeng, Z.Z. and Piao, S.L. (2015) MODIS Based Estimation of Forest Above-ground Biomass in China. Remote Sensing, 7, 5534-5564.
[6]
Thenkabail, P.S., Stucky, N., Griscom, B.W., Ashton, M.S., Diels, J., Van der Meer, B. and Enclona, E. (2004) Biomass Estimations and Carbon Stock Calculations in the Oil Palm Plantations of African Derived Savannas Using IKONOS Data. International Journal of Remote Sensing, 25, 1-27. http://dx.doi.org/10.1080/01431160412331291279
[7]
Eckert, S. (2012) Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data. Remote Sensing, 4, 810-829. http://dx.doi.org/10.3390/rs4040810
[8]
Baghdadi, N., le Maire, G., Bailly, J.-S., Osé, K., Nouvellon, Y., Zribi, M., Lemos, C. and Hakamada, R. (2014) Evaluation of ALOS PALSAR-L Band for the Estimation of Eucalyptus Plantation Aboveground Biomass in Brazil. IEEE Transactions on Geoscience and Remote Sensing, 721-724.
[9]
Zhang, Y. and Jing, Z.-X. (2001) Estimating Paddy Rice Biomass Using Radarasat-2 Data Based on Artificial Neutral Network. International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013), 423-426.
[10]
Pierce, L., Liang, P., Dobson, M.C., Kellndorfer, J., Barros, O., dos Santos, J.R. and Soares, J.V. (2003) Regrowth Biomass Estimation in Amazon Using JERS-1/RADARSAT SAR Composites. IEEE Transactions on Geoscience and Remote Sensing, 3, 2075-2077. http://dx.doi.org/10.1109/igarss.2003.1294297
[11]
Englhart, S., Keuck, V. and Siegert, F. (2011) Aboveground Biomass Retrieval in Tropical Forests. The Potential of Combined X- and L-Band SAR Data Use. Remote Sensing of Environment, 115, 1260-1271.
http://dx.doi.org/10.1016/j.rse.2011.01.008
[12]
Kumar, S., Pandey, U., Kushwaha, S.P.S., Chatterjee, R.S. and Bijiker, W. (2012) Tropical Forest from Envisat ASAR Using Modeling Approach. Journal of Applied Remote Sensing, 6.
[13]
Cartus, O., Santoro, M. and Kellndorfer, J. (2012) Mapping Forest Aboveground Biomass in the Northeastern United States with ALOS PALSAR Dual-Polarization L-Band. Remote Sensing of Environment, 124, 466-478.
http://dx.doi.org/10.1016/j.rse.2012.05.029
[14]
Ranson, K.J. and Sun, G.Q. (1994) Mapping Biomass of Northern Forest Using Multifrequency SAR Data. IEEE Transcationson Geoscience and Remote Sensing, 32, 388-396. http://dx.doi.org/10.1109/36.295053
[15]
Hamdan, O., Aziz, H.K. and Rahman, A. (2011) Remotely Sensed L Band SAR Data for Tropical Forest Biomass Estimation. Journal of Tropical Forest Science, 23, 318-324.
[16]
Tanase, M.A., Panciera, R., Lowell, K., Tian, S., Hacker, J.M. and Walker, J.P. (2014) Airborne Multi-Temporal L-Band Polarimetric SAR Data for Biomass Estimation in Semi-Arid Forests. Remote Sensing of Environment, 145, 93-104. http://dx.doi.org/10.1016/j.rse.2014.01.024
[17]
Goh, J.Y., Miettinen, J., Chia, A.S., Chew, P.T. and Liew, S.C. (2013) Biomass Estimation in Humid Tropical Using Combination of Both ALOS PALSAR and SPOT 5 Satellite Image. Asian Journal of Geoinformatics, 13, 1-10.
[18]
Asari, N., Suratman, M.N., Jaafar, J. and Khalid, M.M. (2013) Estimation of above Ground Biomass for Oil Plantations Using Algometric Equations. 4th International Conference on Biology, Environment and Chemistry, 58, 110-114.
[19]
Hairiah, K., Dewi, S., Agus, F., Velarde, S., Ekadinata, A., Rahayu, S. and van Noordwijk, M. (2010) Measuring Carbon Stocks across Land Use Systems: A Manual. World Agroforestry Centre (ICRAF), SEA Regional Office, Bogor, 154 p.
[20]
Chavan, B. and Rasal, G. (2012) Total Sequestered Carbon Stock of Mangifera indica. Journal of Environment and Earth Science, 2, 37-49.
[21]
Bwalya, J.M. (2012) Estimation of Net Carbon Sequestration Potential of Citrus under Different Management Systems Using the Life Cycle Approach.
[22]
Chave, J., Andalo, C., Brown, S., Cairns, M.A., Chambers, J.Q., Eamus, D., Folster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J.P., Nelson, B.W., Ogawa, H., Puig, H., Riera, B. and Yamakura, T. (2005) Tree Allometry and Improved Estimation of Carbon Stocks and Balance in Tropical Forests. Oecologia, 145, 87-99.
http://dx.doi.org/10.1007/s00442-005-0100-x
