Above Ground Biomass Assessment from Combined Optical and SAR Remote Sensing Data
Main Article Content
Abstract
Today the carbon content in the atmosphere is predominantly increasing due to green house gas emission and deforestation etc. Forest plays a key role in absorbing carbon dioxide from atmosphere and stores in form of wood biomass which contains nearly 70-80% of global carbon. Spatial distribution of biomass cannot be obtained by inventory techniques so the application of remote sensing in biomass assessment is introduced to solve the problem. 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 can be called as above ground biomass. 20 ground sample plots of 30m*30m utilized for biomass calculation from allometric equations. Optical remote sensing calculates the biomass based on the spectral indices of SAVI and RVI by regression analysis (R²=0.813). Synthetic aperture radar is an emerging technique uses high frequency wavelengths for biomass estimation. HV back scattering 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 SAR back scattering increases the plantation biomass accuracy to (R²=0.859) compared to optical (R²=0.788) and SAR (R²=0.742).
Article Details
Article Accepting Policy
The editorial board of Thai-Nichi Institute of Technology is pleased to receive articles from lecturers and experts in the fields of business administration, languages, engineering and technology written in Thai or English. The academic work submitted for publication must not be published in any other publication before and must not be under consideration of other journal submissions. Therefore, those interested in participating in the dissemination of work and knowledge can submit their article to the editorial board for further submission to the screening committee to consider publishing in the journal. The articles that can be published include solely research articles. Interested persons can prepare their articles by reviewing recommendations for article authors.
Copyright infringement is solely the responsibility of the author(s) of the article. Articles that have been published must be screened and reviewed for quality from qualified experts approved by the editorial board.
The text that appears within each article published in this research journal is a personal opinion of each author, nothing related to Thai-Nichi Institute of Technology, and other faculty members in the institution in any way. Responsibilities and accuracy for the content of each article are owned by each author. If there is any mistake, each author will be responsible for his/her own article(s).
The editorial board reserves the right not to bring any content, views or comments of articles in the Journal of Thai-Nichi Institute of Technology to publish before receiving permission from the authorized author(s) in writing. The published work is the copyright of the Journal of Thai-Nichi Institute of Technology.
References
J. M. B. Carreiras, M. J. Vasconcelos, and R. M. Lucas, “Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa),” Remote Sensing of Environment, vol. 121, pp. 426–442, Jun. 2012.
Å. Rosenqvist, A. Milne, R. Lucas, M. Imhoff, and C. Dobson, “A review of remote sensing technology in support of the Kyoto Protocol,” Environmental Science & Policy, vol. 6, no. 5, pp. 441–455, Oct. 2003.
L. Ji, B. K. Wylie, D. R. Nossov, B. Peterson, M. P. Waldrop, J. W. McFarland, J. Rover, and T. N.Hollingsworth, “Estimating aboveground biomass in interior Alaska with Landsat data and field measurements,”International Journal of Applied Earth Observation and Geoinformation, vol. 18, pp. 451–461, Aug. 2012.
X. Wang, Y. Pang, Z. Zhang, and Y. Yuan, “Forest aboveground biomass estimation using SPOT-5 texture indices and spectral derivatives,” in 2014 IEEE Geoscience and Remote Sensing Symposium, 2014, pp. 2830–2833.
G. Yin, Y. Zhang, Y. Sun, T. Wang, Z. Zeng, and S. Piao, “MODIS Based Estimation of Forest Aboveground Biomass in China,” PLOS ONE, vol. 10, no. 6, p. e0130143, 2015.
P. S. Thenkabail, N. Stucky, B. W. Griscom, M. S. Ashton, J. Diels, B. van der Meer, and E. Enclona, “Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data,”International Journal of Remote Sensing, vol. 25, no. 23, pp. 5447–5472, Dec. 2004.
S. Eckert, “Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data,” Remote Sensing, vol. 4, no. 4, pp. 810–829, Mar. 2012.
N. Baghdadi, G. L. Maire, J. S. Bailly, K. Osé, Y. Nouvellon, M. Zribi, C. Lemos, and R. Hakamada, “Evaluation of ALOS/PALSAR L-Band Data for the Estimation of Eucalyptus Plantations Aboveground Biomass in Brazil,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 8, pp. 3802–3811, Aug. 2015.
Z. Jing, Y. Zhang, and K. Wang, “Estimating Paddy Rice Biomass Using Radarsat-2 Data Based on Artificial Neural Network,” in International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013), 2013, pp. 423–426.
L. Pierce, P. Liang, and M. C. Dobson, “Regrowth biomass estimation in the amazon using JERS-1/RADARSAT SAR composites,” in Geoscience and Remote Sensing Symposium, 2002. IGARSS ’02. 2002 IEEE International, 2002, vol. 4, pp. 2075–2077 vol.4.
S. Englhart, V. Keuck, and F. Siegert, “Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use,” Remote Sensing of Environment, vol. 115, no. 5, pp. 1260–1271, May 2011.
S. Kumar, U. Pandey, and S. P. Kushwaha, “Tropical forest from Envisat ASAR using modeling approach,” Journal of Applied Remote Sensing, vol. 6, no. 1, 2012.
O. Cartus, M. Santoro, and J. Kellndorfer, “Mapping forest aboveground biomass in the Northeastern United States with ALOS PALSAR dual-polarization L-band,” Remote Sensing of Environment, vol. 124, pp. 466–478, Sep. 2012.
K. J. Ranson and G. Sun, “Mapping Biomass for a Northern Forest Ecosystem Using Multi- Frequency Sar Data,” in Geoscience and Remote Sensing Symposium, 1992. IGARSS ’92. International, 1992, vol. 2, pp. 1220–1222.
O. Hamdan, H. K. Aziz, and K. A. Rahman, “Remotely sensed L-band sar data for tropical forest biomass estimation,” ResearchGate, vol. 23, no. 3, pp. 318–327, Jul. 2011.
M. A. Tanase, R. Panciera, K. Lowell, S. Tian, J. M. Hacker, and J. P. Walker, “Airborne multi-temporal L-band polarimetric SAR data for biomass estimation in semi-arid forests,” Remote Sensing of Environment, vol. 145, pp. 93–104, Apr. 2014.
J. Goh, J. Miettinen, A. S. Chia, P. T. Chew, and S. C. Liew, “Biomass Estimation in Humid Tropical Forest Using a Combination of ALOS PALSAR and Spot 5 Satellite Imagery,” Asian Journal of Geoinformatics, vol. 13, no. 4, 2014