STAND AGE MODEL FOR MAPPING SPATIAL DISTRIBUTION OF RUBBER TREE USING REMOTELY SENSED DATA IN KEDAH, MALAYSIA
DOI:
https://doi.org/10.11113/jt.v78.8303Keywords:
Rubber tree identification, rubber tree age classification, remote sensing, vegetation indicesAbstract
This study attempt to develop stand age model of rubber tree by using remote sensing data. Rubber tree is one of the important biomass that has been considered as the essential part in global warming reduction plan due to its beneficial carbon sequestration capability. The spatial distribution of rubber tree based on different age was most highlighted as the focus of this study. Felda Lubuk Merbau in state of Kedah has been selected as a study area and Landsat 8 OLI-TIRS data was utilized to map rubber tree and differentiate them based on age group. The relationship between vegetation indices namely NDVI, SAVI and EVI to different age stages of rubber tree were discussed.
References
Dessler, Andrew. 2012. Introduction to Modern Climate Change. Cambrige University Press, New York, NY, USA.
Neelin, David J. 2011. Climate Change and Climate Modelling. Cambrige University Press, New York, NY, USA.
Prentice, I. C., Farquhar, G. D., Fasham, M. J. R., Goulden, M. L., Heimann, M., Jaramillo, V. J., Kheshgi, H. S., Le Quéré, C., Scholes, R. J., Walace, D. W. R. 2001. The Carbon Cycle and Atmospheric Carbon Dioxide in Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der Linden, P. J., Dai, X., Maskell, K., Johnson, C. A. (Eds.). Climate Change 2001: The Scientific Basis, Cambridge University Press, Cambridge. 183-237.
Ravindranath, N.H. and Ostwald, M. 2008. Carbon Inventory Methods: Handbook for Greenhouse Gas Inventory, Carbon Mitigation and Roundwood Production Projects. Springer Science, Business Media B.V.
Dong, J., Kaufmann, R. K., Myneni, R. B., Tucker, C. J., Kauppi, P. E., Liski, J., Buermann, W., Alexeyev, V., Hughes, M. K. 2003. Remote Sensing Estimates Of Boreal And Temperate Forest Woody Biomass: Carbon Pools, Sources, and Sinks. Journal of Remote Sensing of Environment. 84(2003): 393-410. Elsevier Science Inc.
Turner, D. P., Guzy, M., Lefsky, M. A., Ritts, D. W., van Tuyl, S., Law, E. B. 2004. Monitoring Forest Carbon Sequestration with Remote Sensing and Carbon Cycle Modelling. Journal of Environmental Management. 33(4): 457-466. Springer-Verlag, New York.
Krukanont, P. and Prasertsan, S. 2004. Geographical Distribution Of Biomass And Potential Sites Of Rubber Wood Fired Power Plants In Southern Thailand. Journal of Biomass and Biology. 26(2004): 47-59. Elsevier Ltd.
Senf, C., Pflugmacher, D., van der Linden, S., Hostert, P. 2013. Mapping Rubber Plantations and Natural Forest in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series. Remote Sensing. 5: 2795-2812.
Shidiq, I. P. A., Mohd Hasmadi,I., & Kamarudin, N. 2014. Initial Results Of The Spatial Distribution Of Rubber Tress In Peninsular Malaysia Using Remotely Sensed Data For Biomass Estimate. IOP Conference Series: Earth and Environmental Science. 18. doi:10.1088/1755-1315/18/1/012135.
Li, Z., Fox, J. M. 2011. Rubber Tree Distribution Mapping in Northeast Thailand. International Journal of Geosciences.. 573-584.
Li, Z., & Fox, J. M. 2012. Mapping Rubber Tree Growth In Mainland Southeast Asia Using Time-Series MODIS 250 m NDVI and Statistical Data. Applied Geography. 32(2): 420-432. doi:10.1016/j.apgeog.2011.06.018.
Dong, J., Xiao, X., Sheldon, S., Biradar, C., & Xie, G. 2012. Mapping Tropical Forests And Rubber Plantations In Complex Landscapes By Integrating PALSAR And MODIS Imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 74: 20-33. doi:10.1016/j.isprsjprs.2012.07.004.
Dong, J., Xiao, X., Chen, B., Torbick, N., Jin, C., Zhang, G., & Biradar, C. 2013. Mapping Deciduous Rubber Plantations Through Integration Of PALSAR And Multi-Temporal Landsat Imagery. Remote Sensing of Environment. 134: 392-402. doi:10.1016/j.rse.2013.03.014.
Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering. 1973. Monitoring Vegetation Systems in the Great Plains with ERTS. Third ERTS Symposium, NASA. SP-351 I:309-317
Huete, A. R. 1988. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment. 25: 295-309.
Liu, H. Q., Huete, A. R. A. 1995. Feedback Based Modification Of The NDV I To Minimize Canopy Background And Atmospheric Noise. IEEE Transactions on Geoscience and Remote Sensing. 33: 457-465.
Matsushita, B., Yang, W., Chen, J., Onda, Y., Qiu, G. 2007. Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-Density Cypress Forest. Sensors. 7: 2636-2651.
Suratman, M. Z. 2003. Applicability of Landsat TM Data for Inventorying and Monitoring of Rubber (Hevea Brasiliensis) Plantations in Selangor, Malaysia: Lingkages to Policies. Faculty of Forestry. The University of British Columbia.
Hamdan, O., Aziz, H. K., Mohd Hasmadi, I. 2014. L-band ALOS PALSAR for Biomass Estimation of Matang Mangroves, Malaysia. Remote Sensing of Environment. 155: 69-78.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
