Prediction of Teak Volume Using Sentinel-2 Satellite Imagery Data at Thong Pha Phum Plantation, Kanchanaburi Province

Authors

  • Weeraphart Khunrattanasiri Department of Forest Management, Faculty of Forestry, Kasetsart University
  • Yutthana Thoenglom Department of Forest Management, Faculty of Forestry, Kasetsart University

Keywords:

linear regression, teak volume, vegetation index, Sentinel-2 satellite imagery data Thong Pha Phum plantation

Abstract

The teak volume prediction is an important data for planning a teak plantation management. This study aimed to investigate the vegetation indices using Sentinel-2 satellite imagery data for teak volume prediction based on data from Thong Pha Phum Plantation, Kanchanaburi province. The linear regression was used to explain the relationship between teak volume and vegetation indices from Sentinel-2 satellite imagery data. Ratio Vegetation Index (RVI) Difference Vegetation Index (DVI), Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil Adjusted Vegetation Index (SAVI) and Atmospherically Resistant Vegetation Index (ARVI) were used as the independent variables in the equation. The results revealed that SAVI (values with L equal to 0.5) was the best vegetation index for teak volume prediction based on Sentinel-2 satellite imagery data. The equation was teak volume (y) = 10.064 (SAVI)-98.736 and the coefficient of determination was 0.73. The mean absolute error (MAE) and the root mean squared error (RMSE) were used to check the equation precision for teak volume prediction. The MAE was 1.52 and the RMSE was 1.92. The teak volume calculated based on the given equation was 199,366.68 cubic meters and the average of teak volume per rai was calculated to be 18.51 cubic meters.

References

Chang, J. and M. Shoshany. 2016. Mediterranean Shrublands Biomass Estimation using Sentinel-1 and Sentinel-2. pp. 5300-5303. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing: IEEE Xplore.

European Space Agency. 2015. Sentinel-2 User Handbook. Paris: European Space Agency. 64 p.

Forest Industry Organization. 2014. Sustainable Forest Management Thong Pha Phum Plantation. Bangkok: Ministry of Natural Resources and Environment. 14 p. [in Thai]

Gitelson, A.A., Y.J. Kaufman and M.N. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58(3): 289-298.

Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25(3): 295-309.

Huete, A.R., K. Didan, T. Miura, E.P. Rodriguez, X. Gao and L.G. Ferreira. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83(1-2): 195-213.

Husch, B., T. Beers and J.A. Kershaw. 2003. Forest Mensuration. Hoboken: John Wiley & Sons, Inc. 456 p.

Jordan, C.F. 1969. Derivation of leaf‐area index from quality of light on the forest floor. Ecology 50(4): 663-666.

Kaufman, Y.J. and D. Tanre. 1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing 30(2): 261-270.

Kriegler, F., W. Malila, R. Nalepka and W. Richardson. 1969. Preprocessing Transformations and Their Effects on Multispectral Recognition. pp. 97-131. In Proceedings of the 6th International Symposium on Remote Sensing of Environment. Ann Arbor, MI: University of Michigan.

Lillesand, T.M., R.W. Kiefer and J.W. Chipman. 2015. Remote Sensing and Image Interpretation. Hoboken: John Wiley & Sons, Inc. 736 p.

Nuthammachot, N., W. Phairuang, P. Wicaksono and T. Phairuang. 2018. Estimating Aboveground Biomass on Private Forest Using Sentinel-2 Imagery. Journal of Sensors 2018(2): 1-11. DOI:10.1155/2018/6745629.

Prasomsin, P. 1991. A Manual of Forest Mathematics Operations. Bangkok: Department of Forest Management, Faculty of Forestry, Kasetsart University. 138 p. [in Thai]

Ravindranath, M.H. 1981. Manual of Research Methods for Crustacean Biochemistry and Physiology. Madras: Central Marine Fisherriess Research Institute. 172 p.

Redowan, M., R. Akter, M. Islam, K.M. Masum and M.S.H. Chowdhury. 2015. Estimating growing stock volume in a Bangladesh forest site using Landsat TM and field-measured data. International Journal of Geomatics and Geosciences 6(2): 1607-1619.

Sangcharoen, R. 1973. A role of teak in economic development. KU News 18(6): 36-49. [in Thai]

Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8(2): 127-150.

Downloads

Published

2023-08-22

How to Cite

Khunrattanasiri, W., & Thoenglom, Y. (2023). Prediction of Teak Volume Using Sentinel-2 Satellite Imagery Data at Thong Pha Phum Plantation, Kanchanaburi Province. Journal of Agricultural Research and Extension, 40(2), 130–145. retrieved from https://li01.tci-thaijo.org/index.php/MJUJN/article/view/251019

Issue

Vol. 40 No. 2 (2023): May - August 2023

Section

Research Article

License

Copyright (c) 2023 Journal of Agricultural Research and Extension

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This article is published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0), which allows others to share the article with proper attribution to the authors and prohibits commercial use or modification. For any other reuse or republication, permission from the journal and the authors is required.