Potential of Normalized Difference Vegetation Index for Mapping of Soft Clay Area in Paddy Fields of Kedah, Malaysia

https://doi.org/10.22146/ijg.43617

Muhammad Rendana(1*), Wan Mohd Razi Idris(2), Sahibin Abdul Rahim(3), Zulfahmi Ali Rahman(4), Tukimat Lihan(5)

(1) Universitas Sriwijaya, Bukit Besar, Palembang, Indonesia
(2) Environmental Science Programme, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysi
(3) Environmental Science Program, Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
(4) Environmental Science Programme, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysi
(5) Environmental Science Programme, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysi
(*) Corresponding Author

Abstract


Mapping of soft clay area in paddy fields uses remote sensing and GIS technique is the fastest way to obtain an accurate location of soft clay in a large scale area. It can be an alternative way to change conventional method like in-situ observation that is expensive and labor intensive. Therefore, this study aimed to investigate the normalized difference vegetation index (NDVI) to map soft clay area in paddy fields Kedah, Malaysia. To analyze soft clay area comprehensively, the study was carried out in three different periods; before paddy planting, after paddy planting and harvest. Ground-truth data of soft clay area was collected from study area during fieldwork activity and compared with NDVI values that produced from Landsat 8 image. Result of study showed NDVI map in period of before paddy planting could be a good indicator for mapping soft clay area because it gave a higher accuracy value than the other periods, with overall accuracy (85%) and kappa coefficient (0,84). Total area of soft clay from the highest value was showed in period of before paddy planting (1.856,97 ha), followed by after paddy planting (656,73 ha) and harvest (401,85 ha) periods, respectively.

Keywords


Normalized Difference Vegetation Index; Paddy cultivation; Remote sensing; Soft clay

Full Text:

PDF


References

Ahmed, M., Else, B., Eklundh, L., Ardo, J., & Seaquist, J. (2017). Dynamic Response of NDVI to Soil Moisture Variations during Different Hydrological Regimes in the Sahel Region. International Journal of Remote Sensing, 38, 5408-5429.

Ajith, K., Geethalakshmi, V., Ragunath, K. P., Pazhanivelan, S., & Dheebakaran, G. (2017). Rice yield prediction using MODIS - NDVI (MOD13Q1) and land based observations. International Journal of Current Microbiology and Applied Sciences, 6(12), 2277-2293.

Azizul, G. (2008). Soil hardpan improvement technique using vibrator subsoiler for rice mechanization farm. Bulletin Teknologi Tanaman, 5, 1-4.

Bambang, R., & Ahmad, B. S. (2017). Identification of paddy field using Landsat image in Karawang Regency, West Java. IOP Conference Series: Earth and Environmental Science, 54(1), 1-8.

Chen, T., De Jeu, R., Liu, Y. Y., Van der Werf, G. R., & Dolman, A. J. (2014). Using Satellite Based Soil Moisture to Quantify the Water Driven Variability in NDVI: A Case Study over Mainland Australia. Remote Sensing Environment, 140, 330–338.

GopalaPillai, S., & Tian, L. (1999). In-field variability detection and yield prediction in corn using digital aerial imaging. Transactions of the American Society of Agricultural Engineers, 42, 1911-1920.

Holzman, M. E., Rivas, R., & Bayala, M. (2014). Subsurface soil moisture estimation by VI-LST method. IEEE Geoscience Remote Sensing Letter, 11, 1951-1955.

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

Jensen, J. R. (1996). Introductory digital image processing: a remote sensing perspective. New York: Prentice hall, Inc.

Jwan, A. D., Shattri, B. M., & Helmi, Z. M. S. (2013). NDVI Differencing and Post-classification to Detect Vegetation Changes in Halabja City, Iraq. IOSR Journal of Applied Geology and Geophysics, 1(2), 1-10.

Kamaruzaman, J., Hasmadi, M. I., & Nurul, H. M. A. (2009). Spectral separability of tropical forest tree species using airborne hyperspectral imager. Journal of Environmental Science and Engineering, 3(1), 37-41.

Mallick, K., Battacharya, B. K., & Patel, N. K. (2009). Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI. Agricultural Forest Meteorology, 149, 1327-1342.

Mosleh, M. K., & Hassan, Q. K. (2014). Development of a remote sensing-based “Boro” rice mapping system. Remote Sensing, 6(3), 1938-1953.

Nadzim, M. N., Ayob, A. H., Eddy, H. S., Ahmad, S. S. S., & Mohamed, F. I. (2014). The replacement soil method for increasing in soil strength at soft soil area. Journal of Technology, 70, 45-47.

New Straits Time. (2013, June 1). Berita Jun 2013. New Straits Time Malaysia. Retrieved from https://www.nst.com.my.

Rendana, M., Idris, W. M. R., Rahim, S. A., Rahman, Z. A., Lihan, T., & Jamil, H. (2018). Reclamation of acid sulphate soils in paddy cultivation area with organic amendments. AIMS Agriculture and Food, 3, 358-371.

Rendana, M., Idris, W. M. R., Rahim, S. A., Rahman, Z. A., Lihan, T., & Jamil, H. (2019a). Effects of Organic Amendment on Soil Organic Carbon in Treated Soft Clay in Paddy Cultivation Area. Sains Malaysiana, 48(1), 61-68.

Rendana, M., Idris, W. M. R., Rahim, S. A., Rahman, Z. A., Lihan, T., & Jamil, H. (2019b). Field investigation of soil organic carbon and soil respiration rate from paddy cultivation area of Alor Senibong Kedah, Malaysia. AIP Conference Proceedings, 2111(1), 1-7.

Rendana, M., Rahim, S. A., Idris, W. M. R., Lihan, T., & Rahman, Z. A. (2016). Mapping nutrient status in oil palm plantation using geographic information system. Asian Journal of Agricultural Research, 10(3), 144-153.

Rendana, M., Rahim, S. A., Idris, W. M. R., Lihan, T., & Rahman, Z. A. (2017). Soil erosion assessment in Tasik Chini catchment using remote sensing and GIS techniques. Sains Malaysiana, 46(4), 529-535.

Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the great plains with erts. Proceedings of Third ERTS Symposium, 1, 309-317.

Shafian, S., & Maas, S. J. (2015) Index of soil moisture using raw Landsat image digital count data in Texas High Plains. Remote Sensing, 7, 2352-2372.

Wang, X., Hongjie, X., Guan, H., & Zhou, X. (2007). Different Responses of MODIS-Derived NDVI to Root-Zone Soil Moisture in Semi-Arid and Humid Regions. Journal of Hydrology, 340, 12–24.



DOI: https://doi.org/10.22146/ijg.43617

Article Metrics

Abstract views : 2856 | views : 2108

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2020 Muhammad Rendana, Wan Mohd Razi Idris, Sahibin Abdul Rahim, Zulfahmi Ali Rahman, Tukimat Lihan

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

Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)

ISSN 2354-9114 (online), ISSN 0024-9521 (print)

Web Analytics IJG STATISTIC