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6. Calculating Vegetation Indices
Use the NDVI (Vegetation Index) to transform multispectral data into a single image band representing vegetation distribution. The NDVI (Normalized Difference Vegetation Index) values indicate the amount of green vegetation present in the pixel—higher NDVI values indicate more green vegetation. Calculating NDVI uses the standard algorithm: (Valid results fall between -1 and +1).

The calculation of NDVI by randomly points sampling inside actual rubber plantation at the end of February. The results shown that NDVI of R1 are range between 0.67-0.736, R2 are range between 0.574-0.673 and R3 0.551-0.64 respectively. (See Figure 4) The calculation of NDVI by randomly points sampling inside actual oil palm plantation at the end of February. The results shown that NDVI of P1 are range between 0.719-0.777, P2 are range between 0.638-0.714 and P3 0.627-0.666 respectively. (See Figure 5)


Fig 4 The relationship between actual rubber plantation NDVI and rubber land suitability classe

Fig 5 The relationship between actual oil palm plantation NDVI and oil palm land suitability classes
7. Crop suitability modeling.
Finally the rubber suitability and oil palm suitability maps were superimposed, then all polygon were union together to get a model for the area selection of those two crops. The resulting crop suitability model (Fig. 6) shows 9 land suitability classes resulting from the combination of highly (R1, P1), moderately (R2, P2) and not suitable (R3, P3) areas for rubber and oil palm planting.

Conclusion
The results obtained from this study indicate that our new method gives better result than the older methods. The application of GIS and multi-factor evaluation using AHP could provide a superior database and guide map for decision makers considering crop substitution between rubber and oil palm in order to achieve better agricultural production.

One somewhat notable limitation of this new method is that it requires a knowledgeable assessment of the relative importance of all factors. This can be carried out to some extent using the pairwise comparison method, but to rate the relative preferences level for two factors using the scale for pairwise comparison still needs priority-setting experience for the specific plant from senior researchers. To set the correct relative preference level without personal bias in this study required not only reliable soil theory information and the writer’s experience, but also the experience of a number of senior researchers.


Fig 6 Crops substitution model for Krabi, Thailand
References

• Nakorn Sarakoon, Somyot Sinthurahat, and Sutat Dansagoonpon. 1998. “Agro-ecological Zoning Analysis for Oil Palm in The South of Thailand ”. Horticulture Research Institute and Rubber Research Institute, Dept. of Agriculture. 266 p.
• Saaty, T.L.1980, The Analytic Hierarchy Process, McGraw-Hill, New York.
• Somyot Sinthurahat. 1992. “Elaboration of Land Evaluation Model of Rubber Cultivation in Peninsular Thailand”. PhD Thesis, ITC, State University of Ghent, Belgium. 261 p.
• Sutat Dansagoonpon, and Somyot Sinthurahat. 1999. “Agro-ecological Zoning for Rubber (Hevea Brasiliensis) Plantation in Southern Thailand Using Land Evaluation, Remote Sensing and GIS”. Rubber Research Institute, Dept. of Agriculture. 242 p.
• Sys, C., Van Ranst, E., and Debaveye, J. 1992. “Land Evaluation. Part I (Principles in Land Evaluation and Crop Production Calculation)”. International Training Center (ITC) for Post-Graduate Soil Scientists. State University of Ghent, Belgium, 274 p.

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