AI SearchLand suitability and crop substitution modeling for sugarcane and cassava in Kamphaeng-Phet Province, Thailand
Call Number: AIT Thesis no.RS-13-11 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. RS-13-11Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2013Description: 95 leaves : ill. (some col.) + 1 online resourceSubject(s): Online resources: Dissertation note: Thesis (M.Sc.) - Asian Institute of Technology, 2013 Summary: Thailand is importing highly expansive energy sources such as petroleum products. Moreover, cassava and sugarcane can be used as food and alternative energy source. Therefore, this study suggests the use of biofuel crops to be utilized as source of energy. Kampaeng-phet province was selected as research area in this research because it has potential for many industries to support growing these crops and extent the plantation. This study has three objectives. The first objective was land evaluation for cassava and sugarcane by considering 10 factors including soil texture, soil depth, soil drainage, soil fertility, soil pH, surface water, irrigation, rainfall, temperature and slope. Then, the weights of factors were investigated by analytic hierarchy process (AHP) and Fuzzy AHP. It was found that Fuzzy AHP had approximately more accuracy than AHP to evaluate the land suitability for cassava and sugarcane plantation. The second objective was to classify agricultural area using HJ-1A satellite image. Two methods of classifications in this study were pixel base classification (maximum likelihood) and object based classification. For this objective, it was found that object based classification had overall accuracy(76.27%)more than pixel based classification (64.55%). The last objective was to make crop substitution model for extending cassava and sugarcane plantation regarding land suitability, economics (revenue and profit).The different scenarios showed many different options for planting biofeul crops. Crop substitution modeling regarding land suitability had area to substitute to grow sugarcane (605.3 sq.km) was more than cassava (278.68 sq.km). The modeling regarding revenue (real prices in2012) had area to substitute to grow sugarcane (1535.46 sq.km) was more than cassava (14.09 sq.km).However, the model regarding profit had area that can be used to substitute to cassava (1196.76 sq.km) had area more than sugarcane (453.65sq.km)because cassava had lower capital cost than sugarcane.
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Asian Institute of Technology Library AIT Publications | AIT Thesis no.RS-13-11 (Browse shelf(Opens below)) | 2 | Available | 30050120588842 | |||||||||||||
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Asian Institute of Technology Library AIT Publications | AIT Thesis no.RS-13-11 (Browse shelf(Opens below)) | 3 | Available | 30050120993430 |
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Remote Sensing and Geographic Information Systems
Thesis (M.Sc.) - Asian Institute of Technology, 2013
Thailand is importing highly expansive energy sources such as petroleum products. Moreover, cassava and sugarcane can be used as food and alternative energy source. Therefore, this study suggests the use of biofuel crops to be utilized as source of energy. Kampaeng-phet province was selected as research area in this research because it has potential for many industries to support growing these crops and extent the plantation. This study has three objectives. The first objective was land evaluation for cassava and sugarcane by considering 10 factors including soil texture, soil depth, soil drainage, soil fertility, soil pH, surface water, irrigation, rainfall, temperature and slope. Then, the weights of factors were investigated by analytic hierarchy process (AHP) and Fuzzy AHP. It was found that Fuzzy AHP had approximately more accuracy than AHP to evaluate the land suitability for cassava and sugarcane plantation. The second objective was to classify agricultural area using HJ-1A satellite image. Two methods of classifications in this study were pixel base classification (maximum likelihood) and object based classification. For this objective, it was found that object based classification had overall accuracy(76.27%)more than pixel based classification (64.55%). The last objective was to make crop substitution model for extending cassava and sugarcane plantation regarding land suitability, economics (revenue and profit).The different scenarios showed many different options for planting biofeul crops. Crop substitution modeling regarding land suitability had area to substitute to grow sugarcane (605.3 sq.km) was more than cassava (278.68 sq.km). The modeling regarding revenue (real prices in2012) had area to substitute to grow sugarcane (1535.46 sq.km) was more than cassava (14.09 sq.km).However, the model regarding profit had area that can be used to substitute to cassava (1196.76 sq.km) had area more than sugarcane (453.65sq.km)because cassava had lower capital cost than sugarcane.
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