Monitoring agricultural drought using MODIS temperature vegetation dryness index in Mae Nam Chi Basin, Thailand | |
| Author | Kaesorn Jumpa |
| Call Number | AIT Thesis no.RS-05-08 |
| Subject(s) | Drough forecasting Soil moisture--Remote sensing Soil temperature--Remote sensing |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science |
| Publisher | Asian Institute of Technology |
| Abstract | Drought occurs when there is lack of water in a particular area and is usually caused by reduced amount of rainfall over that particular area. The impact of drought is usually noticed in the agricultural land only on its onset. For that reason agricultural drought monitoring in near-real time is very important. This research brings a method of predicting drought and monitoring its severity in near real time using Terra MODIS images. Terra MODIS images with reflective band of 250 m resolution and thermal infrared band of 1 km resolution with high temporal frequency data were used in this study. The satellite images from 2001 to 2003 were analyzed. The temperature vegetation dryness index (TVDI), which integrates normalized difference vegetation index and land surface temperature was used to detect surface moisture and in monitoring drought in Mae Nam Chi basin. Year 2003 has higher TVDI value while, 2001 has lower TVDI value when compared within the analysis years. Accordingly the same year (2003) received less rainfall compared to other years and was declared drought year where as 2001 was a normal year. The difference NDVI (DEV NDVI) was selected to develop the model for drought risk prediction. TVDI and DEVNDVI were used to detect drought occurrence during the dry season and was found to be reliable. A verification drought index when compared with the drought risk map shows a significant correlation than annual rainfall during dry season. However during hot season TVDI shows significant correlation than DEVNDVI when compared with drought risk level but it has no correlation with annual rainfall with both TVDI and DEVNDVI were used to compute drought risk prediction model using linear regression during the dry season. The model was tested with the data collected within 2001 to 2003. 19th October as the starting of dry season of every year was considered for compare on of the difference in the results. The results showed that the value for 2003 was much higher than other two years at the beginning of dry season; hence it was declared severe drought year. The model was also applied to the recent data collected on 18th February 2005 as it can illustrate drought risk area during present time. Validation assessment of the model with monthly rainfall proved to be reliable for drought monitoring and perdition. This model proved useful for near real time drought monitoring and prediction. However some limitation has to be considered as the model here was applied to non irrigated land areas. |
| Year | 2005 |
| Type | Thesis |
| School | School of Advanced Technologies (SAT) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | Tripathi, Nitin Kumar |
| Examination Committee(s) | Kusanagi, Michiro ;Srisaang Kaojarern ;Vivarad Phonekeo |
| Scholarship Donor(s) | Ministry of Agricultural and Cooperative of Thailand |
| Degree | Thesis (M.Sc.) - Asian Institute of Technology, 2005 |