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Charat Mongkolsawat, Nagon Wattanakij, Thapanee Kamchai,

Exploration of Spatio-Temporal Drought Patterns using Satellite-Derived Indices for Crop management in Northeastern Thailand. Charat Mongkolsawat, Nagon Wattanakij, Thapanee Kamchai, Khaesaet Mongkolsawat and Duangjai Chuyakhai.

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Charat Mongkolsawat, Nagon Wattanakij, Thapanee Kamchai,

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  1. Exploration of Spatio-Temporal Drought Patterns using Satellite-Derived Indices for Crop management in Northeastern Thailand Charat Mongkolsawat, Nagon Wattanakij, Thapanee Kamchai, Khaesaet Mongkolsawat and Duangjai Chuyakhai Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  2. Content • Introduction • Objective • Study Area • Method • Results and Discussion • Conclusion Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  3. 1. Introduction • Drought analysis requires a number of input data, normally no full coverage of information available for the entire areas. • Lack of water has profound impact on crop management particularly in the areas where irrigation is not available. Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  4. 1. Introduction (Cont.) • Soil moisture and vegetation covers are the most direct and important indicators of drought events. • Satellite data offers effective opportunities instead of collecting huge volume of climatic data. Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  5. 2. Objective • To explore spatio-temporal drought pattern with satellite-derived indicators. • To identify some of the satellite derived indicators best suited for the Northeast Thailand. Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  6. 3. Study Area • Northeast Thailand Area:170,000 km2. Rainfall:1,000-2,000 mm./year Forest:Deciduous and Evergreen forests Topography: Gently undulating terrain with small hills Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  7. Geology Cassava Topography Soil Forest Sugarcane Paddy Field Rainfall Rubber 3. Study Area (Cont.) Landuse Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  8. 4. Method Data source: • Rainfall data over 70 stations in Northeast Thailand of 8 years (2001-2008). • Multitemporal Terra-Modis data of the 16 day composite image data at 250 m resolution during the period 2001-2008 available from http://daac.ornl.gov Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  9. 4. Method (Cont.) Satellite derived indices: The Normalized Difference Vegetation Index NDVI= (ρNIR - ρRed) / (ρNIR + ρRed) Where ρNIR and ρRed are the reflectance values at 0.857 μm and 0.645 μm, respectively The Normalized Difference Water Index NDWI= (ρNIR - ρSWIR) / (ρNIR + ρSWIR) Where ρNIR and ρSWIR are the reflectance values at 0.857 μm and 1.65 μm, respectively Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  10. 4. Method (Cont.) Satellite derived indices: The Normalized Difference Drought Index NDDI= (NDVI- NDWI) / (NDVI + NDWI) Where NDVI = The Normalized Difference Vegetation Index NDWI = The Normalized Difference Water Index Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  11. 4. Method (Cont.) Rainfall Analysis: • Mean annual rainfall, mean 16 days rainfall and their standard deviations for 8 years record were analyzed at the entire stations. • The cumulative rainfalls summed over the preceding months and its slope gradient for each year. • Spatial interpolation of mean annual rainfall for 8 years performed using Inverse Distance Weighted method. Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  12. 4. Method (Cont.) • The method was based on the relationship between rainfall and the indices, including comparison of the changes in NDVI and NDWI values of pairs of images for different dates. Phenology of vegetation provides information on the spatio-temporal pattern of drought. Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  13. 5. Results Temporal Variability: • Show relationship between cumulative rainfall and NDVINDWI and NDDI values cover. Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  14. 5. Results (Cont.) • The high NDVI and NDWI values are strongly correlated with the greenness of the areas in contrary to the NDDI value. • Increase of NDDI values occurs during the dry period. Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  15. Spatial NDVI over the Northeast • The onset of greenness increase with increasing NDVI value occurs in May. • The NDVI value response to rainfall is remarkedly identified and reaches maximum in October.

  16. Spatial NDWI over the Northeast • The NDWI value response to rainfall is similar to the NDVI but the NDWI is more sensitive to water content than the NDVI.

  17. Spatial NDDI over the Northeast • When the area is dried out the NDDI value is increased. The NDDI value is more sensitive than the NDVI-NDWI difference.

  18. 5. Results (Cont.) Spatial Variability of Rainfall: • The difference in spatial rainfall is shown by the step of standard deviation. • A board pattern of increasing rainfall from southwest to northeast is evident. Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  19. NDVI/ NDWI images differencing and their associated histograms of the changes • The NDVI value is substantially sensitive to vegetation cover but the NDWI is sensitive to both vegetation cover and water content. • The severity of the change can be derived from the SD steps, the greater step the higher changes.

  20. 6. Conclusion • In conclusion cumulative rainfall has a significant impacton vegetation development to which the satellite derived-indices are great correlated. The NDDI value is more sensitive to the severity of drought than the difference between NDVI and NDWI. Changes in phonological state of different vegetation covers identify the spatio-temporal pattern of drought. The changes represent the dNDVI and dNDWI values of multi-date images covering over diverse vegetation types. With availability and rapid access of satellite data and difficulty in gathering the continuous spatial coverage of climatic data, the satellite derived indices can be used to monitor the drought condition for the vast extent. Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

  21. Thank you for your attention Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

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