Approaches to exploring drought using satellite data
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The document discusses the use of satellite data to analyze drought patterns in Northeast Thailand, highlighting the importance of soil moisture and vegetation indicators. It details objectives, methods, and results from a study analyzing rainfall and satellite-derived indices such as NDVI, NDWI, and NDDI from 2001 to 2008. The findings indicate that NDDI is particularly sensitive to drought severity and can serve as a reliable tool for monitoring drought conditions over large areas.
Approaches to exploring drought using satellite data
- 1. Approaches to exploring drought using satellite dataCharat Mongkolsawat and Thapanee KamchaiGeo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand
- 3. Objective
- 4. Study Area
- 5. Method
- 6. Results and Discussion
- 7. ConclusionGeo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand
- 8. 1. Introduction Drought analysis requires a number of input data, normally no full coverage of information available for the entire areas.
- 9. 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
- 10. 1. Introduction (Cont.) Soil moisture and vegetation covers are the most direct and important indicators of drought events.
- 11. 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
- 12. 2. Objective To explore spatio-temporal drought pattern with satellite-derived indicators.
- 13. 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
- 14. 3. Study AreaNortheast ThailandArea:170,000 km2.Rainfall:1,000-2,000 mm./yearForest:Deciduous and Evergreen forestsTopography: Gently undulating terrain with small hillsGeo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand
- 15. GeologyCassavaTopographySoilForestSugarcanePaddy FieldRainfallRubber3. Study Area (Cont.)LanduseGeo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand
- 16. 4. MethodData source: Rainfall data over 70 stations in Northeast Thailand of 8 years (2001-2008).
- 17. Multitemporal Terra-Modis data of the 16 day composite image data at 250 m resolution during the period 2001-2008 and 2010 available from WIST (https://wist.echo.nasa.gov)Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand
- 18. 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.
- 19. The cumulative rainfalls summed over the preceding months and its slope gradient for each year.
- 20. 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
- 21. 4. Method (Cont.) Based on relationship between rainfall and satellite derived-indices.
- 22. Satellite-derived indices NDVI, NDWI and NDDI Comparison of the changes in NDVI and NDWI values of pairs of images for different dates.Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand
- 23. 4. Method (Cont.)Satellite derived indices:The Normalized Difference Vegetation IndexNDVI= (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 IndexNDWI= (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
- 24. 4. Method (Cont.)Satellite derived indices:The Normalized Difference Drought IndexNDDI= (NDVI- NDWI) / (NDVI + NDWI)Where NDVI = The Normalized Difference Vegetation Index NDWI = The Normalized Difference Water Index(Proposed by Gu et al 2007)Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand
- 25. 4. Method (Cont.) 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.
- 26. dNDVI = NDVI1 - NDVI2
- 28. The step of SD from the mean dNDVI or dNDWI determines the magnitude of the change.
- 29. -1SD to 1SD = no change
- 30. The severity of drought can be derived from the SD step, the greater SD step the higher change.Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand
- 31. 5. Results DroughtWetSpatial Variability of Rainfall:20012002200320042005200620072008The difference in spatial rainfall is shown forthe years 2001 2002 2004 2005 2006 2007 and 2008. A board pattern of increasing rainfall from southwest to northeast is evident.2001-2008Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand
- 32. 5. Results (Cont.)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
- 33. 5. Results (Cont.) The high NDVI and NDWI values are strongly correlated with the greenness of the areas in contrary to the NDDI value.
- 34. 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
- 35. 5. Results (Cont.)Drought identification for the dry period of 2010 With the mean values of NDVI, NDWI and NDDI the drought severity for the year 2010 can be identified.
- 36. No distinction of NDVI values with increasing or decreasing water content can be observed.
- 37. Greater response to drought is remarkably identified by NDWI and NDDI values.
- 38. NDDI values are more sensitive and evident for drought identification as a result of greenness and water content.Geo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand
- 39. Spatial NDVI over the Northeast The onset of greenness increases with increasing NDVI value occuringin May.
- 40. The NDVI value response to rainfall is remarkably identified by the slope and reaches maximum in October.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.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.NDVISpatial NDVI over the Northeast Spatio-temporal NDVI for the years 2001-2008 and 2010
- 41. NDWISpatial NDWI over the Northeast Spatio-temporal NDWI for the years 2001-2008 and 2010
- 42. Spatial NDDI over the Northeast Spatio-temporal NDDI for the years 2001-2008 and 2010
- 43. 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.
- 44. The severity of the change can be derived from the SD steps, the greater step the higher changes.NDVI/ NDWI images differencing and their associated histograms of the changes NDVINDWIComparison of pairs of NDVI/ NDWI images differencing and the histograms of the changes in 2010 and the previous years.
- 45. 6. ConclusionIn 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 NDVI value. Changes in phonological state of different vegetation covers identify the spatio-temporal pattern of drought. The establishment of the temporal means of NDVI, NDWI and NDDI values for the period 2001-2008 can be used for comparison of drought severity. Moreover, the changes in image indices between different dates provide the magnitude of drought severity of which the higher step of SD determines the greater magnitude. 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
- 46. Thank you for your attentionGeo-informatics Center for the Development of Northeast Thailand, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand