1. Study on applying MODIS image into drought indicator analysis in Taiwan Yuh-Lurng Chung, Chaur-Tzuhn Chen Chen-Ni Hsi, Shih-Ming Liu 2004.11.04 Yuh-Lurng Chung, Chaur-Tzuhn Chen Chen-Ni Hsi, Shih-Ming Liu 2004.11.04

2. Introduction When drought occurs, due to insufficient water supply, the variation/change of leaves in aridity can be sensed by spectral reflection of multi-temporal satellites.

3. Overseas researches of employing satellite images to efficiently forecast and manage drought has achieved great outcomes.

4. Introduction This research employ MODIS images to select sensitive thermal bands suitable for monitoring drought. And by using bands for the calculation of all kinds of vegetation indices, it is expected to find proper and practical indices for drought monitoring, which could be used for future management and determination of drought disaster.

5. Using bands for the calculation of all kinds of vegetation indices, it is expected to find proper and practical indices for drought monitoring, which could be used for future management and determination of drought disaster.

6. Research Data/Information and Methodology 1. Study Area Include whole Taiwan area of 19 districts. 2. Required Data/Information Precipitation Data Information concerned includes the records of all rainfall stations from the January of 1991 to the March of 2004 in the entire Taiwan area.

7. Legend Boundary Rainfall station Locations of All Taiwan Rainfall Stations

8. MODIS Images Band Band Width (μm) Central Wavelength (μm) Required NeΔT (K) Primary use* 20 3.660 － 3.841 3.78820.05O,L 21 3.929 － 3.989 3.99212.00Fire,volcano 22 3.929 － 3.989 3.97190.07A,L 23 4.020 － 4.080 4.05670.07A,L 29 8.400 － 8.700 8.52880.05L 31 10.78 － 11.28 11.01860.05A,L 32 11.77 － 12.27 12.03250.05A,L * A:atmospheric studies, L:land studies, O:oceanstudies Thermal Bands of MODIS Image Seven MODIS Bands for Monitoring Earth’s Surface

9. By utilizing the information of surface regression through Kriging Model, our approach then can get drought indicator and drought amount of this area. Two images of the dry season (January 25, 2004) and wet season (June 30, 2004) are accordingly chosen for further analysis. By utilizing the information of surface regression through Kriging Model, our approach then can get drought indicator and drought amount of this area. Two images of the dry season (January 25, 2004) and wet season (June 30, 2004) are accordingly chosen for further analysis.

10. Research Methodology Select clear MODIS images without cloud Rainfall of continuous 30 days Data from rainfall stations Cumulative rainfall of all Drought Amount>130m m Threshold of Each County and City Climate Drought Indices NO YES Preprocessing of MODIS images Use MODIS images of dry and wet seasons to select thermal bands for drought monitor Locate sample grassland areas Choose index for drought Specify Ranges of Dry and Wet Seasons Using Climate Drought Indices Calculate all indices& select some for preliminary analysis

11. Discussions on Applying Drought Indices to Drought Monitor Normalized Thermal Index (NTI) Normalized Difference Vegetation Index (NDVI)

12. Normalized Difference Water Index (NDWI) Shortwave Infrared Water Stress Index (SIWSI)

13. Results and Discussion Characteristics of Taiwan Rainfall Data Based on the rainfall data of 355 rainfall stations from 1992 to 2003, clearly shows different standards (levels) of different places in different periods. It also indicates the relativity of drought definition due to spatial and temporal factors.

14. Historical Curves of the first decile values of Cumulative Taipei Taoyuan Hsinchu Miaoli Taichung Janghua Nantou Yunlin Yilan Hualien Taitung Chiayi Tainan Kaohsiung January March May July September November Month Precipitation within 30 consecutive days recorded by rainfall stations in each county in whole Taiwan Province

15. Application of MODIS Images to Select Thermal Bands for Drought Monitor Original MODIS Image MODIS Images After Geometric Correction

16. Extraction of Sample Sites of Grasslands Sample Boundary Legend From land-use maps we query all natural grasslands from the database of ArcGIS. And after removing those sample cloud hovering, we mark those sample sites on the extracted images of natural grasslands without cloud covered.

17. Select Thermal Bands of MODIS images for Drought Monitor Extracts data for the seven MODIS bands, and compares the seven bands of dry season and wet season to find out what are the real differences.

18. Differences of Thermal Infrared Band Values of MODIS Images of Grasslands in Dry and Wet Seasons Differences of Mean Thermal Infrared Bands of Taiwan Grasslands MODIS Images in Dry and Wet Seasons

19. The Calculation of Normalized Thermal Index (NTI) NTI Number of Pixels NTI STD Mean STD = Mean= NTI Image and Histogram In Dry Season NTI Image and Histogram In Wet Season The research done by Robert et al. (2002) about monitoring volcano indicates that NTI value are ranged between -0.850 ~ - 0.950 due to the high surface temperature of the volcanic region.

20. Statistics of NTI Values of MODIS Images in Dry and Wet Season, and T-test MODIS NTI Images Sample Numbers NTI Average P(T<=t)Difference In Dry Season36600-0.199 0Distinct In Wet Season366000.058 Note: T-test with confidence interval 5%

21. The Calculation of Normalized Difference Vegetation Index (NDVI) MODIS NDVI Images in Dry Season MODIS NDVI Images in Wet Season NDVI Number of Pixels NDVI STD= Mean= STD= Mean=

22. MODIS NDVI Images Samples NDVI Average P(T<=t)Difference In Dry Season780.88 2.84E-12significant In Wet Season780.94 Statistics of NDVI Values of MODIS Images in Dry and Wet Season, and T-test Dry NDVI Wet NDVI Sample NDVI Differences of MODIS Grassland Images in Dry and Wet Seasons

23. The Calculation of Normalized Difference Water Index (NDWI) MODIS NDWI Images in Dry Season MODIS NDWI Images in Wet Season NDWI Number of Pixels NDWI STD Mean STD Mean

24. Statistics of NDWI Values of MODIS Images in Dry and Wet Seasons, and T-test MODIS NDWI Images Samples NDWI Average P(T<=t)Difference In Dry Season780.52 7.67E-08significant In Wet Season780.63 Difference of MODIS NDWI Images of Grasslands in Dry and Wet Seasons Dry NDWI Wet NDWI Sample

25. Difference Between NDVI and NDWI of MODIS Images of Grasslands in Dry and Wet Seasons

26. NDVI in Dry Season NDVI in Wet Season NDWI in Dry Season NDWI in Wet Season NDVI in DS 1.000 NDVI in WS 0.079 1.000 NDWI in DS 0.461** 0.045 1.000 NDWI in WS 0.197 0.799** 0.023 1.000 **: Distinct Correlation as distinction level if 0.01(**). DS: Dry Season WS: Wet Season **: Distinct Correlation as distinction level if 0.01(**). DS: Dry Season WS: Wet Season Correlation Matrix Between NDVI and NDWI in DS and WS

27. Calculation of Shortwave Infrared Water Index (SIWSI) SIWSI Number of Pixels MODIS SIWSI Images of Taiwan in Dry Season MODIS SIWSI Image of Taiwan in Wet Season SIWSI Number of Pixels STD Mean

28. Statistics and T-test Table of SIWSI Values of grasslands in Dry and Wet Seasons (MODIS Images) SIWSI Differences for Sample Sites of Grasslands in Dry and Wet Seasons, based on MODIS Images MODIS SIWSI Images Number of Samples SIWSI MeanP(T<=t)differences Dry Season78-0.29 0.3214Insignificant Wet Season78-0.31

29. Conclusion This research indicates MODIS images with 36 bands have substantial potential in drought sensing. It is hereby possible to replace the NOAA-AVHRR satellite images with MODIS images, for more precise image data/information.

30. The MODIS Band 22 at the spatial resolution of 1,000 m is the most sensitive thermal bands to drought. And the NTI is the unique index of sensing thermal energy only available in MODIS images. Furthermore, this research hence utilizes the important wave bands which are chosen from the Band 22 to calculate NTI.

31. We can conclude that the NDVI, NDWI and NTI are sensitive to the monitoring of surface vegetation status, water content of vegetation and surface temperature respectively. As a result, they hereby have practical usages for drought forecast and monitoring.

32. The End