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Yuei -An Liou

AgMIP -Pakistan Kickoff Workshop & International Seminar on Climate Change. Application of Remote Sensing Technologies to Mitigate the Impacts of Climate Change on Crop Production. Yuei -An Liou. Center for Space and Remote Sensing Research National Central University, Taiwan

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Yuei -An Liou

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  1. AgMIP-Pakistan Kickoff Workshop & International Seminar on Climate Change Application of Remote Sensing Technologies to Mitigate the Impacts of Climate Change on Crop Production Yuei-An Liou Center for Space and Remote Sensing Research National Central University, Taiwan President, Taiwan Group on Earth Observations Email: yueian@csrsr.ncu.edu.tw

  2. Yesterday Center for Space and Remote Sensing Research

  3. Today Hydrology Remote Sensing Laboratory (HRSL) -- Briefing -- Crop Yield Remote Sensing

  4. HRSL (1/2) • Land surface processes modeling (freezing, prairie) • Land surface monitoring (soil moisture, evapotranspiration, heat flux, biomass) • Land land use/change studies: precision farming, agricultural applications (Taiwan, China, Thailand, 311 Japan); natural disasters monitoring & mitigation & reduction; regional climate (heat island effect) • Atmosphere (water vapor, typhoon, profiles & waves) by microwave radiometers, ground- and space-based GNSS approach (radio occultation, e.g. Formosat3) • Weather forecast (typhoon, extreme weather events) 4

  5. HRSL (2/2) • Cryosphere and Global Warming • Glaciers: Arctic (2010 Ilulissat Icefjord) Antarctica Mainland China 5

  6. Content - Crop Yield Remote Sensing Remote Sensing on Crop Production The Great East Japan Earthquake (311) Impacts of Climate Change on Crop Production

  7. Remote Sensing on Crop Production – motivations • With the advantage of assessing environmental change over a large area, remotely sensed imageries have been extensively used to acquire a wide variety of information of the earth’s surface. • As the globe is facing more and more unpredictable natural disasters, the application of remotely sensed technique on estimation of lost crop yield is vital for taking further mitigation actions linking to climate change.

  8. Remote Sensing on Crop Production • A newly developed Rice field Identification and riCe yield Estimate (RICE) algorithm is utilized to perform remote sensing of crop production. The RICE algorithm consists of masking (including forest, building, cloud, and products of water area & DEM), identification of rice field, and rice yield estimate. MOD44W(white: water) SRTM DEM

  9. Remote Sensing on Crop Production-Flowchart

  10. Remote Sensing on Crop Production • Images pre-processing: 1) Mosaic images; 2) Convert Map Projection as Geographic Lat/Lon-WGS84 (or other coordinate system), & locate and resample the study areas; 3) Stack Layers; 4) Calculate MODIS including NDVI, EVI, LSWI, and NDBI (spatial resolution: 250 m). MODIS Images Text MATLAB

  11. Remote Sensing on Crop Production irrigation LSWI(Land Surface Water Index)≧NDVI or EVI (Vegetation index) NDVI=Normalized Difference Vegetation Index & EVI= Enhanced Vegetation Index

  12. Remote Sensing on Crop Production-case study 1 MODIS-derived Spatial distribution of paddy over Taiwan • Changhua 2006 2006 2007 2007 Data from National Land Surveying and Mapping Center(2006) 2008 2008 First period of paddy Second period of paddy

  13. Remote Sensing on Crop Production-case study 1 MODIS-derived Spatial distribution of paddy over Taiwan • Yunlin 2006 2006 First period of paddy Published by Agriculture and Food Agency(2006) 2007 2007 Second period of paddy Published by Agriculture and Food Agency(2006) 2008 2008 First period of paddy Second period of paddy

  14. Remote Sensing on Crop Production-case study 1 MODIS-derived Spatial distribution of paddy over Taiwan • Chiayi 2006 2006 First period of paddy Published by Agriculture and Food Agency(2006) Second period of paddy Published by Agriculture and Food Agency(2006) 2007 2007 Taiwan 2008 2008 First period of paddy Second period of paddy

  15. Remote Sensing on Crop Production-case study 1 MODIS-derived Spatial distribution of paddy over Taiwan • Tainan 2006 2006 First period of paddy Published by Agriculture and Food Agency(2006) 2007 2007 2008 2008 Second period of paddy Published by Agriculture and Food Agency(2006) First period of paddy Second period of paddy

  16. Remote Sensing on Crop Production-case study 1 Comparison of MODIS-imagery-derived and official paddy yields. (Unit: ton)

  17. Remote Sensing on Crop Production-case study 2 • Northeastern Thailand is one of the representative rainfed lowland rice agriculture areas in Asia, where rice yield is limited due to unstable rainfall and poor soil. • Heavy monsoon rainfall over central and northern Thailand began in July 2011 and lasted until October, causing a great impact on national agriculture. • We applied the RICE algorithm by using the MODIS data to estimate the loss of paddy yield after the severe flooding events.

  18. Remote Sensing on Crop Production-case study 2 • The flooded map over northeast Thailand in 2011 was drawn by THA_flood map_111013 (from OCHA, United Nations Office for the Coordination of Humanitarian Affairs) using Editor tool of GIS. Severe flooding area on Northeast Thailand

  19. Remote Sensing on Crop Production-case study 2 • Rice paddy map comparison using MODIS data (a) and (b) IRRI data. IRRI is the abbreviation of International Rice Research Institute

  20. Remote Sensing on Crop Production-case study 2 • To predict the toll of rice paddy, we overlay the flooded map with the estimated rice paddy from MODIS imagery. The influenced region by the severe flooded area is approximately 7,890,850.86 ha,which occupied 43.13% of the northeast Thailand area.

  21. Remote Sensing on Crop Production-case study 2 • The damage of rice paddy • The rice paddy planted area influenced by severe flooded area is about 123,950 ha, which is 2.32% of total rainfed rice planted area (5,336,369 ha). The corresponding affected rainfed rice yield is about 227,304 tons. • Even though the rice planted area is not seriously influenced by the severe flood, the rice planted condition and harvest in the region would be likely influenced in the near future.

  22. The Great East Japan Earthquake (311) • A 9.0 magnitude earthquake struck Japan on 11 March 2011, triggered an extremely destructive tsunami that hit the Tohoku region of Japan severely. • On 12 September 2012, a Japanese National Police Agency report confirmed 15,883 deaths, 6,144 injured, and 2,676 people missing across twenty prefectures, as well as 129,225 buildings totally collapsed, with a further 254,204 buildings 'half collapsed', and another 691,766 buildings partially damaged.

  23. Impacts of Climate Change on Crop Production • The Tohoku region is located in the northeastern portion of Honshu, the largest island of Japan. • Miyagi and Fukushima are the most damaged prefectures by the Great East Japan Earthquake.

  24. Impacts of Climate Change on Crop Production • 2010 Official rice production (MAFF) MAFF: Ministry of Agriculture, Forestry, and Fishes of Japan.

  25. Impacts of Climate Change on Crop Production • The pre- and post-earthquake MODIS multi-spectral images (250 m) are collected in Tohoku after tsunami from the MODIS Website (http://modis.gsfc.nasa.gov/).

  26. Impacts of Climate Change on Crop Production • The standard MODIS products are organized in a tile system with the sinusoidal projection. • We obtained 23 tiles including Jan., Apr., May, June, July, Nov., and Dec. 2010 of MODIS Surface Reflectance 8-Day L3 Global 250 m (MOD09Q1) and 500 m (MOD09A1) imageries from NASA LP DAAC (http://lpdaac.usgs.gov/) to calculate vegetation indices.

  27. Impacts of Climate Change on Crop Production • Images mosaic using ENVI

  28. Impacts of Climate Change on Crop Production • Indices calculation ρ means reflectance, NIR is near infrared (841-845 nm), the wavelength of red band is 620-670 nm, blue band is 459-479 nm, and SWIR is shortwave infrared (1628-1652 nm).

  29. Impacts of Climate Change on Crop Production • Parameters used for each mask to distinguish paddy from other land cover • Exclude (image) areas with cloud cover.

  30. Impacts of Climate Change on Crop Production • According to the historical data from Japan MAFF, the flooding period of Fukushima and Miyagi is May per year. • Determination formula of Paddy: where T (threshold) can be varying and indeed depends on the local rice planting system, such as flooding/transplanting practices, and single, early, or late rice growth period. In this study, a global threshold value of 0.05 recommended by Xiao et al. is adopted. LSWI + T≧ EVI or LSWI + T≧ NDVI

  31. Impacts of Climate Change on Crop Production • Comparison of total rice field and yield in Miyagi andFukushima derived from MODIS imagery with the statistic data from MAFF.

  32. Impacts of Climate Change on Crop Production • Disaster loss in rice field. • The disaster losses in rice field are subsequently calculated, 1,932.52 ha for Miyagi and 718.43 ha for Fukushima, accounting for 2.63% and 0.89% of the total rice planting areas of the two prefectures, respectively.

  33. Impacts of Climate Change on Crop Production • Disaster loss in rice yield Image: Dave Tappin Image: Dr. Toshiaki Mizuno http://biofreshblog.com/2011/04/04/how-the-japanese-earthquake-may-drastically-impact-freshwater-ecosystems/ http://www.bgs.ac.uk/research/highlights/2011/japanTsunamiFieldWork.html

  34. Impacts of Climate Change on Crop Production-Conclusions • The disaster losses in rice field are found to be 1,932.52 ha for Miyagi and 718.43 ha for Fukushima. They will result in corresponding expected losses of rice yield by 9,472.60 tons and by 2,939.10 tons, respectively, equivalent to a direct total loss of $US 31 Mio in a year (based on an exchange rate of 1 USD vs. 80 JPY). Mio= million

  35. Impacts of Climate Change on Crop Production-Conclusions • It is thus estimated that the direct economic loss in total agricultural products will be around $US 1411 Mio in a year since rice yield of Miyagi and Fukushima accounts for about 2.2 % of the value of all kinds of agricultural products.

  36. Impacts of Climate Change on Crop Production-Conclusions • Nevertheless, the situation is even worse with the contamination of nuclear radiation. • It is inevitably that economic impact will persist for decades. Remote Sensing Natural disaster Statistics Data Resource loss Economic impact GIS

  37. Conclusion • Satellite imagery can be used to monitor the environmental change after severe natural disaster timely. • A Rice field Identification and riCe yield Estimate (RICE) algorithm is developed to identify the rice paddy/field and estimate its yield, which is useful to assess the loss in rice paddy production associated with disasters immediately. • Impacts of climate change on crop production may be conducted in future with the application of the RICE algorithm.

  38. Reference (remote sensing) • Liou, Y.-A.*, H.-C. Sha, T.-M. Chen, T.-S. Wang, Y.-T. Li, Y.-C. Lai, M.-H. Chiang, and L.-T. Lu, 2012/12: Assessment of disaster losses in rice field and yield after tsunami induced by the 2011 Great East Japan earthquake. Journal of Marine Science and Technology, 20(6), 618-623, doi: 10.6119/JMST-012-0328-2. • Chang, T.-Y., Y.C. Wang, C.-C. Feng, A.D. Ziegler, T. W. Giambelluca, and Y.-A. Liou, 2012/6: Estimation of Root Zone Soil Moisture using Apparent Thermal Inertia with MODIS Imagery over the Tropical Catchment of Northern Thailand. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5 (3), pp. 752-761, doi: 10.1109/JSTARS.2012.2190588. (June 2012) • Lin, C.Y., H.-M. Hsu, Y.-F. Sheng, C.-H. Kuo, and Y.-A. Liou, 2011, Mesoscale Processes for Super Heavy Rainfall of Typhoon Morakot (2009) over Southern Taiwan, Atmospheric Chemistry and Physics, 11, 345–361, 2011, doi:10.5194/acp-11-345-2011. • Wang, Y.-C., T.-Y. Chang, Y.-A. Liou, and A. Ziegler, 2010: Terrain correction for increased estimation accuracy of evapotranspiration in a mountainous watershed. IEEE Geosci. Remote SensingLetters, 7(2), pp. 352-356, April 2010, doi: 10.1109/LGRS.2009.2035138. • Chang, T.-Y., Y.-A. Liou*, C.-Y. Lin, C.-S. Liu, and Y.-C. Wang, 2010/7: Evaluation of surface heat fluxes in Chiayi plain of Taiwan by remotely sensed data. Int. J. Remote Sensing, 31(14), pp. 3885-3898, DOI: 10.1080/01431161.2010.483481. • Lin, C.-Y., F. Chen, J.C. Huang, W.-C. Chen, Y.A. Liou, and W.-N. Chen, 2008b: Urban heat island effect and its impact on boundary layer development and land-sea circulation over Northern Taiwan, Atmospheric Environment, 42, 5639-5649, doi:10.1016/j.atmosenv.2008.03.01.

  39. Reference (GNSS Meteorology/Climate -1) • Chane Ming, F., C. Ibrahim, S. Jolivet, P. Keckhut, Y.-A. Liou, and Y. Kuleshov, 2013: Observation and a numerical study of gravity waves during tropical cyclone Ivan~(2008), Atmos. Chem. Phys. Discuss., 13, 10757-10807, doi:10.5194/acpd-13-10757-2013, 2013. • Pavelyev, A.G., Y.-A. Liou, et al., 2012/1: Identification and localization of layers in the ionosphere using the eikonal and amplitude of radio occultation signals. Atmos. Meas. Tech., 5, 1–16, doi:10.5194/amt-5-1-2012. • Aragon-Angel, Angela,  Yuei-An Liou, et al., 2011/09: Improvement of retrieved FORMOSAT-3/COSMIC electron densities validated by using Jicamarca DPS measurements. Radio Science, Vol 46, RS5001, DOI:10.1029/2010RS004578, 1 SEP 2011. • Pavelyev, A.G., K. Zhang, S.S. Matyugov, Y.-A. Liou, et al. 2011/02: Analytical model of bistatic reflections and radio occultation signals. Radio Science, Vol. 46, RS1009, doi:10.1029/2010RS004434. • Chen, Q.-M., S.-L. Song, S. Heise, Y.-A. Liou*, et al., 2011/1: Assessment of ZTD derived from ECMWF/NCEP data with GPS ZTD over China, GPS Solutions, 15 (4), pg. 415-425, DOI 10.1007/s10291-010-020. • Pavelyev, A.G., Y.-A. Liou*, et al., 2010: Analytical model of electromagnetic waves propagation and location of inclined plasma layers using occultation data. Progress in Electromagnetics Research (PIER), pp. 177-202, July 2010, doi: 10.2528/PIER10042707. • Pavelyev, A.G., Y.-A. Liou*, et al., 2009: Eikonal acceleration technique for studying of the earth and planetary atmospheres by radio occultation method, Geophys. Res. Lett., 36, L21807, doi:10.1029/2009GL040979. • Lee, C. C., Y.-A. Liou, et. al, 2008: Nighttime medium-scale traveling ionospheric disturbances detected by network GPS receivers in Taiwan. J. Geophys.Res., Vol. 113,A12316, doi:10.1029/2008JA013250, 2008.

  40. Reference (GNSS Meteorology/Climate -2) • Wang, C., Y.-A. Liou*, and T. Yeh (2008), Impact of surface meteorological measurements on GPS height determination, Geophys. Res. Lett., 35, L23809, doi:10.1029/2008GL035929. • Chiu, T.-C., Y.A. Liou*, W.-H. Yeh, and C.-Y. Huang, 2008: NCURO data retrieval algorithm in FORMOSAT-3 GPS radio constellation mission, IEEE Trans. Geosci. Remote Sensing, Vol. 46, No. 11, doi:10.1109/TGRS.2008.2005038. • *Liou, Y.-A., A.G. Pavelyev, et. al, 2007: FORMOSAT-3 GPS radio occultation mission: preliminary results, IEEE Trans. Geosci. Remote Sensing, Vol. 45, No. 10, pp. 3813-3826, doi:10.1109/TGRS.2007.903365. • Pavelyev, A.G., Y.-A. Liou*, et al. , 2010: Analytical model of electromagnetic waves propagation and location of inclined plasma layers using occultation data. Progress in Electromagnetics Research (PIER), pp. 177-202, July 2010, doi: 10.2528/PIER10042707. • *Liou, Y.-A., and A. G. Pavelyev (2006), Simultaneous observations of radio wave phase and intensity variations for locating the plasma layers in the ionosphere, Geophys. Res. Lett., 33, L23102, doi:10.1029/2006GL027112. • *Liou, Y.-A., A.G. Pavelyev, et al., 2006: Application of GPS radio occultation method for observation of the internal waves in the atmosphere, J. Geophys.Res., 111, D06104, doi: 10.1029/2005JD005823. • *Liou, Y.A., A.G. Pavelyev, and J. Wickert, 2005: Observation of the gravity waves from GPS/MET radio occultation data. J. Atmos. Solar-Terr. Phys., 67(3), 219-228, February 2005, doi:10.1016/j.jastp.2004.08.001. • *Liou, Y.-A., Y.-T. Teng, T. Van Hove, and J. Liljegren, 2001b: Comparison of precipitable water observations in the near tropics by GPS, microwave radiometer, and radiosondes. J. Appl. Meteor, 40(1), 5-15.

  41. Thanks for your attention

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