yuei -an liou

Application of Remote Sensing Technologies to Mitigate the Impacts of

Climate Change on Crop Production

Yuei-An Liou

AgMIP-Pakistan Kickoff Workshop &

International Seminar on Climate Change

Center for Space and Remote Sensing Research National Central University, Taiwan

President, Taiwan Group on Earth ObservationsEmail: [email protected]

Yesterday

Center for Space and Remote Sensing Research

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Today

Hydrology Remote Sensing Laboratory (HRSL)

-- Briefing-- Crop Yield Remote Sensing

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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)

5

HRSL (2/2)Cryosphere and Global Warming

Glaciers:

Arctic (2010 Ilulissat Icefjord)

Antarctica

Mainland China

Content - Crop Yield Remote Sensing

Remote Sensing on Crop Production

The Great East Japan Earthquake (311)

Impacts of Climate Change on Crop Production

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Remote Sensing on Crop Production – motivations

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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.

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

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Remote Sensing on Crop Production-Flowchart

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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).

Text MODISImages MATLAB

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Remote Sensing on Crop Production

LSWI(Land Surface Water Index) NDVI or EVI (Vegetation ≧

index)

irrigation

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NDVI=Normalized Difference Vegetation Index & EVI= Enhanced Vegetation Index

Remote Sensing on Crop Production-case study 1

Spatial distribution of paddy over Taiwan Changhua

Data from National Land Surveying and Mapping Center(2006)

2006 2006

2007 2007

2008 2008First period of paddy Second period of paddy

MODIS-derived

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Spatial distribution of paddy over Taiwan Yunlin

First period of paddy Published by Agriculture and Food Agency(2006)

Second period of paddy Published by Agricultureand Food Agency(2006)

2006 2006

2007 2007

2008 2008First period of paddy Second period of paddy

MODIS-derived

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Spatial distribution of paddy over Taiwan Chiayi


2006 2006

2007 2007

2008 2008Taiwan

First period of paddy Second period of paddy

MODIS-derived


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Spatial distribution of paddy over Taiwan Tainan



2006 2006

2007 2007

2008 2008

First period of paddy Second period of paddy

MODIS-derived

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Year 2006 2007 2008Area First Second First Second First SecondChanghua

Official 175333 134944 176124 81776 182582 90870RICE Estimate 164823 78115 130915 82804 168526 70614Diff (%) -6 -42 -26 1 -8 -22

Yunlin

Official 182087 90132 169861 52673 200635 64914RICE Estimate 224110 65544 193073 74599 163658 67244Diff (%) 23 -27 14 42 -18 4

Chiayi

Official 116633 84556 115354 62002 119001 66593RICE Estimate 129334 70584 122636 64753 97215 74482Diff (%) 11 -17 6 4 -18 12

Tainan

Official 95827 53294 94775 37234 96036 39393RICE Estimate 84276 46087 102124 37794 90433 43420Diff (%) -12 -14 8 2 -6 10

Overall error 7.6 -26.5 1.6 9.5 -12.7 0.2

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

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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.


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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

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Rice paddy map comparison using MODIS data (a) and (b) IRRI data.


IRRI is the abbreviation of International Rice Research Institute

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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.


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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.


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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.

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http://en.wikipedia.org/wiki/National_Police_Agency_(Japan)

http://en.wikipedia.org/wiki/Prefectures_of_Japan

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.

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Impacts of Climate Change on Crop Production 2010 Official rice production (MAFF)

Region Miyagi Fukushima

Tohoku Japan

Rice area(ha)

73,400 80,600 419,300 1,625,000

(%) 4.52 4.96 25.80 100Rice

yield(ha)400,000 445,700 2,339,00

08,478,000

(%) 4.72 5.26 27.59 100

MAFF: Ministry of Agriculture, Forestry, and Fishes of Japan.

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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/).


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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.


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Images mosaic using ENVI


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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).

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Parameters used for each mask to distinguish paddy from other land cover

Exclude (image) areas with cloud cover.

Parameters

Paddy Cloud Building

Forest Snow

Study area LSWI + 0.05≧ EVI or LSWI + 0.05≧ NDVI

Blue ref. ≧0.08

NDBI>0 NDVI≧0.6 or EVI>0.45 & LSWI>0.1

NDSI>0.4


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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≧ ≧

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Comparison of total rice field and yield in Miyagi and Fukushima derived from MODIS imagery with the statistic data from MAFF.


Statistics MAFF MODIS Diff. Diff. (%)

Miyagi (ha) 73,400 76,676 3,275 4.46(ton) 400,000 412,066 12,066 3.01

Fukushima (ha)

80,600 89,050 8,450 10.48

(ton) 445,700 478,752 33,052 7.41

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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.


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Disaster loss in rice yield


Statistics Disaster loss of rice yield (ton)

Disaster loss of rice yield (%)

Miyagi 9,472.60 2.37Fukushima 2,939.10 0.66

http://biofreshblog.com/2011/04/04/how-the-japanese-earthquake-may-drastically-impact-freshwater-ecosystems/

Image: Dr. Toshiaki Mizuno

http://www.bgs.ac.uk/research/highlights/2011/japanTsunamiFieldWork.html

Image: Dave Tappin

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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).

Impacts of Climate Change on Crop Production-Conclusions

Mio= million

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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.


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Nevertheless, the situation is even worse with the contamination of nuclear radiation.

It is inevitably that economic impact will persist for decades.


Natural disaster

Resource loss

Economic impact

Remote Sensing

GIS Statistics Data

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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.

Conclusion

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Reference (remote sensing)

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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 Sensing Letters, 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.

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http://adsabs.harvard.edu/cgi-bin/nph-abs_connect?fforward=http://dx.doi.org/10.1109/LGRS.2009.2035138

http://dx.doi.org/10.1080/01431161.2010.483481

Reference (GNSS Meteorology/Climate -1)

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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.

http://dx.doi.org/10.2528/PIER10042707

Reference (GNSS Meteorology/Climate -2)

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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.

http://dx.doi.org/10.2528/PIER10042707

http://www.sciencedirect.com/science?_ob=JournalURL&_cdi=6062&_auth=y&_acct=C000057545&_version=1&_urlVersion=0&_userid=2489623&md5=73142616fc784aafb7690c8a972891fb

http://www.sciencedirect.com/science?_ob=IssueURL&_tockey=

Thanks for your attention

yuei -an liou

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