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Yann Chemin

Reflectance of AVHRR L1B correlated to MODIS L13A reflectance for a Diachronic vegetation study of Gujarat Environment. Yann Chemin. Gujarat Water Harvesting Movement.

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Yann Chemin

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  1. Reflectance of AVHRR L1B correlated to MODIS L13A reflectancefor a Diachronic vegetation study of Gujarat Environment Yann Chemin

  2. Gujarat Water Harvesting Movement For the last 30 years, a substantial return to the ancient techniques of water harvesting has led to livelihoods improvement by providing domestic water and especially agricultural groundwater irrigation. The rising of the water aquifer lenses locally has proven to ameliorate the vegetation cover. The International Water Management Institute sub-office of Anand (Gujarat), wanted to assess this potential vegetation cover change.

  3. Setting up a methodology • Availability of free Internet download of NOAA AVHRR L1B from November 1988, was a first choice because of the radiometric corrections and the 1Km spatial resolution. • The availability of the free MODIS NDVI products ready from Internet for 2001 was also the easiest quality choice. • The question arises: How to make them comparable?

  4. How to proceed onward? • Propagating NOAA AVHRR to MODIS NDVI product would be wasting the internal MODIS qualities:1) narrow band responses2) 250 m pixels responses integrated in the 1Km product. • Propagating NDVI from MODIS to NOAA AVHRR, possible?1) NOAA AVHRR has larger bands than Modis => Lower Contrast.2) NOAA AVHRR has “coarser effective” pixels. • Alexandridis and Chemin (2001)=> Zhanghe Irrigation District (Hubei, China) Similar method used, Landsat 7 based. - Modis (250m) and L7 (30m) are sharingthe same NDVI histogram becauseof Modis pixel size and narrow bands. - NOAA AVHRR failed because of low R&NIRcontrast in a very small study area.

  5. Problems ahead… • No atmospheric corrections in NOAA AVHRR L1B • How to transfer Modis NDVI internal histogram nature to NOAA AVHRR?

  6. “Two problems solved by one solution?” 2 Problems: NOAA AVHRR Atmospheric correction NDVI from Modis to NOAA AVHRR 1 Solution: Property and Assumption: Band-wise reflectance of Desert and Water are having constant values over time => Linear Regression possible (van Grien and Owe, 1992) - Red and NIR transfer from Modis to AVHRR - NDVI Propagation from Modis to AVHRR is then possible

  7. Identification of invariant reflectance pixels • Geo-statistical model • Modis NDVI multi-date input • Calculates Mean(t) in R and NIR • Calculates StDev(t) in R and NIR • Calculates C.V.(t) in R and NIR • Manual Thresholds applied(i.e. mean > 95% && CV +/- 5%) • Returns R&NIR Invariant pixels maps for the ground feature under consideration (Water/Desert).

  8. Invariant locations • Neighbouring pixels are joined together to form larger invariant locations. • The largest pool of invariants is kept. • A combined map of the two invariant locations (water and Desert) is processed. • The Invariant final map is ready to use.

  9. Invariant map Highest reflectance values were found in the Rann of Khachchh Unable to find in-land open-water bodies invariant, even the biggest water reservoir gets really drier at some point before summer. Ocean points should be preferred in future work.

  10. From Reflectance to NDVI… • Original NOAA AVHRR Red and NIR bands are then corrected by linear regression generated from the invariant water and desert points as extrems. • Finally, AVHRR NDVI is calculated out ofthe Redcorr. andthe NIRcorr. bands. • AVHRR NDVI =>End of April 1989

  11. MODIS to AVHRR 2001 Test match

  12. Where do the mismatched pixels go? Red NIR

  13. Results of the propagation to 1989 AVHRR All Gujarat Mean AVHRR1989 Mean Modis 2001 Data available for Modiswas not covering early monthsof 2001 at the time of study Pre-monsoon is best to assessgroundwater effect on vegetation

  14. District-wise results (Low NDVI)

  15. District-wise Results (High NDVI)

  16. Conclusions • Can we answer to the question of “Water harvesting movement impact on the Gujarat vegetation?” No, not yet. • The methodology is not calibrated with many images yet, it is not enough robust at this time • The principle is simple and the concept is physically based. • It has some clear advantages for repeating processing of images where atmospheric correction is needed while less meteorological data / no atmospheric sounding is available.

  17. Thank you… Special Thanks to: IWMI-IN for supporting this research www.iwmi.org • yann@sltnet.lk FOR MORE INFO...

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