1. 1 _________________________________________________________________________________________________________________________________________________________________ Improvement of Local Maximum Fitting (LMF) for High Temporal Remote Sensing Data Using Meteorological Data _________________________________________________________________________________________________________________________________________________________________ Examination Committee: Dr. Kiyoshi Honda (Chairperson) Prof. Seishiro Kibe (Member) Dr.Vivarad Phonekeo (Member) External committee: Mr. Wataru Ohira By Salinthip Kungvalchokechai RS & GIS FoS, SET

2. 2 _______________________________________________________________________________________________________________________________________ Contents _______________________________________________________________________________________________________________________________________ 1. 1. Introduction Background: Local Maximum Fitting (LMF) Statement of the Problems Objective 2. 2. Methodology 3. 3. Results 4. Conclusion & Recommendation

3. 3 Introduction _______________________________________________________________________________________________________________________________________ The seasonal vegetation changes are monitored by high frequent satellite observation. Original satellite data is contaminated of noise by cloud and haze, especially in rainy season, which degrade an original satellite NDVI data. Local Maximum Fitting (LMF) is developed to remove the effect of cloud and haze. (Sawada, H., 2001) Many fluctuate noise in rainy season

4. 4 Background: LMF ______________________________________________________________________________________________________________________________________ Min Max Filter  Window filter size = the number of window which we use to filter noise at the considering point. 3 Step of LMF: Local Maximum Fitting

5. 5 Background: LMF (cont.) _____________________________________________________________________________________________________________________________________ Simulate the fluctuation by limited number of Harmonic Waves. Fitting Model To avoid overfitting & Maximize prediction stability AIC ( Akaike Information Criterion ) AIC = D{log(2 )+1}+2(j+1)

6. 6 Data NDVI MODIS 8-days images of Suphanburi (Spatial resolution 250 m.) Year 2005, 2006, 2007; 46 images/yr ( Total = 138) Sugarcane ( Number of function = 6, Window size = 3 ) Statement of the Problems ________________________________________________________________________________________________________________________________________________________ LMF line is lower than maximum original NDVI (see ) The number of peaks are different from real pattern (sugarcane has 1 peak per cycle) (see ) Too large W : Revisable value will exceed the real NDVI Too small W : Cannot remove noise

7. 7 Objective _____________________________________________________________________________________________________________________________________ To improve LMF algorithm  To adjust Min Max filter size according to the weather condition.  Use multiple previous years information for repeating pattern in case similar pattern of same type of crop. (Best Max Value Method for revision data) (Best Max Value Method for revision data) ■ To improve a set of software

8. 8 Data  Modis surface reflectance products 8-Day L3 (250m.)(year 2005-2007)  TERRA (MOD 09Q1), AQUA (MYD 09Q1) (46 images / year)  Suphanburi occupies in 2 land tiles (Total = 552 images)  Ground Data (year 2003)  GIS land use map (year 2000)  Average monthly rainfall historical statistical data Methodology ______________________________________________________________________________________________________________________________________ Study Area : Suphanburi Province

9. 9 Methodology: Overview ______________________________________________________________________________________________________________________________________ 1 Min Max Filter 2 3 Adjust Min Max window filter size depend on weather condition Different pattern of crop in each year Similar pattern of same type of crop in each year Min AIC Harmonic Curve Fitting Model Best Max Value Method Outcome (Expected LMF) MODIS surface reflectance products 8-days(TERRA,AQUA) 4 Cloud-daysRainfall Improvement Improvement : Utilization of Multi-year Information Average Monthly Historical Data NIR surface reflectance (TERRA)

10. 10 – – Global : WMO, FAO, LocClim – – Local : Meteorological Department _____________________________________________________________________________________________________________________________________________________ 1. Min Max Filter _____________________________________________________________________________________________________________________________________________________ Adjust window size depending on weather condition Concept 1: Depends on Average Monthly Rainfall Historical Statistical Data 13 Dry Season W = 3 W = 5 W = 6 More Rainfall : Bigger Window Size Thai Meteorological Department Heavy Rainy season

11. 11 Heavy Rainy season Min Max Filtering (Cont.) ________________________________________________________________________________________________________________________________________ Experiment: Varies Window Size depends on average rainfall data Min Max Filter (WIN=3) Original NDVI Min Max Filter (WIN=6) Original NDVI Heavy Rainy season

12. 12 Min Max Filtering(cont.) ________________________________________________________________________________________________________________________________________ Min Max Filtering (cont.) ________________________________________________________________________________________________________________________________________ Result: Min Max Filter Graph □ Varies Window Size depends on average rainfall data Dry seasonW = 3 Rainy seasonW = 5 Heavy Rainy seasonW = 6 Heavy Rainy season Min Max Filter (WIN=3,5,6,5,3 ) Original NDVI Mean Total Rainfall : F mean 0 ≤ F mean ≤ 100  w = 3 100 < F mean ≤ 200  w = 5 200 < F mean  w = 6

13. 13 Define cloud-day: Cloud Mask Method (cont.) ____________________________________________________________________________________________________________________________________________ 1. Min Max Filter (cont.) ____________________________________________________________________________________________________________________________________________ NIR Reflectance Threshold = 3500 TERRA 2007 Band 43 (Dec.) Cloud MaskRGB=b2,b1,b2 Concept 2 : Depends on continuous cloud-days NIR surface reflectance (TERRA)Cloud Mask

14. 14 (cont.) _____________________________________________________________________________________________________________________________________________________ 1. Min Max Filter (cont.) _____________________________________________________________________________________________________________________________________________________ N left, N right = The number of cont. cloud-days at the left or right side of considering point W left, W right = Window filter size at left, right side of considering point Concept 2 : Depends on continuous cloud-days (cont.) W left = N left + 4 W right = N right + 4 NDVI after filter = Min [ Max left, Max right ] Min Max Filter (WIN depends on cont. cloud-days) Original NDVI Min Max Filter (WIN=3) DOY Min Max Filter (W depends on cloud-days) remove noise > W = 3

15. 15 _____________________________________________________________________________________________________________________________________________________ 2. Best Max Value Method _____________________________________________________________________________________________________________________________________________________ Case : Similar pattern in each year Choose maximum NDVI value within analysis period   Maximum value does not have cloud More chance to capture good data Justify similar or not similar pattern in each year: Modeling Method (r 2 ) Stable Agriculture field  Repeat Same Pattern

16. 16 Adjust graph each year to have no trend and intersection point of NDVI axis   Calculate Slope (c 1 ), Remove trend ( ) and intersection point of NDVI axis (c 0 ) by Linear Regression   NDVI no trend at time axis = NDVI after filter – – c 0 Choose maximum value within analysis period pattern Adjust graph each year to have trend and intersection point of NDVI axis back NDVI after adjust trend and intersection point of NDVI axis back = pattern (cont.) _____________________________________________________________________________________________________________________________________________________ 2. Best Max Value Method (cont.) _____________________________________________________________________________________________________________________________________________________ Remove noise using 3 yrs pattern Min Max Filter has trend After adjust trend and intersection point of NDVI axis back pattern

17. 17 Improved LMF Result ________________________________________________________________________________________________________________________________________ Original NDVI LMF (WIN=3) LMF (WIN depends on cont. cloud-days, Best Max) LMF (WIN=3,5,6,5,3,Best Max) Original NDVI LMF (WIN=3) Both Improved LMF are lifted higher, and approach original NDVI Top Points

18. 18 Accuracy Assessment _____________________________________________________________________________________________________________________________________  Improved LMF (WIN depends on Rainfall data)*: rmsd = 36.88  Improved LMF (WIN depends on Cont. cloud-days)*: rmsd = 37.04  Prior LMF (One WIN = 3): rmsd = 44.92 (* Including Best Max) y1i = original top point NDVI y2i = after LMF NDVI n = number of original NDVI top point

19. 19 Plot comparison NDVI before and after LMF varies 58 pixels classify by 12 types land use Accuracy Assessment (cont.) _____________________________________________________________________________________________________________________________________ Rainfall SugarcaneCassava

20. 20 Conclusion & Recommendation Rainfall – Global data acquisition Rainfall – Global data acquisition Cont. cloud-days – More complicate to identify cloud-day Cont. cloud-days – More complicate to identify cloud-day – Uncertain obtained cloud-day – Uncertain obtained cloud-day One window size – Cannot remove noise in longer rainfall period One window size – Cannot remove noise in longer rainfall period Recommendation – Recommendation – Improvement cloud-day identification using more suitable method 2 3 1 W=3

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