PROCESSING OF MODIS/SPOT VEGETATION NDVI IMAGERY TO CHARACTERIZE VEGETATION DYNAMICS OF LCCS based LAND COVER DATA SETS (VeDAS) Antonio Di Gregorio, Valerio Capecchi, Fabio Maselli FAO-Global Land Cover Network Presented by: Craig von Hagen – FAO-SWALIM

The project is aimed at developing and testing a methodology suitable to characterize the land cover polygons contained in GLCN data sets, and in general any polygon classified with LCCS, in terms of average vegetation dynamics. MODIS NDVI imagery have been used. Modis is currently unique in providing the spatial resolution (250 m) and temporal acquisition frequency (16 days) which are necessary to retain average vegetation features of the polygons considered. Objective

MODIS Test Tiles Different AFRICOVER/GLCN data sets falling into five MODIS tiles have been used

Development and testing of a procedure for purifying polygon NDVI profiles Each AFRICOVER polygon due to process of interpretation (visual interpretation) is never fully homogeneous in terms of vegetation physiognomy. The total of impurity (heterogeneity of land cover) was estimated to be up to 20% of the total surface. A method was developed and tested to remove or reduce such impurity and to isolate NDVI profiles of the dominant cover type. An automatic procedure was developed to purify the polygons from pixels with anomalous multitemporal NDVI profiles: The procedure, implemented in Fortran 77 code, computes the average NDVI profile of the examined polygon. Then the Euclidean distance of each pixel from the average profile is computed, and the pixel with largest Euclidean distance is removed. The process is iterated until the number of excluded polygons reaches a predefined threshold. The average profile tends to converge towards that of the less anomalous, likely dominant cover type of the polygon.

Purification and computation of mean NDVI profiles of all polygons The polygon purification procedure was applied to all polygons of pure classes for Tanzania extracted from the study tile. In all cases, 20% of the polygon pixels were excluded and the average profiles of the remaining pixels were computed, obtaining typical NDVI profiles for each polygon. All polygon profiles were filtered by a moving average of order 3 to reduce residual irregularities and to obtain smoother, more easily usable profiles.

Conversion and smoothing of the polygon NDVI profile The use of polygon NDVI profiles defined on the basis of 16-day periods pose several difficulties, since they are not correspondent to monthly periods which are generally used as basic temporal steps Thus, a procedure was devised to transform the 23-values profiles into 24-value profiles corresponding to half-month periods. This was obtained by computing different filtering weights for each output semi-monthly period which differently considered the original 16-day NDVI images depending on the temporal distances between the input and output periods

B Example of real data Transformation of input into output NDVI profiles (over 23 and 24 periods, respectively) by means of the procedure developed. The original shape of the profiles is only slightly smoothed and maintains its general integrity. Moreover, the irregularity of the input profile is mostly removed with consequent improvement of its interpretability.

Steps The VEDAS automatic procedures is able to: Read NDVI data from native format; Convert the data into a generic Lat/Long system; Subset image of different size; Extract additional data quality layer; Combine selectable numbers of years for building data time series; Define the pixel by pixel quality of the data time series; Account for different quality thresholds for pixels and time series; Flag and discard pixels according to quality criteria; Compute multi-temporal average and variance from the good pixels; Take in input any ‘rasterized’ vector layer and define the amount of good quality pixels, polygon by polygon, according to the criteria and thresholds chosen; Flag and discard bad quality polygons; Extract average profiles of NDVI, polygon by polygon; Purify the average profiles discarding a predefined percentage of probably mixed pixels (generally 20%);

Steps - cont Purify the average profiles discarding a predefined percentage of probably mixed pixels (generally 20%); Transform the 23-value NDVI profiles into 24-value profiles which smoothes the profiles by moving average; Generate a coding of the 24-value profiles according to the criteria defined in the relevant summary report.

Steps Take in input any ‘rasterized’ vector layer and define the amount of good quality pixels, polygon by polygon:

A: Herbs B: Herbaceous crops Extraction and preliminary analysis of NDVI profiles of exemplary polygons

C: Trees closed D: Others (one tree closed needle leaf, in yellow, and two herbs seasonally flooded) Exemplary profiles of selected polygons.

A Three average NDVI profiles of the herbaceous class within a south-east to north-west transect (zone 5, 13, 22) are shown in A. (The Y scale is in NDVI*10000 ) Their regular variation from typical monomodality to typical bimodality can be clearly appreciated.

Code A: Code B Profiles of 2HC in Tanzania corresponding to the code A and B N_Max. NDVI Max 1 (x10) NDVI Min (x10) Month ONSET Month END N_Max. NDVI Max 1 (x10) NDVI Min (x10) Month ONSET Month END

Conclusions The current project, carried out during a six-month period, has produced the following deliverables Procedures to extract the polygon NDVI profiles Procedures to purify the polygon NDVI profiles Procedure to transform the NDVI profiles from 23 to 24 values Procedure to codify the NDVI profiles into 12 monthly values (extended codification) Procedure to further summarize the NDVI profiles into a code of 5 values (restricted codification) Results of testing all procedures in three study areas (Tanzania, Sudan, Somalia)

APPLICATION OF THE VEDAS PROCEDURE IN SOMALIA BASIC IDEA: information on average yearly phenological behavior of distinct vegetation types can serve a multitude of applications/end users

VEDAS PROCEDURE SUCCESFULLY APPLIED IN MORE COMPLEX SITUATIONS (AFRICOVER POLYGONS) THAN SOMALIA TEST AREAS. TEST AREAS AS THE ADVANTAGE TO BE INTERNALLY VERY HOMOGENEOUS IN TERMS OF VEGETATION TYPES IT IS A MAJOR ADVANTAGE THAT ALLOW USER TO PROPERLY EVALUATE THE CAPABILITIES AND LIMITS OF THE DATA SET WITHOUT THE INTERFERENCE OF VEGETATION TYPES MISTAKES OR DISOMOGENEITY TEST AREAS HOWEVER SHOULD BE SELECTED WITH LOCAL KNOWLEDGE AND ACCORDING TO A SPECIFIC USE SELECTION OF THE ACTUAL TEST AREAS HAS NOT FOLLOWED THESE PRINCIPLES. THEY SHOULD BE CONSIDERED A FIRST ‘EVALUATION’ STEP A MORE PRECISE TEST AREAS SELECTION SHOULD BE CONSIDERED

ORIGINALLY IT WAS FORESEEN TO SELECT AROUND 1000 TEST AREAS. THE NUMBER WAS REDUCED APP. 500 TO BETTER CONTROL THE INTERNAL POLYGON HOMOGENEITY AND TO REDUCE DATA REDUNDANCY H - closed to open herbaceous terrestrial SAV - trees/shrub savannah SR - sparse herbaceous / sparse shrubs TO - open trees TC - closed trees SO - open shrubs SC - closed shrubs A - agriculture (pf= post-flooding; i= irrigated) WH - herbaceous aquatic TB - Tiger bush (mixed class of shrubs and herbaceous; shrubs open 40-65% fragmented).

USING MODIS DATA SET ( ) IN THE VEDAS PROCEDURE HAS RESULTED ON REJECTING APP.45% OF THE TEST AREAS. MAIN REASON: PERSISTENT CLOUDY CONDITION OVER A LONG PERIOD ADDITIONAL REASONS: LIMITED NUMBER OF YEARS ANALYZED HEAVY QUALITY CONTROL ON MODIS DATA

ALTERNATIVE SOLUTIONS APPLIED: INCREASE AREA SIZE USE OF SPOT VEGETATION DATA ADVANTAGES OF SPOT VEGETATION: LARGER DATA SET (8 YEARS PASSAGES; COMPLETE YEARS STARTS IN 1999 UP TO THE DELAY PRESENT) LESS STRICT QUALITY CONTROL DISAVANTAGES: PIXEL RESOLUTION 16 TIME LARGER (250 m AGAINST 1 Km) MODIS SPOT VEGETATION

COMPARISON OF RESULTS FOR TWO DIFFERENT VEGETATION TYPES FOR MODIS AND SPOT VEGETATION Modis SPOT- Veg

INTER ANNUAL NDVI VARIATION AND STANDARD DEVIATION Average Standard Deviation

NEXT STEPS - ANALYSIS OF RESULTS LINKED TO LOCAL KNOWLEDGE AND SPECIFIC APPLICATIONS

NEXT STEPS – ‘ECO-REGIONS’ BY COMBINING WITH CLIMATE AND ELEVATION Code A: Code B N_Max. NDVI Max 1 (x10) NDVI Min (x10) Month ONSET Month END N_Max. NDVI Max 1 (x10) NDVI Min (x10) Month ONSET Month END

Cross correlation analysis The basic idea is to fix a 10-day period (say 10-day period starting 1 January) and to perform a cross correlation with the remaining day periods; In this way we get 35 images showing the correlation between decad N and decad N +K with K > 0. Then we move to the following 10-day period (i.e. 10-day period starting 11 January) and we perform the cross correlation analysis with the remaining 10-day periods up to the decad starting 1 January. The interest of this study could be the investigation of the correlation of NDVI values of the current growing season with the previous one. So, ¯finally, what we get at the end of the algorithm is 36x36 images (including the trivial auto-correlation image of a 10-day period with itself) showing correlation coefficient

Other Ideas Link to Murray Watson’s historical monitoring sites Talk to ILRI, related work Possibility of availability of 1km soil moisture data from ENVISAT ASAR