1. Dissertation Committee Ranga B. Myneni Yuri Knyazikhin Curtis E. Woodcock Crystal B. Schaaf Jan Bogaert Ph.D. Dissertation Defense by Jiannan Hu Assessment and Refinement of the MISR LAI and FPAR Product

2. 2 Part 1: Introduction Part 2: Assessment of the performance of the MISR LAI and FPAR algorithm Part 3: Analysis of the MISR LAI/FPAR product for spatial and temporal coverage, accuracy and consistency Part 4: Development of a rank based algorithm for aggregating land cover maps Part 5: Summary of main results Part 6: Future directions Contents

3. 3 Part 1: Introduction MISR instrument LAI and FPAR

4. 4 MISR observational attributes Multi-angle Imaging SpectroRadiometer (MISR) is on the EOS Terra platform MISR consists of nine pushbroom cameras in four spectral bands (NIR, Red, Green, Blue) A swath is a 360 km wide 7 minutes to observe each scene at all 9 angles Contiguous zonal coverage: 9 days at equator

5. 5 MISR Surface Reflectance Products Bidirectional Reflectance Factor (BRF) Parallel beam Directional Hemispherical Reflectance (DHR) Parallel beam Hemispherical Directional Reflectance Factor (HDRF) BiHemispherical Reflectance (BHR)

6. 6 LAI and FPAR: Definition LAI: green leaf area index, one-sided green leaf area per unit ground area FPAR: fraction of photosynthetically active radiation (0.4-0.7  m) absorbed by the vegetation Importance LAI and FPAR govern the exchange of energy, momentum and mass (water and carbon dioxide, for example) between the Earth’s surface and the atmosphere LAI and FPAR

7. 7 Research Objectives To investigate the performance of the MISR LAI&FPAR algorithm as a function of input and model uncertainties To analyze the MISR LAI&FPAR product for spatial and temporal coverage, accuracy and consistency To develop a spatial aggregation algorithm that represents fine resolution land cover information from satellite data at coarser resolutions with minimal information loss

8. 8 To specify uncertainties in the MISR Surface Reflectance Product To investigate the performance of the algorithm as a function of uncertainties To refine the LAI/FPAR algorithm Part 2: Assessment of the performance of the MISR LAI/FPAR algorithm

9. 9 Data flow chart of the MISR LAI/FPAR algorithm BRF BHR MISR Level 2 Surface Parameters View Angle Sun Angle MISR Geometric Parameters MISR LAI/ FPAR Algorithm LAI FPAR Biome Identification Map Input Output A principal objective of the MISR LAI and FPAR algorithm is to retrieve LAI and FPAR without requiring a static, pre-specified global biome map

10. 10 Forward Backward View Geometry The maximum deviation in view zenith angles is for camera A Fore 4.95 degrees Aft 4.84 degrees Deviation decreases with increasing view zenith angle NAABBCCDD Uncertainties due to variation in view zenith angles -70.5 -60 -45.6 -26.1 0 26.1 45.6 60 70.5 These variations fall within the view angle bin limits in the Look Up Table

11. 11 Uncertainties in the MISR surface Reflectance Product – Data used (1) Data density distribution function in the Red and NIR space for Broadleaf Forests Six-Biome Classification Map of Africa

12. 12 The mean and standard deviation of HDRFs at red and NIR wavelengths derived from pixels located around the data peak for: Grasses & Cereal Crops Broadleaf Forests Uncertainties in the MISR Surface Reflectance Product – Spatial analysis (2) Uncertainties in HDRFs are larger at large view angles and greater in the near- infrared channel than the red channel

13. 13 Histograms of Standard Deviation/Mean of data from path 178 for 3 different days, Mar 1, Mar 17 and April 2, 2001. Plots are for grasses & cereal crops and broadleaf forests at Blue, and NIR bands. Uncertainties in the MISR Surface Reflectance Product –Temporal analysis (2) Large uncertainties may be due to errors in pixel geolocation, or may be due to atmospheric correction

14. 14 Uncertainties in the MISR Surface Reflectance Product – Summary (3) TR TN SR SN Uncertainties from the temporal analysis are greater than the uncertainties from spatial analysis for most biome types Uncertainties derived from temporal analysis are taken as the upper bounds of uncertainties in observations Standard deviations derived from spatial analysis are taken as lower bounds of uncertainties in observations TR, TN – temporal coefficient of variation at red and NIR spectral bands, respectively SR, SN – spatial coefficient of variation atred and NIR spectral bands, respectively

15. 15 Performance of the algorithm as a function of uncertainties Objectives: To evaluate an upper limit of acceptable uncertainties in observations which allow the algorithm to discriminate between pure biome types, to minimize the impact of biome misidentification on LAI retrievals, and to maximize the number of successful retrievals

16. 16 Performance Index (PI) Retrieval Index (RI): the conditional probability of retrieving a LAI value given biome type Biome Classification Map Six Biome types LAI Mean and Dispersion MISR Biome Identification Algorithm MISR LAI/FPAR algorithm Both BHR and BRF tests passed: QA = 0 Only BHR test passed: QA = 1 Only BRF test passed: QA = 2 Biome Identification (BI): the probability to identify the biome type Performance Index (PI) characterizes the ability of the algorithm to retrieve biome and LAI value simultaneously

17. 17 Optimal set of relative uncertainties

18. 18 Concurrently valid LAI retrievals and correct biome identification occurs, on average, in about 23% of the pixels, given the current level of uncertainties in the MISR surface reflectance data The other 77% of the LAI values are retrieved using incorrect information about the biome type Performance Index for optimal set of uncertainties What is impact of biome misidentification on LAI retrievals?

19. 19 Impact of Biome Misidentification on LAI Retrievals On average, with a probability of 70% and higher, the high and intermediate quality retrievals agree with reference values to within 25% uncertainties Uncertainties in the observations are the limiting factor in controlling the LAI uncertainty, not the ability to classify the biome type LAI misr is the LAI retrieved by the MISR LAI/FPAR algorithm without using information on biome type LAI ref is a LAI retrieved by the MISR LAI/FPAR algorithm using the BCM

20. 20 MISR nadir camera HDRFs were used to derive the NDVI NDVI values were regressed against both LAI and FPAR High quality LAI and FPAR retrievals (QA=0) were used MISR LAI/FPAR algorithm does not use NDVI as input Test of Physics The biome specific relationships between the retrieved LAI/FPAR and the measured NDVI values conform to both theoretical and empirical results

21. 21 An upper limit of acceptable uncertainties in the MISR surface reflectances which allows the algorithm to discriminate between pure biome types, to minimize the impact of biome misidentification on LAI retrievals, and to maximize the spatial coverage of retrievals was empirically evaluated On average, for about 23% of pixels, both LAI and biome type can be simultaneously specified at the current level of uncertainties in the MISR surface reflectance product In about 70% of the cases examined, uncertainties in the observations were the limiting factor in controlling the LAI uncertainty, not the ability to classify the biome type. This investigation underlies the transition of the MISR LAI/FPAR product from beta to provisional status Part 1: Conclusions

22. 22 Analysis Analysis of information content of MISR surface reflectances LAI and FPAR retrievals over sparse vegetation Consistency and complementarity of the MISR product suite that includes LAI, FPAR, BHRPAR Validation Analysis of spatial and temporal coverage of MISR LAI product over validation sites Validation of MISR LAI product Part 2: Analysis of the MISR LAI/FPAR product for spatial and temporal coverage, accuracy and consistency

23. 23 Information content of MISR surface reflectance data Angular signatures in spectral space Angular signatures for five land covers Temporal variation in the mean signature of a broadleaf forest Angular signature: BRFs in nine view angles in red and NIR spectral space Land type dependent Captures vegetation seasonality slope intercept length location Spring Autumn Summer

24. 24 LAI and FPAR retrievals over sparse vegetation Sparse vegetation: Information in single angle data is insufficient to retrieve LAI and FPAR accurately (Shrubs constitute 25% of global vegetation or 32 million 1 km pixels) Multi-angle measurements are required for LAI & FPAR retrievals in sparse vegetation canopies

25. 25 Energy absorption by ground below vegetation BHRPAR is the angle-integrated PAR reflectance FGROUND (at PAR wavelengths) = 1-BHRPAR-FPAR North America from 15 August 2003 (path 32, orbit 19461, blocks 54-60) Multi-angle measurements provide new products: FGROUND

26. 26 Gap fraction Canopy Structure (1) Extinction coefficient (2) Mean leaf inclination (3) Gap fraction FGROUND and Gap Fraction at solar zenith angle =

27. 27 Validation sites The mean MISR BHR availability is around 25%

28. 28 Availability of MISR surface reflectance Area -- 2 by 2 degree Year -- 2000 The spatial and temporal coverage of input is mainly limited by cloud contamination Retrieval Applicability Mask

29. 29 Temporal variation in the Retrieval Index The algorithm provides retrievals in 50-80 percent of pixels with suitable input

30. 30 Temporal variation in mean LAI values derived from MODIS and MISR LAI products Seasonality

31. 31 These anomalies have been satisfactorily resolved in the Collection 4 product Refinement of the MISR LAI algorithm Earlier versions of the algorithm overestimated LAI in grasses&cereal crops and broadleaf crops

32. 32 Validation of MISR LAI product at a cropland site in France 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6Non-biome1 ETM+ LAI (30m) Reference LAI (1.1 km) Stage 1 Validation: Product accuracy has been estimated using a small number of independent measurements obtained from selected locations and time periods and ground-truth/field program effort Site: Alpilles, France Time: February 26 – March 15, 2001 Land Cover Type: Croplands Results: Collection 4 MISR LAI is accurate to within 0.2LAI with a precision of 30%

33. 33 Validation of MISR LAI product at other sites Reference LAI Data: C4 MODIS LAI Standard Product Area: 0.5 by 0.5 degree Biome Classification Map: Collection 3 MODIS Land Cover Classification Map Comparison with MODIS LAI Konza: MISR LAI agrees with MODIS LAI Agro: MISR LAI underestimates MODIS LAI by 0.4 LAI Mongu: MISR LAI agrees with MODIS LAI Harvard Forests: MISR LAI underestimates MODIS LAI by 0.9LAI

34. 34 The use of multi-angle data improves the quality of LAI retrievals in sparse vegetation canopies MISR LAI, FPAR and BHRPAR can be used to derive at least three measures of canopy structure – Beer’s law extinction coefficient, mean leaf inclination and the gap fraction or vegetation ground cover The spatial and temporal coverage of the LAI/FPAR product is mainly limited by cloud contamination The early version of the algorithm overestimated LAI values in grasses and broadleaf crops The MISR LAI product from the recalibrated algorithm (version 3.3) shows structural and phenological variability in agreement with field data. The product is accurate to within 0.5 LAI in herbaceous vegetation and savannas and is an overestimate by about 1 LAI in broadleaf forests This investigation underlies the transition of the MISR LAI/FPAR product from provisional to validated status Part 2: Conclusions

35. 35 Objectives: To develop a spatial aggregation algorithm that represents fine resolution land cover information from satellite data at coarser resolutions with minimal information loss To implement the algorithm at continental scale To evaluate the algorithm by comparing with random and majority algorithm Part 3: A rank based algorithm for aggregating land cover maps with minimal information loss

36. 36 Block Rank for each class number of remaining blocks containing a subpixel of class j the number of blocks to be assigned to class j, Class Rank For the class characterized by the highest -values, a block of that class is initially aggregated Ranked aggregation algorithm Example Original Image Aggregated Image Sequence Blank Image

37. 37 Original Image Aggregated Images Majority – 4 cases Random – 24 cases Ranked – 1 cases Ranked Algorithm Sequence Examples of aggregation techniques Approaches: Consecutive Nonconsecutive 124 8 163264 Resolution (km) 1 2 128 Consecutive Nonconsecutive

38. 38 MODIS IGBP Land cover map Original Image (1km, 8960x9216) Ranked Consecutive Random Consecutive Majority Input data Grasslands Permanent Wetlands Croplands Urban and Built-Up Mixed Forests Snow and Ice Water Evergreen Broadleaf Forest Evergreen Needleleaf Forest Deciduous Needleleaf Forest Deciduous Broadleaf Forest Mixed Forests Closed Shrublands Savannas Open Shrublands Woody Savannas Cropland/Natural Vegetation Mosaic Unclassified & Fill Value Aggregated map (128 km, 70x72)

39. 39 Metrics (1) – Diversity of the classes r : 2, 4, …, 128 Relative Index: is used to characterize the landscape pattern differences between the aggregated images and the original image Shannon diversity index More sensitive to the richness of the components Simpson diversity index Less sensitive to the richness of the components Places more weight on the common classes p i the relative area of i-th class

40. 40 Metrics (2) Proportion estimation error Shows the over- or underestimation of class areas the number of patches the number of pixels Fragmentation index: Quantifies the patch property.

41. 41 Metrics (3) Contagion: measures the degree to which the image is composed of a few large or several small patches measures both class interspersion (intermixing of classes) and class dispersion (the spatial distribution of the class) Accuracy: quantifies product quality is defined as the average percent fraction of all pixels in the original land cover map

42. 42 Metrics (4) – Similarity and Euclidean distance: is the length of the distance between two images by accounting for the 7 metrics introduced earlier The larger it is, the less similar two images are Percentage similarity: The larger it is, the more similar two images are.

43. 43 A rank based land cover aggregation algorithm which prioritizes the subpixel block in terms of majority, adjacency, and ambiguity is implemented at continental scale Ranked aggregation algorithm better conserves the complex patterns in the original image Images generated with ranked aggregation algorithm are found to be more similar to the original image than those created by random and majority aggregation algorithms Reference: Hu J., et al., (2005), A rank based algorithm for aggregating land cover maps with minimal information loss. Remote Sens. Environ., (submitted for publication) Part 3: Conclusions

44. 44 An optimal set of configurable parameters which allows the MISR LAI/FPAR algorithm to discriminate between pure biome types, to minimize the impact of biome misidentification on LAI retrievals, and to maximize the spatial coverage of retrievals was empirically evaluated The spatial and temporal coverage of the MISR LAI and FPAR product are mainly limited by cloud contamination LAI retrievals show structural and phenological variability in agreement with field data. The product is accurate to within 0.5  units in herbaceous vegetation and savannas and overestimates LAI by about 1  unit in broadleaf forests A rank based land cover aggregation algorithm that represents fine resolution land cover information from satellite data at coarser resolutions with minimal information loss was developed Part 4: Summary of Main Results

45. 45 This research demonstrated the performance, accuracy and consistency of the MISR LAI product However… Multiangle data which provide vegetation angular signature information need to be further exploited to produce a more accurate biome identification map to apply the ranked aggregation algorithm More effort will be put in the validation of the energy partition by comparing with the field data in the future Nonconsecutive ranked aggregation algorithm will be explored Part 5: Future Directions