The MODIS Land Cover and Land Cover Dynamics Products

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• The MODIS Land Cover and Land Cover Dynamics
  Products
• A.H. Strahler (PI), Mark Friedl, Xiaoyang Zhang,
  John Hodges,
• Crystal Schaaf, Amanda Cooper, and Alessandro
  Baccini
• http//geography.bu.edu/landcover/
• Center for Remote Sensing and Dept. of Geography
• Boston University

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MODIS Land Cover Five Sets of Labels

• IGBPInternational Geosphere-Biosphere Project
  labels
• 17 classes of vegetation life-form
• UMD University of Maryland land cover class
  labels
• 14 classes without mosaic classes
• LAI/FPAR Classes for LAI/FPAR Production
• 6 labels including broadleaf and cereal crops
• BGC Biome BGC Model Classes
• 6 labels leaf type, leaf longevity, plant
  persistence

• Plant Functional Types (Future)
• Plant functional types to be used with the
  community land model (NCAR, Bonan)
• Exact classes TBD

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MODIS Land Cover Where Does it Come From?

• MODIS Data
• 16-day Nadir BRDF-Adjusted Reflectances (NBARs)
  assembled over one year of observations
• 7 spectral bands, 0.42.4 ?m, similar to Landsat
• 16-day Enhanced Vegetation Index (EVI)
• Training Data
• gt1,500 training sites delineated from high
  resolution satellite imagery (largely Landsat)
• Classifier
• Uses decision tree classifier with boosting

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MODIS Land Bands
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MODIS Geolocation

• Geolocation accuracy specification is 300 m (2 ?)
  and goal is 100 m (2 ?) at nadir
• Geolocation goal driven by Land 250 m change
  product requirements
• Goal is currently being met

? Land 550 CPs from 126 TM Scenes Ocean 4600
island points from SeaWifs library
Ground Control PointsLand
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MODIS Data Levels

• Level 1
• Radiometrically corrected, geolocated radiances
• Level 2
• Products derived from Level 1 data without
  geometric resampling
• Level 2G (MODIS Land)
• Forward-binned into integerized sinusoidal
  projection without resampling
• Level 3
• Products resampled using geolocation information
  to a standard family of map grids often
  multitemporal or composited
• Level 4
• Products derived from multiple data sources by
  modeling

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IGBP Land Cover Units (17)

• Natural Vegetation (11)
• Evergreen Needleleaf Forests
• Evergreen Broadleaf Forests
• Deciduous Needleleaf Forests
• Deciduous Broadleaf Forests
• Mixed Forests
• Closed Shrublands
• Open Shrublands
• Woody Savannas
• Savannas
• Grasslands
• Permanent Wetlands

• Developed and Mosaic Lands (3)
• Croplands
• Urban and Built-Up Lands
• Cropland/Natural Vegetation Mosaics
• Nonvegetated Lands (3)
• Snow and Ice
• Barren
• Water Bodies

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The Land Cover Input Database

• 242 Features From MODIS
• Temporal and spectral information 16-day
  composites
• Uses Surface Reflectance (NBAR)
• View-angle corrected surface reflectance, 7 land
  bands
• And Enhanced Vegetation Index (EVI)
• Plus (in the future).
• Spatial Texture from 250-m Band 2
• Standard deviation-to-mean ratio in Band 2
  (near-infrared)
• Snow Cover
• MODIS Snow Cover Product, number of days with
  snow cover
• Land Surface Temperature
• MODIS Land Surface Temperature, maximum value
  composite
• Directional Information
• Bidirectional reflectance information from BRDF
  product

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Global Composite Map of Nadir BRDF-Adjusted
Reflectance (NBAR) April 722 2001
no data
True color, MODIS Bands 2, 4, 3
10 km resolution, Hammer-Aitoff
projection, produced by MODIS BRDF/Albedo Team
MODLAND/Strahler et al.
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MODIS Nadir BRDF-Adjusted Reflectance
May 25June 9 2001 False Color Image NIRRedGreen
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NBAR Time Trajectories
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MODIS 500 m Vegetation Indices
(September 30 October 15, 2000
NDVI
MOD13A1 16 day Composite
EVI
MODLAND/Huete et al
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EVI
NDVI
EVI shows better dynamic range, less saturation
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Advanced Technology Classifiers

• Supervised Mode
• Use of supervised mode with training sites
• Allows rapid reclassifications for tuning
• Decision TreesC4.5 Univariate Decision Tree
• Fast algorithm
• Uses boosting to create multiple trees and
  improve accuracy, estimate confidence
• Neural NetworksFuzzy ARTMAP
• Uses Adaptive Resonance Theory in building
  network
• Presently not in use. Too slow does not handle
  missing data well.

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Decision Tree Classification

• Goal
• Optimal prediction of class labels from a set of
  feature values
• Basic Approach
• Supervised learning using training data
• Key Attributes
• Nonparametric
• Able to handle noisy or missing features
• Adept at capturing nonlinear, hierarchical
  patterns

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DTs Basic Theory

• Tree Structure
• Root node (all data), internal nodes and terminal
  or leaf nodes (predictions)
• Building the Decision Tree
• Recursive partitioning of training data into
  successively more homogeneous subsets
• Multiple Leaf Nodes per Class
• Leaf nodes identify class assignment
• Sub-classes allocated individual leaves

Root
Internal nodes
Leaf nodes
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Postclassification Processing

• Application of Prior Probabilities
• Use of priors to remove training site count
  biases (sample equalization)
• Application of global and moving-window priors
  from earlier products
• Increases accuracies, reduces speckle
• Use of external maps of prior probabilities to
  resolve confusions
• Agriculture/natural vegetation confusion in some
  regions
• Use of city lights DMSP data to enhance urban
  class accuracy (to come)
• Filling of Cloud-Covered Pixels from Earlier Maps
• Use of at-launch (EDC DISCover v. 2) or
  provisional product when there are not sufficient
  values to classify a pixel with confidence

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Using Priors to Classify Cereal and Broadleaf
Crops
Broadleaf Crop Intensity from USDA Statistics
Cereal Crop Intensity from USDA Statistics
MODIS Map of Broadleaf Crops in Continental
United States
MODIS Map of Cereal Crops in the Continental
United States
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Provisional Land Cover Product June 01
MODIS data from Jul 00Jan 01
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Consistent Year Land Cover Product June 02IGBP
MODIS data from Nov 00Oct 01
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Consistent Year Land Cover Product, Nov 00Oct 01
Mixed Forest
Evergreen Needleleaf Forest
Cropland/Natural Vegetation Mosaic
Cropland
Deciduous Broadleaf Forest
Urban
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Classification Confidence Map
Second Most-Likely Class
Lower Confidence
Second choice omitted with very high confidence
level
High Confidence
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Rondonia Comparison

• Note better delineation of land cover pattern

Consistent Year
Confidence
EDC DISCover v.2
Provisional Product
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Land Cover Validation

• Validation Plan Utilizes Multiple Approaches
• Level 1 Comparisons with existing data sources
• Examples
• Global AVHRR land cover datasets DISCover, UMd
• Humid Tropics Landsat Pathfinder
• Forest Cover FAO Forest Resources Assessment
• Western Europe CORINE
• United States USGS/EPA MLRC
• United States California Timber Maps (McIver and
  Woodcock)
• MODIS and Bigfoot test site comparisons

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Validation Levels, Cont.

• Level 2 Quantitative studies of output and
  training data
• Per-pixel confidence statistics
• Aggregated by land cover type and region
• Describe the accuracy of the classification
  process
• Test site cross-comparisons
• Confusion matrices globally and by region
• Provides estimates of errors of omission and
  commission
• Level 3 Sample-based statistical studies
• Random stratified sampling according to proper
  statistical principles
• Costly, but needed for making proper accuracy
  statements
• CEOS Cal-Val Land Product Validation Land Cover
  Activity

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Confidence Values by Land Cover Type (Preliminary)

• IGBP Class Confidence
• 1 Evergreen Needleleaf 68.3
• 2 Evergreen Broadleaf 89.3
• 3 Deciduous Needleleaf 66.7
• 4 Deciduous Broadleaf 65.9
• 5 Mixed Forest 65.4
• 6 Closed Shrubland 60.0
• 7 Open Shrubland 75.3
• 8 Woody Savanna 64.0

• IGBP Class Confidence
• 9 Savanna 67.8
• 10 Grasslands 70.6
• 11 Permanent Wetlands 52.3
• 12 Cropland 76.4
• 14 Cropland/Nat. Vegn. 60.7
• 15 Snow and Ice 87.2
• 16 Barren 90.0
• Overall Confidence 76.3

Includes adjustment for prior probabilities.
Urban and Built-Up (13), Water(17) classes
omitted. Pixels filled from prior data omitted.
Based on preliminary data, subject to change.
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Confidence Values by Continental Region
(Preliminary)

• Region Confidence, percent
• Africa 79.4
• Australia/Pacific 83.2
• Eurasia 76.8
• North America 71.9
• South America 78.5
• Overall Confidence 76.3

Includes adjustment for prior probabilities.
Urban and Built-Up (13), Water(17) classes
omitted. Pixels filled from prior data omitted.
Based on preliminary data, subject to change.
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Cross Validation with Training Sites

• Cross-Validation Procedure
• Hide 10 percent of training sites, classify with
  remaining 90 percent repeat ten times for ten
  unique sets of all sites
• Provides confusion matrix based on unseen
  pixels where whole training site is unseen
• Not a stratified random sample, but a reasonable
  indication of within-class accuracy

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Confusion Matrix (Preliminary)

• Global Test Site Confusion MatrixConsistent Year
  Product, After Priors

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AccuraciesConsistent Year Product (Preliminary)
Based on Global Test Site Confusion Matrix

• Dataset Training Site Accuracy
• Before priors 78.6
• After priors 71.0
• After priors, first two classes 84.0

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Overall Accuracies

• Proper accuracy statements require proper
  statistical sampling
• AVHRR state of the art has been 6070 percent,
  depending on class and region
• MODIS accuracies are falling in 7080 percent
  range
• Most mistakes are between similar classes
• Land cover change should NOT be inferred from
  comparing successive land cover maps

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Land Cover Dynamics

• Primary Objectives
• Quantify interannual change
• Uses change vectors comparing successive years
• Identifies regions of short-term climate
  variation
• Under development with Eric Lambin, Frederic Lupo
  at UCL, Belgium
• Quantify phenology
• Greenup, maturity, senescence, dormancy
• Values of VI, EVI at greenup and peak, plus
  annual integrated values
• Uses logistic functions fit to time trajectories
  of EVI

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Land Cover DynamicsDefining Phenological
Attributes
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Web Site http//geography.bu.edu/landcover
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References

• Friedl, M.A., D. Muchoney, D.K. McIver, A.H.
  Strahler, and J.C.F. Hodges 2000
  Characterization of North American land cover
  from AVHRR Data, Geophysical Research Letters,
  vol. 27, no. 7, pp. 977-980.
• Friedl, M.A., C. Woodcock, S. Gopal, D. Muchoney,
  A.H.Strahler, and C. Barker-Schaaf 2000. A note
  on procedures used for accuracy assessment in
  land cover maps derived from AVHRR data,
  International Journal of Remote Sensing, vol. 21,
  pp.1073-1077.
• Friedl, M.A., Brodley, C.E. and A.H. Strahler
  1999 Maximizing land cover classification
  accuracies at continental to global scales, IEEE
  Transactions on Geoscience and Remote Sensing,
  vol. 37, pp. 969-977.
• Friedl, M.A. and C.E. Brodley 1997 Decision tree
  classification of land cover from remotely sensed
  data, Remote Sensing of Environment, vol. 61,
  pp. 399-409.
• Mciver, D.K. and M.A. Friedl 2002. Using prior
  probabilities in decision-tree classification of
  remotely sensed data, Remote Sensing of
  Environment, Vol. 81, pp. 253-261.
• McIver, D.K. and M.A. Friedl 2001. Estimating
  pixel-scale land cover classification confidence
  using non-parametric machine learning methods,
  IEEE Transactions on Geoscience and Remote
  Sensing. Vol 39(9), pp. 1959-1968.
• Muchoney, D., Borak, J, Chi, H., Friedl, M.A.,
  Hodges, J. Morrow, N. and A.H. Strahler 1999
  Application of the MODIS global supervised
  classification model to vegetation and land cover
  mapping of Central America, International Journal
  of Remote Sensing, Vol 21, no 6 7, pp.
  1115-1138.
• Muchoney, D. M., and Strahler, A. H., 2001, Pixel
  and site-based calibration and validation methods
  for evaluating supervised classification of
  remotely sensed data, Remote Sens. Environ., in
  press.