Global land cover mapping from MODIS: algorithms and early results M.A. Friedl a,*, D.K. McIver a, J.C.F. Hodges a, X.Y. Zhang a, D. Muchoney b, A.H. Strahler a, C.E. Woodcock a, S. Gopal a, A. Schneider a, A. Cooper a, A. Baccini a, F. Gao a, C. Schaaf a aDepartment of Geography and Center for Remote Sensing, Boston University, 675 Commonwealth Avenue, Boston, MA 02215, USA bConservation International, 1919 M Street, NW Suite 600, Washington, DC 20036, USA Received 6 April 2001; received in revised form 4 December 2001; accepted 10 February 2002

MODIS Improved Upon Existing Techniques of Global Land Cover Mapping Prior to 1994 global land cover mapping was compiled from a variety of data sources of varying dates, scales and classification schemes In 1994 AVHRR data was used for the first global scale remote sensing based land cover mapping project

AVHRR Land Cover Mapping in the 1990’s 1994 project used maximum likelihood monthly composited AVHRR NDVI at 1˚ spatial resolution 1998 decision tree classification technique used for 8 km resolution map 2000 unsupervised classification of monthly composited NDVI data for 1 km resolution map – supervised classification was also done in 2000

Improvements with MODIS MODIS’s TERRA has 7 spectral bands that were designed for land applications MODIS has enhanced spectral, spatial, radiometric, and geometric qualities. Greatly improves monitoring and mapping land cover changes Algorithms used with MODIS intended for operational mapping – rapid turnaround

Friedl et al Sought to: Describe the MODIS based land cover mapping activities –Provide an overview of the MODIS products including the classification methodologies –Provide a summary of the early results

MODIS Products 1 km spatial resolution maps 28 km  ¼° spatial resolution for the global modeling community (to data intensive at higher res?) Measure of annual land cover change at 1 km resolution employing a change-vector algorithm (properties caused by climate)

MODUS Land Cover Classification Algorithm (MLCCA) Supervised classification methodology –Uses a database of sites intended to represent the majority of the Earth’s land cover features –Database is then used to train the supervised classification of the MODUS data

International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) Classification Scheme

Land Cover Classes Used with MODIS The IGBP classes are used but the MODUS product also includes secondary classifications for each pixel to make it useful for a wide variety of models

System for Terrestrial Ecosystem Parameterization (STEP) Because global land cover is highly diverse STEP was designed to provide a classification free and versatile database structure for site based characterization of global land cover Future global mapping efforts based on MODIS or other sources will not need to be based on IGBP or any other classification scheme

STEP Sites STEP sites number 1373 and range from 1 to 200 km 2 – locations with a high degree of land cover change were excluded Site selection at first, “performed opportunistically based on available Landsat data.”

Experts From Around the World Reviewed the Sites in a Complex Process

…If no Consensus can be Reached the Site is Rejected

Assessing the STEP Coverage Oversampled in Central and North America Used Olson land cover class map overlaid with STEP to check other areas of the world Some subclasses found that are either not sampled or undersampled. Eight Olson classes for agricultural areas were not sampled in Africa

STEP Coverage of the Continents is Considered Good Except for Eurasia (Some Would Say That’s Two Continents…It’s, at Least, One Very Big Continent, With Most of the World’s People in it)

Eurasia has Complex Vegetation and Landcover

MLCCA Inputs -Nadir BRDF-adjusted Reflectance (NBAR) is a result of MODIS using a range of zenith angles for viewing, this allows for correction of directional reflectance effects -Cloud screening and atmospheric correction are also used

Robust and Repeatable Algorithms Univariate Decision Tree C4.5 Artificial Neural Network (ARTMAP) …both are distribution-independent, they assume nothing about frequency distribution of the data …C4.5 has become the primary classification algorithm

Assessing the Results Admittedly difficult to assess but the global distribution of land cover appears to be realistic Biggest problem areas are high latitudes (>70°) and distinguishing between agriculture and vegetation Boosting has been used to enhance the data with per pixel estimates of classification confidence