Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data M.C. Hansen a, * , R.S. DeFries a,b , J.R.G. Townshend a,c , R. Sohlberg a , C. Dimiceli a , M. Carroll a a Department of Geography, University of Maryland, 2181 LeFrak Hall, College Park, MD 20742, USA b Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, USA c Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA Received 1 May 2001; received in revised form 21 February 2002; accepted 12 March 2002 Abstract The continuous fields Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products are 500-m sub-pixel representations of basic vegetation characteristics including tree, herbaceous and bare ground cover. Our previous approach to deriving continuous fields used a linear mixture model based on spectral endmembers of forest, grassland and bare ground training. We present here a new approach for estimating percent tree cover employing continuous training data over the whole range of tree cover. The continuous training data set is derived by aggregating high-resolution tree cover to coarse scales and is used with multi-temporal metrics based on a full year of coarse resolution satellite data. A regression tree algorithm is used to predict the dependent variable of tree cover based on signatures from the multi- temporal metrics. The automated algorithm was tested globally using Advanced Very High Resolution Radiometer (AVHRR) data,as a full year of MODIS data has not yet been collected. A root mean square error (rmse) of 9.06% tree cover was found from the global training data set. Preliminary MODIS products are also presented, including a 250-m map of the lower 48 United States and 500-m maps of tree cover and leaf type for North America. Results show that the new approach used with MODIS data offers an improved characterization of land cover. D 2002 Elsevier Science Inc. All rights reserved. 1. Introduction Tree cover mapping has grown in importance as the need to quantify global tree stocks has increased. Tree cover is an important variable for modeling of global biogeochemical cycles and climate (Sellers et al., 1997; Townshend et al., 1994). Additionally, tree cover mapping has taken on increased importance in the policy arena. Quantifying carbon stocks has been deemed a necessity in global treaties regard- ing release and sequestration of carbon to and from the atmosphere (IGBP, 1998). The use of tree cover mapping in assessing the condition of global ecosystems is also important (Ayensu, Claasen, Collins, et al., 1999). In order to meet the needs of the users of such data, the remote sensing community has begun to promote the benefits of the synoptic, standardized view provided by satellite data (DeFries, Han- sen, Townshend, Janetos, & Loveland, 2000). One of the annual Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products is the vegetation continuous fields layers. The layers include percent bare ground, herba- ceous and tree cover and, for tree cover, percent evergreen, deciduous, needleleaf and broadleaf. These maps have the potential to meet many of the needs of both the scientific and policy communities. This paper describes an improved methodology for deriving percent tree cover estimates over previous methodologies. The procedure is presented along with a global Advanced Very High Resolution Radiometer (AVHRR) application and two examples using MODIS data. Continuous fields of vegetation properties offer advan- tages over traditional discrete classifications. By depicting each pixel as a percent coverage, areas of heterogeneity are better represented. Discrete classes do not allow for the depiction of variability for spatially complex areas (DeFries, Field, Fung, et al., 1995). Many spatially complex areas occur because of anthropogenic land cover change. By using proportional estimates, sub-pixel cover can be mapped with the prospect of measuring change over time. Since the 0034-4257/02/$ - see front matter D 2002 Elsevier Science Inc. All rights reserved. PII:S0034-4257(02)00079-2 * Corresponding author. Tel.: +1-301-314-2585. E-mail address: mhansen@geog.umd.edu (M.C. Hansen). www.elsevier.com/locate/rse Remote Sensing of Environment 83 (2002) 303 – 319