For Peer Review OnlyDifferentiation of Semi-Arid Vegetation Types based on Multi-Angular Observations from MISR and MODIS Journal: International Journal of Remote Sensing Manuscript ID: TRES-LET-2006-0032.R1 Manuscript Type: Research Letter Date Submitted by the Author:n/a Complete List of Authors: Su, Lihong; Montclair State University, Earth & Environmental Studies Chopping, Mark; Montclair State University, Department of Earth and Environmental Studies Rango, Albert; USDA, ARS Jornada Experimental Range Martonchik, John; NASA Jet Propulsion Laboratory Peters, Debra; USDA, ARS Jornada Experimental Range Keywords:BIDIRECTIONAL REFLECTANCE, CLASSIFICATION, SEMI-ARID LAND, VEGETATION Keywords (user defined):http://mc.manuscriptcentral.com/tres Email: [email protected] Journal of Remote SensingFor Peer Review Only1Differentiation of Semi-Arid Vegetation Types based on Multi-Angular Observations from MISR and MODISLIHONG SU(1), MARK J. CHOPPING(1), ALBERT RANGO(2), JOHN V.MARTONCHIK(3), DEBRA P. C. PETERS(2)1) Department of Earth and Environmental Studies, Montclair State University, Montclair, New Jersey 070432) USDA, ARS Jornada Experimental Range, Las Cruces, New Mexico 88003, USA3) NASA Jet Propulsion Laboratory, Pasadena, California 91109, USAAbstractMapping accurately vegetation type is one of the main challenges for monitoring arid and semi-arid grasslands with remote sensing. The multi-angle approach has been demonstrated to be useful for mapping vegetation types in deserts. The paper presents a study on the use of directional reflectance derived from two sensor systems, using two different models to analyse the data and two different classifiers as a means of mapping vegetation types. The Multiangle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) provide multi-spectral and angular, off-nadir observations. In this study, we demonstrate that reflectance from MISR observations and reflectance anisotropy patterns derived from MODIS observations are capable of working together to improve classification accuracy. The patterns are Corresponding author Page 1 of 12http://mc.manuscriptcentral.com/tres Email: [email protected] Journal of Remote Sensing123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960For Peer Review Only2described by parameters of the Modified Rahman-Pinty-Verstraete and the RossThin-LiSparseMODIS bidirectional reflectance distribution function (BRDF) models. The anisotropy patterns derived from MODIS observations are highly complementary to reflectance derived from radiances observed by MISR. Support vector machine algorithms exploit the information carried by the same data sets more effectively than the maximum likelihood classifier.1. IntroductionDifferentiation of semi-arid vegetation types is a classification problem (Kremer and Running, 1993). It implies a large number of classes that differ more subtly than the broader categories assigned to regional or global classification schemes. The land surface scatters solar radiation anisotropically. Multiple view angle data could provide information on canopy structure and disturbance that is inaccessible using single view angle technologies. The factors derived from multiangle measurements have been suggested to be integrated into land cover classification activities (Abuelgasim et al. 1996, Pinty et al. 2002, Zhang et al. 2002, Su et al. 2005). The reflectance anisotropy patterns depend on the three-dimensional character and optical properties of the surface and can be used to characterize the surface target; it should be reasonable to introduce them as additional discriminatory variables into a classification procedure. This study investigates the use of directional reflectance from two different sensor systems, two different models analysis of the directional reflectance, and two different classifiers as a means of mapping Page 2 of 12http://mc.manuscriptcentral.com/tres Email: [email protected] Journal of Remote Sensing123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960For Peer Review Only3vegetation types in deserts. This study emphasizes a distinctive capability provided by combining these patterns derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) observations and reflectance derived from the Multiangle Imaging SpectroRadiometer (MISR) observations for differentiation of semi-arid vegetation types.The Modified Rahman-Pinty-Verstraete (MRPV) model (Engelsen, et al., 1996; Diner et al., 1999) and the RossThin-LiSparseMODIS (RTnLS) model (Wanner et al., 1995; Strahler and Muller, 1999) were applied to invert bidirectional reflectance distribution function (BRDF) parameters from MISR and MODIS reflectance. The MRPV model uses the following three parameters describing the anisotropy of surface reflectance: (1) 0, giving the diffuse reflectance; (2) k, representative of the bowl or bell shape of the surface anisotropy; (3) b, describing the predominance of forward or backward scattering. The RTnLS model is a semi-empirical kernel-driven model thatconsists of two kernel terms and a constant term. The volumetric kernel represents the scattering properties of a turbid medium, the geometric-optical kernel captures the shadowing effect of sparse vegetation, and the constant term is for the isotropic scattering. The weights for these three terms are called vol, geo, and iso, respectively. RTnLS uses these weights to describe the anisotropy of surface reflectance. Support vector machines (SVM) and maximum likelihood classifier (MLC) use same inputs, and show the same tendency in terms of accuracy improvement, however, SVM can exploit the information carried by the same data sets more efficiently.Page 3 of 12http://mc.manuscriptcentral.com/tres Email: [email protected] Journal of Remote Sensing123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960For Peer Review Only42. Study AreasOur study area lies within the