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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #214338

Title: Differentiation of semi-arid vegetation types based on multi-angular observations from MISR and MODIS

item Rango, Albert
item Peters, Debra

Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/20/2006
Publication Date: 11/15/2007
Citation: Su, L., Chopping, M.J., Rango, A., Martonchik, J.V., Peters, D.C. 2007. Differentiation of semi-arid vegetation types based on multi-angular observations from MISR and MODIS. International Journal of Remote Sensing. 28:1419-1424.

Interpretive Summary: The Terra satellite has a Multiangle Imaging SpectroRadiometer (MISR) and a Moderate Resolution Imaging Spectroradiometer (MODIS). Thus for these two important instruments have not been used in a complementary fashion. The Jornada Experimental Range and the Sevilleta National Wildlife Refuge were the study areas in an attempt to distinguish between different vegetation types. It was shown that the anisotropy patterns of surface reflectance from MODIS are complementary to the nine angular views of MISR. The potential is revealed to increase classification accuracy of different vegetation types when the two instruments are used together. Users needing discrimination of different vegetation over large areas should benefit form this approach.

Technical Abstract: Mapping 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. This letter presents a study on the use of directional reflectance derived from two sensor systems, using two different models to analyze the data and two different classifiers as a means of mapping vegetation types. The multiangle Imaging SpecroRadiometer (MISR) and the Moderation Resolution Imaging Specroradiometer (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 increase classification accuracy. The patterns are described 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.