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

TitleDifferentiation of semi-arid vegetation types based on multi-angular observations from MISR and MODIS
Publication TypeJournal Article
Year of Publication2007
AuthorsSu L., Chopping M., Rango A., Martonchik J.V., Peters DC
JournalInternational Journal of Remote Sensing
Volume28
Pagination1419-1424
Date Published11/2007
ARIS Log Number214338
Keywordsbidirectional reflectance, classification, semi-arid land
AbstractMapping accurately vegetation type is one of the main challenges for monitoring arid and semiarid 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.
URL/files/bibliography/07-032.pdf
DOI10.1080/01431160601085995