Variations in reflectance with seasonality and viewing geometry: implications for semi-arid vegetation mapping with MISR data

Abstract

The purpose of this article is to understand the effect of multi-temporal multi-angle data on vegetation community type mapping in desert regions. Based on data from the multi-angle imaging spectroradiometer (MISR), a set of 46 multi-temporal classification experiments were carried out in the Jornada Experimental Range in New Mexico, USA. Besides multi-angle observations, bidirectional reflectance distribution function (BRDF) model parameters were also used as input data for the classifications. The experiments used two widely accepted BRDF models, the Rahman–Pinty–Verstraete (RPV) model and the Ross-thin Li-sparse reciprocal (RTnLS) model. The experiments show that multi-temporal multi-angle classifications can yield a more accurate mapping than multi-temporal nadir classifications, and multi-temporal BRDF model parameters combined with a single nadir image can provide an accuracy roughly the same as all multi-temporal multi-angle observations for the vegetation mapping. These findings opened not only a path of reducing data dimensionality for multi-temporal multi-angle classifications, but also a way of merging products of both MISR and moderate resolution imaging spectroradiometer (MODIS) to improve semi-arid vegetation mapping.

Acknowledgements

The Jornada data sets were provided by the Jornada Basin Long-Term Ecological Research (LTER) project. The MISR data were obtained from the NASA Langley Atmospheric Sciences Data Center. The authors also thank the anonymous referees for their detailed comments for improving our presentation.