Improved semi-arid community type differentiation with the NOAA AVHRR via exploitation of the directional signal

Mark J. Chopping, Albert Rango, Jerry C. Ritchie

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Mapping semi-arid vegetation types at the community level is extremely difficult for optical sensors with large ground footprints such as the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). Attempts to usc solar wavelength AVHRR data in community type differentiation have often resulted in unacceptable classification errors which are usually attributed to noise from topographic and soil background variations, inaccurate reflectance retrieval and poor registration. One source of variation which is rarely accounted for adequately is the directional signal resulting from the combined effects of the surface bidirectional reflectance distribution function (BRDF) and the variation of viewing and illumination geometry as a function of scan angle, season, latitude and orbital overpass time. In this study, a linear semiempirical kernel-driven (LiSK) BRDF model is used to examine the utility of the directional signal in community and cover type differentiation over discontinuous but statistically homogeneous semi-arid canopies in Inner Mongolia Autonomous Region (IMAR), China, and New Mexico (NM), USA. This research shows that the directional signal resulting from the physical structure of the canopy-soil complex can be retrieved to provide information which is highly complementary to that obtained in the spectral domain.

Original languageEnglish
Pages (from-to)1132-1149
Number of pages18
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number5
StatePublished - May 2002


  • Desert regions
  • Environmental factors
  • Geometric modelling
  • Image classification
  • Optical reflection
  • Optical scattering
  • Remote sensing
  • Satellite applications
  • Vegetation mapping


Dive into the research topics of 'Improved semi-arid community type differentiation with the NOAA AVHRR via exploitation of the directional signal'. Together they form a unique fingerprint.

Cite this