Name
Title | ||
|---|---|---|
Improved semi-arid community type differentiation with the NOAA AVHRR via exploitation of the directional signal |
Abstract | ||
|---|---|---|
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 use 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. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/TGRS.2002.1010900 | IEEE T. Geoscience and Remote Sensing |
Keywords | Field | DocType |
image classification,vegetation mapping,Advanced Very High Resolution Radiometer,China,Inner Mongolia Autonomous Region,NOAA AVHRR,New Mexico,USA,canopy-soil complex,cover type differentiation,desert regions,directional signal,environmental factors,geometric modelling,illumination geometry,image classification,linear semiempirical kernel-driven model,optical reflection,optical scattering,optical sensors,remote sensing,satellite applications,semi-arid community type differentiation,semi-arid vegetation types mapping,soil background,solar wavelength AVHRR data,surface bidirectional reflectance distribution function,topographic variations,vegetation mapping,viewing geometry | Bidirectional reflectance distribution function,Vegetation,Topographic map,Remote sensing,Advanced very-high-resolution radiometer,Radiometry,Bruit,Latitude,Contextual image classification,Mathematics | Journal |
Volume | Issue | ISSN |
40 | 5 | 0196-2892 |
Citations | PageRank | References |
3 | 0.70 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. J. Chopping | 1 | 3 | 1.38 |
| A. Rango | 2 | 29 | 3.44 |
| J. C. Ritchie | 3 | 3 | 0.70 |