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Accueil du site → Doctorat → Royaume-Uni → 1998 → Linear semi-empirical kernel-driven bidirectional reflectance distribution function models in monitoring semi-arid grasslands from space

University of Nottingham (1998)

Linear semi-empirical kernel-driven bidirectional reflectance distribution function models in monitoring semi-arid grasslands from space

Chopping, M J.

Titre : Linear semi-empirical kernel-driven bidirectional reflectance distribution function models in monitoring semi-arid grasslands from space.

Auteur : Chopping, M J.

Université de soutenance : University of Nottingham

Grade : Doctor of Philosophy (Ph.D.) : 1998

Résumé
Of the five sources of information available in optical remote sensing data from space (spectral, temporal, locational, directional and wave polarity), little use has been made of the last two in mapping and monitoring semi-arid grasslands. This research aims to explore the impact and information potential of the directional variation in the satellite signal through sampling the surface BRDF (Bidirectional Reflectance Distribution Function) with data from wide-swath sensors and in particular the Advanced Very High Resolution Radiometer (AVHRR). The capabilities and limitations of current and near-future spaceborne sensors for sampling the BRDF and calibrating BRDF models are examined. Unaccounted for, BRDF phenomena make use of the spectral reflectance data derived from spectral radiance measurements from these sensors highly problematic : the data are inconsistent. The nature and extent of the impact of the BRDF on visible and near-infrared reflectance data are investigated using observations over different grassland communities in a semi-arid zone in Inner Mongolia, both at ground level (field radiometry) and from the AVHRR. These communities are characterised by vegetation, soil and bidirectional spectral reflectance parameters obtained via field survey at the peak of the growing season.Models designed to account for BRDF phenomena are reviewed with special attention apportioned to recently-developed linear semi-empirical kernel-driven (LiSK) models suitable for operational use. These models are evaluated for their ability to describe and explain the BRDF, adjust multiangular data to a various common sun-sensor geometries and provide unique surface information from the directional signal which is useful in community type differentiation in semi-arid grasslands. The sensitivity of the models to the limited angular sampling provided by spaceborne sensors is also assessed. The research shows that LiSK models provide considerable scope for improving data quality and for retrieving useful additional surface information, even when adjusted against the sparse angular samples provided by two AVHRR sensors.

Mots clés : Remote sensing ; Satellite ; GIS Geomagnetism Cartography Geodesy Pattern recognition systems Pattern perception Image processing Geomagnetism Pattern recognition systems

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