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Inferring Vegetation Characteristics Using A Knowledge-based System

A knowledge-based system for inferring physical and biological surface properties of vegetation using nadir and/or directional reflectance data as input has been developed. A portion of this system has been developed to discriminate between user defined vegetation classes using spectral and directional reflectance relationships. This discrimination program was used to classifying targets into user defined ground cover and plant height classes. User defined classes were sparse--0-30% ground cover, intermediate--3@60% ground cover, and dense--60- 100% ground cover of vegetation, and >1Om and <10m vegetation height. The program “learns” class descriptions from samples (both positive and negative) of spectral, directional reflectance data of natural surfaces (bare soils, natural vegetation, and agricultural vegetation). The system is designed to handle any combination of directional view angles. The system finds class descriptions which contain the most important features that distinguish each class from the others. The explicit relationships used in the class descriptions include greater-than relationships between combinations of two view angles and maximum and minimum value relationships. The class descriptions are used to classify an unknown target using the same directional views. The program was tested by learning class descriptions of various categories of percent ground cover and vegetation height. These class descriptions were then used to classify an array of target data.