Full text: Mesures physiques et signatures en télédétection

application of ai techniques to infer vegetation characteristics 
D.S. Kimes and J.A. Smith 
NASA/Goddard Space Flight Center 
Greenbelt, Maryland (USA) 
Tel. 301-286-4927 
P.A. Harrison 
JJM Systems Inc. 
1225 Jefferson Davis Hwy., Suite 412 
Arlington, Virginia (USA) 
P.R. Harrison 
U.S. Naval Academy 
Annapolis, Maryland (USA) 
Traditionally, the remote sensing community has relied totally on spectral knowledge to extract vegetation 
characteristics. However, there are other knowledge bases that can be used to significantly improve the accu 
racy and robustness of inference techniques. Using AI (artificial intelligence) techniques a knowledge-based 
system (VEG) was developed that integrates input spectral measurements with diverse knowledge bases. 
These knowledge bases consist of data sets of directional reflectance measurements, knowledge from litera 
ture, and knowledge from experts which are combined into an intelligent and efficient system for making 
vegetation inferences. VEG accepts spectral data of an unknown target as input, determines the best tech 
niques for inferring the desired vegetation characteristic(s), applies the techniques to the target dam, and pro 
vides a rigorous estimate of the accuracy of the inference. Currently VEG has been developed to (1) infer 
spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles (2) infer per 
cent ground cover from any combination of nadir and/or off-nadir view angles (3) infer unknown view 
angle(s) from known view angle(s) (known as view angle extension), and (4) discriminate between user- 
defined vegetation classes using spectral and directional reflectance relationships developed from an 
automated learning algorithm. The errors for these techniques were generally very good ranging between 2 
and 15% (proportional rms). The system is designed to aid scientists in developing, testing, and applying new 
inference techniques using directional reflectance data. 
KEY WORDS: Vegetation, Directional Reflectance, Knowledge-based System, Artificial Intelligence 
Few accurate inference techniques of vegetation characteristics (e.g. hemispherical reflectance, ground 
cover, photosynthetic capacity, leaf area index, biomass, vegetation height, etc.) have been developed using 
data that can be realistically collected from conventional sensor platforms. The success of fundamental 
remote sensing science efforts that accurately infer vegetation characteristics will determine the degree and 
scope of the success of science performed using Earth Observing System (EOS) data. Inference systems must 
be developed that deal with the variable nature of EOS data (e.g. variable off-nadir viewing capabilities, 
varying solar zenith angles, various sensor wavelengths, cloud cover problems, missing dam, and atmospheric

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