HR: 1340h
AN: SF43A-0774    [Abstracts]
TI: Testing Hyperspectral Indices for Crop Identification and Stress Detection
AU: * Lobell, D B
EM: dlobell@stanford.edu
AF: Carnegie Institution of Washington, Department of Global Ecology 260 Panama St, Stanford, CA 94305 United States
AU: * Lobell, D B
EM: dlobell@stanford.edu
AF: Stanford University, Dept. Geological and Environmental Sciences, Stanford, CA 94305 United States
AU: Asner, G P
EM: gpa@stanford.edu
AF: Carnegie Institution of Washington, Department of Global Ecology 260 Panama St, Stanford, CA 94305 United States
AU: Asner, G P
EM: gpa@stanford.edu
AF: Stanford University, Dept. Geological and Environmental Sciences, Stanford, CA 94305 United States
AU: Ortiz-Monasterio, I
EM: i.ortiz-monasterio@cgiar.org
AF: CIMMYT, Apdo. Postal 6-641, Mexico, D.F., 06600 Mexico
AB: Many agricultural applications can benefit from the rapidly expanding array of remote sensing technologies. Here we evaluate recent hyperspectral images acquired by EO-1 Hyperion over an agricultural region in Mexico. Two applications that have seen limited success with multispectral sensors -- crop discrimination and stress detection -- were investigated using a combination of ground measurements, decision tree modeling, and multi-temporal image analysis. Results indicate substantial improvements over multispectral approaches for both applications.
DE: 1694 Instruments and techniques
DE: 1640 Remote sensing
SC: Special Focus: Advances in Data Acquisition, Management, Analysis and Display [SF]
MN: 2004 AGU Fall Meeting