Functional Data Analysis of Multi-Angular Hyperspectral Data on Vegetation

Functional Data Analysis of Multi-Angular Hyperspectral Data on Vegetation

Abstract - The surface reflectance anisotropy can be estimated by directional reflectance analysis through the collection of multi-angular spectral data. Proper characterization of the surface anisotropy is an important element in the successful interpretation of remotely sensed signals. A signal received by a sensor from a vegetation canopy is affected by several factors. One of them is the sensor zenith angle. Functional data analysis can be used to assess the distribution and variation of spectral reflectance due to sensor zenith angle. This paper examines the effect of sensor zenith angles on the spectral reflectance of vegetation, example on cotton leaves. The spectra were acquired in a green house trial in order to address the question ‘how much information can be obtained from multi-angular hyperspectral remote sensing on vegetation?’ The goals of the functional data analysis applied in this paper is to examine the Functional Data Analysis approach was applied to analysis multi-angular hyperspectral data on cotton, highlighting various characteristics of cotton spectra due to sensor view angles, and to infer directional variation in an outcome or dependent variable with different zenith angles.

Sugianto, Sugianto; Remote Sensing and GIS Laboratory, Syiah Kuala University, Banda Aceh 23111, Indonesia

Laffan, Shawn; School of Biological Earth and Environmental Science, The University of New South Wales, Australia.

Aceh International Journal of Science and Technology; Vol 1, No 1: January - April 2012

2088-9860

Graduate School of Syiah Kuala University

2012-04-01

http://jurnal.unsyiah.ac.id/AIJST/article/view/12/11

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article

http://jurnal.unsyiah.ac.id/AIJST/article/view/12

10.13170/aijst.1.1.12