Title

Comparing Canonical Correlation Analysis with Partial Least Squares Regression in Estimating Forest Leaf Area Index with Multitemporal Landsat TM Imagery

Document Type

Article

Publication Date

2012

Digital Object Identifier (DOI)

http://dx.doi.org/10.2747/1548-1603.49.1.92

Abstract

The leaf area index (LAI) of plant canopies is an important structural variable for assessing terrestrial ecosystems. This research examined the use of multitemporal Landsat TM imagery to estimate and map LAI in mixed natural forests in the southeastern USA. The performances of canonical correlation analysis (CCA) and partial least squares (PLS) regression techniques were evaluated for feature extraction to estimate forest LAI. The experimental results indicate that use of multitemporal TM imagery can improve the accuracy of estimating the forest LAI, and that CCA analysis outperforms PLS regression for feature extraction.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

GIScience & Remote Sensing, v. 49, issue 1, p. 92-116

Share

COinS