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)
https://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
Scholar Commons Citation
Pu, Ruiliang, "Comparing Canonical Correlation Analysis with Partial Least Squares Regression in Estimating Forest Leaf Area Index with Multitemporal Landsat TM Imagery" (2012). School of Geosciences Faculty and Staff Publications. 359.
https://digitalcommons.usf.edu/geo_facpub/359
