NDLI: Mapping tree species in a boreal forest area using RapidEye and LiDAR data

Content Provider	IEEE Xplore Digital Library
Author	Xiaohui Yang Rochdi, N. Jinkai Zhang Banting, J. Rolfson, D. King, C. Staenz, K. Patterson, S. Purdy, B.
Copyright Year	2014
Description	Author affiliation: Alberta Environ. & Sustainable Resource Dev., Edmonton, AB, Canada (Patterson, S.) \|\| Dept. of Geogr., Univ. of Lethbridge, Lethbridge, AB, Canada (Xiaohui Yang; Rochdi, N.; Jinkai Zhang; Banting, J.; Rolfson, D.; King, C.; Staenz, K.) \|\| Alberta Innovates, Energy & Environ. Solutions, Edmonton, AB, Canada (Purdy, B.)
Abstract	Tree species composition is an indicator of forest type. It is also a required attribute in forest inventory, biomass and stand volume estimation. Accurate mapping tree species is essential for forest management purposes. In this paper the performances of LiDAR, RapidEye data, and their combination on tree species classification were investigated in a boreal forest. Both Random forest (RF) and support vector machine (SVM) classification methods were performed. Results indicated that combined LiDAR and RapidEye data improved the classification accuracy significantly, compare to using each type of data separately. The RF classifier outperformed SVM for tree species classification. Six variables that contributed most to classification accuracy were digital elevation model, slope, canopy height, red-edge NDVI, and red-edge and Near infrared bands of RapidEye data.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	69
Ending Page	71
File Size	290935
Page Count	3
File Format	PDF
ISBN	9781479957750
DOI	10.1109/IGARSS.2014.6946357
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-13
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Laser radar Radio frequency Support vector machines Accuracy Vegetation Remote sensing Input variables Support Vector Machine Random Forest
Content Type	Text
Resource Type	Article

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