Estimation of tree species proportions of forest compartments using ranging scatterometer

Author

Torma, Markus ; Hyyppa, Juha

Author_Institution

Inst. of Photogrammetry & Remote Sensing, Helsinki Univ. of Technol., Espoo, Finland

Volume

2

fYear

1999

fDate

1999

Firstpage

776

Abstract

Tree species proportions of forest stands were estimated using a ranging scatterometer called HUTSCAT. The employed estimation method was a multilayer perceptron neural network with error backpropagation training algorithm. Different methods based on intensity and/or shape of measured profiles were tested. The best classification accuracy of the main tree species was about 88% and the mean error of estimation for tree species proportions was 0.26

Keywords

airborne radar; backpropagation; feature extraction; forestry; geophysical signal processing; image classification; multilayer perceptrons; radar imaging; remote sensing by radar; vegetation mapping; HUTSCAT; classification accuracy; error backpropagation training algorithm; estimation method; forest compartments; forest stands; intensity; multilayer perceptron neural network; ranging scatterometer; shape; tree species proportions; Backscatter; Chirp modulation; Laboratories; Materials testing; Radar measurements; Remote sensing; Shape measurement; Soil; Space technology; Spaceborne radar;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1999. IGARSS '99 Proceedings. IEEE 1999 International

Conference_Location

Hamburg

Print_ISBN

0-7803-5207-6

Type

conf

DOI

10.1109/IGARSS.1999.774437

Filename

774437