Authors

J. J. Angelo; B. W. Duncan;J. F. Weishampel

Comments

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Abbreviated Journal Title

Remote Sens.

Keywords

lidar; classification algorithms; support vector machines; oak scrub; Florida; time since fire; prescribed burning; disturbance ecology; Remote Sensing

Abstract

Disturbance plays a fundamental role in determining the vertical structure of vegetation in many terrestrial ecosystems, and knowledge of disturbance histories is vital for developing effective management and restoration plans. In this study, we investigated the potential of using vertical vegetation profiles derived from discrete-return lidar to predict time since fire (TSF) in a landscape of oak scrub in east-central Florida. We predicted that fire influences vegetation structure at the mesoscale (i.e., spatial scales of tens of meters to kilometers). To evaluate this prediction, we binned lidar returns into 1m vertical by 5 x 5 m horizontal cells and averaged the resulting profiles over a range of horizontal window sizes (0 to 500 m on a side). We then performed a series of resampling tests to compare the performance of support vector machine (SVM), k-nearest neighbor (k-NN), logistic regression, and linear discriminant analysis (LDA) classifiers and to estimate the amount of training data necessary to achieve satisfactory performance. Our results indicate that: (1) the SVMs perform significantly better than the other classifiers, (2) SVM classifiers may require relatively small training data sets, and (3) the highest classification accuracies occur with averaging over windows representing sizes in the mesoscale range.

Journal Title

Remote Sensing

Volume

2

Issue/Number

2

Publication Date

1-1-2010

Document Type

Article

Language

English

First Page

514

Last Page

525

WOS Identifier

WOS:000208401200008

ISSN

2072-4292

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