Remote Sens. 2010, 2(2), 514-525; doi:10.3390/rs2020514
Communication

Using Lidar-Derived Vegetation Profiles to Predict Time since Fire in an Oak Scrub Landscape in East-Central Florida

Received: 7 January 2010; in revised form: 2 February 2010 / Accepted: 3 February 2010 / Published: 11 February 2010
(This article belongs to the Special Issue LiDAR)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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 × 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.
Keywords: lidar; classification algorithms; support vector machines; oak scrub; Florida; time since fire; prescribed burning; disturbance ecology
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MDPI and ACS Style

Angelo, J.J.; Duncan, B.W.; Weishampel, J.F. Using Lidar-Derived Vegetation Profiles to Predict Time since Fire in an Oak Scrub Landscape in East-Central Florida. Remote Sens. 2010, 2, 514-525.

AMA Style

Angelo JJ, Duncan BW, Weishampel JF. Using Lidar-Derived Vegetation Profiles to Predict Time since Fire in an Oak Scrub Landscape in East-Central Florida. Remote Sensing. 2010; 2(2):514-525.

Chicago/Turabian Style

Angelo, James J.; Duncan, Brean W.; Weishampel, John F. 2010. "Using Lidar-Derived Vegetation Profiles to Predict Time since Fire in an Oak Scrub Landscape in East-Central Florida." Remote Sens. 2, no. 2: 514-525.

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