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Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA

Posted date: April 10, 2009
Publication Year: 
2009
Authors: Falkowski, Michael J.; Evans, Jeffrey S.; Martinuzzi, Sebastian; Gessler, Paul E.; Hudak, Andrew T.
Publication Series: 
Scientific Journal (JRNL)
Source: Remote Sensing of Environment. 113: 946-956.

Abstract

Quantifying forest structure is important for sustainable forest management, as it relates to a wide variety of ecosystem processes and services. Lidar data have proven particularly useful for measuring or estimating a suite of forest structural attributes such as canopy height, basal area, and LAI. However, the potential of this technology to characterize forest succession remains largely untested. The objective of this study was to evaluate the use of lidar data for characterizing forest successional stages across a structurally diverse, mixed-species forest in Northern Idaho. We used a variety of lidar-derived metrics in conjunction with an algorithmic modeling procedure (Random Forests) to classify six stages of three-dimensional forest development and achieved an overall accuracy>95%. The algorithmic model presented herein developed ecologically meaningful classifications based upon lidar metrics quantifying mean vegetation height and canopy cover, among others. This study highlights the utility of lidar data for accurately classifying forest succession in complex, mixed coniferous forests; but further research should be conducted to classify forest successional stages across different forests types. The techniques presented herein can be easily applied to other areas. Furthermore, the final classification map represents a significant advancement for forest succession modeling and wildlife habitat assessment.

Citation

Falkowski, Michael J.; Evans, Jeffrey S.; Martinuzzi, Sebastian; Gessler, Paul E.; Hudak, Andrew T. 2009. Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA. Remote Sensing of Environment. Remote Sensing of Environment. 113: 946-956.