Shrub detection using disparate airborne laser scanning acquisitions over varied forest cover types

ABSTRACT

We explore the possibility of extending the national forest inventory-based point data of understory presence using region-wide, disparate lidar data for the southeastern USA. For this, we developed a simple inferential model that helps to understand the basic underlying relationships and associations between lidar predictor metrics and forest understory shrub presence over a wide range of forest types and topographic conditions. The model (a least absolute shrinkage and selection operator-based logistic regression model) had fair predictive performance (accuracy = 62%, kappa = 0.23). Hence, we were able to propose a set of biophysically meaningful predictor variables that represent understory (4), canopy (3), topographic conditions (1), and sensor characteristics (1). The single most important predictor variable was the understory layer canopy density, which is the ratio of lidar returns in the understory to those near the ground. Hence, we demonstrate that the interplay of several factors affects understory vegetation condition. Overall, our work highlights the potential value of using lidar to characterize understory conditions.

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Acknowledgements

The authors would like to acknowledge LISA, the statistical consulting group of the statistics department, Virginia Tech for their help during this work. This work was funded by the PINEMAP project (pinemap.org) sponsored by the USDA’s National Institute of Food and Agriculture (NIFA). We would also like to acknowledge the US Department of Agriculture (USDA) McIntire-Stennis Formula Grant.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Institute of Food and Agriculture (NIFA) and US Department of Agriculture (USDA).