Discriminating amongst spatial configurations and climax size of trees in forests along varying physical gradients from time since last disturbance is a significant component of applied forest management. Understanding what has led to the existing vegetation's structure has important implications for monitoring succession and eco-hydrological interactions within the critical zone: the near-surface environment where rock, soil, air, and biota interact and regulate ecosystem services. This research demonstrates the utilities of local indicators of spatial association (LISA) to (1) quantify natural variation in forest structure as derived from aerial Light Detection and Range (LiDAR) across topographically complex landscapes at ecologically relevant scale, i.e., individual trees; (2) map previously recorded but poorly defined forest disturbances; and (3) link scalable topographic indices to observed tree size distributions. We first selected a priori undisturbed and disturbed stands scanned by aerial LiDAR that included: preservation, mechanical fuels treatment, and logging in two similar semi-arid forest ecosystems. Next, we compared two topographic indices: the topographic position index (TPI) and topographic wetness index (TWI) to climax tree height and two related LISAs: the Getis-Ord Gi and Anselin local Moran's I. Gi and I measure local spatial clustering, producing z-scores that represent the significance of each statistic. The tallest trees in each study area were found to be located on negative TPI and greater TWI values (valley bottom positions and higher wetness conditions, respectively). Stands of trees with positive z-scores (i.e., overstory trees with similarly tall neighbors) were most likely to be located in the a priori undisturbed areas. Disturbed locations, on the other hand, were defined by neighborhoods of negative z-scores (i.e., shorter trees with short neighbors). Future applications include locating and reconstructing historical disturbances and developing a consistent strategy for characterizing stands for forest inventory monitoring.
Swetnam T.L., Lynch A.M., Falk D.A., Yool S.R., and Guertin D.P. (2015): Discriminating disturbance from natural variation with LiDAR in semi-arid forests in the southwestern USA. Ecosphere 6(6): art97. DOI: 10.1890/ES14-00384.1
This Paper/Book acknowledges NSF CZO grant support.