Forests are an important global resource, playing key roles in both the environment and the economy. The implementation of quality national monitoring programs is required for the generation of robust national statistics, which in turn support global reporting. Conventional monitoring initiatives based on samples of field plots have proven robust but are difficult and costly to implement and maintain, especially for large jurisdictions or where access is difficult. To address this problem, air photo-and satellite-based large area mapping and monitoring programs have been developed; however, these programs also require ground measurements for calibration and validation. To mitigate this need for ground plot data we propose the collection and integration of light detection and ranging (lidar) based plot data. Lidar enables accurate measures of vertical forest structure, including canopy height, volume, and biomass. Rather than acquiring wall-to-wall lidar coverage, we propose the acquisition of a sample of scanned lidar transects to estimate conditions over large areas. Given an appropriate sampling framework, statistics can be generated from the lidar plots extracted from the transects. In other instances, the lidar plots may be treated similar to ground plots, providing locally relevant information that can be used independently or integrated with other data sources, including optical remotely sensed data. In this study we introduce the concept of "lidar plots" to support forest inventory and scientific applications, particularly for large areas. Many elements must be considered when planning a transect-based lidar survey, including survey design, flight and sensor parameters, acquisition considerations, mass data processing, and database development. We present a case study describing the acquisition of over 25 000 km of lidar data in Canada's boreal forests in the summer of 2010. The survey, which included areas of managed and unmanaged forests, resulted in the production of more than 17 million 25 x 25 m lidar plots with first returns greater than 2 m in height. We conclude with insights gained from the case study and recommendations for future surveys.
