Seeing the Trees in the Forest

TLDR

The study aims to develop robust processing and analysis techniques that enable estimation of plot‑level tree height from small‑footprint lidar data by measuring individual trees on the 3‑D lidar surface. The authors processed lidar data by fusing it with multispectral optical imagery, applying local filtering with variable square and circular windows, and then used regression models with cross‑validation to estimate tree height on 0.017‑ha plots. Regression models explained 97 % of pine tree height variance and 79 % for deciduous trees, with circular‑window filtering improving pine models and square‑window filtering slightly better for deciduous trees, and data fusion yielding superior pine height estimates without needing to calibrate window size for deciduous plots.

Abstract

The main study objective was to develop robust processing and analysis techniques to facilitate the use of small-footprint lidar data for estimating plot-level tree height by measuring individual trees identifiable on the three-dimensional lidar surface. Lidar processing techniques included data fusion with multispectral optical data and local filtering with both square and circular windows of variable size. The lidar system used for this study produced an average footprint of 0.65 m and an average distance between laser shots of 0.7 m. The lidar data set was acquired over deciduous and coniferous stands with settings typical of the southeastern United States. The lidar-derived tree measurements were used with regression models and cross-validation to estimate tree height on 0.017-ha plots. For the pine plots, lidar measurements explained 97 percent of the variance associated with the mean height of dominant trees. For deciduous plots, regression models explained 79 percent of the mean height variance for dominant trees. Filtering for local maximum with circular windows gave better fitting models for pines, while for deciduous trees, filtering with square windows provided a slightly better model fit. Using lidar and optical data fusion to differentiate between forest types provided better results for estimating average plot height for pines. Estimating tree height for deciduous plots gave superior results without calibrating the search window size based on forest type.