Seeing the Trees in the Forest

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.

Estimating tree heights with images from an unmanned aerial vehicle

“…Local maximum filters were based on the window size set by the user. This filter moves the pre-defined window over the CHM and then compares the centre cell's value with the surrounding pixels within a circular window in order to define the centre pixel as a maximum (Popescu et al 2003;Kini & Popescu 2004;Popescu & Wynne 2004;McGaughey 2014). The algorithm uses the CHM to identify local maximums and produces a text file based result.…”

Integrating Hands-On Undergraduate Research in an Applied Spatial Science Senior Level Capstone Course

“…Popescu and Wynne (2004) and Popescu, Wayne and Nelson (2002) found LiDAR and multispectral data fusion were satisfactory in estimating forest plot-level tree height accounting for 97% of the variation. O'Beirne (2012) calculated coefficient of determinations ranging from 0.92 to 0.96 comparing LiDAR data to field height measurements of trees in an urban environment.…”

Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation Clustering

“…According to the statistics, single-tree detection algorithms that use active RS data, such as ALS, have grown rapidly in recent years, accounting for 80% of the total increase in RS area, at the same time, those algorithms using passive data or a fusion of passive and active data accounts for 8% and 12% respectively [6]. To date, there are many methods for single-tree detection based on ALS data acquired for forest mapping, precision forestry management, and forest timber evaluation [7][8][9][10][11][12].…”