The application of lidar data to assist with forest inventory is common around the world. However, the determination of tree species is still somewhat elusive. Lidar data collected using UAS (uncrewed aircraft systems) platforms offer high density point cloud data for areas from a few to several hundred hectares. General point cloud metrics computed using these data captured differences in the crown structure that proved useful for species classification. For our study, we manually adjusted plot and tree locations to align field trees and UAS lidar point data and computed common descriptive metrics using a small cylindrical sample of points designed to capture the top three meters and leader of each tree. These metrics were used to train a random forest classifier to differentiate between two conifer species, Douglas fir and western hemlock, common in the Pacific Northwest region of the United States. Our UAS lidar data had a single swath pulse density of 90 pulses/m² and an aggregate pulse density of 556 pulses/m². We trained classification models using both height and intensity metrics, height metrics alone, intensity metrics alone, and a small subset of five metrics, and achieved overall accuracies of 91.8%, 88.7%, 78.6%, and 91.5%, respectively. Overall, we showed that UAS lidar data captured morphological differences between the upper crowns of our two target species and produced a classification model that could be applied over large areas.

A fundamental question of forestry is that of species composition: which species are present, and which are not. However, traditional forest measurements needed to map species over large areas can be both time consuming and costly. In this study, we combined airborne light detection and ranging (LiDAR) data with extensive field data from the Long-Term Ecosystem Productivity study located near Sappho, Washington, USA to increase the accuracy of our GIS data and to differentiate between red alder (Alnus rubra Bong.) and other dominant tree species. We adjusted plot and tree locations using LiDAR canopy height models (CHMs) by manually matching tree canopies on the CHMs with tree stem maps based on field data. We then used the adjusted tree locations and metrics computed from LiDAR point cloud data to create a classification model to identify and map red alder. The manual matching of field stem maps to CHMs improved tree locations, allowing us to create model training data. These data were used to create a random forest model that discriminated between red alder and conifer species with an accuracy of 96%. Our findings highlight the potential of LiDAR to improve coordinates of individual trees as well as discriminate between selected coniferous and deciduous tree species using LiDAR data collected in leaf-off conditions in Pacific Northwest ecosystems.

Forest understory vegetation contributes to aboveground and belowground carbon budgets and affects terrestrial ecosystem function, yet quantifying understory biomass at large spatial scales is difficult. Increasingly, forest management operations are aided by unmanned aerial vehicles (UAVs) that collect imagery and airborne light detection and ranging (LiDAR) data, potentially providing a dataset for understory monitoring. In this study, we evaluated the suitability of two different remote sensing datasets (A and B) for predicting understory biomass amounts (field average of 2 Mg ha-1) using five different machine learning models. We compared (1) Acquisition A LiDAR and multispectral data, (2) Acquisition A multispectral data only, (3) Acquisition B SfM photogrammetry point cloud and multispectral data, and (4) Acquisition B multispectral data only. Remotely sensed data were temporally linked to ground plots in open post-harvest conditions, where we employed a method for quick in-field biomass estimation via calibrated photos. Our best model was a stochastic gradient boosting model built with LiDAR and multispectral data (testing R2=0.43, training R2=0.67). The multispectral-only model from Acquisition A performed similarly (testing R2=0.44, training R2=0.53), despite larger bias in shaded areas, suggesting an alternative streamlined method for creating wall-to-wall estimates. Structure-for-motion was unfit for modeling biomass under all tested conditions, indicating the importance of data acquisition and post-processing. Our study provides a novel framework for evaluating low-volume understory biomass efficiently and creating landscape scale predictions using limited ground measurements and remotely sensed data.