Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand

Abstract

Forests in the Southwestern United States are becoming increasingly susceptible to large wildfires. As a result, forest managers are conducting forest fuel reduction treatments for which spatial fuels and structure information are necessary. However, this information currently has coarse spatial resolution and variable accuracy. This study tested the feasibility of using unmanned aerial vehicle (UAV) imagery to estimate forest canopy fuels and structure in a southwestern ponderosa pine stand. UAV-based multispectral images and Structure-from-Motion point clouds were used to estimate canopy cover, canopy height, tree density, canopy base height, and canopy bulk density. Estimates were validated with field data from 57 plots and aerial photography from the U.S. Department of Agriculture National Agriculture Imaging Program. Results indicate that UAV imagery can be used to accurately estimate forest canopy cover (correlation coefficient (R 2 ) = 0.82, root mean square error (RMSE) = 8.9%). Tree density estimates correctly detected 74% of field-mapped trees with a 16% commission error rate. Individual tree height estimates were strongly correlated with field measurements (R 2 = 0.71, RMSE = 1.83 m), whereas canopy base height estimates had a weaker correlation (R 2 = 0.34, RMSE = 2.52 m). Estimates of canopy bulk density were not correlated to field measurements. UAV-derived inputs resulted in drastically different estimates of potential crown fire behavior when compared with coarse resolution LANDFIRE data. Methods from this study provide additional data to supplement, or potentially substitute, traditional estimates of canopy fuel.

UAV‐derived estimates of forest structure to inform ponderosa pine forest restoration

“…; Shin et al. ), the high‐density plots used in our study were on average much denser (up to 778 TPH) than the high‐density conditions (>300 TPH) considered elsewhere. We provide the first accuracy assessment of UAV image and SfM data in very high‐density conditions and note that when stem density increases beyond approximately 500 TPH, UAV image‐derived estimates cannot provide adequate accuracies.…”

“…; Shin et al. ). The omission and commission analysis quantifies the rate of correctly identified trees or true positive (TP), omitted or false negative (FN) and incorrectly included trees or false‐positive (FP) trees.…”

“…The ideal parameters were achieved by iteratively and incrementally testing them with our data and based on the results from previous research (Shin et al. ). The final parameters were as follows: distance threshold (DT) 1 of 1.4 m, DT 2 of 1.7 m, minimum height of 2 m, individual point height for triggering DT value of 12 m and the search radius of 0 m. The segmentation and associated tree metric calculation processes yielded a list of all trees segmented with their unique identification number, their geographic coordinates (UTM in meters), heights (m) and two opposing canopy diameters (m).…”

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

“…Considering the studies developed by Shin et al [138], White et al [134] and Fernández-Guisuraga et al [136], authors used MSP sensors. Shin et al [138] evaluated the feasibility of using UAV imagery to estimate forest canopy fuels and its structure in a ponderosa pine (Pinus ponderosa) stand with a small Gambel oak (Quercus gambelii) component. The results obtained indicate that UAV imagery can be used to accurately estimate forest canopy cover (R 2 = 0.82, RMSE = 8.9%).…”

Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery

“…In this context, remote sensing platforms are being used as a capable tool for mapping burned areas, evaluating the characteristics of active fires and characterizing post-fire ecological effects and regeneration [7]. In the past decade, the use of unmanned aerial vehicles (UAVs) has increased for agroforestry applications [8] and are now being used for forest fire prevention [9], canopy fuel estimation [10], fire monitoring [11,12] and to support firefighting operations [13]. Likewise, studies using UAV-based imagery in post-fire monitoring have been concerned with surveying [14], calibrating satellite-based burn severity indices [15], assessing post-fire vegetation recovery [16], mapping fire severity [17,18], studying forest recovery dynamics [19] and sapling identification [20].…”