Improving Deciduous Forest Inventory Plot Center Measurement Using Unoccupied Aerial Systems Imagery

Abstract

Abstract Field-based forest inventory plots are fundamental for many forest studies. These on-the-ground measurements of small samples of forested areas provide foresters with key information such as the size, abundance, health, and value of their forests. Recently, forest inventory plots have begun to be used as ground validation for tree features automatically extracted from remotely sensed data sets. Additionally, machine learning methods for feature extraction rely heavily on large quantities of training data and require these field forest inventory measurement datasets for algorithm training. Undermining the usefulness of forest inventory plot data as validation or training data is the positional uncertainty of plot location measurements. Because global navigation satellite systems (GNSS) cannot reliably measure plot center coordinates under thick tree canopy, plot center coordinates usually contain multiple meters of horizontal error. We present a method for reliably measuring plot center coordinates in which plot centers are individually marked with low-cost targets, allowing plot centers to be manually measured from orthoimagery captured during the leaf-off season. Our plot center measurements are shown to have less than 10 cm of horizontal error, an improvement of an order of magnitude over traditional GNSS methods.