Abstract
Improvements in computer vision combined with current structure-from-motion photogrammetric methods (SfM) have provided users with the ability to generate very high resolution structural (3D) and spectral data of the forest from imagery collected by unmanned aerial systems (UAS). The products derived by this process are capable of assessing and measuring forest structure at the individual tree level for a significantly lower cost compared to traditional sources such as LiDAR, satellite, or aerial imagery. Locating and delineating individual tree crowns is a common use of remotely sensed data and can be accomplished using either UAS-based structural or spectral data. However, no study has extensively compared these products for this purpose, nor have they been compared under varying spatial resolution, tree crown sizes, or general forest stand type. This research compared the accuracy of individual tree crown segmentation using two UAS-based products, canopy height models (CHM) and spectral lightness information obtained from natural color orthomosaics, using maker-controlled watershed segmentation. The results show that single tree crowns segmented using the spectral lightness were more accurate compared to a CHM approach. The optimal spatial resolution for using lightness information and CHM were found to be 30 and 75 cm, respectively. In addition, the size of tree crowns being segmented also had an impact on the optimal resolution. The density of the forest type, whether predominately deciduous or coniferous, was not found to have an impact on the accuracy of the segmentation.
Highlights
The objectives of this study are to (1) compare the accuracies of individual tree crowns delineated from unmanned aerial systems (UAS)-based canopy height models (CHM) and natural color orthoimagery using maker-controlled watershed segmentation, (2) provide insight into how accuracies change with spatial resolution, crown size, and forest type, and (3) facilitate the consideration of choosing the right data and in the future
The undersegmentation accuracy (Ua) shows a downward trend while the oversegmentation accuracy (Oa) demonstrates an upward trend before the spatial resolution approaches 74 cm
This research compared the use of a CHM with the lightness band for the delineation of individual tree crowns based on the maker-controlled watershed algorithm
Summary
Maintaining the diversity of these products and services involves the development and implementation of forest management practices, which requires detailed forest inventory information at varying scales, such as stand-level basal area and diameter at breast height (DBH), and/or crown size and tree height at the single tree level [4,5,6]. The conventional way to gather this forest inventory information is to carry out periodic field surveys based on statistical sampling [7,8]. The high cost in time and expense, as well as the difficulties in accessing specific sampling locations, make it an inefficient and often impractical approach [9,10]. Over the last few years, unmanned aerial systems (UAS), carrying a variety of sensors ranging from standard consumer-grade cameras to more expensive and complex multispectral or light detection and ranging
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