A Comparison of Forest Tree Crown Delineation from Unmanned Aerial Imagery Using Canopy Height Models vs. Spectral Lightness

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.

A Comparison of Methods for Determining Forest Composition from High-Spatial-Resolution Remotely Sensed Imagery

“…Next, the 'ultrahigh-quality' settings were selected to create the dense point cloud, digital elevation model (DEM), and orthomosaic. This maximum-quality setting ensured that DEM was generated using the full resolution of the imagery, which is the foundation of the segmentation process in the next section [80]. For each study area, an ultrahigh-resolution true-color (RGB) orthomosaic and DEM were generated.…”

“…The results of these segmentation parameter combinations were evaluated both qualitatively (i.e., visually) and quantitatively in comparison to manually digitized reference trees (i.e., polygons) at several of our study areas. For the quantitative assessment, we calculated the over-segmentation accuracy (Oa), under-segmentation accuracy (Ua), and quality rate (QR) of each parameter (see Gu et al [80]) combination for over 200 digitized reference trees [87,88]. The equations for Oa, Ua, and QR are included below.…”

“…In these three equations, r i denotes the i-th reference polygon, and s i represents the i-th segmented polygon that overlaps with r i . The symbol ∩ is defined at the intersection between two polygons, while ∪ is their union [80,87,88].…”

Mapping and Monitoring Forest Cover

“…The paper regarding large juniper forests also introduces the concept of carbon sequestration, which is particularly important to our environment today [3]. The final three papers deal with higher spatial resolution remotely sensed imagery [4][5][6]. The paper by Ganz et al [4] demonstrates the benefits of having a detailed forest map of a German forest created from high spatial resolution imagery.…”

“…Many experiences and suggestions provided by this paper can help other countries to more effectively and efficiently map and monitor their forests. The final paper by Gu et al [6] brings the analysis of forests down to the individual tree level. This paper reports on efforts using a UAS to collect imagery that can be used to delineate individual tree crowns and emphasizes some of the computer processing and algorithm developments that have made such techniques possible [6].…”