Individual Tree Crown Segmentation in Two-Layered Dense Mixed Forests from UAV LiDAR Data

Abstract

In forests with dense mixed canopies, laser scanning is often the only effective technique to acquire forest inventory attributes, rather than structure-from-motion optical methods. This study investigates the potential of laser scanner data collected with a low-cost unmanned aerial vehicle laser scanner (UAV-LS), for individual tree crown (ITC) delineation to derive forest biometric parameters, over two-layered dense mixed forest stands in central Italy. A raster-based local maxima region growing algorithm (itcLiDAR) and a point cloud-based algorithm (li2012) were applied to isolate individual tree crowns, compute height and crown area, estimate the diameter at breast height (DBH) and the above ground biomass (AGB) of individual trees. To maximize the level of detection rate, the ITC algorithm parameters were tuned varying 1350 setting combinations and matching the segmented trees with field measured trees. For each setting, the delineation accuracy was assessed by computing the detection rate, the omission and commission errors over three forest plots. Segmentation using itcLiDAR showed detection rates between 40% and 57%, while ITC delineation was successful at segmenting trees with DBH larger than 10 cm (detection rate ~78%), while failed to detect trees with smaller DBH (detection rate ~37%). The performance of li2012 was quite lower with the higher detection rate equal to 27%. Errors and goodness-of-fit between field-surveyed and flight-derived biometric parameters (AGB and tree height) were species-dependent, with higher error and lower r2 for shorter species that constitute the lowermost layer of the forest. Overall, while the application of UAV-LS to delineate tree crowns and estimate biometric parameters is satisfactory, its accuracy is affected by the presence of a multilayered and multispecies canopy that will require specific approaches and algorithms to better deal with the added complexity.

A new generation of sensors and monitoring tools to support climate-smart forestry practices

“…ALS data alone represent an effective source of data for detecting and delineating tree crowns in conifer-dominated forests (Hastings et al 2020) by processing the raw point cloud to compute a wide range of vegetation metrics from the height probability distributions and from the relative frequency distributions of vegetation heights. In mixed temperate forests, successful crown delineation using ALS data is lower than in coniferous forests due to the physical canopy traits that in turn influence tree height, crown architecture (crown spreading and leaf-display), and how crowns interact with neighbouring crowns (Hastings et al 2020;Torresan et al 2020). However, the integration of ALS data with aerial high-resolution multi-spectral or hyperspectral images (e.g., Dalponte et al 2019) as well as with high resolution aerial NIR images (Persson et al 2004) allow for tree species classification.…”

Metodologia de integração de dados para mapeamento florestal na RPPN Reserva Volta Velha - Pe. Piet van der Aart, Itapoá - Santa Catarina, Brasil

“…É notório a versatilidade de tamanho e capacidade de equipamento embarcado para o mapeamento da vegetação. O uso de sensores hiperespectral e LiDAR mostram constantes evolução na análise e tratamento da informação para o inventário florestal (TORRESAN et al, 2020).…”

Modelling the Diameter Distribution of Savanna Trees with Drone-Based LiDAR

“…The under-detection of small trees has also been observed when using high-density UAV-LS point-clouds in settings where large overstorey trees occlude smaller understorey trees. For example, Torresan et al [24] applied a dense UAV-LS point cloud collected in a dense mixed forest in Italy, and showed the under-segmentation of small understorey trees. However, Camarretta et al [25] used UAV-LS data to model DBH across all size-classes in a restored Eucalypt forest in Tasmania, Australia.…”