Prediction of Diameter Using Height and Crown Attributes: A Case Study

Abstract

Recent advances in remote sensing provide increasingly detailed forest information in a timely and cost-effective manner. Individual tree stem diameter, an important variable for operational forest inventory, cannot be determined directly from remotely sensed data; stem diameter must be estimated from ancillary measures of tree crown, tree height, and/or measures related to stand structure. In this study, we developed predictive models of diameter as a function of height and crown attributes using a nonlinear mixed-effects approach. Long-term silvicultural experiments provided data for several species: black spruce, Douglas-fir, and lodgepole pine. Addition of crown area and crown area of larger trees to diameter-height models significantly improved predictive performance, resulting in low root mean squared error values between 0.9 and 1.8 cm (10–11% of mean diameter). Our models indicated the need to include additional explanatory variables at wider levels of spacing and thinning. This case study highlights several practical implications for developing and refining individual tree diameter models based on variables that can be remotely sensed.

Using Tree Height, Crown Area and Stand-Level Parameters to Estimate Tree Diameter, Volume, and Biomass of Pinus radiata, Eucalyptus globulus and Eucalyptus nitens

“…The functions using only HT to predict DBH were able to explain 93%-98% of the variability in field measured DBH. This is higher than the Adj-R 2 of 0.77 reported by Gonzalez-Benecke et al [5] for P. palustris but similar to the R 2 values reported by Filipescu et al for several conifer species, which ranged between 0.84-0.91 [24]. The functions in model set I did demonstrate some degree of heteroscedasticity such that prediction errors increased with increasing tree size.…”

“…Equations to predict tree DBH from tree height and stand parameters has been reported for several important tree species [5,11,[22][23][24]; however, there is currently limited information for Eucalyptus globulus Labill. (E. globulus), Eucalyptus nitens (H.Deane & Maiden) Maiden (E. nitens), and Pinus radiata D. Don (P. radiata).…”

Bridging the genotype–phenotype gap for a Mediterranean pine by semi‐automatic crown identification and multispectral imagery

“…Although ground‐based and UAV data were collected over different years, the intertree variability in height estimated through RGB‐derived imagery agreed with ground‐based measurements. Similarly, crown area was a good indicator of stem diameter, as reported elsewhere for other species (Lockhart et al ., 2005; Filipescu et al ., 2012), including Pinus spp. (Pretzsch et al ., 2015).…”

Remote Sensing Technologies for Assessing Climate-Smart Criteria in Mountain Forests

“…With reference to the age structure indicator, information concerning the ageclass structure of forests, and for uneven-aged forests, their diameter distributions, is important for understanding the history of forests and their likely future development, for assessing the harvesting potential, and for providing insights into biodiversity and recreation, which are generally more favorable in uneven-aged and old even-aged forests than in young even-aged forests (FOREST EUROPE 2015). It is known that the diameter of a tree can generally be modelled as a function of tree height or tree crown or measures related to stand structure (Filipescu et al 2012) and derived from LiDAR data (Thomas et al 2008;Salas et al 2010;Bergseng et al 2015;Spriggs et al 2017;Arias-Rodil et al 2018). Recently, harvester-mounted and ALS data (Maltamo et al 2019) as well as SPOT-5 satellite imagery and field sample data (Peuhkurinen et al 2018) have been used to estimate stand-level stem diameter distribution.…”