Trevor A. Jones

Estimating Stem Diameter Distributions in a Management Context for a Tolerant Hardwood Forest Using ALS Height and Intensity Data

Abstract Two types of nonparametric modeling techniques and various metrics derived from airborne laser scanning (ALS) data were examined in terms of their utility for modeling stem diameter distributions in an uneven-aged tolerant hardwood forest in Ontario, Canada. Using an area-based approach (ABA), the frequency distribution of trees across 6 size classes was predicted using k-nearest neighbor (k-NN) imputation and Random Forest (RF) regression. Predictor variables derived from ALS height and intensity data were divided into 3 groups: height only, intensity only, and all metrics. Prediction results demonstrated that the first 2 groups of predictor variables exhibited similar predictive accuracy, whereas the synergy of both resulted in enhanced performance. The utility of intensity-based metrics was corroborated by an importance measure obtained from RF. The size class-specific stem density estimation approach based on RF was more accurate and flexible than the simultaneous estimation approach based on k-NN models. After the predicted diameter distributions were grouped into 9 structural groups, heterogeneous accuracy scores revealed the challenges for predicting select diameter distributions. Although successes were observed for certain size classes, there remains additional research (e.g., development of additional metrics or data types) to be done to accurately predict a complete range of size classes.

Delineating Individual Tree Crowns in an Uneven-Aged, Mixed Broadleaf Forest Using Multispectral Watershed Segmentation and Multiscale Fitting

Delineating individual tree crowns (ITCs) in high-spatial-resolution images can help to improve forest inventory and management. However, single-band watershed segmentation methods often fail to delineate broadleaf species, particularly in uneven-aged stands when a single-scale parameter is used to fit segments to reference crowns of different sizes. In this study, we present multispectral watershed segmentation and multiscale fitting method for ITC delineation, and the method involves two steps: 1) multispectral watershed segmentation to produce multiscale segmentation for subsequent fitting, which takes full advantage of boundary information contained in the spectral contrast of multiple bands; and 2) multiscale fitting to identify optimal parameters to best fit each ITC, rather than selecting a single parameter value based on its overall fit to the image as a whole. We evaluate the effectiveness of the proposed method using two multispectral images from a mixed broadleaf forest in Central Ontario, Canada. Our results show that multispectral watershed segmentation at multiple spatial scales produces ITC maps of higher quality than the commonly used multiresolution segmentation method. The automated multiscale fitting produces ITC maps of higher quality than the best single-scale segmentation.

Effect of size and number of calibration plots on the estimation of stem diameter distributions using airborne laser scanning

Stem diameter distribution is a crucial forest inventory variable in operational forest management. Compared to ground based forest mensuration (e.g., diameter at breast height (DBH)), airborne laser scanning (ALS) offers a cost effective alternative for modelling forest inventory variables. The objective of this study is to determine the impact of the size and number of sample plots on modelling diameter distributions in an unevenaged tolerant hardwood forest using discrete return ALS data. With the size of the sample plots ranging from 0.04 to 0.25 ha, DBH distributions were divided into six structural classes, estimated by two categories of non-parametric methods: k-nearest neighbor (k-NN) imputation and the random forest (RF). Sensitivity analysis demonstrated that the size of sample plots has a stronger impact on model performance than the number of plots. In addition, RF was found to be the most accurate model, regardless of the size and number of sample plots.

Forest harvest residue recovery in semi-mechanized hardwood selection operations

Semi-mechanized single-tree selection operations make important contributions to wood supply in parts of the northern hardwood forest region of northeastern North America. However, harvest residue recovery has not yet been integrated within this silvicultural system, due to low recovery rates, low mechanization, and small harvest blocks. As bioenergy policy incentives and markets continue to strengthen, there is a need to determine whether these partial harvest systems can contribute to local and regional energy wood supply. We assessed residue recovery and procurement costs during semi-mechanized single-tree selection operations in central Ontario, Canada. Logging contractors recovered 1.7–3.5 oven dry tonnes (ODt) of harvest residue per hectare by reducing the diameter at which trees were delimbed and topped. The time spent delimbing and topping increased by 10.8% but no extra time or machinery was required to recover the residue to roadside landings. Supply chain scenarios that included terminal chipping indicate that the harvest residue could be brought to market at a delivered cost