Murray Woods

LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

Over the past two decades there has been an abundance of research demonstrating the utility of airborne light detection and ranging (LiDAR) for predicting forest biophysical/inventory variables at the plot and stand levels. However, to date there has been little effort to develop a set of protocols for data acquisition and processing that would move governments or the forest industry towards cost-effective implementation of this technology for strategic and tactical (i.e., operational) forest resource inventories. The goal of this paper is to initiate this process by examining the significance of LiDAR data acquisition (i.e., point density) for modeling forest inventory variables for the range of species and stand conditions representing much of Ontario, Canada. Field data for approximately 200 plots, sampling a broad range of forest types and conditions across Ontario, were collected for three study sites. Airborne LiDAR data, characterized by a mean density of 3.2 pulses m−2 were systematically decimated to produce additional datasets with densities of approximately 1.6 and 0.5 pulses m−2. Stepwise regression models, incorporating LiDAR height and density metrics, were developed for each of the three LiDAR datasets across a range of forest types to estimate the following forest inventory variables: (1) average height (R2(adj) = 0.75–0.95); (2) top height (R2(adj) = 0.74–0.98); (3) quadratic mean diameter (R2(adj) = 0.55–0.85); (4) basal area (R2(adj) = 0.22–0.93); (5) gross total volume (R2(adj) = 0.42–0.94); (6) gross merchantable volume (R2(adj) = 0.35–0.93); (7) total aboveground biomass (R2(adj) = 0.23–0.93); and (8) stem density (R2(adj) = 0.17–0.86). Aside from a few cases (i.e., average height and density for some stand types), no decimation effect was observed with respect to the precision of the prediction of the majority of forest variables, which suggests that a mean density of 0.5 pulses m−2 is sufficient for plot and stand level modeling under these diverse forest conditions across Ontario.

Allometric equations for integrating remote sensing imagery into forest monitoring programmes

Abstract Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able – for the first time – to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed – specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the worlds forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large‐scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.

Parametric vs. nonparametric LiDAR models for operational forest inventory in boreal Ontario

Parametric and nonparametric predictions of forest inventory attributes from airborne LiDAR data are compared for a forest management unit in boreal Ontario. For the parametric approach, seemingly unrelated regression models were calibrated by forest type (SUR) and for all forest types combined (SUR_All). For the nonparametric approach, randomForest (RF) and k-nearest neighbours (kNN) were implemented. Calibration data consisted of 442 circular 0.04 ha plots covering a range of development stages within eight forest types. Results were validated on 64 independent plots distributed across the same forest types. Predicted variables included top height, merchantable basal area, and gross merchantable volume. In general, RF and SUR predictions were the most accurate and precise, whereas kNN and SUR_All predictions were less reliable. Prediction accuracy and precision varied markedly with forest type, with no single method producing results that were consistently best. None of the methods extrapolated well, underscoring the need to capture the full range of population variation during calibration. Parametric predictions were improved by forest-type stratification, necessitating a population forest-type layer prior to application. In contrast, forest type was not an important predictor in the nonparametric solutions. RF can offer significant operational advantages over parametric regression without loss of accuracy or precision.

A Comparison of Point Clouds Derived from Stereo Imagery and Airborne Laser Scanning for the Area-Based Estimation of Forest Inventory Attributes in Boreal Ontario

Abstract Point clouds derived from the photogrammetric pixel matching of 35-cm Leica ADS40 imagery (∼2.4 points/m2) were compared to those derived from airborne laser scanning (ALS; 1.1 returns/m2) in terms of their capacity to predict core forest inventory attributes at 400-m2 resolution on a boreal landscape in northeastern Ontario, Canada. These attributes described average stem size (top height, dominant–codominant height, quadratic mean stem diameter, mean stem volume) and growing stock (basal area, gross merchantable stem volume, sawlog volume, stem density), as calibrated from 426 400-m2 plots distributed across 8 forest types. Predictions derived from image-based point clouds for 10 independent validation plots in each forest type exhibited accuracies equivalent to ALS, however, some losses in precision were evident. Excluding mean stem volume and stand density, losses in precision corresponded to increases in coefficients of variation (CVs) of 4 percentage points or fewer for predicted versus observed plot values. CVs for mean stem volume and stand density increased by as many as 11 percentage points with image-based predictions. This result implies that forest inventories that are supported by an accurate, preexisting digital terrain model can be acceptably updated with optical imagery as the primary data source. Résumé Les nuages de points provenant de la mise en correspondance de pixels photogrammétriques de l’imagerie du Leica ADS40 de 35 cm (∼2,4 points/m2) ont été comparés à ceux obtenus du balayage laser aéroporté <<airborne laser scanning>> (BLA <<ALS>>; 1,1 retour/m2) pour ce qui est de leur capacité de prévoir les attributs principaux d’inventaire forestier à une résolution de 400 m2 sur un paysage boréal dans le nord-est de l’Ontario au Canada. Ces attributs ont décrit la taille moyenne des tiges (hauteur maximale, hauteur dominante–codominante, diamètre quadratique moyen des tiges, volume moyen des tiges) et le matériel sur pied (surface terrière, volume marchand brut des tiges, volume des grumes de sciage, densité des tiges), tel qu’étalonné à partir de 426 placettes de 400 m2 réparties dans 8 types de forêts. Les prévisions obtenues des nuages de points à partir d’une image pour 10 placettes de validation indépendantes dans chaque type de forêt ont affiché des exactitudes équivalentes à celles obtenues du BLA; cependant, certaines pertes de précision étaient évidentes. À l’exception du volume moyen des tiges et de la densité des peuplements, les pertes de précision correspondaient aux augmentations des coefficients de variation (CV) de 4 points de pourcentage ou moins pour les valeurs de placettes prévues par opposition à celles observées. Les CV pour le volume moyen des tiges et la densité des peuplements ont augmenté de 11 points de pourcentage selon les prévisions à partir d’une image. Ce résultat autorise à penser que les inventaires forestiers qui sont soutenus par un modèle numérique de terrain préexistant et exact pourraient être mis à jour convenablement avec l’imagerie optique comme principale source de données.

A Two-Level Approach for Species Identification of Coniferous Trees in Central Ontario Forests Based on Multispectral Images

This study aims to provide detailed spatial information of valuable tree species to support improved management of winter habitat of white-tailed deer. To achieve this, we proposed a novel approach using information from two spatial scales and a suite of methods for analysis and classification of remotely sensed data. High-spatial resolution, multispectral images were employed to test the proposed method. A new structure-based remote sensing feature [local binary pattern (LBP) index] was developed and proved to be effective for species classification. A simple but effective fusion approach based on information entropy theory was proposed to incorporate features derived from different methods and their uncertainties. Based on tenfold cross validation, an overall accuracy (OA) of 77% was obtained for the classification of three tree species groups. The proposed approach has high potential to improve species mapping for operational ecological modeling.